WO2010125707A1 - Système de recharge et support contenant un programme de recherche - Google Patents

Système de recharge et support contenant un programme de recherche Download PDF

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Publication number
WO2010125707A1
WO2010125707A1 PCT/JP2009/070729 JP2009070729W WO2010125707A1 WO 2010125707 A1 WO2010125707 A1 WO 2010125707A1 JP 2009070729 W JP2009070729 W JP 2009070729W WO 2010125707 A1 WO2010125707 A1 WO 2010125707A1
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dimension
matching table
matching
unit
target data
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PCT/JP2009/070729
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English (en)
Japanese (ja)
Inventor
美穂子 北村
稔樹 村田
達哉 介弘
さより 下畑
幾夫 折原
篤司 池野
茂博 加藤
美穂 田中
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沖電気工業株式会社
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Publication of WO2010125707A1 publication Critical patent/WO2010125707A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G06F16/367—Ontology

Definitions

  • the present invention relates to a search system and a search program storage medium. For example, by repeatedly asking questions in a dialog, the user's needs and values are extracted, and a variety of services and contents are included. Therefore, the present invention can be applied to an information search system that searches for items that match user needs and values.
  • Communication through IT will change from text information to moving images and conversations stored by individuals, broadcast images and movie images, product information and sensor information, production history and traffic history, Expanded to various information media such as climate information, and its usage phase is dynamic to various aspects such as information home appliances, in-vehicle terminals, electronic tags, public transportation, corporate production / distribution sites, market stores, etc. It is expected to expand.
  • Conventional information retrieval / analysis technology generally accepts keyword input, searches information on the network for information that matches the input keyword, and introduces them in descending order of the number of hits.
  • keyword automatic expansion technology for displaying keywords used together with input keywords, a technology for recommending products and the like through personal communication by many users, and the like.
  • the user can search a lot of information including the input keyword, but these information statistically summarizes the results searched by many other users. Information, not the real needs or desired personalized information of the user.
  • the inventor of the present application makes use of a laddering technique that draws out the needs and values of the user by repeating questions that are gradually delved into the dialogue, and the user and the system repeat the dialogue.
  • Proposed information retrieval / analysis technology that retrieves the needs of services and contents that were difficult for users to express, and searches for a wide variety of services and contents that match them. (JP 2009-193532, JP 2009-193533).
  • a question sentence is generated based on domain knowledge and presented to the user, the answer from the user is interpreted at a semantic level by intention analysis, information extracted from the user and the search target in advance
  • a search is realized by matching an analysis (for example, an analysis result to be interpreted from a user's answer) and a result of analyzing the search target data.
  • Ontology defines and systematizes knowledge about a specific domain, knowledge across multiple domains, or general-purpose knowledge.
  • knowledge refers to a term / vocabulary and its meaning, and the relationship between other terms / vocabulary, and the domain refers to a field such as medicine, engineering, real estate, automobile, and finance.
  • Ontology can systematically express the concept of words and the relationship between words, so in an interactive search system, it is possible to use what is described in the ontology for each domain as domain knowledge. .
  • the present invention uses the concept of multidimensional ontology in view of the above problems.
  • a matching table is required in which the search key of the target data corresponding to the user's answer and the degree of correspondence are entered.
  • This matching table is expressed as a network indicating the relationship between words, and can be expressed by an ontology. Therefore, by considering this matching table as an ontology and dynamically switching the matching table according to the user's intention and situation, the user's intention is accurately analyzed from the user's remarks, and according to the user's intention and situation.
  • a search system and a search program capable of dynamically switching conditions and selection criteria necessary for a search are provided.
  • the search system uses a matching table having a correspondence relationship between a value to be searched for and a value of target data corresponding to this value and a weight for the correspondence.
  • a target data storage unit for storing one or more target data to be searched;
  • a dimension-specific matching table storage unit that stores one or more dimension-specific matching tables having different weights depending on the dimension of the combination of conditions;
  • a basic matching table storage unit that stores a basic matching table in which the above weights are set when there is no condition;
  • a dimension-specific matching table extraction unit that extracts each dimension-specific matching table from the dimension-specific matching table storage unit, Of the extracted matching tables for each dimension, the values entered by overwriting the basic matching table with the contents of each dimension matching table in descending order of dimension
  • a multidimensional matching table generation unit for generating a multidimensional matching table for searching for target data from And a matching unit that searches for one or a plurality of target data that matches a value to be searched from the target data storage unit using the multi-dimensional matching table generated by the multi-dimensional matching table generation unit.
  • the search program of the second aspect of the present invention uses a matching table having a correspondence relationship between a value to be searched for and a value of target data corresponding to this value and a weight of the correspondence, from among target data to be searched.
  • the search program is A target data storage unit for storing one or more target data to be searched;
  • a dimension-specific matching table storage unit that stores one or a plurality of dimension-specific matching tables having different weights depending on the dimension of a combination of conditions;
  • a basic matching table storage unit that stores a basic matching table in which the above weights are set when there is no condition;
  • a dimension-specific matching table retrieval process for retrieving each dimension-specific matching table that meets the condition from the dimension-specific matching table storage unit, Of the extracted matching tables for each dimension, the contents of the matching table for
  • Multi-dimensional matching table generation processing for generating a multi-dimensional matching table for searching for target data from input values, Using the multidimensional matching table generated by the multidimensional matching table generation unit to perform a matching process for searching for one or more target data that matches a value to be searched from the target data storage unit .
  • the user's intention is accurately analyzed from the user's remarks, and the conditions and selection criteria necessary for the search are dynamically switched according to the user's intention and situation. be able to.
  • 1 is an overall configuration diagram illustrating an overall configuration of an interactive search system according to a first embodiment. It is explanatory drawing explaining the basic concept of a multidimensional ontology (the 1). It is explanatory drawing explaining the basic concept of a multidimensional ontology (the 2). It is a flowchart which shows the whole flow of the dialog control system of 1st Embodiment. It is explanatory drawing explaining a property definition (table format). It is explanatory drawing explaining a property definition (network format). It is a flowchart which shows the multidimensional ontology creation process of 1st Embodiment. It is explanatory drawing explaining the user data which the domain knowledge manager acquired. It is explanatory drawing explaining the dimension list
  • generation process of a multidimensional ontology. It is a flowchart which shows the dialogue control process linked with the search process of 1st Embodiment. It is a flowchart which shows the matching process using the multidimensional ontology of 1st Embodiment. It is explanatory drawing explaining the user data which the domain knowledge manager acquired. It is explanatory drawing explaining the matching table of a base dimension. It is explanatory drawing explaining the matching table of a "/ person / personality / current occupation cook" dimension. It is explanatory drawing explaining the matching table made multi-dimensional. It is explanatory drawing explaining the user data which the domain knowledge manager acquired.
  • the multi-dimensional ontology refers to an ontology that holds a large number of semantic spaces in a multi-dimensional manner and can instantly switch the semantic space to be used according to user data or target data / situations obtained through dialogue.
  • the current ontology only expresses the concept of words and the relationship between words from a certain point of view. Therefore, even if the current ontology is used for an interactive search system, static data Can only be obtained.
  • an ontology can be maintained in a multidimensional manner, and a mechanism for automatically moving between dimensions whenever the information extracted from the user changes is proposed.
  • 2 and 3 are explanatory diagrams for explaining the basic concept of the multidimensional ontology. 2 and 3 exemplify a case where the interactive search / analysis system 1 of the first embodiment is used for an occupation introduction system.
  • the dimension in which the property is set is positioned according to the priority, and the base dimension and a special dimension other than the base (hereinafter referred to as a sub dimension). ).
  • the base dimension is the dimension with the lowest priority, and defines all classes and properties defined in the sub dimensions. Further, only necessary classes and properties are defined for each sub dimension, and the higher the number of conditions, the higher the priority. When the number of conditions is the same, for example, priorities are assigned in advance by the developer.
  • FIGS. 2 and 3 illustrate a 6-dimensional ontology including the base dimension.
  • circles on each sub dimension indicate classes and properties defined in each sub dimension.
  • FIG. 2 shows “when nothing (data) is acquired” from the user.
  • the dialogue has not yet progressed.
  • the dialogue proceeds using classes and properties (not shown in FIG. 2) defined in the base dimension.
  • the interactive search system 1 includes at least a dialogue management unit 10, an intention analysis unit 20, a domain knowledge management unit 30, a matching unit 40, and a matching target analysis unit 50. .
  • the interactive search system 1 is connected to a data providing server 80 that provides data to the user, acquires the target data held by the data providing server 80, and stores it in the expansion target data 54 of the matching target analyzing unit 50.
  • the user starts the interactive search system 1 by accessing the Web server 70 using the browser 90.
  • the user interface can be widely applied to a PC having a communication function, a portable terminal, a dedicated terminal, or the like, but may be provided with a voice synthesizing / recognizing unit that converts a voice uttered by the user into a text. Good.
  • the dialog management unit 10 manages the progress of a dialog with a user who wants to search, and includes a dialog control unit 11 that controls the progress of the dialog.
  • the dialogue control unit 11 receives a multidimensional ontology from the domain knowledge management unit 30 and creates a question sentence (also referred to as a system utterance sentence) related to the progress of the dialogue based on the multidimensional ontology.
  • a question sentence also referred to as a system utterance sentence
  • the dialogue control unit 11 gives an answer sentence (also referred to as a user utterance sentence) from the user to the question sentence to the intention analysis unit 20 to analyze the contents of the user utterance sentence.
  • the dialogue control unit 11 receives the analysis result of the user utterance, the dialogue control unit 11 stores the analysis result of the user utterance as user data in the extended user data 53 of the matching target analysis unit 50.
  • the dialogue control unit 11 upon receiving the intention analysis result from the intention analysis unit 20, the dialogue control unit 11 gives the intention analysis result to the domain knowledge management unit 30 to generate a multidimensional ontology used for the progress of the dialogue, and the domain knowledge management unit Using the multidimensional ontology from 30, the next question sentence is determined and the dialogue proceeds.
  • the dialogue control unit 11 instructs the matching unit 40 to match user data and target data.
  • Various timings for instructing the matching are set, for example, when a matching instruction is received from the user, when an answer sentence for each question sentence is obtained, or when all question items are completed. be able to.
  • the intention analysis unit 20 receives a user utterance sentence answered by the user in response to the question sentence acquired from the dialogue control unit 11, and analyzes the contents of the user utterance sentence. In addition, the intention analysis unit 20 gives the analyzed intention analysis result to the dialogue control unit 11 in an ontology format. Furthermore, the intention analysis unit 20 performs dynamic switching processing of domain knowledge dictionary and dynamic switching processing based on analysis target sentence information as multidimensional intention analysis processing.
  • the intention analysis unit 20 includes an intention analysis execution unit 21, an intention analysis dictionary 22, and a domain knowledge dictionary manager 23.
  • the intention analysis execution unit 21 refers to the intention analysis dictionary 22 and performs a morpheme analysis unit 211 that performs morphological analysis on the user utterance sentence, and refers to the intention analysis dictionary 22 to construct a syntax for the user utterance sentence. It has a syntax analysis unit 212 that performs analysis.
  • the intention analysis execution unit 21 acquires a class defined in the property definition of the domain knowledge DB 32 by performing morphological analysis and syntax analysis on the user utterance sentence, and gives the acquired class to the domain knowledge management unit 30.
  • the intention analysis dictionary 22 is a dictionary group for analyzing the contents of a user utterance sentence.
  • a morpheme dictionary for example, a Japanese morpheme dictionary
  • a syntax dictionary for example, a Japanese syntax dictionary
  • a base dimension for example, a base dimension
  • each dimension This includes domain knowledge dictionaries that are automatically generated from dimension class definitions.
  • the domain knowledge dictionary manager 23 receives the extended user data from the user data management, rearranges the priorities of the dimension-specific domain dictionaries to be looked up according to the extended user data, This is set in the analysis dictionary 22.
  • the domain knowledge dictionary manager 23 receives the analysis result of the target data (target sentence information) from the data providing server 80 from the intention analysis execution unit 21, and creates a dictionary for each dimension according to the contents of the target data. Sorting is performed, and the sorted syntax dictionary by dimension is set in the intention analysis dictionary 22.
  • the domain knowledge dictionary manager 23 includes a feature extraction unit 231, a condition rearrangement unit 232, and a dictionary setting unit 233.
  • the feature extraction unit 231 extracts a value of a dimension condition when a value that is a dimension condition is included from the intention analysis result.
  • the condition rearrangement unit 232 rearranges the dimension-specific domain dictionary or the dimension-specific syntax dictionary according to a predetermined priority order with respect to the dimension condition extracted by the feature extraction unit 231. Further, the dictionary setting unit 233 sets the domain domain dictionary or the dimension syntax dictionary sorted by the condition sorting unit 232 in the intention analysis dictionary 22.
  • the domain knowledge management unit 30 manages domain knowledge with knowledge of multidimensional ontology for each domain.
  • the domain knowledge management unit 30 includes a domain knowledge manager 31 and a domain knowledge DB 32.
  • the base dimension for constructing the multidimensional ontology and the property definition of each dimension, the class definition and the inference definition, the dimensional condition for constructing the multidimensional ontology, and the priority of the dimensional condition are set.
  • a dimension priority definition table to be defined, a generated multidimensional ontology, and a matching table are stored.
  • the domain knowledge manager 31 receives the intention analysis result from the dialogue control unit 11, extracts features from the intention analysis result, and generates a multidimensional ontology while referring to the property definition and dimension priority definition table of the domain knowledge DB 32. Is.
  • the domain knowledge manager 31 stores the generated multidimensional ontology in the domain knowledge DB 32.
  • the domain knowledge manager 31 regards the matching table as a kind of ontology, and generates a matching table using the multidimensional ontology (hereinafter also referred to as a multidimensional matching table).
  • the matching unit 40 performs matching processing while referring to the multidimensional matching table.
  • the domain knowledge manager 31 of the domain knowledge management unit 30 includes a feature extraction unit 311, a condition matching unit 312, and a multidimensional ontology generation unit 313.
  • the feature extraction unit 311 confirms whether or not a feature (a value corresponding to the dimension condition) as a dimension condition is set in the intention analysis result obtained by analyzing the contents of the user utterance sentence.
  • the feature is extracted.
  • the user data DB 53 managed by the user data management unit 51 stores user information obtained from the user utterance by the dialogue control unit 11.
  • This user data DB 53 holds a user data path indicating the storage location (storage location) of user information and a user data class name indicating the value of user information.
  • the condition collation unit 312 collates the dimension value extracted by the feature extraction unit 311 with the contents of the dimension priority definition table.
  • the multi-dimensional ontology generation unit 313 generates a multi-dimensional ontology or a multi-dimensional matching table by superimposing the dimensional ontology on the basic ontology according to the priority of the dimension priority definition table.
  • the matching unit 40 performs matching between user data stored in the user data management unit 51 and target data stored in the target data management unit 50, and matches conditions desired by the user.
  • the matching unit 40 gives the multi-dimensional matching table received from the dialogue control unit 11 to the matcher 42, or gives the matched information to the dialogue control unit 11 to the match control unit 11, user data and target data And a matcher 42 for performing the matching process.
  • the matching target analysis unit 50 stores user data and target data to be matched, expands to a form that can be easily matched, and stores the expanded user data and target data.
  • the matching target analysis unit 50 includes a user data management unit 51, a target data management unit 52, extended user data 53, and expansion target data 54.
  • FIG. 4 is a flowchart showing the overall flow of the interactive search system 1.
  • the user who uses the interactive search system 1 accesses the designated URL using the browser 90 and activates the interactive search system 1 through the Web server 70.
  • the dialog control unit 11 refers to the property definition stored in the domain knowledge DB 32 through the domain knowledge manager 31 and has the highest priority among the properties having the current pointer in the domain. Select.
  • FIG. 5 is a configuration diagram showing the configuration of the property definition
  • FIG. 6 is a diagram showing a part of the property definition in a network format.
  • the property definition shown in FIG. 5 is an example of a property definition serving as a multidimensional base.
  • the property relationship of each class is described with “human” as a vertex.
  • the property definition has items of a definition area, a property, a value area, and option information.
  • the option information is provided with an importance indicating the degree of determining whether the question is an essential question and a priority indicating the degree of determining the order of questions (that is, the flow of dialogue).
  • the option information defines various functions for the system to smoothly proceed with the conversation, such as a connection sentence generated before the question sentence and various received sentences generated based on the intention analysis result. Yes.
  • property information is described in option information, for example, group and acquisition target.
  • property information can be set without being limited to this.
  • the domain knowledge manager 31 gives the basic question sentence set in the item of the basic question sentence to the dialog control unit 11, and the dialog control unit 11 uses this as a system utterance sentence. It transmits to the user side through the Web server 70.
  • the domain knowledge manager 31 searches and selects a property having the range class in the domain. At this time, when there are a plurality of properties having this range class as the domain, the domain knowledge manager 31 looks at the priority of the option information and selects the property with the highest priority. If the selected property is “acquisition target”, the domain knowledge manager 31 gives the basic question sentence to the dialogue control unit 11, and the dialogue control unit 11 transmits this to the user side as a system utterance sentence. .
  • the priority can be used to achieve the transition to a property having a range class in the domain.
  • FIG. 6 is a configuration diagram in which a part of the property definition shown in FIG. 5 is expanded in a network format.
  • circles indicate classes (that is, domain and value range in FIG. 5), and lines connecting between classes indicate properties.
  • the range when “human” is the vertex and the property is “personality” is “personality”
  • the range when the property is “strength” is “strength that can be used for work”.
  • the value range when the property is “an opportunity to change jobs” is “reason for job change”
  • the value range when the property is “work experience” is “current job”.
  • the definition area to be selected as the initial value is set in the property definition.
  • a pointer is set for the first domain domain class “human” in FIG.
  • the domain knowledge manager 31 sees the range class “personality” in the case of setting “personality” having the highest priority among the properties of the domain class “human” pointed to by the initial pointer.
  • the domain knowledge manager 31 has the domain class “with the highest priority among the properties having the range class“ personality ”as the domain. Select the property of “Personality”-Property “Site visit purpose”-Range class “Site visit purpose”.
  • the domain knowledge manager 31 gives the basic question message of this property “Please tell us the purpose of coming to this site” to the dialog control unit 11, and the dialog control unit 11 uses this basic question message as a system utterance Send to the user side. In this way, a question from the system is started (step S101).
  • the user who has received the system utterance sentence replies, for example, “I want to know what his / her job is”, and transmits this as a reply sentence to the dialog control unit 11 through the Web server 70 (step S102).
  • the dialogue control unit 11 When the dialog control unit 11 receives the user utterance, the dialogue control unit 11 gives the user utterance to the intention analysis unit 20.
  • the intention analysis execution unit 21 uses the intention analysis dictionary 22 to perform morphological analysis and syntactic analysis on the user utterance sentence, and as a result, the intention of the user “I want to know the right job” is obtained. Analyzing and giving the intention analysis result to the dialogue control unit 11 (step S103).
  • the intention analysis result of the intention analysis unit 20 is given to the domain knowledge manager 31, a multidimensional ontology is generated by the domain knowledge manager 31, and the generated multidimensional ontology is stored in the domain knowledge DB 32.
  • the dialogue control unit 11 Upon receiving the intention analysis result from the intention analysis unit 20, the dialogue control unit 11 inquires of the domain knowledge manager 31 about the next question and determines the next question (step S104).
  • the domain knowledge manager 31 refers to the property definition and shifts to the domain class “personality” —the property “current job type” —the range class “job type”, which has the next highest priority after the range class “site visit”. Then, the basic question sentence “What is your current job type?” Is given to the dialogue control unit 11, and the dialogue control unit 11 determines this as a system utterance and transmits it to the user side (step S105).
  • the dialogue between the system and the user is realized by repeatedly presenting the next question sentence and receiving the answer sentence from the user.
  • the search process is processed in the dialog control process (S104), and when the dialog control unit 11 receives a search request from the user, the search result is displayed (details are described in A-3-4).
  • step S104 if the next question is not determined in step S104, that is, if all the questions are finished, the operation of the system is finished.
  • FIG. 7 is a flowchart showing the operation of the multi-dimensional ontology creation process in the domain knowledge manager 31.
  • the domain knowledge manager 31 receives the intention analysis result from the dialogue control unit 11. Moreover, the domain knowledge manager 31 acquires the user data of the user from the user data management unit 51 (step S201).
  • the user data of the user from the user data management unit 51 has the contents shown in FIG. As shown in FIG. 8, the user data is described in an ontology format.
  • the class of the property “nickname” is the class “ ⁇ -chan”, and similarly, the property “stage of job change activity” —the class “sent resume” and the property “life event” ”—Class“ restructuring / bankruptcy ”, property“ current industry ”—class“ pharmaceutical ”, property“ current job type ”—class“ sales ”.
  • the user data indicates a case where the class for the property obtained from the user's answer is used as the data.
  • the user data may be a new class inferred from the user's answer class.
  • the contents may be expanded data.
  • the feature extraction unit 311 of the domain knowledge manager 31 compares the user data with the conditions of all dimensions registered in the domain knowledge DB 32, and extracts the dimension conditions that meet the conditions (step S202). That is, the feature extraction unit 311 confirms whether or not the user data includes values that serve as conditions for all dimensions, and if there is a value that serves as a condition, extracts a value that serves as a condition for that dimension. .
  • condition matching unit 312 rearranges the dimension condition values extracted by the feature extraction unit 311 in descending order of predetermined priority (step S203), and checks whether there is a dimension condition value. And stored in the dimension priority definition table (step S204).
  • the one with a large number of dimension conditions is given higher priority.
  • priorities for each dimension are set in advance, and rearrangement is performed based on the priorities.
  • condition matching unit 312 refers to the dimension list shown in FIG. 9, confirms the priority of the condition values of each dimension, and sorts the condition values of each dimension according to the dimension list.
  • the multidimensional ontology generation unit 313 generates a multidimensional ontology by superimposing the dimensional ontology on the basic ontology according to the priority of the dimension priority definition table (step S205).
  • FIG. 11 is an explanatory diagram for explaining multidimensional ontology generation processing.
  • FIG. 11A shows job class and class options defined in the base dimension.
  • the option acceptance sentence of the class “job type” -class “sales” is defined as “You are doing sales work”.
  • the option acceptance sentence of class “job type” -class “sales” is rewritten to “sales business even in a highly competitive industry”.
  • the contents of the class option definition defined in the base dimension can be changed.
  • an appropriate acceptance sentence can be output according to the job type of the user.
  • the multidimensional ontology generation unit 313 outputs the generated multidimensional ontology to the dialogue control unit 11 (step S206).
  • the generated multidimensional ontology is stored in the domain knowledge DB 32.
  • the domain knowledge manager 31 performs the multidimensional ontology creation process described with reference to FIG. 7 on the target data acquired from the data providing server 80 to determine the intention of the target data. Analyzed and stored in the expansion target data 54.
  • FIG. 12 is a flowchart showing the dialog control process in the interactive search process.
  • the intention analysis unit 20 performs intention analysis on the user utterance, and when the intention analysis result is given to the dialogue control unit 11 (step S301), the dialogue control unit 11 converts the intention analysis result into the extended user data 53. (Step S302).
  • the matching unit 20 starts the matching process.
  • the domain knowledge manager 31 generates a multidimensional matching table, and uses it to search for extension target data from the extended user data, and rearranges the target data in order from the highest search similarity (step S303). .
  • the similarity between all the information obtained from the user and all the target data is calculated and presented to the user in order from the target data having the highest similarity.
  • the dialogue control unit 11 receives the matching result from the matching unit 40 and transmits the matching result to the user side (step S305).
  • step S304 when there is no display request from the user side (step S304), the dialogue control unit 11 does not transmit the matching result and proceeds to step S306.
  • step S306 the dialogue control unit 11 receives the multidimensional ontology from the domain knowledge manager 31, and determines the next question (property). Then, the dialogue control unit 11 transmits the next question sentence to the user side, and ends the process (step S307).
  • FIG. 13 is a flowchart showing the operation of matching processing using a multidimensional ontology.
  • the domain knowledge manager 31 is processing for generating a multidimensional matching table by regarding the matching table as a kind of ontology.
  • the matching unit 40 requests the domain knowledge manager 31 to create a matching table, and the domain knowledge manager 31 regards the matching table as a kind of ontology and generates a multidimensional matching table (step S401). .
  • the domain knowledge manager 31 Upon receiving the matching table creation request, the domain knowledge manager 31 acquires all user data from the extended user data 53.
  • the operation flow of the domain knowledge manager 31 is represented by a flow in which S204 in FIG. 7 does not exist.
  • the feature extraction unit 311 compares the user data with the conditions of all dimensions registered in all the matching tables, and extracts the dimension conditions that meet the conditions (S202).
  • the condition matching unit 312 arranges the dimension conditions extracted by the feature extraction unit 311 in descending order of priority according to a certain standard, and stores them in the dimension priority definition table (S203). ).
  • the multidimensional ontology generation unit 313 generates a multidimensional matching table by superimposing the dimension-specific matching table on the base dimension matching table according to the priority of the dimension priority definition table (S205). (S206).
  • FIG. 14 shows user data (network format) of the nickname “Chan” whose current job type is cook.
  • FIG. 15 shows a configuration example of a base dimension matching table.
  • FIG. 15A shows the base dimension matching table in a table format
  • FIG. 15B shows the base dimension matching table in a network format.
  • the base dimension matching table includes “user data”, “importance”, and “target data” as items.
  • the item “user data” indicates a property (condition) in the user data.
  • the item “target data” indicates a property (condition) in the target data.
  • the item “importance” indicates a weight for a property in matching.
  • the property name is different depending on the relationship between the user data and the target data. This is because the condition name (property name) of the recruitment guidelines (data) of the company recruiting the job change and the condition name extracted from the corresponding user This is because (property name) is different.
  • FIG. 16 shows a dimension matching table referred to when the feature extraction unit 311 extracts the features.
  • the multidimensional ontology generation unit 313 overwrites the base dimension matching table shown in FIG. 15 with the dimension matching table shown in FIG. As a result, as shown in FIG. 17, it is possible to generate a multidimensional matching table in which “the importance of qualification” is changed to “1.0”.
  • FIG. 19 shows a dimension matching table referred to when the feature extraction unit 311 extracts the features.
  • the multidimensional ontology generation unit 313 overwrites the base dimension matching table shown in FIG. 15 with the dimension matching table shown in FIG. As a result, as shown in FIG. 20, it is possible to generate a multidimensional matching table in which the qualification importance is changed to “0.1” and the experience years importance is changed to “0.8”.
  • the matching unit 40 that has received the multidimensional matching table from the domain knowledge manager 31 calculates the degree of similarity with all target data based on the multidimensional matching table (step S402).
  • the matching unit 40 calculates the degree of similarity by weighting with the “importance” described in the matching table using the multidimensional matching table.
  • the matching unit 40 rearranges the target data in descending order of similarity, and uses this as a matching result.
  • the matching unit 40 receives the multidimensional matching table shown in FIG. 17 and is similar to the target data of “Company A” and “Company B” in FIG. A case where the degree is calculated will be described.
  • the matching unit 40 outputs matching results in the order of “Company A” and “Company B” as shown in FIG.
  • the matching unit 40 receives the multi-dimensional matching table shown in FIG. 19 and compares it with the target data of “Company C” and “Company D” in FIG. A case where the similarity is calculated will be described.
  • the matching unit 40 outputs matching results in the order of “Company C” and “Company D” as shown in FIG.
  • FIG. 23 is a flowchart showing processing of the intention analysis execution unit 21 related to multidimensional intention analysis processing (domain knowledge dictionary dynamic switching processing).
  • FIG. 24 is a flowchart showing processing of the domain knowledge dictionary manager 23 related to multidimensional intention analysis processing (domain knowledge dictionary dynamic switching processing).
  • the intention analysis unit 20 receives a user utterance from the dialogue control unit 11 (step S501).
  • the attached information is set as user data in the extended user data 53 (step S502).
  • the intention analysis execution unit 21 divides the sentence into one sentence unit (step S503), and the morpheme analysis unit 211 performs morpheme analysis on the user utterance sentence (step S504).
  • the unit 212 performs syntax analysis (step S505).
  • the intention analysis execution unit 21 performs intention analysis with reference to the intention analysis dictionary 22. At this time, the intention analysis execution unit 21 gives the user data to the domain knowledge dictionary manager 23 (step S506).
  • step S601 when user data is received from the intention analysis execution unit 21 (step S601), the user domain knowledge dictionary manager 23 matches the dimensional condition with the feature extraction unit 231 collating the user data with the dimensional condition of each dimension. A value is extracted (step S602).
  • the condition rearrangement unit 232 rearranges the dimensional domain knowledge dictionaries in priority order according to a certain standard with respect to the dimension condition extracted by the feature extraction unit 311 (step S603), and the dictionary setting unit 233 has a predetermined priority.
  • the dimension-specific domain knowledge dictionary is set in the intention analysis dictionary 22 so as to be looked up by rank (step S604).
  • step S507 of FIG. 23 the intention analysis unit 20 performs a dictionary lookup from the domain knowledge dictionary according to dimensions in the order set by the domain knowledge dictionary manager 23, and the intention according to the information obtained from the dialogue with the user. Is generated (step S507).
  • a domain knowledge dictionary according to dimensions as shown in FIGS. 28A to 28C is automatically generated according to the class definition of the “address” class.
  • the “Osaka” class is under the “address” class
  • the “Takatsuki City” class, the “Ibaraki City” class, and so on are under the “Osaka” class.
  • “Takatsuki City” class, “Ibaraki City” class, etc. are also shown together.
  • the feature extraction unit 231 extracts features from the user data and extracts the property “workplace” -class “Osaka”. To do.
  • the condition rearrangement unit 232 selects a domain knowledge dictionary classified by dimension that satisfies the property “workplace” -class “Osaka” extracted by the feature extraction unit 231 and rearranges the domain knowledge dictionary classified by dimension according to a predetermined priority. I do. By rearranging the dimensional dictionaries, the dictionary can be looked up from the high-priority dimensional domain knowledge dictionary.
  • condition rearranging unit 232 sets the domain knowledge dictionary according to dimension of FIG. 28 (A) in which the feature extracting unit 231 meets the condition of the property “place of work” ⁇ class “Osaka” as the highest priority,
  • the domain knowledge dictionary of the “Kansai” class which is a higher class of the “Osaka” class, is rearranged as the next highest priority.
  • the intention analysis unit 20 receives the user utterance sentence “I am Mita” for the system utterance sentence “Where are you?”
  • the morpheme analysis unit 211 performs morpheme analysis on the user utterance sentence “It is Mita”, obtains the morpheme analysis result “Mita: noun: auxiliary verb”, and the syntax analysis unit 212 performs the syntax analysis. To obtain a syntax analysis result as shown in FIG.
  • the analysis target sentence dynamic switching process is a multidimensional intention analysis process when constructing extension target data from target data.
  • the dynamic switching process of the sentence to be analyzed can be performed using the flowcharts shown in FIGS. 23 and 24, and will be described here with reference to FIGS.
  • “user data” is described as “target data”.
  • FIG. 30A shows the target data given to the intention analysis unit 20.
  • additional information “analysis target: target data” is attached to the target data. Accordingly, the intention analysis execution unit 21 adds “analysis target: target data” to the target data (steps S5001 and S502).
  • the intention analysis execution unit 21 divides the target data into one sentence unit (step S503), performs morphological analysis on each sentence of the target data (step S504), and constructs the syntax. Analysis is performed (step S505).
  • the intention analysis execution unit 21 gives the user data to the domain knowledge dictionary manager 23 (step S506).
  • the feature extraction unit 231 compares the user data with all the dimension conditions registered in the dimension-specific matching table, and sets each dimension condition. A suitable dimension condition is extracted (steps S601 and S602).
  • the condition rearrangement unit 232 rearranges the dimensional domain knowledge dictionaries into priorities according to a certain standard with respect to the dimension conditions extracted by the feature extraction unit 231 (step S603), and the dictionary setting unit 233 has a predetermined priority.
  • the dimension-specific domain knowledge dictionary is set in the intention analysis dictionary 22 so as to be looked up by rank (step S604).
  • FIG. 34 is a flowchart showing a process in the dialog control unit 11 in the dialog control process linked with the matching result.
  • FIG. 35 is a flowchart showing the matching process in the dialogue control process linked to the matching result.
  • the domain knowledge manager 31 analyzes the intention of the target data with respect to the target data acquired from the data providing server 80 and stores it in the extended target data 54 on the premise of performing the dialog control process in conjunction with the search process.
  • the dialogue control unit 11 receives an intention analysis result from the intention analysis unit 20 (step S703), stores it as user data in the extended user data 53 (step S702), and starts a matching process (step S703).
  • FIG. 36 assumes that “ ⁇ -chan” is user data, and the dialogue control unit 11 stores the user data shown in FIG. 36 in the extended user data 53.
  • the matching unit 40 extracts all the expansion target data that completely matches the user data stored in the expansion target data 54 (step S801).
  • FIG. 37 shows an example of the expansion target data that perfectly matches the user data.
  • FIG. 37A shows the recruitment data (target data) of “Company A” and the property “Company name” -class “ “Company A”, property “language” -class “Java (registered trademark)”, property “years of experience” -class “3 years or more”, and so on.
  • FIG. 37B shows recruitment data (target data) of “Company B”, property “Company name” -class “Company B”, property “Language used” —class “Java (registered trademark)”, ...
  • the matching unit 40 requests the domain knowledge manager 31 to create a matching table (step S802).
  • the domain knowledge manager 31 generates a multi-dimensional matching table by the method similar to the processing described in (A-3-4), and gives this to the matching unit 40.
  • FIG. 38 is a multidimensional matching table given to the matching unit.
  • the matching unit 40 calculates the similarity with the user data for all target data based on the multidimensional matching table (step S803), and rearranges the target data in descending order of similarity (step S804).
  • the matching unit 40 checks whether there is an ambiguous matching with respect to the target data having the highest similarity (step S805).
  • ambiguous matching refers to matching when there is a condition value in the target data, but no condition value exists in the user data.
  • FIG. 39 is a diagram for explaining a case where the user data of “Chan” in FIG. 36 is matched with the target data of “Company A” in FIG. 37A based on the multi-dimensional matchon table in FIG. It is.
  • Step S704 the process proceeds to step S704 in FIG. 34.
  • steps S705 to S708 correspond to steps S304 to S307 in FIG.
  • values for the target data of “Company A” include “job type—programmer”, “use language—Java (registered trademark)”, and “year of experience—more than 3 years”.
  • the matching unit 40 sets the deep question about the property “year of experience” and gives it to the domain knowledge manager 31 (step S806).
  • the domain knowledge manager 31 sets the property “experience years” as the next question sentence, and the basic question sentence “Please tell me the concrete years of experience” of the property “experience years” to the dialogue control unit 11. Give the next question sentence.
  • step S704 since the following question message is set in the dialog control unit 11, the basic question message "Please tell me the specific years of experience.” Output to the side.
  • the Fukahori question will not be asked because it is not based on years of experience.
  • the question can be freely controlled according to the state of the target data that is the search result.
  • the dictionary to be applied can be dynamically switched according to the user's intention and situation. Can be selected.
  • a search target related to a question or matching to the user can be also intended by the user.
  • the dictionary to be applied can be dynamically switched according to the information related to the target data, so that a selection criterion for accurate information search can be selected.
  • Multidimensionalization of intention analysis means that an analysis engine (a dictionary used at the time of analysis) is dynamically used according to a value acquired from a user.
  • the mechanism to use the analysis engine dynamically can be realized by applying the mechanism to use the ontology dynamically through the domain knowledge manager.
  • a person who wants a full-time employee can make an analysis with the annual income (or monthly income) as a default, and a part-time person with an hourly wage as the default salary.
  • Job information is usually tagged with information.
  • dictionaries for each tag value, it is possible to analyze with high accuracy. For example, in the case of a job title tag, analysis is performed using a dictionary that presupposes the input of “job title”. In the case of the “senior PR” tag, analysis is performed using a dictionary based on the input of the contents described by the employee.
  • Multi-dimensional search means that search conditions are dynamically used according to a value acquired from a user.
  • the presentation request is not necessarily after the search, and may be searched at any time.
  • the matching method is a simple example, but various methods can be widely applied as the matching method. For example, perfect matching may be used, or matching may be arranged in order as in the first embodiment.

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Abstract

L'invention porte sur un système de recherche comportant : une unité de mémorisation de données cibles mémorisant des données cibles ; une unité de mémorisation de tableaux de correspondance séparés par dimension mémorisant des tableaux de correspondance séparées par dimension ayant différents poids en fonction des dimensions, comprenant des combinaisons de conditions ; une unité de mémorisation de tableau de correspondance de base mémorisant un tableau de correspondance de base, dans laquelle sont réglés des poids pour le cas dans lequel il n'y a pas de conditions ; et une unité de mémorisation de priorité de dimension. Lorsque des conditions sont réglées à partir d'une valeur de recharge, on extrait de l'unité de mémorisation de tableaux de correspondance séparés par dimension séparée des tableaux de correspondance séparés par dimension qui correspondent auxdites conditions ; on génère ensuite une table de correspondance multidimensionnelle par écrasement du tableau de correspondance de base par les contenus de chaque tableau de correspondance séparé par dimension, selon un ordre de priorité ascendant des priorités de dimension, et on utilise le tableau de correspondance multidimensionnelle pour réaliser une correspondance.
PCT/JP2009/070729 2009-04-30 2009-12-11 Système de recharge et support contenant un programme de recherche WO2010125707A1 (fr)

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