WO1998045775A1 - Knowledge-based information retrieval system - Google Patents
Knowledge-based information retrieval system Download PDFInfo
- Publication number
- WO1998045775A1 WO1998045775A1 PCT/CA1998/000306 CA9800306W WO9845775A1 WO 1998045775 A1 WO1998045775 A1 WO 1998045775A1 CA 9800306 W CA9800306 W CA 9800306W WO 9845775 A1 WO9845775 A1 WO 9845775A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- case
- attributes
- attribute
- new problem
- candidate
- Prior art date
Links
- 238000000034 method Methods 0.000 claims abstract description 55
- 238000012545 processing Methods 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 2
- 238000004422 calculation algorithm Methods 0.000 description 33
- 230000006870 function Effects 0.000 description 22
- 230000008569 process Effects 0.000 description 14
- 238000005516 engineering process Methods 0.000 description 7
- 101150087199 leuA gene Proteins 0.000 description 5
- 238000003745 diagnosis Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 208000035143 Bacterial infection Diseases 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000002730 additional effect Effects 0.000 description 1
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 208000022362 bacterial infectious disease Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000001125 extrusion Methods 0.000 description 1
- 208000019622 heart disease Diseases 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000003607 modifier Substances 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
Definitions
- the present invention relates to knowledge-based decision support system for solving problems, and more particularly, to systems using case-based reasoning, that search for and present stored solution cases that most closely relate to a new problem.
- the traditional form of knowledge-based technology is paper documents that contain a variety of knowledge such as facts, rules, procedures, designs, and troubleshooting and problem-solving methods.
- the contents of the paper documents cannot be manipulated, are difficult to maintain, and often only accessible to a limited number of specialists.
- the rules and procedures are encoded and manipulated by computer programs.
- This form of knowledge-based technology that offers decision support using rules is called a rule-based expert system.
- CBR case-based reasoning
- the present invention is directed to a method for assisting a user in solving a new problem case within a selected domain.
- the method comprises the steps of providing a database comprising global domain knowledge relating to components of the selected domain, local domain knowledge, and a plurality of previously solved cases in the selected domain, each of the previously solved cases including a plurality of case attributes, said case attributes comprising case attribute names and known values associated therewith, said local domain knowledge comprising associations between the case attributes of a previously solved case; prompting the user to select a component of the domain and to select from the case attributes a set of attributes considered to be relevant to the new problem case and to provide current values for each of the new problem case attributes; searching the database of previously solved cases for candidate solved cases that include one or more of the new problem case attributes selected by the user and generating a list of said candidate solved cases; matching the candidate solved cases to the new problem case by comparing the current value for each of the new problem case attributes to the known value for the same case attribute in each of the candidate solved cases; ranking the candidate solved cases in order of
- the local domain knowledge preferably comprises importance factors for the case attributes within a previously solved case, the importance factors being utilized in determining of which attributes questions should first be asked, precedent constraints linking case attributes within a previously solved case, the precedent constraints enabling questions related to the unanswered attributes to be generated only if the precedent constraints are satisfied, and match operators which enable values for case attributes relating to the new problem case to be matched with the known values of previously solved cases.
- the invention is also directed to a computer system for assisting a user in solving a new problem case relating to a domain.
- the system comprises storage means for storing local domain knowledge and previously solved case records in a database.
- Each of said previously solved case records comprising a plurality of case attribute fields, said case attribute fields comprising case attribute names and associated values.
- the local domain knowledge comprises associations between the case attributes of a previously solved case.
- the system also comprises interface means for interfacing with the user, comprising output means for outputting to the user a list of case attributes of the previously solved case records, and input means for enabling the user to select from the list of case attributes a set of problem case attributes considered to be relevant to the problem case, and to input current values for case attributes relating to a new problem case, and processing means coupled to the storage means and the interface means for processing the current values of the problem case attributes.
- the processing means comprises searching means for searching the previously solved cases for solution candidate cases; matching means for matching the solution candidate cases to the new problem case by comparing the current values of the problem case attributes with stored values for the same case attributes for each of the solution candidate cases; ranking means for ranking the solution candidate cases in order of relevance based upon the similarity and creating a list of solution candidate cases based upon said ranking; and question generation means for generating additional questions based upon unanswered attributes in the solution candidate cases for which values have not yet been provided by the user, to assist the user to enter additional current values for case attributes.
- the present invention is further directed to a method for assisting a user in solving a new problem case within a selected domain, comprising the steps of providing a database comprising global domain knowledge relating to components of the selected domain, local domain knowledge, and a plurality of previously solved cases in the selected domain, each of the previously solved cases including a plurality of case attributes, said case attributes comprising case attribute names and known values associated therewith, said local domain knowledge comprising associations between the case attributes of a previously solved case, prompting the user to select a component of the domain and to select from the case attributes a set of attributes considered to be relevant to the new problem case and to provide current values for each of the new problem case attributes, searching the database of previously solved cases for candidate solved cases that include one or more of the new problem case attributes selected by the user and generating a list of said candidate solved cases, and matching the candidate solved cases to the new problem case by comparing the current value for each of the new problem case attributes to the known value for the same case attribute in each of the candidate solved cases.
- Figure 1 is a schematic diagram of a system made in accordance with a preferred embodiment of the subject invention
- Figure 2 is an conceptual illustration of the function of the subject invention.
- Figure 3a and 3b is a flow chart showing the method implemented by the reasoning engine of the subject system to retrieve and present potential solution cases and questions;
- Figure 4 is a flow chart showing the method used by the searcher of the subject system
- Figures 5a and 5b are a flow chart showing the method used by the matcher of the subject system
- Figure 6 is a flow chart showing the method used by the ranker of the subject system
- Figures 7a and 7b are a flow chart showing the method used by the question generator of the subject system.
- Figure 8 is a block diagram of the attribute type hierarchy of the subject system.
- Figure 9 is an illustration of the SIM function definition about a previously solved case using the quad-tuple representation.
- Figure 10 is a graph of the encoding of the "near_to" matching algorithm.
- Figure 11 is a graph of the encoding of the "fuzzy_less_than” matching algorithm.
- Figure 12 is a graph of the encoding of the "fuzzy_greater _than" matching algorithm.
- Figure 13 is a graph of the encoding of the "range" matching algorithm.
- Figure 14 is a graph of the encoding of the "greater_than" matching algorithm.
- Figure 15 is a graph of the encoding of the "less_than” matching algorithm.
- Case-based reasoning system 10 assists users in solving a new problem by retrieving case information on known problems and their solutions, within a particular domain, such as a complex apparatus, and comparing information about the new problem with the solved case information.
- Case-based reasoning system 10 comprises a database 12 comprising case database 13 and global domain model 14, a reasoning engine 16 comprising a matcher 36 and a question generator 48, and a user interface 22.
- Case database 13 includes for each of a plurality of previously solved cases, various case attributes 15, and attribute values 17, as well as local domain knowledge 19 in the form of attribute importance factors 21, precedence constraints 23 and match operators 25.
- Case database 13 may be altered and new cases added without affecting the existing case history. Each case stores only the information relevant to that case, thus there is no record of domain knowledge in the case beyond the components relevant to the case. For example, if a new jet engine component is added to a domain of jet aircraft, the existing cases would not have to be updated as they do not apply to the new engine type nor is it required that they store information about it. This flexibility is achieved by utilizing a third- normal form relational database for the storage of case and domain information in separate tables.
- the case and domain data is preferably stored in a SQL-92 compliant relational database.
- the database engine may be a Borland Interbase Server bundled with a Delphi 2.0 Developer, although other SQL-92 compliant database servers, such as Oracle, Sybase or MicroSoft SQL Server, can be used.
- Database 12 includes system administration tables, domain tables, case tables, and problem report tables.
- the system adminstration tables are typically used by the case administrator to maintain meta level database control, and to control data access, grants of user rights, etc.
- the domain tables provide the necessary descriptive language to represent a case, and typically include equipment, component and hierarchy tables.
- the case tables preferably include a case header table and a case detail table.
- the problem report tables are used to record information about a new problem generated during a user session, and are similar to the case tables.
- a user will describe a new problem by specifying an attribute and a value for the attribute.
- a problem in the area of extrusion equipment maintenance A user knows that "the die end is leaking".
- the goal of system 10 is to assist the user in making observations that identify the cause of the leakage.
- the various root causes i.e. stored cases
- the relevant question to identify the root cause could be "when was the seal last changed?", “is the seal rubbing the chill roll?", "have the tightening bolts sunk in?”, and so on.
- Question generator 48 selects and orders such questions for presentation to the user.
- Question generator 48 receives its input from matcher 36, case database 13, and global domain model 14, and sends its output to user interface 22.
- Matcher 36 provides the overall similarity between a new problem case and stored cases in case database 13, case database 13 provides various pieces of local domain knowledge 19, and global domain model 14 provides the format and content of the questions.
- domain knowledge can be specified at two levels of granularity: (1.) global level; the knowledge applies to the whole case base; and
- Precedence constraint 23 is a place holder (i.e. representation) for this kind of local domain knowledge 19. The representation method assumes conjunction when more than one precedent is specified. This assumption is reasonable since a typical case has 6-7 attributes and a few simple dependencies. However, collectively, over a family of cases, the number of constraints can be substantial and their use can accurately filter out many irrelevant and annoying questions.
- the Importance factor 21 is used for ordering the questions and for the overall similarity (OSIM) computation.
- Each stored previously solved case under consideration i.e., a Candidate Solved Case
- key observations i.e., most important
- secondary observations i.e., of lesser importance than the key observations
- the key observations, precedent constraints 23 permitting, should be made first followed by the next set of observations and so on.
- Match operators 25 are definitions of which attribute matching algorithm is to be used to compare the value of that attribute to the corresponding attribute in the new problem case.
- the result of the matching algorithm is a local (SIM) definition of similarity.
- SIM local
- These definitions of similarity are a kind of domain knowledge used for computing the overall similarity (OSIM) of a candidate solved case with a new problem case.
- the overall similarity (OSIM) of candidate solved cases strongly affects the question generation process.
- Figure 2 illustrates an overview of the problem solving process.
- the user enters the new problem description at step 170, this initial information 172 provides a description 174.
- the description 174 is used to form criteria 176 for selection of solved problem cases from the case database 13. Cases that match the selection criteria 176 are extracted at step 178 and then ranked at step 180. The ranked cases are presented to the user and additional questions asked at step 182. If the user is satisfied with the cases presented (step 196) then the new problem case is either a known solved case 188 or a new case which will be examined by an expert forum 192 and then entered into the case database 13 at step 194. If the user is not satisfied with the previously solved cases that have been retrieved, the answers to the questions asked results in refinement at step 198 resulting in a new description at step 174 and the new selection criteria 176 are established, thus repeating the entire process.
- Figure 3a and 3b illustrates the steps of the method 100 carried out by the reasoning engine 16 of system 10 made in accordance with the subject invention.
- a user through the user interface 22 first selects a component (block 26) from the case database 13.
- a domain may consist of many components.
- the domain of a jet aircraft may consist of components for a jet engine, hydraulics system and other components.
- the jet engine may consist of sub-components that describe particularly complex components within the engine, for example the turbine assembly.
- the user specifies as many case attributes 50 and their values 52 as are known for the new problem to define a new problem case 30. For example, if the user is dealing with the turbine assembly component discussed above, the user may provide values for the basic "attributes" of that component. These values will be the information recorded about the new problem case. Examples of attributes in the turbine assembly component may be operating temperature or blade fractures.
- Each case attribute 50 has information on valid values 52 stored in the case database 13.
- searcher module 32 For each attribute 50 in the new problem case 30, searcher module 32 searches the case database 13 to find all previously solved cases that have the case attribute 50. The cases selected are added to a list of candidate solved cases 33. Once created, the list of candidate solved cases 33 is read by searcher module 32 and any duplicate cases are deleted.
- Matcher module 36 then reads each case in the list of candidate solved cases 33, and calculates a similarity value SIM for each case attribute 50 in common with the new problem case 30. The calculation of the value of SIM is based upon the type of the case attribute 50. Once a SIM value has been computed for each of the case attributes 50 in common with the new problem case 30 and a given candidate solved case, matcher module 36 then calculates an overall similarity OSIM. OSIM is the overall similarity between the new problem case 30 and a given candidate solved case.
- the list of candidate solved cases 33 is updated to add the OSIM value for each candidate solved case and the new list of candidate solved cases and OSIMs 39 created as input to ranker module 40.
- Ranker module 40 reorders the list of candidate solved cases and OSIMs 39 in decreasing order of OSIM. Then based upon a selection criteria 42 such as: first five, all, or if OSIM is greater than a certain value; a list of ranked candidate solved cases 41 is created as input to the question generator module 48.
- Question generator module 48 reads a case from the list of ranked candidate solved cases 41, the corresponding local domain knowledge 19 and global domain knowledge 14 from the database 12, and generates questions based thereupon, in a manner hereinafter described.
- question generator 48 builds a list of unanswered attributes, i.e. case attributes which have not yet had a value provided by the user, and have had all precedence requirements met ("LA").
- LA precedence requirements met
- attribute identifier the rank of the attribute (i.e. the higher the OSIM ranking of the case, the higher the ranking of the attribute)
- attribute importance category i.e. how important is it toward the confirmation of the root cause of the existing case)
- a vote value calculated by multiplying the importance value of the attribute with its OSIM. If an attribute is found that already exists on the LA, the vote value is increased by adding to it the value of the current attribute importance multiplied by its OSIM.
- the LA is sorted by: OSIM rank descending, and vote descending. Questions are then posed to the user at step 43 regarding the unanswered case attributes sorted highest in the sorted LA. If the user is satisfied (block 45) with a case or cases selected by the ranker 40, the session may be terminated (block 46). If the user is not satisfied with the presented cases, the user may answer the questions displayed to the user by module 43 thereby providing more information on the new problem case. The further information provided by the user adds to the definition of the new problem case 30 and the process 100 repeats.
- Figure 4 illustrates the steps of searcher module 32.
- the new problem case 30 provided by the user will have a number of attributes, n.
- Step 202 sets a counter, i, that will indicate the current attribute being searched for.
- Step 204 checks to ensure the counter i is not greater than the number of attributes n in the new problem case 30. If all attributes in the new problem case 30 have been searched for, then the process jumps to step 212. If not all attributes have been searched for the process moves to step 206, extracts all previously solved cases from the case database 13, that have a value for the attribute i. These previously solved cases are added to the list of candidate solved cases 208 and the value of i incremented at step 210.
- Step 212 reads the list of candidates solved cases 208 and discards any duplicate candidate solved cases, thereby creating a list of unique candidate solved cases 33.
- Figures 5a and 5b illustrate the steps of matcher module 36.
- the matcher 36 accepts as input the list of unique candidate solved cases 33 created by the searcher 32.
- the list of unique candidate solved cases 33 will have a number of cases, n.
- Step 222 sets a counter, i, that will indicate the current candidate solved case being examined.
- Step 224 checks to ensure the counter i is not greater than the number of candidate solved cases in the list of unique candidate solved cases 33.
- step 226 If all cases in the list of unique candidate solved cases 33 have been examined then the matcher 36 has completed its function and exits via step 226. If all cases in the list of unique candidate solved cases 33 have not been examined, then the matcher 36 proceeds to step 228.
- the current candidate solved case i.e. case #i
- Step 228 sets a counter, j, that will indicate the current attribute being examined in the current candidate solved case.
- Step 230 checks to ensure the counter j is not greater than the number of attributes m in the current candidate solved case. If the current attribute j is not greater than the number of attributes m in the current candidate solved case, then step 232 is invoked.
- Step 232 checks to see if the current attribute j is in the new problem case 30 provided by the user. If the attribute j is in the new problem case 30, then step 236 adds the attribute j to a list of attributes common to the new problem case 30 and the current candidate solved case, then increments the value of the counter j at step 234. If the attribute j of the current candidate solved case is not in the new problem case 30, then step 236 is not invoked and value of the counter j is incremented at step 234. From step 234 the matcher 36 returns to step 230.
- step 230 determines the SIM for each attribute in the list of common attributes 236. Once each SIM has been calculated, for the attributes the current candidate solved case has in common with the new problem case 30 (i.e. the list created by step 236), then step 240 calculates the OSIM for the current candidate solved case. The OSIM and the current candidate solved case are added to the list of candidate solved cases and OSIMs 39. The value of the counter i is incremented at step 244 and the matcher 36 returns to step 224.
- Figure 6 illustrates the steps of ranker module 40.
- the ranker 40 accepts as input the list of candidate solved cases and OSIMs 39 created by the matcher 36.
- the first function performed by the matcher 40 is to sort the list of candidate solved cases and OSIMs 39 in descending order of OSIM. This function is performed at step 252, which creates a list of sorted candidate solved cases and OSIMs 254.
- a system defined selection criteria is then applied at 256 to determine which cases are to be displayed to the user and these cases are output in a list of selected and ranked candidate solved cases 41.
- Step 260 initializes an empty list of attributes LA.
- Step 262 initializes a counter i which will indicate the number of the current candidate solved case being examined from the list of selected and ranked solved candidate cases 41.
- Step 264 checks to see if their are any more candidate solved cases to be examined, if there are candidate solved cases left, the process moves to step. 266.
- Step 266 uses the case information stored in the case database 13 to create a list of attributes in the current candidate solved case that are enabled and have had their precedents met, designated as LEUA.
- Step 268 then initializes a counter j that will be used to step through the attributes in the list LEUA.
- Step 270 if the last attribute in list LEUA has not been examined, the process moves to step 274.
- Step 274 checks to see if the attribute j is in the list of attributes LA. If it is not, the attribute, it's rank, importance and vote are added to list LA by step 276. If it is in the list LA, then the vote value for that attribute is incremented by adding the attribute importance multiplied by the OSIM to the current vote at step 278. Both steps 276 and 278 then proceed to step 280 where the value of j is incremented.
- Step 280 proceeds to step 270 and the next attribute in LEUA is checked. If at step 270 all the attributes in LEUA have been examined, step 270 proceeds to step 272 where the counter i is incremented.
- Step 272 then proceeds to step 264 where the number of the current candidate solved case in the list of selected and ranked solved candidate cases 41 is examined. If there are no more cases in the list of selected and ranked solved candidate cases, then step 264 proceeds to step 282.
- Step 282 sorts the list of attributes LA by rank descending, importance descending and vote descending and passes the sorted list LA to step 284.
- Step 284 checks database 12 for a question to ask for attribute at the top of the sorted list LA and poses the question to the user at step 286.
- Figure 8 lists attribute types 300 based on their properties.
- System 10 allows for case attributes having various types of values.
- attribute categorization only symbolic 301 has distinct subtypes. It is the subtypes that are used to categorize and evaluate attributes. Thus, attributes may be categorized into eleven distinct types as shown below.
- the similarity between two values of an attribute is computed by a similarity computation scheme.
- the generally applicable (i.e., global) similarity computation scheme does not consider any contextual or local information.
- the local or contextual information resides in the cases.
- the global scheme is used by default. If a local scheme resides in a case it will overrule the global scheme for that particular case.
- the system should allow disabling of local schema. This would allow a knowledge engineer to determine the impact of local schema on the quality of output produced by the system.
- Only symbolic logical attributes do not require a similarity computation scheme because they are always exact matches. Lack of a similarity computation scheme implies exact matching.
- a symbolic attribute can be assigned symbol/labels as values. For example, a temperature may be "high”, “medium”, or "low”.
- a numeric attribute can be assigned numbers as values, e.g.: 1.56, or 10.
- the symbolic nominal attribute type accepts a symbolic value.
- the attribute CITY can be assigned a value like "Hamilton”, “Toronto”, “Guelph”, or “St. Catherines”, or an attribute ENGINE LOCATION can be assigned a value like "Left -1", “Left-2", “Right-1", or “Right-2”.
- Symbolic nominal attributes possess the following properties: a) Default Value: The default value is the usual selection that a user makes for the attribute. For example, "Toronto" as a value for the attribute CITY. It is not necessarily the normal value. Specification of a default value is optional.
- b) Normal Value Since the present invention is a diagnostic system, it deals primarily with deviations from normal. The system is designed to ignore normal states. Specification of this property for nominal values is optional. Nominal values typically do not have normal value settings. When this property is unspecified, the attribute is not used for matching unless it is included in the stored case.
- Similarities between any two values of a symbolic nominal attribute may be explicitly represented in a matrix.
- the level of similarity is specified by linguistic labels such as none, very low, low, medium, high, very high, exact. These labels can be converted to numeric values based on a linear scale, or by a non-linear scale that conforms to psychological notions of distance (See for example, adverb membership modifiers such as are used in fuzzy sets).
- the symbolic logical attribute is a special case of Symbolic-Nominal (see the attribute type taxonomy in Figure 8).
- a logical attribute can assume only two values. For example,
- a multi-valued attribute allows a user to assign one or more values to the attribute. This attribute type exists solely as a user convenience. For reasoning, these values are transformed into symbolic-logical-attributes with True-False or
- the multi-valued-attribute "Fault code” can assume values FOl, F02, F03 and so on.
- FOl and F03 the system performs an internal translation into attribute-values "Fault code F01"-present and "Fault code F03"-present.
- Multi-value logical-attribute-references This property specifies the list of references to symbolic logical attributes, the order in which it appears in the selection option in the user interface, and the label associated with it. For example, the attribute "Fault code”has a logical-attribute reference, comprising label "FOl", its sequence number at the interface: 1, and the associated reference logical attribute ID.
- a multi-valued attribute is never used in case representation. Instead, the component logical attributes are used. This attribute type does not possess properties for normal value or default value.
- Symbolic-Ordinal Values assigned to this attribute type are symbolic labels that have an implicit order.
- the temperature of a component may be "Normal”, “Warm”, “Hot”, “Very Hot”,or “Extremely hot”. Notice that these are subjective observations and are less precise than exact measurements such as 44.5 degrees.
- the symbolic ordinal attribute type inherits its properties from the symbolic and numeric attribute types. These include the following: a) Normal Value - as for the symbolic nominal type. b) Default Value - as for the symbolic nominal type. c) Similarity computation regular quad-tuple - as for the numeric type.
- Order number This is a real number which indicates the relative ordering of the symbolic values. For example, Normal (1), Warm (2), Hot (3), Very hot (4), and extremely Hot (5). By default, the values are set at equal intervals. However, the knowledge engineer may override the defaults to increase or decrease the similarity between adjacent symbols.
- the similarity computation regular quad-tuple is based on the ordinal value property.
- Numeric This attribute can be assigned a real or integral number as a value. For example, the "Temperature” is "47.5" degrees. The following properties are available: a) Default Value: This is specified when the attribute is created, and represents a subjective estimate of the most typical attribute value. This facet is required. b) Normal Value: This indicates that the attribute may not be relevant for reasoning. Unlike its symbolic counterpart, this is a range. The range consists of an upper bound and a lower bound. For example, in the attribute "Water level” normal condition refers to any value less than 4.5 m. In this case, the lower bound is - ⁇ and the upper bound is 4.5. This facet is required and must be specified when the attribute is created.
- Min The minimum valid value that the attribute can assume. For example, the attribute "Voltage" cannot be less than 0. If unspecified, the system will not impose a lower limit on values entered by the user.
- Max The maximum valid value that the attribute can have. For example, based on practical limits, the attribute
- Similarity computation regular quad-tuple This is a set of four parameters defined in standard attribute units. These four parameters define the attribute similarity function at a particular value. The attribute similarity function is used to compute similarity of two values. If unspecified, the matching is exact. For details, refer to the similarity computation schemes described later.
- Unit this is the standard dimensional unit associated with attribute values in the case base. For example, the motor current is stored in amperes. If this property is unspecified, the attribute is considered non-dimensional (i.e., None).
- This type of attribute is computed based on two or more numeric type attributes. For example, the percentage drop in voltage is computed as (Rated-Observed) /Rated. In this example, the representation allows comparison of equipment that do not share the same voltage ratings.
- a numeric computed attribute inherits all numeric properties. In addition, it has the following property of Computation-formula:
- attributes can be used with the method and apparatus of the present invention, such as: a) Time. b) Date. c) Symbolic Taxonomic. These are attributes organized in a "is-a-type of" hierarchy. For example, types of house. d) User defined. This comprises any type not covered by those specified in this document that the user needs.
- case based reasoning consists of an attribute-by-attribute comparison of the new problem description and each solved case.
- several matching schemes will be implemented. During case creation, a knowledge engineer will be able to select the matching scheme most suitable to the problem at hand.
- the default_fuzzy_match algorithm corresponds to the earlier implementation's handling of symbolic matching.
- a lookup table defined in the domain model is used to retrieve the similarity of any two values of the attribute.
- attribute name knife edge quality attribute values: new, sharp, corroded, dull similarity table: n ew sharp corroded dull n ew 1.0 0.8 0.3 0.0 sharp 1.0 0.6 0.0 corroded 1.0 0.5 dull 1.0
- custom_fuzzy_match may be used with nominal symbolic attributes.
- custom_fuzzy_match The custom_fuzzy_match algorithm is essentially identical to default_fuzzy_match. However, a customized lookup table stored as part of the case is used instead of the default, global lookup table. Custom_fuzzy_match is provided for special cases in which the default_fuzzy_match table is not suitable. It is not anticipated that custom_fuzzy_match will be frequently used. Because of storage and performance penalties associated with custom_fuzzy_match, default_ fuzz y_ m atch should be used whenever possible. Custom_fuzzy_match is available for nominal symbolic attributes.
- the range algorithm may be applied to ordered, integer, and floating point attributes.
- the less_than algorithm returns 1.0 if the attribute value falls on or below the specified threshold, and 0.0 if it is above the threshold. For example, consider a case involving precipitation. For arguments sake, assume precipitation only occurs below 15,000 feet altitude. The following representation would be used: altitude less_than 15000.0
- the less_than algorithm may be applied to ordered, integer, and floating point attributes.
- the fuzzy_less_than algorithm is similar to the less_than algorithm. The only distinction is a gradual rather than abrupt transition from 1.0 to 0.0 in the similarity score at the threshold value.
- the greater_than algorithm returns 1.0 if the attribute value falls on or above the specified threshold, and 0.0 if it is below the threshold. For example, consider a case involving a hydraulic seal leakage. The problem only occurs when the pressure differential across the seal is more than 4 atmospheres. The following representation would be used: pressure differential greater_than 4.0
- the greater_than algorithm may be applied to ordered, integer, and floating point attributes.
- the fuzzy_greater_than algorithm is similar to the greater_than algorithm. The only distinction is a gradual rather than abrupt transition from 1.0 to 0.0 in the similarity score at the threshold value.
- the near_to algorithm returns 1.0 if the attribute value in the problem description exactly matches the value in the stored case.
- the match level decreases to 0.0 as the values move apart from each other. The calculation is performed according to the following equation:
- the near_to algorithm may be applied to integer and floating point attributes.
- the range algorithm returns 1.0 if the attribute value falls on or within the specified limits, and 0.0 if it is outside the limits. For example, consider a case involving ice buildup on an aircraft wing. For arguments sake, assume icing only occurs between 10,000 and 15,000 feet altitude. The following representation would be used: altitude range 10000.0, 15000.0
- the range algorithm may be applied to ordered, integer, and floating point attributes.
- the similarity-computation schemes are classified according to the applicable attribute types.
- the following discussion deals with the computation of attribute similarity i.e.: the mapping from two values of a particular attribute to a number in the range 0.0 to 1.0.
- the superscript "nc” on a variable refers to the variable's value in new problem case.
- the superscript “sc” refers to the variable's value in the solved case.
- the label “val” refers to value.
- Figure 9 illustrates a similarity function definition using a quad- tuple representation, utilizing four parameters.
- Computation of numeric similarities is based on four parameters (hence, the name quad-tuple or set of four). These four parameters are a, b, c, and d.
- the parameters are defined relative to a reference value. This reference value is usually the value in the stored case.
- the value of a, b , c, and d are specified in the units associated with the attribute.
- Comparison schemes based on the standard deviation information for an attribute may be implemented as well. Parameters for such schemes will be defined in standard deviation units.
- quad-tuple parameters The interpretation of the quad-tuple parameters is as follows: a. If the new value is lower than the reference value by this amount or more it is considered completely dissimilar. For example, consider a scenario where the temperature is lower by 15 degrees, resulting in significantly difference operating characteristics. b. If the new value is lower than the reference value by this amount or less it is considered essentially identical, (i.e., the decision maker is not concerned by the difference). For example, consider a scenario where the temperature is lower by 5 degrees, but the difference does not affect the outcome. c. If the new value is greater than the reference value by this amount or less it is considered essentially identical. d. If the new value is greater than the reference value by this amount or more it is considered completely dissimilar.
- Figures 10 through 15 illustrate special case membership functions which can be derived by using different parameter settings. Each of these special cases correspond to a local similarity match algorithm as discussed earlier.
- the case specific scheme defines /redefines the global scheme with reference to the value used in a case. Although, this lends a great deal of flexibility for reasoning and for including context specific similarity assessment, the case specific specification should be avoided.
- the same local similarity scheme can be used by symbolic ordinal or even numeric integer types to override other numeric computation schemes.
- the case specific similarity scheme is used to implement the custom_fuzzy_match similarity algorithm.
- the must match scheme enforces a match with the attribute in which it is specified. That is, if the similarity with the specified attribute value is less than a preset threshold similarity value the overall similarity of the case (OSIM) is zero.
- OSIM overall similarity of the case
- the must match scheme is used locally in conjunction with any of the local similarity schemes. By default, the must match scheme is not enabled.
- the must match scheme includes a local similarity threshold value.
- the local similarity threshold value is specified by a linguistic label indicating the level of similarity.
- the labels used must be the same as those used in the similarity matrix.
- the local similarity scheme says that the match of motor type with value D.C. is 1 and with value A.C. is 0.
- the must match scheme is interpreted as: the similarity of motor type must be at least exact for the case to be considered.
- the overall similarity (OSIM) between a new problem case and a previously solved case is computed using the various matching functions of matcher module 36.
- Four exemplary matching functions are presented hereinbelow. It is assumed that no attribute weighting (i.e. domain knowledge or contextual knowledge) is provided with a new problem case description. This implies that the attributes of a new problem case are considered as equally weighted (e.g., 1). All the functions presented here are Nearest-Neighbour like because they deviate from the true nearest-neighbour function. The deviations include consideration of only a subset of all possible attributes for matching and the use of local importance of attributes.
- the local importance refers to the importance of an attribute in the context of a previously solved case (i.e., importance of an attribute is dependent on a previously solved case and recorded along with it).
- Nearest- neighbour uses global weights (i.e. importance of an attribute is the same across the whole case base).
- Prior art systems typically implement the true nearest-neighbour, and as a result do not consider local weights, nor can work with a subset of attributes.
- modified cosine matching function (OSIM 4) is considered to be the most sophisticated one. It performs significantly better than the nearest-neighbour when local importance along with subset of all possible attributes are used for matching. It also has the ability to consider contextual variation (i.e., difference between the importance of attributes of new problem case and a previously solved case). While this provides the ability to match contexts, it can make the matching oversensitive.
- the full contrast modified nearest-neighbour (OSIM 1) is a close second choice.
- ⁇ w attributes are assigned according to the CSC. For those attributes not in CSC they are equally weighted using a defined scheme.
- OSIM relevant attributes, is ⁇ ( W ,NC)2 X( w fCSC)2 capable of complete contrast and can take into account weights from NC and CSC Table 5: OSIM computing example
Abstract
Description
Claims
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU69137/98A AU6913798A (en) | 1997-04-08 | 1998-04-03 | Knowledge-based information retrieval system |
GB9922102A GB2338097B (en) | 1997-04-08 | 1998-04-03 | Knowledge-based information retrieval system |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US08/835,558 | 1997-04-08 | ||
US08/835,558 US5822743A (en) | 1997-04-08 | 1997-04-08 | Knowledge-based information retrieval system |
Publications (1)
Publication Number | Publication Date |
---|---|
WO1998045775A1 true WO1998045775A1 (en) | 1998-10-15 |
Family
ID=25269824
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CA1998/000306 WO1998045775A1 (en) | 1997-04-08 | 1998-04-03 | Knowledge-based information retrieval system |
Country Status (5)
Country | Link |
---|---|
US (1) | US5822743A (en) |
AU (1) | AU6913798A (en) |
CA (1) | CA2206155C (en) |
GB (1) | GB2338097B (en) |
WO (1) | WO1998045775A1 (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1218831A1 (en) * | 1999-09-22 | 2002-07-03 | Infoglide Corporation | System and method for performing similarity searching |
EP1811520A2 (en) * | 2006-01-17 | 2007-07-25 | Omron Corporation | Factor estimating device, method and program recording medium therefor |
CN106663144A (en) * | 2014-08-29 | 2017-05-10 | 皇家飞利浦有限公司 | Method and apparatus for hierarchical data analysis based on mutual correlations |
US10565539B2 (en) | 2014-11-07 | 2020-02-18 | International Business Machines Corporation | Applying area of focus to workflow automation and measuring impact of shifting focus on metrics |
Families Citing this family (226)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6571251B1 (en) * | 1997-12-30 | 2003-05-27 | International Business Machines Corporation | Case-based reasoning system and method with a search engine that compares the input tokens with view tokens for matching cases within view |
US6026393A (en) * | 1998-03-31 | 2000-02-15 | Casebank Technologies Inc. | Configuration knowledge as an aid to case retrieval |
US6263362B1 (en) * | 1998-09-01 | 2001-07-17 | Bigfix, Inc. | Inspector for computed relevance messaging |
US7197534B2 (en) | 1998-09-01 | 2007-03-27 | Big Fix, Inc. | Method and apparatus for inspecting the properties of a computer |
US8914507B2 (en) | 1998-09-01 | 2014-12-16 | International Business Machines Corporation | Advice provided for offering highly targeted advice without compromising individual privacy |
US6193518B1 (en) | 1998-11-20 | 2001-02-27 | Tina M. Nocera | Method for developing answer-options to issue-questions relating to child-development |
US6424969B1 (en) | 1999-07-20 | 2002-07-23 | Inmentia, Inc. | System and method for organizing data |
US6519578B1 (en) | 1999-08-09 | 2003-02-11 | Mindflow Technologies, Inc. | System and method for processing knowledge items of a knowledge warehouse |
US6519590B1 (en) * | 1999-08-09 | 2003-02-11 | Mindflow Technologies, Inc. | System and method for performing a mindflow process using a mindflow document archive |
US6629096B1 (en) * | 1999-08-09 | 2003-09-30 | Mindflow Technologies, Inc. | System and method for performing a mindflow process |
US6876991B1 (en) | 1999-11-08 | 2005-04-05 | Collaborative Decision Platforms, Llc. | System, method and computer program product for a collaborative decision platform |
US6571236B1 (en) * | 2000-01-10 | 2003-05-27 | General Electric Company | Method and apparatus for problem diagnosis and solution |
US6421571B1 (en) | 2000-02-29 | 2002-07-16 | Bently Nevada Corporation | Industrial plant asset management system: apparatus and method |
US7505921B1 (en) | 2000-03-03 | 2009-03-17 | Finali Corporation | System and method for optimizing a product configuration |
US8645137B2 (en) | 2000-03-16 | 2014-02-04 | Apple Inc. | Fast, language-independent method for user authentication by voice |
US20020147652A1 (en) * | 2000-10-18 | 2002-10-10 | Ahmed Gheith | System and method for distruibuted client state management across a plurality of server computers |
US7885820B1 (en) * | 2000-07-19 | 2011-02-08 | Convergys Cmg Utah, Inc. | Expert system supported interactive product selection and recommendation |
US7031951B2 (en) * | 2000-07-19 | 2006-04-18 | Convergys Information Management Group, Inc. | Expert system adapted dedicated internet access guidance engine |
US6745172B1 (en) | 2000-07-19 | 2004-06-01 | Whisperwire, Inc. | Expert system adapted data network guidance engine |
US7111010B2 (en) | 2000-09-25 | 2006-09-19 | Hon Hai Precision Industry, Ltd. | Method and system for managing event attributes |
WO2002027528A1 (en) * | 2000-09-25 | 2002-04-04 | Metaedge Corporation | Method and system for managing event attributes |
DE10107352B4 (en) * | 2001-02-13 | 2007-11-22 | T-Mobile Deutschland Gmbh | Method and device for handling complaints on mobile phones |
US6944619B2 (en) | 2001-04-12 | 2005-09-13 | Primentia, Inc. | System and method for organizing data |
US7401136B2 (en) | 2001-07-27 | 2008-07-15 | Dell Products L.P. | Powertag: manufacturing and support system method and apparatus for multi-computer solutions |
ITFI20010199A1 (en) | 2001-10-22 | 2003-04-22 | Riccardo Vieri | SYSTEM AND METHOD TO TRANSFORM TEXTUAL COMMUNICATIONS INTO VOICE AND SEND THEM WITH AN INTERNET CONNECTION TO ANY TELEPHONE SYSTEM |
GB2382678A (en) * | 2001-11-28 | 2003-06-04 | Symbio Ip Ltd | a knowledge database |
US7389280B2 (en) * | 2002-02-22 | 2008-06-17 | Lifecom, Inc. | Computer-based intelligence method and apparatus for assessing selected subject-area problems and situations |
US20070094197A1 (en) * | 2002-02-22 | 2007-04-26 | Datena Stephen J | Medical diagnosis including graphical user input |
JP4132962B2 (en) * | 2002-05-16 | 2008-08-13 | パイオニア株式会社 | Interactive information providing apparatus, interactive information providing program, and storage medium storing the same |
AU2003279115A1 (en) * | 2002-10-03 | 2004-04-23 | Whisperwire, Inc. | System and method for bundling resources |
US20040139107A1 (en) * | 2002-12-31 | 2004-07-15 | International Business Machines Corp. | Dynamically updating a search engine's knowledge and process database by tracking and saving user interactions |
US7216121B2 (en) * | 2002-12-31 | 2007-05-08 | International Business Machines Corporation | Search engine facility with automated knowledge retrieval, generation and maintenance |
US20040158561A1 (en) * | 2003-02-04 | 2004-08-12 | Gruenwald Bjorn J. | System and method for translating languages using an intermediate content space |
US7225176B2 (en) * | 2003-03-26 | 2007-05-29 | Casebank Technologies Inc. | System and method for case-based reasoning |
US7409593B2 (en) * | 2003-06-30 | 2008-08-05 | At&T Delaware Intellectual Property, Inc. | Automated diagnosis for computer networks |
US7324986B2 (en) * | 2003-06-30 | 2008-01-29 | At&T Delaware Intellectual Property, Inc. | Automatically facilitated support for complex electronic services |
US20050038697A1 (en) * | 2003-06-30 | 2005-02-17 | Aaron Jeffrey A. | Automatically facilitated marketing and provision of electronic services |
US7237266B2 (en) * | 2003-06-30 | 2007-06-26 | At&T Intellectual Property, Inc. | Electronic vulnerability and reliability assessment |
US20050108598A1 (en) * | 2003-11-14 | 2005-05-19 | Casebank Technologies Inc. | Case-based reasoning system and method having fault isolation manual trigger cases |
US20050193004A1 (en) * | 2004-02-03 | 2005-09-01 | Cafeo John A. | Building a case base from log entries |
US7392295B2 (en) | 2004-02-19 | 2008-06-24 | Microsoft Corporation | Method and system for collecting information from computer systems based on a trusted relationship |
US7584382B2 (en) * | 2004-02-19 | 2009-09-01 | Microsoft Corporation | Method and system for troubleshooting a misconfiguration of a computer system based on configurations of other computer systems |
US7389444B2 (en) * | 2004-07-27 | 2008-06-17 | Microsoft Corporation | Method and system for troubleshooting a misconfiguration of a computer system based on product support services information |
US8244689B2 (en) | 2006-02-17 | 2012-08-14 | Google Inc. | Attribute entropy as a signal in object normalization |
US7769579B2 (en) | 2005-05-31 | 2010-08-03 | Google Inc. | Learning facts from semi-structured text |
US8095392B1 (en) | 2005-01-20 | 2012-01-10 | Owen Daniel L | System, method and computer program product for facilitating informed decisions relating to family risk |
JP2006268405A (en) * | 2005-03-24 | 2006-10-05 | Hitachi Ltd | Supporting device for creating customer value generation scenario, system and method |
US7587387B2 (en) | 2005-03-31 | 2009-09-08 | Google Inc. | User interface for facts query engine with snippets from information sources that include query terms and answer terms |
US8682913B1 (en) | 2005-03-31 | 2014-03-25 | Google Inc. | Corroborating facts extracted from multiple sources |
US9208229B2 (en) | 2005-03-31 | 2015-12-08 | Google Inc. | Anchor text summarization for corroboration |
US7831545B1 (en) | 2005-05-31 | 2010-11-09 | Google Inc. | Identifying the unifying subject of a set of facts |
US7567976B1 (en) * | 2005-05-31 | 2009-07-28 | Google Inc. | Merging objects in a facts database |
US8996470B1 (en) | 2005-05-31 | 2015-03-31 | Google Inc. | System for ensuring the internal consistency of a fact repository |
US8677377B2 (en) | 2005-09-08 | 2014-03-18 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US7492949B1 (en) | 2005-09-20 | 2009-02-17 | Patrick William Jamieson | Process and system for the semantic selection of document templates |
US7856100B2 (en) * | 2005-12-19 | 2010-12-21 | Microsoft Corporation | Privacy-preserving data aggregation using homomorphic encryption |
CA2530928A1 (en) * | 2005-12-20 | 2007-06-20 | Ibm Canada Limited - Ibm Canada Limitee | Recommending solutions with an expert system |
US8260785B2 (en) * | 2006-02-17 | 2012-09-04 | Google Inc. | Automatic object reference identification and linking in a browseable fact repository |
US7991797B2 (en) | 2006-02-17 | 2011-08-02 | Google Inc. | ID persistence through normalization |
US8700568B2 (en) | 2006-02-17 | 2014-04-15 | Google Inc. | Entity normalization via name normalization |
US9318108B2 (en) | 2010-01-18 | 2016-04-19 | Apple Inc. | Intelligent automated assistant |
US8122026B1 (en) | 2006-10-20 | 2012-02-21 | Google Inc. | Finding and disambiguating references to entities on web pages |
US8347202B1 (en) | 2007-03-14 | 2013-01-01 | Google Inc. | Determining geographic locations for place names in a fact repository |
US8977255B2 (en) | 2007-04-03 | 2015-03-10 | Apple Inc. | Method and system for operating a multi-function portable electronic device using voice-activation |
US8239350B1 (en) | 2007-05-08 | 2012-08-07 | Google Inc. | Date ambiguity resolution |
FI20075349L (en) * | 2007-05-14 | 2008-11-15 | Stiftelsen Arcada | Method and arrangement in quality control |
US7966291B1 (en) | 2007-06-26 | 2011-06-21 | Google Inc. | Fact-based object merging |
US7970766B1 (en) | 2007-07-23 | 2011-06-28 | Google Inc. | Entity type assignment |
US8738643B1 (en) | 2007-08-02 | 2014-05-27 | Google Inc. | Learning synonymous object names from anchor texts |
US7613694B2 (en) * | 2007-08-24 | 2009-11-03 | Omholt Ray E | Computer-implemented method and apparatus for recording and organizing multiple responses to queries used to create a legacy profile in a manner that expands memory recall |
US9053089B2 (en) | 2007-10-02 | 2015-06-09 | Apple Inc. | Part-of-speech tagging using latent analogy |
US8812435B1 (en) | 2007-11-16 | 2014-08-19 | Google Inc. | Learning objects and facts from documents |
US9330720B2 (en) | 2008-01-03 | 2016-05-03 | Apple Inc. | Methods and apparatus for altering audio output signals |
US8065143B2 (en) | 2008-02-22 | 2011-11-22 | Apple Inc. | Providing text input using speech data and non-speech data |
JP5182361B2 (en) * | 2008-03-17 | 2013-04-17 | 富士通株式会社 | Information acquisition support device |
US8996376B2 (en) | 2008-04-05 | 2015-03-31 | Apple Inc. | Intelligent text-to-speech conversion |
US10496753B2 (en) | 2010-01-18 | 2019-12-03 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
US8464150B2 (en) | 2008-06-07 | 2013-06-11 | Apple Inc. | Automatic language identification for dynamic text processing |
US20100030549A1 (en) | 2008-07-31 | 2010-02-04 | Lee Michael M | Mobile device having human language translation capability with positional feedback |
US8768702B2 (en) | 2008-09-05 | 2014-07-01 | Apple Inc. | Multi-tiered voice feedback in an electronic device |
US8898568B2 (en) | 2008-09-09 | 2014-11-25 | Apple Inc. | Audio user interface |
US20100063797A1 (en) * | 2008-09-09 | 2010-03-11 | Microsoft Corporation | Discovering question and answer pairs |
US8712776B2 (en) | 2008-09-29 | 2014-04-29 | Apple Inc. | Systems and methods for selective text to speech synthesis |
US8676904B2 (en) | 2008-10-02 | 2014-03-18 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US9959870B2 (en) | 2008-12-11 | 2018-05-01 | Apple Inc. | Speech recognition involving a mobile device |
US8862252B2 (en) | 2009-01-30 | 2014-10-14 | Apple Inc. | Audio user interface for displayless electronic device |
US8380507B2 (en) | 2009-03-09 | 2013-02-19 | Apple Inc. | Systems and methods for determining the language to use for speech generated by a text to speech engine |
JP5325981B2 (en) * | 2009-05-26 | 2013-10-23 | 株式会社日立製作所 | Management server and management system |
US9858925B2 (en) | 2009-06-05 | 2018-01-02 | Apple Inc. | Using context information to facilitate processing of commands in a virtual assistant |
US10241644B2 (en) | 2011-06-03 | 2019-03-26 | Apple Inc. | Actionable reminder entries |
US10540976B2 (en) | 2009-06-05 | 2020-01-21 | Apple Inc. | Contextual voice commands |
US20120311585A1 (en) | 2011-06-03 | 2012-12-06 | Apple Inc. | Organizing task items that represent tasks to perform |
US10241752B2 (en) | 2011-09-30 | 2019-03-26 | Apple Inc. | Interface for a virtual digital assistant |
US9431006B2 (en) | 2009-07-02 | 2016-08-30 | Apple Inc. | Methods and apparatuses for automatic speech recognition |
US8386410B2 (en) * | 2009-07-22 | 2013-02-26 | International Business Machines Corporation | System and method for semantic information extraction framework for integrated systems management |
US8682649B2 (en) | 2009-11-12 | 2014-03-25 | Apple Inc. | Sentiment prediction from textual data |
US8311838B2 (en) | 2010-01-13 | 2012-11-13 | Apple Inc. | Devices and methods for identifying a prompt corresponding to a voice input in a sequence of prompts |
US8381107B2 (en) | 2010-01-13 | 2013-02-19 | Apple Inc. | Adaptive audio feedback system and method |
US10276170B2 (en) | 2010-01-18 | 2019-04-30 | Apple Inc. | Intelligent automated assistant |
US10679605B2 (en) | 2010-01-18 | 2020-06-09 | Apple Inc. | Hands-free list-reading by intelligent automated assistant |
US10553209B2 (en) | 2010-01-18 | 2020-02-04 | Apple Inc. | Systems and methods for hands-free notification summaries |
US10705794B2 (en) | 2010-01-18 | 2020-07-07 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
WO2011089450A2 (en) | 2010-01-25 | 2011-07-28 | Andrew Peter Nelson Jerram | Apparatuses, methods and systems for a digital conversation management platform |
US8682667B2 (en) | 2010-02-25 | 2014-03-25 | Apple Inc. | User profiling for selecting user specific voice input processing information |
US8713021B2 (en) | 2010-07-07 | 2014-04-29 | Apple Inc. | Unsupervised document clustering using latent semantic density analysis |
US8719006B2 (en) | 2010-08-27 | 2014-05-06 | Apple Inc. | Combined statistical and rule-based part-of-speech tagging for text-to-speech synthesis |
US8719014B2 (en) | 2010-09-27 | 2014-05-06 | Apple Inc. | Electronic device with text error correction based on voice recognition data |
US10762293B2 (en) | 2010-12-22 | 2020-09-01 | Apple Inc. | Using parts-of-speech tagging and named entity recognition for spelling correction |
US10515147B2 (en) | 2010-12-22 | 2019-12-24 | Apple Inc. | Using statistical language models for contextual lookup |
JP5648911B2 (en) * | 2010-12-27 | 2015-01-07 | インターナショナル・ビジネス・マシーンズ・コーポレーションInternational Business Machines Corporation | System, program and method |
US8781836B2 (en) | 2011-02-22 | 2014-07-15 | Apple Inc. | Hearing assistance system for providing consistent human speech |
US9262612B2 (en) | 2011-03-21 | 2016-02-16 | Apple Inc. | Device access using voice authentication |
US10057736B2 (en) | 2011-06-03 | 2018-08-21 | Apple Inc. | Active transport based notifications |
US10672399B2 (en) | 2011-06-03 | 2020-06-02 | Apple Inc. | Switching between text data and audio data based on a mapping |
US8812294B2 (en) | 2011-06-21 | 2014-08-19 | Apple Inc. | Translating phrases from one language into another using an order-based set of declarative rules |
US8706472B2 (en) | 2011-08-11 | 2014-04-22 | Apple Inc. | Method for disambiguating multiple readings in language conversion |
US8994660B2 (en) | 2011-08-29 | 2015-03-31 | Apple Inc. | Text correction processing |
US8762156B2 (en) | 2011-09-28 | 2014-06-24 | Apple Inc. | Speech recognition repair using contextual information |
US10134385B2 (en) | 2012-03-02 | 2018-11-20 | Apple Inc. | Systems and methods for name pronunciation |
US9483461B2 (en) | 2012-03-06 | 2016-11-01 | Apple Inc. | Handling speech synthesis of content for multiple languages |
US9280610B2 (en) | 2012-05-14 | 2016-03-08 | Apple Inc. | Crowd sourcing information to fulfill user requests |
US10417037B2 (en) | 2012-05-15 | 2019-09-17 | Apple Inc. | Systems and methods for integrating third party services with a digital assistant |
US8775442B2 (en) | 2012-05-15 | 2014-07-08 | Apple Inc. | Semantic search using a single-source semantic model |
US10019994B2 (en) | 2012-06-08 | 2018-07-10 | Apple Inc. | Systems and methods for recognizing textual identifiers within a plurality of words |
US9721563B2 (en) | 2012-06-08 | 2017-08-01 | Apple Inc. | Name recognition system |
US9495129B2 (en) | 2012-06-29 | 2016-11-15 | Apple Inc. | Device, method, and user interface for voice-activated navigation and browsing of a document |
US9002769B2 (en) * | 2012-07-03 | 2015-04-07 | Siemens Aktiengesellschaft | Method and system for supporting a clinical diagnosis |
US9576574B2 (en) | 2012-09-10 | 2017-02-21 | Apple Inc. | Context-sensitive handling of interruptions by intelligent digital assistant |
US9547647B2 (en) | 2012-09-19 | 2017-01-17 | Apple Inc. | Voice-based media searching |
US8935167B2 (en) | 2012-09-25 | 2015-01-13 | Apple Inc. | Exemplar-based latent perceptual modeling for automatic speech recognition |
KR20230137475A (en) | 2013-02-07 | 2023-10-04 | 애플 인크. | Voice trigger for a digital assistant |
US10642574B2 (en) | 2013-03-14 | 2020-05-05 | Apple Inc. | Device, method, and graphical user interface for outputting captions |
US10652394B2 (en) | 2013-03-14 | 2020-05-12 | Apple Inc. | System and method for processing voicemail |
US9368114B2 (en) | 2013-03-14 | 2016-06-14 | Apple Inc. | Context-sensitive handling of interruptions |
US10572476B2 (en) | 2013-03-14 | 2020-02-25 | Apple Inc. | Refining a search based on schedule items |
US9977779B2 (en) | 2013-03-14 | 2018-05-22 | Apple Inc. | Automatic supplementation of word correction dictionaries |
US9733821B2 (en) | 2013-03-14 | 2017-08-15 | Apple Inc. | Voice control to diagnose inadvertent activation of accessibility features |
US10748529B1 (en) | 2013-03-15 | 2020-08-18 | Apple Inc. | Voice activated device for use with a voice-based digital assistant |
US11151899B2 (en) | 2013-03-15 | 2021-10-19 | Apple Inc. | User training by intelligent digital assistant |
WO2014144579A1 (en) | 2013-03-15 | 2014-09-18 | Apple Inc. | System and method for updating an adaptive speech recognition model |
WO2014168730A2 (en) | 2013-03-15 | 2014-10-16 | Apple Inc. | Context-sensitive handling of interruptions |
US9922642B2 (en) | 2013-03-15 | 2018-03-20 | Apple Inc. | Training an at least partial voice command system |
WO2014197336A1 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for detecting errors in interactions with a voice-based digital assistant |
US9582608B2 (en) | 2013-06-07 | 2017-02-28 | Apple Inc. | Unified ranking with entropy-weighted information for phrase-based semantic auto-completion |
WO2014197334A2 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
WO2014197335A1 (en) | 2013-06-08 | 2014-12-11 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
EP3937002A1 (en) | 2013-06-09 | 2022-01-12 | Apple Inc. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
US10176167B2 (en) | 2013-06-09 | 2019-01-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
KR101809808B1 (en) | 2013-06-13 | 2017-12-15 | 애플 인크. | System and method for emergency calls initiated by voice command |
JP6163266B2 (en) | 2013-08-06 | 2017-07-12 | アップル インコーポレイテッド | Automatic activation of smart responses based on activation from remote devices |
US20150127631A1 (en) * | 2013-11-05 | 2015-05-07 | International Business Machines Corporation | Best available alternative dialog |
US10296160B2 (en) | 2013-12-06 | 2019-05-21 | Apple Inc. | Method for extracting salient dialog usage from live data |
CA2909413C (en) * | 2014-03-28 | 2019-06-18 | Casebank Technologies Inc. | Methods and systems for troubleshooting problems in complex systems using multiple knowledgebases |
US9620105B2 (en) | 2014-05-15 | 2017-04-11 | Apple Inc. | Analyzing audio input for efficient speech and music recognition |
US10592095B2 (en) | 2014-05-23 | 2020-03-17 | Apple Inc. | Instantaneous speaking of content on touch devices |
US9502031B2 (en) | 2014-05-27 | 2016-11-22 | Apple Inc. | Method for supporting dynamic grammars in WFST-based ASR |
US9715875B2 (en) | 2014-05-30 | 2017-07-25 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US10078631B2 (en) | 2014-05-30 | 2018-09-18 | Apple Inc. | Entropy-guided text prediction using combined word and character n-gram language models |
US9842101B2 (en) | 2014-05-30 | 2017-12-12 | Apple Inc. | Predictive conversion of language input |
US9734193B2 (en) | 2014-05-30 | 2017-08-15 | Apple Inc. | Determining domain salience ranking from ambiguous words in natural speech |
US9430463B2 (en) | 2014-05-30 | 2016-08-30 | Apple Inc. | Exemplar-based natural language processing |
US10289433B2 (en) | 2014-05-30 | 2019-05-14 | Apple Inc. | Domain specific language for encoding assistant dialog |
EP3480811A1 (en) | 2014-05-30 | 2019-05-08 | Apple Inc. | Multi-command single utterance input method |
US9785630B2 (en) | 2014-05-30 | 2017-10-10 | Apple Inc. | Text prediction using combined word N-gram and unigram language models |
US10170123B2 (en) | 2014-05-30 | 2019-01-01 | Apple Inc. | Intelligent assistant for home automation |
US9633004B2 (en) | 2014-05-30 | 2017-04-25 | Apple Inc. | Better resolution when referencing to concepts |
US9760559B2 (en) | 2014-05-30 | 2017-09-12 | Apple Inc. | Predictive text input |
US20150379419A1 (en) * | 2014-06-26 | 2015-12-31 | International Business Machines Corporation | Ghost-pattern analyzer |
US10659851B2 (en) | 2014-06-30 | 2020-05-19 | Apple Inc. | Real-time digital assistant knowledge updates |
US9338493B2 (en) | 2014-06-30 | 2016-05-10 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US10446141B2 (en) | 2014-08-28 | 2019-10-15 | Apple Inc. | Automatic speech recognition based on user feedback |
US9818400B2 (en) | 2014-09-11 | 2017-11-14 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US10789041B2 (en) | 2014-09-12 | 2020-09-29 | Apple Inc. | Dynamic thresholds for always listening speech trigger |
US9886432B2 (en) | 2014-09-30 | 2018-02-06 | Apple Inc. | Parsimonious handling of word inflection via categorical stem + suffix N-gram language models |
US9668121B2 (en) | 2014-09-30 | 2017-05-30 | Apple Inc. | Social reminders |
US9646609B2 (en) | 2014-09-30 | 2017-05-09 | Apple Inc. | Caching apparatus for serving phonetic pronunciations |
US10127911B2 (en) | 2014-09-30 | 2018-11-13 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US10074360B2 (en) | 2014-09-30 | 2018-09-11 | Apple Inc. | Providing an indication of the suitability of speech recognition |
US10552013B2 (en) | 2014-12-02 | 2020-02-04 | Apple Inc. | Data detection |
US9711141B2 (en) | 2014-12-09 | 2017-07-18 | Apple Inc. | Disambiguating heteronyms in speech synthesis |
US9865280B2 (en) | 2015-03-06 | 2018-01-09 | Apple Inc. | Structured dictation using intelligent automated assistants |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US9721566B2 (en) | 2015-03-08 | 2017-08-01 | Apple Inc. | Competing devices responding to voice triggers |
US9886953B2 (en) | 2015-03-08 | 2018-02-06 | Apple Inc. | Virtual assistant activation |
US9899019B2 (en) | 2015-03-18 | 2018-02-20 | Apple Inc. | Systems and methods for structured stem and suffix language models |
WO2016156433A1 (en) | 2015-03-31 | 2016-10-06 | British Telecommunications Public Limited Company | Network operation |
US9842105B2 (en) | 2015-04-16 | 2017-12-12 | Apple Inc. | Parsimonious continuous-space phrase representations for natural language processing |
US10083688B2 (en) | 2015-05-27 | 2018-09-25 | Apple Inc. | Device voice control for selecting a displayed affordance |
US11468101B2 (en) | 2015-05-29 | 2022-10-11 | Kuni Ahi LLC | Context-rich key framework implementations for global concept management |
US10127220B2 (en) | 2015-06-04 | 2018-11-13 | Apple Inc. | Language identification from short strings |
US10101822B2 (en) | 2015-06-05 | 2018-10-16 | Apple Inc. | Language input correction |
US10255907B2 (en) | 2015-06-07 | 2019-04-09 | Apple Inc. | Automatic accent detection using acoustic models |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US10186254B2 (en) | 2015-06-07 | 2019-01-22 | Apple Inc. | Context-based endpoint detection |
GB2541034A (en) | 2015-07-31 | 2017-02-08 | British Telecomm | Network operation |
US10671428B2 (en) | 2015-09-08 | 2020-06-02 | Apple Inc. | Distributed personal assistant |
US10747498B2 (en) | 2015-09-08 | 2020-08-18 | Apple Inc. | Zero latency digital assistant |
US9697820B2 (en) | 2015-09-24 | 2017-07-04 | Apple Inc. | Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks |
US11010550B2 (en) | 2015-09-29 | 2021-05-18 | Apple Inc. | Unified language modeling framework for word prediction, auto-completion and auto-correction |
US10366158B2 (en) | 2015-09-29 | 2019-07-30 | Apple Inc. | Efficient word encoding for recurrent neural network language models |
US11587559B2 (en) | 2015-09-30 | 2023-02-21 | Apple Inc. | Intelligent device identification |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10223066B2 (en) | 2015-12-23 | 2019-03-05 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US10446143B2 (en) | 2016-03-14 | 2019-10-15 | Apple Inc. | Identification of voice inputs providing credentials |
US9934775B2 (en) | 2016-05-26 | 2018-04-03 | Apple Inc. | Unit-selection text-to-speech synthesis based on predicted concatenation parameters |
US9972304B2 (en) | 2016-06-03 | 2018-05-15 | Apple Inc. | Privacy preserving distributed evaluation framework for embedded personalized systems |
US10249300B2 (en) | 2016-06-06 | 2019-04-02 | Apple Inc. | Intelligent list reading |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
DK179309B1 (en) | 2016-06-09 | 2018-04-23 | Apple Inc | Intelligent automated assistant in a home environment |
US10067938B2 (en) | 2016-06-10 | 2018-09-04 | Apple Inc. | Multilingual word prediction |
US10490187B2 (en) | 2016-06-10 | 2019-11-26 | Apple Inc. | Digital assistant providing automated status report |
US10192552B2 (en) | 2016-06-10 | 2019-01-29 | Apple Inc. | Digital assistant providing whispered speech |
US10509862B2 (en) | 2016-06-10 | 2019-12-17 | Apple Inc. | Dynamic phrase expansion of language input |
US10586535B2 (en) | 2016-06-10 | 2020-03-10 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
DK179415B1 (en) | 2016-06-11 | 2018-06-14 | Apple Inc | Intelligent device arbitration and control |
DK201670540A1 (en) | 2016-06-11 | 2018-01-08 | Apple Inc | Application integration with a digital assistant |
DK179049B1 (en) | 2016-06-11 | 2017-09-18 | Apple Inc | Data driven natural language event detection and classification |
DK179343B1 (en) | 2016-06-11 | 2018-05-14 | Apple Inc | Intelligent task discovery |
US10593346B2 (en) | 2016-12-22 | 2020-03-17 | Apple Inc. | Rank-reduced token representation for automatic speech recognition |
DK179745B1 (en) | 2017-05-12 | 2019-05-01 | Apple Inc. | SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT |
DK201770431A1 (en) | 2017-05-15 | 2018-12-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
US20180330802A1 (en) * | 2017-05-15 | 2018-11-15 | Koninklijke Philips N.V. | Adaptive patient questionnaire generation system and method |
CN111259659B (en) * | 2020-01-14 | 2023-07-04 | 北京百度网讯科技有限公司 | Information processing method and device |
US11763182B1 (en) * | 2020-05-07 | 2023-09-19 | Jared Anders Newcombe | Software facilitating decision making method |
CN112861276B (en) * | 2021-01-12 | 2022-11-08 | 北京理工大学 | Blast furnace burden surface optimization method based on data and knowledge dual drive |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5319739A (en) * | 1990-11-22 | 1994-06-07 | Hitachi, Ltd. | Method for retrieving case for problem and inferring solution of the problem and system thereof |
EP0694836A2 (en) * | 1994-07-28 | 1996-01-31 | Hitachi Europe Limited | A case base system |
Family Cites Families (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4658370A (en) * | 1984-06-07 | 1987-04-14 | Teknowledge, Inc. | Knowledge engineering tool |
US4697243A (en) * | 1985-07-25 | 1987-09-29 | Westinghouse Electric Corp. | Methods of servicing an elevator system |
US4763277A (en) * | 1986-01-17 | 1988-08-09 | International Business Machines Corporation | Method for obtaining information in an expert system |
US4964125A (en) * | 1988-08-19 | 1990-10-16 | Hughes Aircraft Company | Method and apparatus for diagnosing faults |
US4985857A (en) * | 1988-08-19 | 1991-01-15 | General Motors Corporation | Method and apparatus for diagnosing machines |
US5282265A (en) * | 1988-10-04 | 1994-01-25 | Canon Kabushiki Kaisha | Knowledge information processing system |
US5107497A (en) * | 1989-07-28 | 1992-04-21 | At&T Bell Laboratories | Technique for producing an expert system for system fault diagnosis |
JP3224226B2 (en) * | 1989-09-22 | 2001-10-29 | 株式会社リコー | Fault diagnosis expert system |
US5224206A (en) * | 1989-12-01 | 1993-06-29 | Digital Equipment Corporation | System and method for retrieving justifiably relevant cases from a case library |
JP3266246B2 (en) * | 1990-06-15 | 2002-03-18 | インターナシヨナル・ビジネス・マシーンズ・コーポレーシヨン | Natural language analysis apparatus and method, and knowledge base construction method for natural language analysis |
US5586218A (en) * | 1991-03-04 | 1996-12-17 | Inference Corporation | Autonomous learning and reasoning agent |
EP0575473B1 (en) * | 1991-03-04 | 2000-04-26 | Inference Corporation | Case-based reasoning system |
US5317677A (en) * | 1992-04-16 | 1994-05-31 | Hughes Aircraft Company | Matching technique for context sensitive rule application |
US5402524A (en) * | 1992-12-22 | 1995-03-28 | Mitsubishi Denki Kabushiki Kaisha | Case-based knowledge source for artificial intelligence software shell |
JP3067966B2 (en) * | 1993-12-06 | 2000-07-24 | 松下電器産業株式会社 | Apparatus and method for retrieving image parts |
US5644686A (en) * | 1994-04-29 | 1997-07-01 | International Business Machines Corporation | Expert system and method employing hierarchical knowledge base, and interactive multimedia/hypermedia applications |
US5715468A (en) * | 1994-09-30 | 1998-02-03 | Budzinski; Robert Lucius | Memory system for storing and retrieving experience and knowledge with natural language |
US5717835A (en) * | 1995-01-11 | 1998-02-10 | International Business Machines Corporation | Simple approach to case-based reasoning for data navigation tasks |
JPH08194618A (en) * | 1995-01-18 | 1996-07-30 | Kobe Steel Ltd | Case base inferring device |
-
1997
- 1997-04-08 US US08/835,558 patent/US5822743A/en not_active Expired - Lifetime
- 1997-05-26 CA CA002206155A patent/CA2206155C/en not_active Expired - Lifetime
-
1998
- 1998-04-03 WO PCT/CA1998/000306 patent/WO1998045775A1/en active Application Filing
- 1998-04-03 AU AU69137/98A patent/AU6913798A/en not_active Abandoned
- 1998-04-03 GB GB9922102A patent/GB2338097B/en not_active Expired - Lifetime
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5319739A (en) * | 1990-11-22 | 1994-06-07 | Hitachi, Ltd. | Method for retrieving case for problem and inferring solution of the problem and system thereof |
EP0694836A2 (en) * | 1994-07-28 | 1996-01-31 | Hitachi Europe Limited | A case base system |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1218831A1 (en) * | 1999-09-22 | 2002-07-03 | Infoglide Corporation | System and method for performing similarity searching |
EP1218831A4 (en) * | 1999-09-22 | 2007-09-19 | Infoglide Corp | System and method for performing similarity searching |
EP1811520A2 (en) * | 2006-01-17 | 2007-07-25 | Omron Corporation | Factor estimating device, method and program recording medium therefor |
EP1811520A3 (en) * | 2006-01-17 | 2008-12-31 | Omron Corporation | Factor estimating device, method and program recording medium therefor |
CN106663144A (en) * | 2014-08-29 | 2017-05-10 | 皇家飞利浦有限公司 | Method and apparatus for hierarchical data analysis based on mutual correlations |
US10565539B2 (en) | 2014-11-07 | 2020-02-18 | International Business Machines Corporation | Applying area of focus to workflow automation and measuring impact of shifting focus on metrics |
Also Published As
Publication number | Publication date |
---|---|
GB9922102D0 (en) | 1999-11-17 |
AU6913798A (en) | 1998-10-30 |
CA2206155A1 (en) | 1998-10-08 |
CA2206155C (en) | 2000-03-21 |
US5822743A (en) | 1998-10-13 |
GB2338097B (en) | 2002-03-06 |
GB2338097A (en) | 1999-12-08 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US5822743A (en) | Knowledge-based information retrieval system | |
US7689544B2 (en) | Automatic indexing of digital image archives for content-based, context-sensitive searching | |
Yang et al. | Using corpus statistics to remove redundant words in text categorization | |
US7734556B2 (en) | Method and system for discovering knowledge from text documents using associating between concepts and sub-concepts | |
Li et al. | An information filtering model on the Web and its application in JobAgent | |
Chajewska et al. | Utility Elicitation as a Classification Problem. | |
US20090106225A1 (en) | Identification of medical practitioners who emphasize specific medical conditions or medical procedures in their practice | |
Chen et al. | Methods for processing and prioritizing customer demands in variant product design | |
Sadiq et al. | Hybrid intelligent technique for text categorization | |
Bordogna et al. | Modeling vagueness in information retrieval | |
Hu | Personalized web search by using learned user profiles in re-ranking | |
Dubitzky et al. | An advanced case-knowledge architecture based on fuzzy objects | |
Radwan et al. | Thyroid diagnosis based technique on rough sets with modified similarity relation | |
Lieber | Strong, fuzzy and smooth hierarchical classification for case-based problem solving | |
Reza Montazemi et al. | A framework for retrieval in case-based reasoning systems | |
Ho et al. | Documents clustering using tolerance rough set model and its application to information retrieval | |
Remadi et al. | The triangular intuitionistic fuzzy extension of the CODAS method for solving multi-criteria group decision making | |
Brini et al. | Relevance feedback: introduction of partial assessments for query expansion. | |
Voglozin et al. | Querying a summary of database | |
Duval | Abduction for explanation-based learning | |
WO2004006124A2 (en) | Text-representation, text-matching and text-classification code, system and method | |
Vale et al. | Improving text retrieval in medical collections through automatic categorization | |
Falchuk et al. | Location-Based Services | |
De Tré et al. | Conjunctive aggregation of extended possibilistic truth values and flexible database querying | |
Hsieh | Rule extraction with rough-fuzzy hybridization method |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AK | Designated states |
Kind code of ref document: A1 Designated state(s): AL AM AT AU AZ BA BB BG BR BY CA CH CN CU CZ DE DK EE ES FI GB GE GH GM GW HU ID IL IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MD MG MK MN MW MX NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT UA UG US UZ VN YU ZW |
|
AL | Designated countries for regional patents |
Kind code of ref document: A1 Designated state(s): GH GM KE LS MW SD SZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE BF BJ CF CG CI CM GA GN ML MR NE SN TD TG |
|
DFPE | Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101) | ||
121 | Ep: the epo has been informed by wipo that ep was designated in this application | ||
ENP | Entry into the national phase |
Ref country code: GB Ref document number: 9922102 Kind code of ref document: A Format of ref document f/p: F |
|
REG | Reference to national code |
Ref country code: DE Ref legal event code: 8642 |
|
NENP | Non-entry into the national phase |
Ref country code: JP Ref document number: 1998542177 Format of ref document f/p: F |
|
122 | Ep: pct application non-entry in european phase | ||
NENP | Non-entry into the national phase |
Ref country code: CA |