JP2019128925A - Event presentation system and event presentation device - Google Patents

Abstract

To provide an event presentation system and an event presentation device for presenting an event in such a way that highly advanced analysis of the event is possible.SOLUTION: The event presentation system comprises: a first specification unit for acquiring an event that corresponds to a character string inputted for search as an abstract event from event information that includes information about an event, and specifying, on the basis of the abstract event, an event following the abstract event from context information in which the front-rear relationship of events is correlated; a second specification unit for specifying, on the basis of the abstract event acquired by the first specification unit, a concrete event that indicates a child event of the abstract event from a parental relation in which the parental relation of events is correlated, and specifying information about a case pertaining to the concrete event from case information that includes information about cases pertaining to events; and an output unit for outputting the abstract event acquired by the first specification unit, the event following the abstract event specified by the first specification unit, and information about the case pertaining to the concrete event of the abstract event specified by the second specification unit.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an event presentation system and an event presentation device, and is suitably applied to, for example, an event presentation system and an event presentation device that outputs information related to an abstract event.

  A system for automatically generating new knowledge from existing knowledge is disclosed (see Patent Document 1). The system described in Patent Literature 1 includes an “input event” input from a user, a “first event” similar to the input event, a “second event” causally related to the first event, and a second event. Search sequentially for "third event" similar to. The system regards the third event that is similar in domain to the input event as an “causal event” that is causal with the input event. In addition, the above system compares the tendency of words in the dependency relationship with the event predicate to determine the similarity between events, and compares the tendency of words in the co-occurrence relationship with the event predicate to compare between events. Determine domain similarity. The system acquires a known causal relationship between the first event and the second event from the causal relationship DB.

JP, 2009-59323, A

  In the system described in Patent Document 1, similar events are grouped into one group in order to detect new causal events. However, the meaning of the group and the outline of the group are not explained, and it is not easy for the user who analyzes the transition of the event to read the meaning and the outline of the group. Therefore, when the user analyzes the event at the group level, there is a problem that the meaning of the event at the group level can not be read and the analysis becomes difficult.

  The present invention has been made in consideration of the above points, and an object of the present invention is to propose an event presentation system and an event presentation device that present events so that they can be analyzed highly.

  In order to solve such a problem, in the present invention, an event corresponding to a character string input for search is obtained as an abstract event from event information including event information, and before and after the event based on the abstract event. A first identification unit that identifies an event after the abstract event from the context information associated with the relationship, and a parent-child relationship of the event is associated based on the abstract event acquired by the first identification unit A second specifying unit that specifies a concrete event indicating a child event of the abstract event from parent-child relationship information, and specifies information on a case related to the concrete event from case information including information on a case related to the event; An abstract event acquired by one specifying unit, an event after the abstract event specified by the first specifying unit, and information on a case relating to a concrete event of the abstract event specified by the second specifying unit; , And an output unit for outputting Was to so that.

  According to the above configuration, it is possible to output an abstract event, an event after the abstract event, and information on a case related to a concrete event of the abstract event.

  Other problems disclosed by the present application and their solutions will be made clear by the description of the embodiments of the invention and the drawings.

  According to the present invention, events can be presented so that the events can be highly analyzed.

It is a figure which shows an example of a structure of the event presentation system by 1st Embodiment. It is a figure showing an example of the flow chart concerning the event presentation processing by a 1st embodiment. It is a figure showing an example of causal model DB by a 1st embodiment. It is a figure showing an example of causal model DB by a 1st embodiment. It is a figure which shows an example of the causal model DB by 1st Embodiment. It is a figure showing an example of causal model DB by a 1st embodiment. It is a figure showing an example of causal model DB by a 1st embodiment. It is a figure which shows an example of the article / document DB by 1st Embodiment. It is a figure which shows an example of the article / document DB by 1st Embodiment. It is a figure which shows an example of text analysis result DB by 1st Embodiment. It is a figure which shows an example of text analysis result DB by 1st Embodiment. It is a figure which shows an example of management DB by 1st embodiment. It is a figure which shows an example of management DB by 1st embodiment. It is a figure which shows an example of management DB by 1st embodiment. It is a figure which shows an example of the search screen of the event by 1st Embodiment. It is a figure which shows an example of the search result display screen of the event by 1st Embodiment. It is a figure which shows an example of the flowchart which concerns on the 1st event abstraction process by 1st Embodiment. It is a figure which shows an example of the flowchart which concerns on the 2nd event abstraction process by 1st Embodiment. It is a figure which shows an example of the flowchart which concerns on the user abstraction level setting process by 1st Embodiment.

  An embodiment of the present invention will now be described in detail with reference to the drawings.

(1) First Embodiment In the present embodiment, various information is collected when analyzing the situation regarding the current situation about company trends, social situations etc., future prospects etc., and the collected information is A configuration (method) will be described that supports combining, information on current situation recognition as new knowledge, future prospects, etc., and analysis by users regarding current situation recognition, future prospects, etc.

  Here, in order to analyze current situation recognition, future prospects, etc., various events occurring in the world are extracted, the causal relationship between events is clarified, and the current event is determined from the causal relationship of past events. It is necessary to estimate the cause-and-effect relationship and the transition of events including those that have not occurred.

  However, the frequency of occurrence of events is low, and analysis of the causal relationship between events is not easy. In this regard, in the present embodiment, a plurality of events are regarded as the same event in consideration of the similarity of events such as the background and meaning, and the like, and a causal relationship between the events is easily provided. As a result, a model (causal model) in which a causal relationship is assigned between events is created, and the transition of the event can be predicted based on the causal model. Here, integrating a plurality of similar events is referred to as event abstraction.

  Therefore, in the present embodiment, meaning is given to the abstracted event, and analysis of the user is supported using the causal model. Here, the user has various purposes. Some users try to analyze from a global perspective, while others try to analyze from a fine perspective. Therefore, the causal model of the event should be constructed according to various levels of abstraction. Therefore, in this embodiment, a causal model is constructed according to various levels of abstraction, a causal model with an appropriate abstraction level is presented according to the user's purpose, and the user's analysis is supported.

  The details of this embodiment will be described below with reference to FIGS. However, the present invention is not limited to the present embodiment, and the present invention can be applied to any situation in which a causal relationship between events is presented.

  In FIG. 1, reference numeral 100 denotes an event presentation system according to the first embodiment as a whole. The event presentation system 100 communicably connects to the map information providing system 160, the vocabulary information providing system 170, and the like via the communication line 150. Each of the event presentation system 100, the map information providing system 160, and the vocabulary information providing system 170 can be realized by one or a plurality of general computers (computers).

  The event presentation system 100 includes a main storage unit 110, an auxiliary storage unit 130, a central control unit 140, a communication unit 141, and an input / output unit 142. These are connected to one another via a bus.

  The main storage unit 110 stores a program. Search word reception unit 111, text analysis unit 112, causal model / event search / acquisition unit 113, article / document search / acquisition unit 114, attribute information search / acquisition unit 115, analysis result output unit 116, management data reception The unit 117, the abstraction level determination unit 118, the analysis target acquisition unit 120, the abstract event generation unit 121, the causal relationship assignment unit 122, and the causal strength / branch number determination unit 123 are program functions (functions of the event presentation system 100). is there. In the case where the main part is described as "○○ part is", the central control device 140 reads each program from the auxiliary storage device 130, loads it into the main storage device 110, and executes each program, The functions of each program (details described later) are realized.

  Although details will be described later, the causal model / event search / acquisition unit 113 acquires an event corresponding to the character string input for the search as an abstract event from the event information including the event information, It is an example of the 1st specific part which specifies the event after the above-mentioned abstract event from the context information with which the context of an event was matched based on the above-mentioned abstract event. The article / document search / acquisition unit 114 specifies a concrete event of the abstract event from the parent-child relationship information associated with the parent-child relationship of the event based on the abstract event acquired by the first specifying unit, and the event It is an example of the 2nd specific | specification part which specifies the information of the case which concerns on the said concrete event from the case information containing the information of the case which concerns on. The attribute information search / acquisition unit 115 is an example of a third specifying unit that specifies information related to the case information specified by the user. The analysis result output unit 116 includes the abstract event acquired by the first specifying unit, the event after the abstract event specified by the first specifying unit, and the abstract event specified by the second specifying unit. It is an example of the output part which outputs the information of the case which concerns on a concrete event of and.

  The auxiliary storage device 130 stores (stores) a causal model DB (database) 131, an article / document DB 132, a text analysis result DB 133, and a management DB 134.

  The map information providing system 160 is a system that provides the event presentation system 100 with map information and the like around the location based on the information on the location provided from the event presentation system 100. The map information providing system 160 communicates with the event presentation system 100 via the communication line 150 to provide map information and the like.

  The vocabulary information providing system 170 is a system that provides the event presentation system 100 with the word abstraction level based on the words provided from the event presentation system 100. The vocabulary information providing system 170 communicates with the event presentation system 100 via the communication line 150 to provide word abstraction.

  As the communication line 150, various networks such as a dedicated line, a wide area network (WAN), a power line network, a wireless network, a public line network, a mobile telephone network, a satellite communication line, etc. other than LAN (Local Area Network) are adopted. can do.

  Although in FIG. 1 the event presentation system 100 performs input and output independently, it is also possible to connect another terminal via the communication line 150 and input and output information using the input and output devices of other terminals. Good. In addition, the function of the event presentation system 100 may be realized by a plurality of computers.

  Subsequently, a series of processes for presenting causality of events in the present embodiment will be described. The process of presenting the causality of an abstract event is shown in FIG. Details of the database used in the process of FIG. 2 will be described later with reference to FIGS. 3A to 6C.

  FIG. 2 is a flowchart relating to a process (event presentation process) of estimating a transition before and after the current event based on the causal relationship of the past event and presenting the transition of the event to the user regarding the event to be searched by the user. An example is shown. FIG. 7 shows an image of a search screen of an event for the user to input an event to be searched, and FIG. 8 shows an image of a search result display screen of an event presenting the transition of the event to the user.

  In step S200, the event presentation system 100 starts an event presentation process.

  In step S201, the search word receiving unit 111 searches a search word (character string input for search) indicating an event to be searched, and an attribute of the event to be searched (search attribute: place, person / organization, event An input with any of the occurrence date and time or a combination thereof is accepted from the user via the input / output device 142 (see FIG. 7).

  In step S202, the causal model / event search / acquisition unit 113 searches for an abstract event including the search word received from the user. More specifically, the causal model / event search / acquisition unit 113 divides the search word into words using a morphological analysis technique, searches each word for the word 502 in the word information table 500 of the text analysis result DB 133, and applies The word ID 501 of the record to be acquired is acquired. The causal model / event search / acquisition unit 113 searches the acquired word ID 501 for the word ID: noun 303 and the word ID: sa variable noun 304 in the event table 300 of the causal model DB 131, and when there is a corresponding record, The event ID 301 of the corresponding record is stored as a search target event (abstract event) in the memory area of the main storage device 110. Further, the causal model / event search / acquisition unit 113 stores the search target attribute in the memory area of the main storage device 110.

  In step S203, the causal model / event search / acquisition unit 113 determines whether or not the event ID 301 can be searched in step S202. If the causal model / event search / acquisition unit 113 determines that the search has been successful, the process proceeds to step S205, and if it is determined that the search can not be performed, the process proceeds to step S204.

  In step S204, the text analysis unit 112 extracts the semantic vector of the word using word embedded vector technology based on the word divided from the search word in step S202, and the word closest to the semantic vector of the word is Are searched from the meaning vector 503 of the word information table 500 of the text analysis result DB 133.

  Here, instead of searching for the closest distance, the distance may be replaced with one within the threshold, and in this case, for the threshold, for example, in the setting contents 602 of the setting table 600 of the management DB 134 It is assumed that the word distance threshold value is set as the setting value 603.

  Then, the text analysis unit 112 replaces the word divided from the search word with the word having the closest distance (or the word whose distance is within the threshold). After that, as in step S203, the causal model / event search / acquisition unit 113 searches the word 502 in the word information table 500 of the text analysis result DB 133 for each word, and acquires and acquires the word ID 501 of the corresponding record. The word ID 501 is searched for the word ID: noun 303 and the word ID: sari noun 304 in the event table 300 of the causal model DB 131, and the event ID 301 of the corresponding record is stored in the memory area of the main storage device 110 as an event to be searched. Do.

  In step S205, the causal model / event search / acquisition unit 113 acquires the abstraction level 305 of the same record for the event ID 301, identifies the abstraction level of the event, and sets the abstraction level of the event to be presented to the user. . Further, the causal model / event search / acquisition unit 113 searches the previous event ID 312 in the causal table 310 based on the event ID 301, and the event (post-event) following the event ID 301 with the subsequent event ID 313 of the corresponding record. Get as. Further, the causal model / event search / acquisition unit 113 searches the post-event ID 313 based on the event ID 301 and acquires the previous event ID 312 of the corresponding record as an event preceding the event ID 301 (pre-event). The causal model / event search / acquisition unit 113 acquires the causal model by repeating the search for the subsequent event and the previous event. It is assumed that the number of repetitions follows a set threshold value, and the threshold value is set to, for example, the setting value 603 of “causal display threshold value” of the setting content 602 of the setting table 600 of the management DB 134.

  Step S205 is not limited to the above-described processing content. For example, the causal model / event search / acquisition unit 113 may acquire a causal model of only subsequent events, or may acquire a causal model of only preceding events. In the case of acquiring causal models of both preceding events and subsequent events, analysis of events is easier than in the case of acquiring causal models of either event.

  Further, for example, when the causal model / event search / acquisition unit 113 determines that the abstraction level of the identified event is different from the abstraction level of the user (see FIG. 11), the causal model / event search / acquisition unit 113 matches the abstraction level of the user. A model may be obtained. If the causal model / event search / acquisition unit 113 determines that the user's abstraction level is higher, the causal model / event search / acquisition unit 113 searches the concrete event ID 322 of the abstract event relation table 320 of the causal model DB 131 to obtain the abstract event ID 323 of the corresponding record. A causal model is acquired as a new abstract event (abstract event suitable for the user). If the causal model / event search / acquisition unit 113 determines that the user's abstraction level is lower, the causal model / event search / acquisition unit 113 searches the abstract event relationship table 320 of the causal model DB 131 for the abstract event ID 323, All the concrete event IDs 322 are set as new abstract events (abstract events suitable for the user), and a causal model is acquired. According to such processing, a causal model can be constructed according to various levels of abstraction, a causal model with an appropriate level of abstraction can be presented according to the purpose of the user, and user analysis can be supported.

  In step S206, the causal model / event search / acquisition unit 113 relates to the event ID (event ID 301, previous event ID 312 and subsequent event ID 313) of the causal model acquired in step S205, and the abstract event relationship table 320 of the causal model DB 131. The abstract event ID 323 is searched, and the concrete event ID 322 of the corresponding record is acquired. More specifically, the causal model / event search / acquisition unit 113 traces back the parent-child relationship in the abstract event relationship table 320 until the lowest concrete event (lowest level of abstraction) is reached, and the lowest concrete event. All of the concrete event IDs 322 of FIG. Note that a data configuration in which an article / document is associated with an event of each abstraction level may be adopted, in which case a concrete event ID 322 corresponding to a concrete event of an abstract event may be acquired.

  In step S207, the article / document search / acquisition unit 114 searches the event ID 331 in the event attribute table 330 of the causal model DB 131 based on the concrete event ID 322 searched in step S206. If an event attribute is input to the memory of the main storage device 110 in step S202, the article / document search / acquisition unit 114 narrows down the corresponding records of the location 332, the person / organization 333, and the date and time 334 using the attribute. . That is, the event ID 331 of the record as a result of search and narrowing down is acquired. The article / document search / acquisition unit 114 searches the event ID 412 of the article / document-event relationship table 410 of the article / document DB 132 based on the event ID 331, and acquires the article / document ID 411 of the corresponding record. The article / document search / acquisition unit 114 searches the article / document ID 401 of the article / document table 400 based on the acquired article / document ID 411, and the article / document 402, publication date 403, and publication source of the corresponding record. 404 is acquired.

  In step S208, the analysis result output unit 116 prepares for screen output. More specifically, the analysis result output unit 116 searches for the abstract event, the previous event, the subsequent event, the event context (causal model) acquired in step S205, and the article / document acquired in step S207. As a result, it is stored in the main storage unit 110 as data to be displayed on the search result display screen of the event.

  In step S209, the attribute information search / acquisition unit 115 obtains the location 332 of the concrete event acquired in step S206 (for example, the search attribute) for each of the abstract event, the previous event, and the subsequent event displayed in step S208. The peripheral map is acquired from the external map information providing system 160 with respect to the location).

  In step S210, the attribute information search / acquisition unit 115 relates to the person / organization 333 that executed the concrete event acquired in step S206 for each of the abstract event, the previous event, and the subsequent event displayed in step S208. The name 342 of the person / organization management table 340 is searched, and the person / organization ID 341 of the corresponding record is acquired. Further, the attribute information search / acquisition unit 115 searches for a parent ID (person / organization) 343 based on the person / organization ID 341, and holds it as child child / organization information. Further, the attribute information search / acquisition unit 115 searches the person / organization ID 341 based on the parent ID (person / organization) 343 of the corresponding record, and holds it as information of the parent person / organization. The attribute information search / acquisition unit 115 repeats the same processing to acquire the person / organization configuration.

  In step S211, the analysis result output unit 116 extracts the abstract event, the pre-event, the post-event, the order of events (causal model), the article / document stored in the main storage device 110 in step S208, and the peripheral acquired in step S209. Is displayed on the search result display screen of the event as a search result (see FIG. 8).

  In step S212, the analysis result output unit 116 receives an article / document selection from the user. If the analysis result output unit 116 determines that there is a selection from the user, it moves the process to step S213, and if it determines that there is no selection, it moves the process to step S214.

  In step S213, the analysis result output unit 116 specifies the event ID 331, the location 332, the person / organization 333, and the date and time 334 associated with the article / document selected by the user from the result of step S207, and the process proceeds to step S209. Move processing.

  In step S214, the event presentation system 100 ends the event presentation process.

  According to the event presenting process, an abstract event that can interpret the meaning of a group of a plurality of similar events and a causal model thereof are presented to the user, so that it is possible to assist the user in analyzing the event.

  Next, the details of each database will be described.

  An example of the causal model DB 131 is shown in FIGS. 3A to 3E. The causal model DB 131 stores data on a causal model of an event. The causal model DB 131 includes an event table 300 (FIG. 3A), a causal table 310 (FIG. 3B), an abstract event relation table 320 (FIG. 3C), an event attribute table 330 (FIG. 3D), and a person / organization management table 340. (FIG. 3E) is comprised.

  The event table 300 stores data on events. The event table 300 is an example of event information, and includes information on an event ID 301, an event 302, a word ID: noun 303, a word ID: sa variable 304, and an abstraction level 305. A key for identifying each record is an event ID 301.

  The event ID 301 is an identifier for uniquely identifying an event. The event 302 is a simple expression of the event, and is composed of a noun indicating the object of the event and a saun noun indicating the action of the event. The word ID: noun 303 is an identifier for uniquely identifying the noun word indicating the object of the event. Word ID: The sa-noun 304 is an identifier for uniquely identifying the word of the sa-noun indicating the action of the event. The degree of abstraction 305 indicates the degree of abstraction of the event, and is calculated from the degree of abstraction of each word of the word ID: noun 303 and the word ID: sa-h.

  The causal table 310 stores data related to the causal relationship between events. The causal table 310 is an example of the context information, and includes information of a link ID 311, a pre-event ID 312, a post-event ID 313, and a strength 314. A key for identifying each record is a link ID 311.

  The link ID 311 is an identifier for uniquely identifying a link indicating a causal relationship between events. The pre-event ID 312 is an identifier for uniquely identifying an event (cause indicating event) that is the cause of the causality among the causal relationships between the events. The post-event ID 313 is an identifier for uniquely identifying an event (an event indicating a result) ahead of the causal in the causal relationship between the events. The strength 314 indicates the strength of causality between events. In the event abstraction, a causal relationship is given in advance by the user (the strength 314 is also given) for the original event. For example, for the strength 314 of the causal relationship between the abstract events of the original event, It is calculated by counting the number of concrete events of the next lower level of abstraction belonging to the abstract event.

  The abstract event relation table 320 stores data on the relation between the pre-abstraction event (child event) and the post-abstraction event (parent event). The abstract event relationship table 320 is an example of parent-child relationship information, and is configured to include information of an abstraction ID 321, a concrete event ID 322, and an abstract event ID 323. A key for identifying each record is an abstraction ID 321.

  The abstraction ID 321 is an identifier for uniquely identifying the relationship between events when an event is abstracted to another event. The concrete event ID 322 is an identifier indicating an event to be abstracted. The abstract event ID 323 is an identifier indicating an abstracted event.

  The event attribute table 330 stores data regarding the location, person / organization, and date / time attributes related to the event. The event attribute table 330 is an example of narrowing information in which an event and a search attribute are associated with each other, and includes information on an event ID 331, a location 332, a person / organization 333, and a date and time 334. A key for identifying each record is an event ID 331.

  The event ID 331 is an identifier for uniquely identifying an event. The place 332 shows the place where the event occurred. The person / organization 333 indicates the subject who has executed the event. The date and time 334 indicates the date and time when the event occurred.

  The person / organization management table 340 stores data on the configuration of the person / organization. The person / organization management table 340 includes information of a person / organization ID 341, a name 342, and a parent ID (person / organization) 343. A key for identifying each record is a person / organization ID 341.

  The person / organization ID 341 is an identifier for uniquely identifying a person / organization. The name 342 shows the name of a person / organization. The parent ID (person / organization) 343 is an identifier for uniquely identifying a person / organization (parent person / organization) higher than the person / organization ID 341 in configuration.

  An example of the article / document DB 132 is shown in FIGS. 4A and 4B. In the article / document DB 132, data on articles and documents for extracting events is stored. The article / document DB 132 includes an article / document table 400 (FIG. 4A) and an article / document-event relationship table 410 (FIG. 4B).

  The article / document table 400 stores data on articles or documents for extracting events. The article / document table 400 is an example of the case information, and includes information of an article / document ID 401, an article / document 402, a publication date 403, and a publication source 404. The key for identifying each record is article / document ID 401.

  The article / document ID 401 is an identifier for uniquely identifying an article or a document. The article / document 402 shows the specific content of an article or document for extracting an event. The publication date 403 indicates the date and time when the article or document was published in a newspaper, a document, or the like. A publication source 404 indicates a newspaper, a document name, etc. in which an article or document is posted.

  The article / document-event relationship table 410 stores articles or documents and data indicating a relationship between events extracted therefrom. The article / document-event relationship table 410 includes information on an article / document ID 411 and an event ID 412. Keys for identifying each record are an article / document ID 411 and an event ID 412.

  The article / document ID 411 is an identifier for uniquely identifying an article or document. The event ID 412 is an identifier for uniquely identifying one event among events extracted from an article or document.

  An example of the text analysis result DB 133 is shown in FIGS. 5A and 5B. The text analysis result DB 133 stores data on an analysis result obtained by analyzing the meaning, part of speech, and the like of a word described in an article or a document. The text analysis result DB 133 is configured to include a word information table 500 (FIG. 5A) and a part-of-speech table 510 (FIG. 5B).

  The word information table 500 stores data related to the meaning of the words described in the article or document. The word information table 500 includes information on a word ID 501, a word 502, a semantic vector 503, an appearance frequency 504, and an abstraction level 505. A key for identifying each record is a word ID 501.

  The word ID 501 is an identifier for uniquely identifying a word. The word 502 indicates the word itself. The semantic vector 503 is a word embedded vector technology which is established as a general technique, and represents the meaning of a word from a sentence of an article or a document as a low dimensional vector such as 200 dimensions. The appearance frequency 504 indicates the frequency with which words appear in articles, newspapers, and the like. The degree of abstraction 505 indicates the degree of abstraction of the word calculated from the frequency of appearance or the degree of abstraction of the word acquired from the vocabulary information providing service.

  The part-of-speech table 510 stores data related to part-of-speech of words extracted from articles or documents. The part-of-speech table 510 includes information on the word ID 511 and the part-of-speech 512. A key for identifying each record is a word ID 511.

  The word ID 511 is an identifier for uniquely identifying a word. The part of speech 512 indicates the part of speech of a word, such as a noun, a verb, or a sabot noun, and the part of speech of the word is extracted from the sentence of the article or document by using “morpheme analysis technology” established as a general technique. It is a thing.

  6A to 6C show an example of the management DB 134. The management DB 134 stores data relating to threshold values, setting values, user information, search history, and the like. The management DB 134 includes a setting table 600 (FIG. 6A), a user information table 610 (FIG. 6B), and a search history table 620 (FIG. 6C).

  The setting table 600 stores data related to setting values (for example, threshold values). The setting table 600 is configured to include information of a setting ID 601, a setting content 602, and a setting value 603. A key for identifying each record is a setting ID 601.

  The setting ID 601 is an identifier for uniquely identifying a setting value related to the system. The setting content 602 indicates the setting content. The setting value 603 indicates a setting value regarding the setting content.

  The user information table 610 stores data related to user information. The user information table 610 is configured to include information of a user ID 611, an interest item 612, and an abstraction degree 613. A key for identifying each record is a user ID 611.

  The user ID 611 is an identifier for uniquely identifying the user. An interest item 612 indicates a set of keywords input by the user, indicating what the user is interested in. The abstraction level 613 indicates the abstraction level of the user's interests, and is calculated from the abstraction level of each word of the interests 612.

  The search history table 620 stores data related to the history searched by the user. The search history table 620 includes information of a user ID 621, a date and time 622, a search word 623, and a search attribute 624. Keys for identifying each record are a user ID 621 and a date and time 622.

  The user ID 621 is an identifier for uniquely identifying a user. The date 622 indicates the date searched by the user. The search word 623 indicates the content searched by the user. The search attribute indicates an attribute (place, person / organization, occurrence date and time of an event) of a search target specified when the user searches.

  FIG. 7 shows an example of an event search screen (input screen) displayed in step S201 of FIG. The event search screen is a screen for the user to input information on an event to be searched for analyzing the transition of future events, and is displayed on the input / output device 142 of the event presentation system 100. The event search screen includes a search word 701, a search attribute 702, a search button 703, and a past search 704.

  A search word 701 is an input field for inputting a keyword related to an event to be searched. The search attribute 702 is an input field for narrowing down events to be searched for by location, person / organization, date and time. A search button 703 is a button for executing a search under the search conditions input by the search word 701 and the search attribute 702. The search screen receives an event search condition from the user. The past search 704 is a list of search words 701 and search attributes 702 searched in the past.

  Note that not only one word but also a plurality of words may be input to the search word 701. In that case, AND conditions, OR conditions, and the like may be set.

  Further, not only one attribute but a plurality of attributes may be input in the search attribute 702. In that case, an AND condition, an OR condition, or the like may be set.

  Further, the person / organization 333 acquired in step S210 has been described as the person / organization executing the event. However, it is assumed that the person / organization of the event object is stored and the person / organization of the event object is stored. The configuration may be acquired.

  FIG. 8 shows an example of the search result display screen of the event displayed in step S211 of FIG. The event search result display screen displays previous and subsequent events from the causal relationship when displaying the events searched by the user, and is displayed on the input / output device 142 of the event presentation system 100.

  The event search result display screen includes a corresponding event 801, a search content 802, an article / document 803 regarding the corresponding event, an article / document 804 of the previous event, a subsequent event (prediction result) 805, an overhead map 806, A human / organization relationship 807, a causal model 808, and a back button 809 are included.

  The corresponding event 801 indicates an event searched as an event to be searched. The search content 802 indicates a search word and a search attribute when the search is performed. The article / document 803 of the corresponding event shows a list of articles / documents associated with specific events of the corresponding event 801. The pre-event article / document 804 indicates an event determined based on a causal relationship as being a previous event with respect to the corresponding event, and indicates an article / document associated therewith. The post-event (predicted result) 805 indicates an event determined based on a causal relationship as being a post-event with respect to the corresponding event, and indicates an article / document associated therewith. In FIG. 8, articles or documents for a predetermined period (for example, the last one year) are displayed as targets.

  The bird's-eye view map 806 is a surrounding map of an occurrence location of an article / document event focused on by the user in the article / document 803 of the applicable event 801 (an example of information related to information on the case specified by the user). Indicates In addition, for example, when the country is input by the search attribute, the peripheral map of the country is initially displayed. The person / organization relationship 807 indicates a configuration of a person / organization to which the person / organization that executed the event of the article / document focused by the user belongs (an example of information related to information on the case designated by the user). In addition, for example, when an organization is input in the search attribute, the configuration of the organization (which may be an administrative division or the like) is initially displayed. The causal model 808 indicates a causal model showing the causal relationship between the event 801, the previous event shown in the previous event article / document 804, the post-event shown in the post-event (predicted result) 805, and the event. A back button 809 is a button for the user to return to the search screen for events (FIG. 7). This screen presents information related to the event to the user.

  Here, as a preparation stage of the event presentation process shown in FIG. 2, an example of a data generation process related to an abstract event stored in the database will be described with reference to FIGS. 9 and 10.

  FIG. 9 shows an example of a flowchart relating to a first event abstraction process that abstracts an event by integrating a plurality of similar events.

  In step S900, the event presentation system 100 starts a first event abstraction process.

  In step S <b> 901, the management data receiving unit 117 receives setting values for leveling the degree of abstraction of events from the management user via the input / output device 142, and sets “abstraction level setting” in the setting content 602 of the setting table 600 of the management DB 134. It stores in the setting value 603 of "number".

  In step S902, the abstraction level determination unit 118 acquires the number of levels of word abstraction of the vocabulary information providing system 170 from the vocabulary information providing system 170, and the set number 603 stored in step S901 and the acquired level division number. Compare with. If the abstraction level determination unit 118 determines that the level division number of the vocabulary information providing system 170 is larger, the process proceeds to step S903. If the abstraction level determination unit 118 determines that the level division number is not larger, the process proceeds to step S904.

  In step S 903, the abstraction level determination unit 118 integrates the number of level divisions in accordance with the setting value 603. For example, when the setting value is “5” and the number of level divisions is “8”, the abstraction level determination unit 118 integrates the second and third of the level divisions and integrates the fourth and fifth And integrate the sixth and seventh. As a result, it is possible to match the level of abstraction of the set values as the first, second, third, fourth, fifth, sixth, seventh, and eighth stages of level division. Note that there may be a plurality of patterns for the integration method, but any pattern may be used. Thus, the abstraction level determination unit 118 resets the abstraction level of the word based on the number of level divisions according to the abstraction level of the set value, and the set abstraction level is used as word information in the text analysis result DB 133. It stores in the word 502 and the abstract degree 505 of the table 500.

  In step S <b> 904, the abstraction level determination unit 118 assigns the number of level divisions to a plurality of abstraction levels with respect to the abstraction level that matches the set value 603. For example, when the setting value is “10” and the number of level divisions is “8”, the abstraction level determination unit 118 sets the fourth of the number of level divisions to the fourth and fifth of the level of abstraction of the setting values. Duplicate assignment and the fifth number of level divisions are assigned to the sixth and seventh abstractions of the setting value. As a result, the first, second, third, fourth, fourth (duplicate allocation), fifth, fifth (duplicate allocation), sixth, seventh, eighth tenth stages of the level division number are set. It can be adjusted to the abstraction level of the value. Although there may be a plurality of patterns for overlapping, any pattern may be used. Thus, the abstraction level determination unit 118 resets the abstraction level of the word based on the number of level divisions in accordance with the abstraction level of the set value (overlapping is allowed as described above), and the set abstraction level Are stored in the word 502 and the abstract degree 505 of the word information table 500 of the text analysis result DB 133.

  In step S905, the text analysis unit 112 extracts words from the learning data (articles, documents, etc.) designated by the management user using the morphological analysis technique, extracts the part of speech of the words, and counts the appearance frequency of the words. Generate word semantic vector with word embedded vector technology (learning). The text analysis unit 112 stores the extracted word, the semantic vector, and the appearance frequency in the word 502, the semantic vector 503, and the appearance frequency 504 of the word information table 500 of the text analysis result DB 133, and assigns a word ID 501 to the part-of-speech table. The same word ID is stored in the word ID 511 in 510, and the extracted part of speech is stored in the part of speech 512.

  In step S <b> 906, the abstraction level determination unit 118 sets, from words with a high appearance frequency 504 in the word information table 500 based on the setting value 603 of “abstraction level setting number” in the setting content 602 of the setting table 600 of the management DB 134. The number of values 603 is grouped in order, and the level of abstraction is assigned so that the level of abstraction of the group with the highest appearance frequency is increased. The degree of abstraction assigned here is overwritten on the degree of abstraction 505. In the process of step S906, the abstraction level of the vocabulary information providing system 170 set in step S903 may be used as it is.

  In step S 907, the text analysis unit 112 extracts words using morphological analysis technology for the events in the analysis target event list designated by the management user. In step S 907, words may be extracted using morphological analysis technology for all events.

  In step S 908, the abstract event generation unit 121 classifies (clusters) words having similar meanings into the same category with respect to the subvariant noun and the noun. More specifically, the abstract event generation unit 121 has the relative distance of the semantic vector 503 in the record in which the word ID 501 in the word information table 500 matches the word ID 511 in which the part of speech 512 in the part of speech table 510 matches the variable noun. The word ID 501 close to is classified into the same category as a word having similar meaning. Similarly, in the part-of-speech table 510, regarding the word ID 511 whose part-of-speech 512 is a noun, the word ID 501 in which the distance of the semantic vector 503 is relatively close among the records having the same word ID 501 in the word information table 500 is similar in meaning. Classified as the same category.

  In step S909, the abstract event generation unit 121 searches the category classified in step S908 for a word ID 501 that is within a threshold distance from the center of the category based on the semantic vector 503, and among the searched words, the word The degree of abstraction 505 of the information table 500 selects a word of one rank (a word of abstraction degree of one of the least abstract words) as a representative word.

  In step S910, the abstract event generation unit 121 sets the representative word of the S-variable noun as a word representing the action of the event, combines the representative word of the noun as the word representing the object of the event, and combines the combined event one level higher. Generate as an abstract event of abstraction level. The abstract event generation unit 121 assigns an event ID 301 to the generated abstract event information (event 302, word ID: noun 303, word ID: saun noun 304, abstraction level 305) and registers the event ID 301 in the event table 300. Further, the abstract event generation unit 121 has a single word of a sabot noun that is a source of an abstract event of a higher abstraction level (one abstract level 505 of the word information table 500 in the category to which the representative word of the sabot noun belongs). Other words below) and the original word of the noun that is the source of the abstract event of the next higher level of abstraction (the abstract level 505 of the word information table 500 in the category to which the representative word of the noun belongs is one lower) The event (combination of The abstract event generation unit 121 assigns an event ID 301 to the generated concrete event information (event 302, word ID: noun 303, word ID: saun noun 304, abstraction level 305), and registers it in the event table 300. Then, the abstract event generation unit 121 associates the generated abstract event with one level of abstraction with the concrete event, and stores them in the concrete event ID 322 and the abstract event ID 323 of the abstract event relation table 320 of the causal model DB 131. .

  In step S 911, the causal relationship assignment unit 122 assigns a causal relationship between abstract events of one or more abstraction levels based on the causal relationship between the concrete events. In assigning the causal relationship, a new link ID 311 is generated in the causal table 310 of the causal model DB 131, and the previous event ID 312 and the subsequent event ID 313 are registered as events to which the causal relationship is given. The causal relationship between the lowest abstraction events (representative events) is assumed to be preset by the user.

  In step S912, the abstract event generation unit 121 classifies words of abstract events having similar meanings into the same category. More specifically, the abstract event generation unit 121 has a meaning in a record in which the word ID 501 of the word information table 500 matches the word ID 511 of the sa variable noun indicating the operation of the abstract event generated in step S910 or step S914. The word IDs 501 whose vectors 503 are relatively close are classified into the same category as words having similar meanings. Similarly, regarding the word ID 511 of the noun indicating the object of the abstract event generated in step S910 or step S914, a word in which the distance of the semantic vector 503 is relatively close in the record in which the word ID 501 of the word information table 500 matches. Classify the ID 501 into the same category as words of similar meaning.

  In step S913, the abstract event generator 121 searches the category classified in step S912 for a word ID 501 that is within a threshold distance from the category centroid based on the semantic vector 503, and the word among the searched words The word with the abstraction level 505 in the information table 500 is selected as the representative word.

  In step S <b> 914, the abstract event generation unit 121 sets the representative word of the S-variable noun as a word representing the action of the event, combines the representative word of the noun as the word representing the object of the event, and combines the combined event one level higher. Generate as an abstract event of abstraction level. The abstract event generation unit 121 assigns an event ID 301 to the generated abstract event information (event 302, word ID: noun 303, word ID: saun noun 304, abstraction level 305) and registers the event ID 301 in the event table 300. Further, the abstract event generation unit 121 has a single word of a sabot noun that is a source of an abstract event of a higher abstraction level (one abstract level 505 of the word information table 500 in the category to which the representative word of the sabot noun belongs). Other words below) and the original word of the noun that is the source of the abstract event of the next higher level of abstraction (the abstract level 505 of the word information table 500 in the category to which the representative word of the noun belongs is one lower) The original event which is combined with the word of) is generated as a concrete event. The abstract event generation unit 121 assigns an event ID 301 to the generated concrete event information (event 302, word ID: noun 303, word ID: saun noun 304, abstraction level 305), and registers it in the event table 300. Then, the abstract event generation unit 121 associates the generated abstract event with a higher abstraction level with the concrete event, and stores them in the concrete event ID 322 and the abstract event ID 323 of the abstract event relation table 320 of the causal model DB 131.

  In step S915, the causality assignment unit 122 assigns a causality between abstract events of one or more abstraction levels, as in step S911. In addition, the causal relationship between the concrete events of the abstract events of the one or more abstraction levels is set in step S911 or in step S915.

  In step S <b> 916, the abstract event generation unit 121 compares the number of repetitions of the processing from step S <b> 912 to step S <b> 915 with the setting value 603 of “abstraction level setting number” in the setting content 602 of the setting table 600 of the management DB 134. If it is determined that the number of repetitions is small, the abstract event generation unit 121 moves the process to step S912, and moves the process to step S917 if it is determined that the number of repetitions reaches the set value 603 or more.

  In step S917, the event presentation system 100 ends the first event abstraction process.

  In step S905, an example of learning an article, document, etc. designated by the management user has been described. However, the event presentation system 100 automatically searches the Internet, an in-house system, etc., and searches for a new article, document, etc. It may be acquired automatically and learned automatically.

  In step S907, the word is extracted from the analysis target event list specified by the management user. However, the combination of the noun that can be the event action and the noun that can be the event object is combined with the event. May be generated as a word extracted from the word used to generate the event.

  FIG. 10 shows an example of a flowchart according to a second event abstraction process of abstracting an event based on the strength of causality and the lesser number of branches. Note that event abstraction may be performed by the first event abstraction process, the second event abstraction process, or by both the first and second event abstraction processes. It may be

  In step S1000, the event presentation system 100 starts a second event abstraction process.

  In step S1001, the abstract event generation unit 121 acquires the causal table 310 of the causal model DB 131.

  In step S1002, the causal strength / branch number determination unit 123 acquires the strength 314 of each link ID 311 in the causal table 310 acquired in step S1001.

  In step S1003, the causal strength / branch number determination unit 123 counts the number of records of the subsequent event ID 313 associated with the previous event ID 312 in the causal table 310 acquired in step S1001, and changes from the previous event to the subsequent event. Get the number of branches of.

  In step S1004, the causal strength / branch number determination unit 123 determines the threshold of the causal relationship strength 314 and the number of branches. Therefore, the causal strength / number-of-branches determination unit 123 determines that the setting content 602 of the setting table 600 of the management DB 134 indicates the setting value 603 of "high threshold of link strength" and "low threshold of link strength" The setting content 602 acquires a setting value 603 of “a high threshold for the number of branches” and “a low threshold for the number of branches” as a threshold for the number of branches. The causal strength / number-of-branches determination unit 123 makes a determination based on these acquired thresholds, the strength 314 of the causality acquired in step S1002, and the number of branches acquired in step S1003.

  If the causal strength / branch number determination unit 123 determines that the strength 314 is greater than the low threshold of the link strength and the number of branches is smaller than the low threshold of the number of branches, the process proceeds to step S1006. If the causal strength / branch number determination unit 123 determines that the strength 314 is larger than the high threshold of the link strength and the number of branches is smaller than the high threshold of the number of branches, the process proceeds to step S1005. If the causal strength / branch number determination unit 123 determines that the result is other than the above, the process proceeds to step S1008.

  In step S1005, the causal strength / branch number determination unit 123 duplicates the event dummy so that one event can be integrated into a plurality of abstract events. A dummy event is represented by an event ID branch number. According to the determination in step S1004, even if the number of branches is abstracted more than the low threshold, it becomes an event spanning multiple abstract events, but since the strength 314 is larger than the high threshold, It is determined that integration is possible.

  In step S1006, the abstract event generation unit 121 abstracts these events into the abstract event (1 event, assuming that one event is formed between the events, since the causal relationship between the events is strong and the number of branches to another is small. Integrated into one lump).

  In step S1007, the abstract event generation unit 121 selects an event at the center between events as the name of an abstract event in the abstract events integrated in step S1006. More specifically, the abstract event generation unit 121 stores an event not at the center of the abstract event in the concrete event ID 322 of the abstract event relation table 320 in the causal model DB 131 and an abstract event at the center to the abstract event ID 323. Store. Also, the abstract event generation unit 121 generates a record in which the abstraction level 305 of the event ID 301 of the event table 300 that matches the abstract event ID 323 is increased by one. Furthermore, the abstract event generation unit 121 registers the event ID of the dummy event generated in step S1005 (the branch number of the original event) in the event ID 301 of the event table 300. In addition, the data of the original event is copied about the other items (event 302, word ID: noun 303, word ID: word 名詞 30, abstract degree 305) of the event table 300.

  In step S1008, the abstract event generation unit 121 determines whether or not processing has been completed for all events. If it is determined that there is an unprocessed event, the abstract event generation unit 121 moves the process to step S1002, and if it is determined that all the processes are performed, the process moves to step S1009.

  In step S1009, the event presentation system 100 ends the second event abstraction process.

  Here, in the process of FIG. 2, the abstraction level of the event presented to the user is determined based on the search word input by the user. In addition or alternatively, an abstraction level suitable for the user may be determined in advance from the user's interests. In addition, an abstraction event and a causal relationship determined from the matter of interest may also be presented to the user. An example of the processing for that is shown in FIG.

  FIG. 11 shows an example of a flowchart relating to user abstraction level setting processing for determining and setting the user's abstraction level from the user's interests.

  In step S1100, the event presentation system 100 starts a user abstraction level setting process.

  In step S <b> 1101, the abstraction level determination unit 118 receives an input of a user's interest from the user via the input / output device 142. Note that the user's interest may be input as a search word in the event presentation system 100, or may be a search word when searching for a target web page, information, etc. on the Internet, or the like May be input.

  In step S <b> 1102, the abstraction level determination unit 118 extracts words of interest, and stores them in a list for each user. More specifically, the abstraction level determination unit 118 divides the interest matter input from the user in step S1101 into words by the morphological analysis technique, and stores each word in the interest matter 612 of the user information table 610 of the management DB 134. . At that time, the abstraction level determination unit 118 stores the user ID 611 in association with the user. The abstraction level determination unit 118 may register the input of the user's interests as a search history in the search history table 620.

  In step S <b> 1103, the abstraction level determination unit 118 determines whether the abstraction level can be acquired for the word (list word) of the matter of interest 612 for each user ID 611, and the word 502 in the word information table 500 of the text analysis result DB 133. And the abstraction level 505 are determined by searching. If the abstraction level determination unit 118 determines that acquisition is possible, the process proceeds to step S1104, and if it is determined that acquisition is not possible, the process proceeds to step S1105.

  In step S1104, the abstraction level determination unit 118 acquires the abstraction level 505 searched in step S1103.

  In step S1105, the abstraction level determination unit 118 determines whether there are other words of interest 612 in the user ID 611. If it is determined that there is another abstraction determination unit 118, the process proceeds to step S1103. If it is determined that there is no other, the process proceeds to step S1106.

  In step S1106, the abstraction level determination unit 118 calculates an average value of the abstraction levels 505 acquired in step S1104, and rounds off the decimal point of the average value to the abstraction level 613 of the user information table 610 as an abstraction level of the user ID 611. Store. In the calculation of the abstract level of the user, the average value has been described as an example, but representative values such as a median value and a mode value may be used.

  In step S <b> 1107, the abstraction level determination unit 118 determines whether there is any other user who has not set the abstraction level 613 with the user ID 611. If the abstraction level determination unit 118 determines that there is another user, the process proceeds to step S1003. If it is determined that there is no other user, the process proceeds to step S1108.

  In step S1108, the event presentation system 100 ends the user abstraction level setting process.

  As described above, the event presentation system 100 abstracts events by grouping similar multiple events into one group and giving the group a word sentence indicating meaning. In addition, a causal relationship is given to the abstracted event. According to such a configuration, the abstracted event and its causal relationship can be presented to the user, and the user can easily analyze the transition of the event at the abstracted level.

  In addition, the event presentation system 100 abstracts events at various levels of abstraction and assigns causal relationships thereof. Therefore, when presenting to the user, it is possible to present an event with an abstraction level and a causal relationship that suits the purpose of the user.

(2) Other Embodiments Although the above embodiment has described the case where the present invention is applied to the event presentation system 100, the present invention is not limited to this, and various other systems, The present invention can be widely applied to an event presentation device and an event presentation method.

  In the above embodiment, the search result display screen is displayed as an example of outputting the search result. However, the present invention is not limited to this and the search result may be printed on paper or the like. Alternatively, the search result may be output as voice, the search result may be sent as a file by e-mail, or other output may be performed.

  In the above-described embodiment, various types of data are described using the XX table for convenience of explanation, but the data structure is not limited and may be expressed as XX information or the like.

  In the above description, information such as programs, tables, and files for realizing each function is stored in a storage device such as a memory, a hard disk, or an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD. Can be put in. Further, control lines and information lines indicate what is considered to be necessary for the description, and not all control lines and information lines in the product are necessarily shown.

  In addition, the above-described configuration may be changed, rearranged, combined, or omitted as appropriate without departing from the gist of the present invention.

  DESCRIPTION OF SYMBOLS 100 ... Event presentation system, 110 ... Main memory, 111 ... Search word reception part, 112 ... Text analysis part, 113 ... Causal model / event search / acquisition part, 114 ... Article / document search・ Acquisition unit, 115 …… Search / acquisition unit of attribute information, 116 …… Analysis result output unit, 117 …… Management data reception unit, 118 …… Abstract level determination unit, 120 …… Analysis target acquisition unit, 121 …… Abstract event generation unit, 122 ... causal relationship assignment unit, 123 ... causal strength / branch number determination unit, 130 ... auxiliary storage device, 140 ... central control device, 141 ... communication device, 142 ... input / output device 150 ... communication line, 160 ... map information providing system, 170 ... vocabulary information providing system.

Claims (6)

The event corresponding to the character string input for the search is acquired as an abstract event from the event information including the information of the event, and the context information associated with the context of the event is acquired based on the abstract event. A first identification unit that identifies an event after an abstract event;
Based on the abstract event acquired by the first specifying unit, the concrete event indicating the child event of the abstract event is specified from the parent-child relationship information associated with the parent-child relationship of the event, and information on the event related to the event is obtained. A second identification unit that identifies information on a case related to the concrete event from the case information included;
A case relating to an abstract event acquired by the first specifying unit, an event after the abstract event specified by the first specifying unit, and a concrete event of the abstract event specified by the second specifying unit An output unit that outputs information;
An event presentation system comprising: The first identifying unit identifies an event before the abstract event from the context information based on the acquired abstract event,
The second identification unit identifies a concrete event of the previous event from the parent-child relationship information based on the event before the abstract event identified by the first identification unit, and the second information unit identifies the previous event information from the case information. Identify the information on the incident related to the concrete event of
The output unit outputs an event before the abstract event identified by the first identifying unit, and information of a case related to a concrete event of the previous event identified by the second identifying unit.
The event presentation system according to claim 1, characterized in that: The first identifying unit identifies the parent event of the abstract event or the child event of the abstract event from the parent-child relationship information when the abstraction level of the user is different from the acquired abstraction level of the abstract event, Based on the context information to identify an event after the event,
The output unit outputs an event identified by the first identifying unit and an event subsequent to the event.
The event presentation system according to claim 1, characterized in that: The second identification unit narrows down information on cases related to the identified concrete event based on the character string indicating the attribute of the search and the narrowing-down information associated with the event and the attribute.
The event presentation system according to claim 1, characterized in that: A third specifying unit for specifying information related to the case information designated by the user;
The output unit outputs related information specified by the third specifying unit;
The event presentation system according to claim 1, characterized in that: The event corresponding to the character string input for the search is acquired as an abstract event from the event information including the information of the event, and the context information associated with the context of the event is acquired based on the abstract event. A first identification unit that identifies an event after an abstract event;
Based on the abstract event acquired by the first identification unit, the concrete event indicating the child event of the abstract event is specified from the parent-child relationship information in which the parent-child relationship of the event is associated, and the event information A second identification unit that identifies information on a case related to the concrete event from the case information included;
A case relating to an abstract event acquired by the first specifying unit, an event after the abstract event specified by the first specifying unit, and a concrete event of the abstract event specified by the second specifying unit An output unit that outputs information;
An event presentation apparatus comprising:

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