JPWO2017104657A1 - Information processing apparatus, information processing method, and computer program - Google Patents

Abstract

Information that can evaluate the consistency of the result of chaining multiple causal relationships is provided. The information processing apparatus extracts a chain of causal relationships composed of at least two causal relationships from a plurality of combinations of information representing elements that cause causal relationships related to events and information representing the resulting elements. A document storage unit that can hold one or more documents, a co-occurrence detection unit that detects co-occurrence of the elements at both ends of the causal chain extracted by the chain extraction unit in the document, and A co-occurrence ratio calculation unit that calculates a co-occurrence ratio regarding the co-occurrence detected by the co-occurrence detection unit; and a result output unit that outputs output information including a result of the calculation by the co-occurrence ratio calculation unit.

Description

  The present invention relates to a technique for analyzing a causal relationship between elements constituting an event.

  A system that visualizes elements constituting a certain event and a causal relationship between each element is known. For example, Patent Document 1 discloses a technique for generating a causal relationship diagram based on whether the relationship between elements constituting the system is increasing or decreasing. Specifically, the technique disclosed in Patent Document 1 visualizes a causal relationship between elements by using a coincidence rate between a change in a cause-side element and a change in a result-side element.

  Patent Document 2 describes a technique for describing a causal relationship structure between nodes by connecting nodes (elements) constituting the system by an arc and generating a simulation program using a simulation model inheriting the structure. Is disclosed.

  Moreover, although it is not a technique related to the analysis of the causal relationship, Patent Literature 3 detects the second proper noun co-occurring with the first proper noun included in the document, and the co-occurrence relationship in time series. A technique for analyzing the transition of the appearance time of each and the subsequent appearance rate of each proper noun is disclosed. Patent Document 4 discloses a technique for reducing data displayed while maintaining the outline of a graph by classifying data having a graph structure into a partial graph and thinning out nodes from the partial graph based on a predetermined standard. Is disclosed.

JP2013-130946A JP 2007-286777 A JP 2008-269014 A Japanese Patent Laying-Open No. 2015-032298

  There are cases where current status analysis and problem solving methods are examined with reference to information representing the visualized causal relationship. If the domains (regions) or conditions to which the plurality of causal relationships are related are not common, there is a problem that the consistency of the chain of the plurality of causal relationships may not be satisfied. That is, when multiple causal relationships are linked, if the domain, condition, or related event related to each causal relationship is not common, the cause element and the resulting element will not match. Problems can arise.

  As an example, a problem that occurs due to an element having a different meaning depending on an event is shown. For example, the element “performance” has a different meaning in an event related to human resource evaluation and an event related to information processing such as a computer. In human resource evaluation, there is a tendency that “the higher the performance person” is, the higher the position is. For example, as shown in FIG. 1A, this causal relationship is expressed using nodes representing “performance” and “job title” and directional links connecting these nodes. On the other hand, in a computer, if “CPU (Central Processing Unit) computing power” is high, there is a tendency that “performance increases” in processing. Similar to the above, this causal relationship is expressed by using, for example, nodes representing “CPU computing power” and “performance” and directional links connecting these nodes (FIG. 1B). FIG. 1C shows the result of combining (chaining) the causal relationships shown in FIGS. 1A and 1B. The result of the chain of causal relationships shown in FIG. 1C leads to a generally meaningless conclusion that “the higher the CPU computing power”, the “higher the job title”. As in the above example, despite the fact that the element “performance” has a different meaning in each event, the result of chaining multiple causal relationships is not consistent due to simply combining the causal relationships. The problem arises.

  On the other hand, the technique disclosed in each of the above patent documents does not always correctly evaluate the consistency of the result of linking a plurality of causal relationships. That is, the technique disclosed in Patent Document 1 only generates a causal relationship diagram representing the relationship between elements based on past data regarding each element. The technique disclosed in Patent Document 2 only generates a simulation model from a causal description format that is easy for humans to understand. Neither Patent Document 3 nor Patent Document 4 is a technique related to the analysis of the causal relationship.

  The present invention has been made in view of the above-described circumstances. That is, the present invention has as one of its main objects to provide an information processing apparatus or the like that provides information that can be used to evaluate the consistency of results obtained by chaining a plurality of causal relationships.

  In order to achieve the above object, an information processing apparatus according to one embodiment of the present invention includes at least two combinations of information representing an element that causes a causal relationship regarding an event and information representing an element that results. A chain extraction unit that extracts a chain of causal relationships composed of two causal relationships; a document storage unit that can hold one or more documents; and the end points of the chain of causal relationships extracted by the chain extraction unit in the document. The co-occurrence detection unit for detecting the co-occurrence of the elements, the co-occurrence rate calculation unit for calculating the ratio of co-occurrence related to the co-occurrence detected by the co-occurrence detection unit, and the result of the calculation by the co-occurrence rate calculation unit A result output unit that outputs output information including the output information.

  In addition, the information processing method according to one aspect of the present invention includes a causal relationship including at least two causal relationships from a plurality of combinations of information representing elements that cause causal relationships related to events and information representing the resulting elements. Extract a chain of relationships, detect co-occurrence in one or more documents of the elements at both ends of the extracted chain of causal relationships, calculate the co-occurrence ratio for the detected co-occurrence, Output the output information including the result.

  The object is also achieved by an information processing apparatus having the above configuration, a computer program that implements the information processing method by a computer, a computer-readable recording medium that stores the computer program, and the like.

  ADVANTAGE OF THE INVENTION According to this invention, the information which can evaluate the consistency of the result in which the some causal relationship was chained can be provided.

FIG. 1A is a diagram illustrating an example of a causal relationship. FIG. 1B is a diagram illustrating another example of the causal relationship. FIG. 1C is a diagram illustrating an example of a causal chain. FIG. 2 is a block diagram illustrating a functional configuration of the information processing apparatus according to the first embodiment of this invention. FIG. 3 is a flowchart illustrating an example of processing performed by the information processing apparatus according to the first embodiment of this invention. FIG. 4 is an explanatory diagram illustrating an example of a method for representing information representing elements constituting an event and a causal relationship between the elements in the first embodiment of the present invention. FIG. 5 is an explanatory diagram showing another example of a method for representing information representing elements constituting an event and a causal relationship between the elements in the first embodiment of the present invention. FIG. 6 is an explanatory diagram illustrating an example of a chain of causal relationships extracted from information in the first embodiment of the present invention. FIG. 7 is an explanatory diagram illustrating an example of a result of calculating a co-occurrence ratio between elements arranged at both ends in a chain of causality in the first embodiment of the present invention. FIG. 8 is a block diagram illustrating a functional configuration of the information processing apparatus according to the second embodiment of this invention. FIG. 9 is a block diagram illustrating a hardware configuration capable of realizing the information processing apparatus according to each embodiment of the present invention.

  Hereinafter, an information processing apparatus and the like that can solve the above-described problem will be described in detail using each embodiment. In addition, the structure of the information processing apparatus described in each following embodiment is an illustration, and the technical scope of this invention is not limited to them. The division (for example, division | segmentation by a functional unit) of the component which comprises the information processing apparatus in each following embodiment is an example which can implement | achieve the said information processing apparatus. The implementation of the information processing apparatus is not limited to the following examples, and various configurations are assumed. That is, the constituent elements constituting the information processing apparatus in each of the following embodiments may be further divided, or one or more constituent elements may be integrated.

  The information processing device described below may be configured using a single device (physical or virtual device), and may be realized using a plurality of separated devices (physical or virtual devices). Also good. When the information processing apparatus includes a plurality of apparatuses, each apparatus may be connected to be communicable via a wired network, a wireless network, or a communication network (communication line) appropriately combining them. Such a communication network may be a physical communication network or a virtual communication network. An information processing apparatus described below or a hardware configuration capable of realizing the components will be described later.

<First Embodiment>
[Description of configuration]
The first embodiment of the present invention will be described below in detail with reference to the drawings. The components described in the following embodiments are merely examples, and the technical scope of the present invention is not limited thereto.

  FIG. 2 is a block diagram illustrating the functional configuration of the information processing apparatus 100 according to the first embodiment of the invention. The information processing apparatus 100 is an apparatus that operates according to a software program (computer program) such as a computer.

  The information processing apparatus 100 includes an input reception unit 101, a chain extraction unit 102, a document storage unit 103, a co-occurrence detection unit 104, a co-occurrence ratio calculation unit 105, and a result output unit 106.

  The input receiving unit 101 (input receiving unit) receives information representing an element constituting an event as an input. More specifically, the input receiving unit 101 receives, as an input, a combination of an element that causes a cause-and-effect relationship related to the event and information that represents a resulting element.

  In the present embodiment, the above-described event represents something (such as a phenomenon) that can occur in an actual environment or a virtual environment such as an information processing apparatus. Hereinafter, an element constituting an event may be simply referred to as an “element”. In addition, information indicating a combination of a cause element and a result element regarding a certain event may be described as “causal relationship information”.

  In the causal relationship information, the elements constituting the event are described or expressed as, for example, some form of data.

  The chain extraction unit 102 (chain extraction unit) extracts a chain of causal relationships including at least two causal relationships from the information received by the input receiving unit 101. Here, a chain of causal relationships including two causal relationships is configured as follows. That is, assuming three elements, they are described as element 1, element 2, and element 3, respectively. Causal relationship (first causal relationship) resulting from element 1 (first element) and resulting from element 2 (second element) and element 2 (second element) as a cause When there is a causal relationship (second causal relationship) that results in the third element), the above-described chain of causal relationships is composed of these two causal relationships and three elements.

  The document storage unit 103 (document storage unit) holds (stores) the document. The document stored in the document storage unit 103 is not particularly limited as long as the document is expected to describe the elements constituting the event and the causal relationship between the elements. Such a document may be, for example, a document published on the WEB (World Wide Web), a newspaper article, or a white paper. Such a document is not limited to the above example, and other appropriate documents can be selected. Note that the document storage unit 103 is not limited to a document composed of text data, and includes information that can be documented using appropriate analysis techniques (speech recognition, image analysis, etc.) such as voice, image, and moving image data. Various data may be held. The document storage unit 103 can store the document using, for example, a well-known file system or a database.

  The co-occurrence detection unit 104 (co-occurrence detection means) detects co-occurrence for the elements at both ends of the causal chain extracted by the chain extraction unit 102 from the document stored in the document storage unit 103.

  Here, both ends of the chain of causal relationships correspond to, for example, element 1 and element 3 among the three elements described in the above example. Hereinafter, the elements at both ends of the causal relationship chain extracted by the co-occurrence detecting unit 104 may be referred to as “causal relationship element pairs”.

  The co-occurrence detection unit 104, for example, when voice, image, video data, or the like is stored in the document storage unit 103, uses a suitable analysis technique such as voice recognition or image analysis to generate a document from those data. May be extracted (character information). Then, the co-occurrence detecting unit 104 may detect the co-occurrence related to the causal relationship element pair from the extracted documentable information.

  The co-occurrence ratio calculation unit 105 (co-occurrence ratio calculation means) calculates the co-occurrence ratio based on the detection result by the co-occurrence detection unit 104. The co-occurrence ratio is information used to determine whether or not the elements (causal relation element pairs) at both ends of the causal relation chain are consistent when a plurality of causal relations are linked. For example, the information processing apparatus 100 or the user can determine whether a causal relationship element pair is consistent based on the co-occurrence ratio calculated by the co-occurrence ratio calculation unit 105. In this case, the co-occurrence ratio calculated by the co-occurrence ratio calculation unit 105 can be an index for determining whether or not a certain causal relationship element pair is consistent.

  Specifically, the co-occurrence ratio calculation unit 105 calculates a value indicating the degree of co-occurrence (for example, the strength of the co-occurrence relation) for a certain causal relationship element pair as the co-occurrence ratio. A method for calculating the proportion of co-occurrence will be described later. Hereinafter, the ratio of co-occurrence may be simply referred to as “co-occurrence ratio”.

  The result output unit 106 (result output unit) outputs output information including the result of the calculation by the co-occurrence ratio calculation unit 105. The method of outputting the output information by the result output unit 106 may be, for example, a method of outputting to an appropriate output device (display device) such as a display (not shown) or a method of outputting to a file. The result output unit 106 may transmit data representing the output information to another information processing apparatus or the like. The method by which the result output unit 106 outputs the output information is not limited to the above, and an appropriate method may be adopted.

  The information processing apparatus 100 may acquire the causal relationship information from an input device (not shown) such as a keyboard, may be acquired from a database or the like (not shown), or another information processing apparatus (for example, a server). Etc.). The method by which the information processing apparatus 100 acquires the causal relationship information is not limited to the above, and an appropriate method may be adopted.

  In the present embodiment, the information processing apparatus 100 includes a control unit, a storage unit, and an input / output unit as a hardware configuration. The control unit is configured using an arithmetic device such as a CPU, for example. The storage unit is configured using, for example, a RAM (Random Access Memory), a ROM (Read Only Memory), an HDD (Hard Disk Drive), and the like. The input / output unit is configured using various interfaces such as an operation unit, a display unit, and a communication interface. The functions of the information processing apparatus 100 described above are realized by the cooperation of these hardware and various programs stored in the storage unit. Note that a hardware configuration capable of realizing the information processing apparatus 100 will be described later.

[Description of operation]
Next, an example of a flow of processing by the information processing apparatus 100 will be described with reference to FIG. The flowchart illustrated in FIG. 3 is an example of the operation of the information processing apparatus 100, and the present embodiment is not limited to this. The execution order of the processing steps in the flowchart may be changed within a range that does not affect the processing result, and one or more processing steps may be executed in parallel. Also, the control of the process illustrated in FIG. 3 is performed, for example, by the control unit configuring the information processing apparatus 100 developing and executing a program stored in the storage unit.

  First, in step S301, the input reception unit 101 receives input of information (causal relationship information) representing elements constituting an event and a causal relationship between elements.

FIG. 4 shows an example of information representing the elements constituting the event and the causal relationship between the elements. In the specific example shown in FIG. 4, the causal relationship information 400 is represented using a table format including a column 401 indicating a cause element and a column 402 indicating a result element. In each of the above columns, data expressed in an appropriate format such as a character string, a numerical value, a code, and a symbol is appropriately set.
The causal relationship information may be expressed using other methods (forms). For example, as such an expression method, an expression method that uniquely defines a graph structure as shown in FIG. 5 may be used. In the graph structure shown in FIG. 5, elements constituting an event are represented using graph nodes, and the causal relationship between the elements is a directed link from the cause element (node) to the result element (node). It is expressed using.

  Next, in step S302, the chain extraction unit 102 extracts a chain of causal relationships including two causal relationships from the received causal relationship information. Hereinafter, a method for extracting the causal chain will be described with reference to a specific example shown in FIG.

  FIG. 6 shows a list of causal relation chains extracted from the causal relation information exemplified in FIG. The causal chain list 600 illustrated in FIG. 6 includes a column 601 indicating an element 1, a column 602 indicating an element 2, and a column 603 indicating an element 3. In each of the columns, data expressed in an appropriate format such as a character string, a numerical value, a code, and a symbol is set.

  To extract the chain of causal relationships shown in FIG. 6, for example, with respect to the causal relationship information illustrated in FIG. 4, the element that is the result (402) in one row and the element that is the cause (401) in another row Find a combination of lines that matches. Alternatively, a pair of nodes whose path length is “2” is searched from the graph structure illustrated in FIG. 5, and three nodes and two links constituting the path are extracted. Good. The method of extracting the causal relationship chain is not limited to the method exemplified above, and other appropriate methods may be adopted.

  Next, in step S303, the co-occurrence detection unit 104 performs co-occurrence regarding elements at both ends of the chain of causal relationships extracted by the chain extraction unit 102 from the documents stored in the document storage unit 103 (causal relationship element pairs). Extract.

  Specifically, for example, the co-occurrence detection unit 104 analyzes the document stored in the document storage unit 103 using a natural language analysis method (for example, morphological analysis, syntax analysis, etc.), and causal elements in the document. Detect pair co-occurrence.

  The range in which the co-occurrence detection unit 104 detects co-occurrence (hereinafter sometimes referred to as “co-occurrence detection range”) may be, for example, the entire document or a range set in units of sentences. It may be a range set in units of paragraphs. The range in which the co-occurrence detection unit 104 detects co-occurrence is not limited to the above, and can be set as appropriate by the user of the information processing apparatus 100, for example. For example, as a result of the analysis, the co-occurrence detecting unit 104 may detect the co-occurrence of the causal relationship element pair based on whether or not the causal relationship element pair is included in a certain co-occurrence detection range.

  The co-occurrence detection unit 104, for example, when voice, image, video data, etc. are held in the document storage unit 103, can be documented from the data using appropriate analysis techniques such as voice recognition and image analysis. Information (character information) may be extracted. Then, the co-occurrence detecting unit 104 may extract the co-occurrence related to the above-described causal relationship element pair from the extracted documentable information.

  Hereinafter, a specific example will be described. For example, assume that the following text is described in the accumulated document.

“Personal computer performance is important, so consider the number of CPU clocks and memory capacity.”
From the above sentence, the co-occurrence detecting unit 104 includes the “CPU clock number” that is element 1 and the element 3 for the first line (600A in FIG. 6) of the causal relation list 600 illustrated in FIG. Detect that “performance” co-occurs.

  As another example, it is assumed that the following text is described.

“If you have high-performing personnel, you can pay a high salary.”
From the above sentence, the co-occurrence detecting unit 104 has “performance” as element 1 and “salary” as element 3 for the fifth line (600B in FIG. 6) of the causal relation list 600 illustrated in FIG. ”Is detected to be co-occurring.

  For example, the co-occurrence detection unit 104 may temporarily hold a co-occurrence detection result and provide the result to the co-occurrence ratio calculation unit 105.

  Next, in step S304, the co-occurrence ratio calculation unit 105 calculates the co-occurrence ratio from the detected co-occurrence. The co-occurrence ratio calculation unit 105 can use, for example, a co-occurrence frequency, a Jaccard coefficient, a Simpson coefficient, a Dice coefficient, a cosine distance, and the like.

  For example, the co-occurrence ratio calculation unit 105 can calculate the co-occurrence frequency by calculating the number of co-occurrence between a certain element x and a certain element y from the detection result of the co-occurrence in step S303. The element x is, for example, an element that causes in a causal relationship element pair, and the element y is an element that results in, for example, a causal relationship element pair (the same applies hereinafter).

Further, the co-occurrence ratio calculation unit 105 can calculate the Jaccard coefficient using the following equation.

  In the above equation, X represents a set of co-occurrence detection ranges (for example, documents, sentences, paragraphs, etc.) including the element x. Y represents a set of co-occurrence detection ranges (for example, documents, sentences, paragraphs, etc.) including the element y. | X∩Y | is the number of elements of the common set of the set X and the set Y, and represents the number of co-occurrence of the element x and the element y. This can be calculated from the result of step S303. | X∪Y | is the number of elements of the union of the set X and the set Y, and represents the number of occurrences of the element x or the element y. The above symbols are the same in the following equations. The range of the Jaccard coefficient is between 0 and 1, and the larger the Jaccard coefficient, the higher the proportion of co-occurrence.

Further, the co-occurrence ratio calculation unit 105 can calculate the Simpson coefficient using the following equation.

  In the above equation, min (| X |, | Y |) represents the smaller of the number of occurrences of element x (| X |) and the number of occurrences of element y (| Y |). The range of the Simpson coefficient is between 0 and 1, and the larger the Simpson coefficient, the higher the proportion of co-occurrence.

The co-occurrence ratio calculation unit 105 can calculate the Dice coefficient using the following equation.

  The range of the Dice coefficient is between 0 and 1, and the larger the Dice coefficient, the higher the co-occurrence ratio.

The co-occurrence ratio calculation unit 105 can calculate the cosine distance using the following equation.

  The range of the cosine distance is between 0 and 1, and the greater the cosine distance, the higher the co-occurrence rate.

  The method of calculating the co-occurrence ratio is not limited to the above example, and the co-occurrence ratio calculation unit 105 may select another appropriate method.

  Finally, in step S305, the result output unit 106 outputs output information including the result of the calculation related to the co-occurrence ratio calculated in step S304.

  The output information may be expressed in a table format as exemplified in FIG. FIG. 7 shows a list of the results of calculating the co-occurrence ratios between the elements at both ends of the causal relation chain illustrated in FIG. In FIG. 7, the output information 700 includes a column 701 indicating element 1, a column 702 indicating element 3, and a column 703 indicating the calculation result of the co-occurrence ratio between element 1 and element 3. In each of the columns, data expressed in an appropriate format such as a character string, a numerical value, a code, and a symbol is set. For example, the result output unit 106 may output, as output data, a table obtained by adding a column indicating the element 2 to the table of FIG.

  For example, the result output unit 106 may adjust the contents to be included in the output information in accordance with the co-occurrence ratio value calculated in step S304. Specifically, the result output unit 106 may adjust the contents included in the output information based on the result of comparing the co-occurrence ratio calculated in step S304 with a certain reference. In this case, the result output unit 106 may output only the combination of the element 1 and the element 3 whose co-occurrence ratio is equal to or less than a reference value (threshold value), for example. The reference value may be set as appropriate by the user.

  As described above, the information processing apparatus 100 according to the present embodiment receives input of information (causal relationship information) indicating the elements and the causal relationship between the elements constituting the event, and the causal relationship including the two causal relationships. Extract the chain. Then, the information processing apparatus 100 detects the co-occurrence of elements at both ends of the extracted chain of causal relationships from the document stored in the document storage unit 103, calculates the co-occurrence ratio, and outputs the calculation result. . Therefore, the information processing apparatus 100 can provide the user with the calculation result of the co-occurrence ratio. For example, when a general document is stored in the document storage unit 103, the co-occurrence detection unit 104 becomes an element at both ends of a chain of causal relationships in a general document (that is, a cause element and a result). Element) co-occurrence is detected. Then, the co-occurrence ratio calculation unit 105 calculates a ratio in which the cause element and the resulting element co-occur in those documents. Since the ratio of co-occurrence represents the frequency of co-occurrence and the strength of the co-occurrence relationship, in this case, the information processing apparatus 100 determines the degree of co-occurrence of elements at both ends of the causal relationship chain in a general document. Can be calculated.

  As described above, the user of the information processing apparatus 100 can confirm whether or not the result of the chain of causality is consistent based on the provided calculation result. For example, by confirming the degree of co-occurrence in a general document (for example, the strength of the co-occurrence relation), the user can check the causal relation of the causal relation extracted from the causal relation information given as input. It can be confirmed whether the result of the chaining is consistent.

  For example, in the case of the specific example shown in FIG. 7, the co-occurrence ratio between “CPU computing ability” and “job title” is “0.001” from the fourth line of output information 700 (700A in FIG. 7). Thereby, the user of the information processing apparatus 100 can recognize that these elements hardly co-occur in the accumulated document. That is, there is a high possibility that these elements belong to different domains or conditions, and the user can recognize the possibility that the chain of causal relationships including these elements is not consistent. As a result, the user can correct a causal error. As described above, the information processing apparatus 100 according to the present embodiment can provide the user with information that can evaluate the consistency of the result of linking a plurality of causal relationships.

<Modification of First Embodiment>
Hereinafter, modifications of the first embodiment will be described. The configuration of the information processing apparatus 100 in the present modification may be the same as that in the first embodiment.

  In the present modification, the co-occurrence ratio calculation unit 105 determines whether or not the result of the chain of certain causal relationships is consistent based on the calculation result of the co-occurrence ratio (that is, in the chained causal relationship, the cause Whether or not the resulting element and the resulting element are consistent).

  Specifically, the co-occurrence rate calculation unit 105 matches the causal relationship based on the result of comparing the co-occurrence rate of the causal element and the resulting element with a certain standard for a certain causal relationship. Determine gender. In this case, the co-occurrence ratio calculation unit 105 includes, for example, a causality including a combination of element 1 (cause element) and element 3 (result element) whose co-occurrence ratio is equal to or less than a certain reference value (threshold value). Regarding the chain of relationships, it may be determined that the causal relationship is not consistent. In addition, the co-occurrence ratio calculation unit 105, for example, for a chain of causal relationships including a combination of element 1 (cause element) and element 3 (result element) where the co-occurrence ratio is greater than a certain reference value. It may be determined that the causal relationship is consistent. The co-occurrence ratio calculation unit 105 may provide the result output unit 106 with the calculation result of the co-occurrence ratio and the determination result.

  The result output unit 106 outputs, for example, a combination of a cause element and a result element included in a chain of causal relationships determined to be inconsistent based on the determination result. Also good.

  The information processing apparatus 100 according to the present modification configured as described above can present the determination result of the causality consistency to the user. For example, by setting an appropriate reference value, the user can collectively confirm a causal relationship chain that is highly likely that the causal element and the resulting element do not match. As described above, the information processing apparatus 100 according to the present modification can provide the user with information that can evaluate the consistency of the result of linking a plurality of causal relationships, as in the first embodiment.

<Second Embodiment>
Next, a second embodiment that is a basic embodiment of the present invention will be described. FIG. 8 is a block diagram illustrating a functional configuration of an information processing apparatus according to the second embodiment of the present invention.

  As illustrated in FIG. 8, the information processing apparatus 800 includes a chain extraction unit 801, a co-occurrence detection unit 802, a document storage unit 803, a co-occurrence ratio calculation unit 804, and a result output unit 805. These components constituting the information processing apparatus 800 are communicably connected using an appropriate communication method.

  The chain extraction unit 801 (chain extraction unit) includes a chain of causal relationships including at least two causal relationships from a plurality of combinations of information indicating elements that cause causal relationships related to events and information indicating the resulting elements. To extract. For example, as in the first embodiment, the chain extraction unit 801 generates a chain of causal relationships from causal relationship information including a plurality of combinations of information representing the cause elements and information representing the resulting elements. It may be extracted. In this case, the causal relationship information and the causal relationship chain may be the same as those in the first embodiment. The chain extraction unit 801 may be provided with the causal relationship information from a user (not shown) of the information processing device 800 or another information processing device, and is held in a storage device (not shown) in the information processing device 800. The causal relationship information may be acquired.

  For example, the chain extraction unit 801 may be configured in the same manner as the chain extraction unit 102 in each of the above-described embodiments, and by the same processing as the chain extraction unit 102, the causal relationship including at least two causal relationships from the causal relationship information. The chain may be extracted.

  A document storage unit 803 (document storage unit) holds one or more documents. Such a document is not particularly limited, for example, as in the first embodiment, and may be any document that is expected to describe the elements constituting the event and the causal relationship between the elements. The document storage unit 803 can store the document using, for example, a known file system or a database.

  For example, the document storage unit 803 may be configured in the same manner as the document storage unit 103 in the first embodiment, and may hold a document by the same processing as the document storage unit 103.

  The co-occurrence detection unit 802 (co-occurrence detection means) detects co-occurrence in the document held in the document storage unit 803 for the elements at both ends of the causal chain extracted by the chain extraction unit 801. For example, the co-occurrence detection unit 802 analyzes the document held in the document storage unit 803, and the cause element and the result element in the extracted chain of causality are the document (or a part thereof). ) May be detected based on whether they are included.

  For example, the co-occurrence detection unit 802 may be configured in the same manner as the co-occurrence detection unit 104 in the first embodiment, and may detect the co-occurrence by the same processing as the co-occurrence detection unit 104.

  The co-occurrence ratio calculation unit 804 (co-occurrence ratio calculation means) calculates the ratio of co-occurrence related to the co-occurrence detected by the co-occurrence detection unit 802. The ratio of such co-occurrence represents, for example, the strength of the co-occurrence relationship related to the co-occurrence detected by the co-occurrence detection unit 802.

  The co-occurrence ratio calculation unit 804 may be configured, for example, in the same manner as the co-occurrence ratio calculation unit 105 in the first embodiment, and the co-occurrence ratio calculation unit 105 calculates the co-occurrence ratio by the same processing as the co-occurrence ratio calculation unit 105. It may be calculated.

  The result output unit 805 (result output unit) outputs output information including the result of calculation by the co-occurrence ratio calculation unit 804. The result output unit 805 may output the output information to a display device (not shown), for example, similarly to the result output unit 106 in the first embodiment, and outputs the calculation result in the form of a file or the like. May be. The method by which the result output unit 805 outputs the calculation result is not limited to the above, and an appropriate method may be selected.

  For example, the result output unit 805 may be configured in the same manner as the result output unit 106 in the first embodiment, and may output the output information by the same processing as the result output unit 106.

  As described above, the information processing apparatus 800 according to the present embodiment extracts a chain of causal relationships including at least two causal relationships from the causal relationship information indicating the elements constituting the events and the causal relationships between the elements. To do. Then, the information processing apparatus 800 detects co-occurrence in the accumulated document and calculates the co-occurrence ratio for the elements at both ends of the extracted chain of causal relationships. Then, the information processing apparatus outputs output information including the calculation result of the co-occurrence ratio. According to such an information processing apparatus 800, the calculation result of the co-occurrence ratio of the elements at both ends of the causal chain can be provided to the user. The user can confirm whether or not the result of the chain of causality is consistent based on the provided calculation result. As described above, the information processing apparatus 800 according to the present embodiment can provide the user with information that can evaluate the consistency of the result of linking a plurality of causal relationships.

<Configuration of hardware and software program (computer program)>
Hereinafter, a hardware configuration capable of realizing each of the above-described embodiments will be described.

  In the following description, the information processing apparatuses (100, 800) described in the above embodiments are collectively referred to simply as “information processing apparatus”. Each component of the information processing apparatus may be simply referred to as “component of the information processing apparatus”.

  The information processing apparatus described in each of the above embodiments may be configured by one or a plurality of dedicated hardware devices. In that case, each component shown in each of the above drawings (FIGS. 2 and 8) is realized by using a hardware (an integrated circuit or a storage device on which processing logic is mounted) that is partially or fully integrated. Also good.

  When the information processing apparatus is realized by dedicated hardware, the components of the information processing apparatus may be realized by, for example, a circuit configuration capable of providing each function. Such a circuit configuration includes, for example, an integrated circuit such as SoC (System on a Chip), a chip set realized using the integrated circuit, and the like. In this case, the data held by the components of the information processing apparatus is, for example, a RAM (Random Access Memory) area or a flash memory area integrated as an SoC, or a storage device (semiconductor storage device or the like) connected to the SoC. May be stored. The data includes, for example, causal relationship information received by the input receiving unit 101, a chain of causal relationships extracted by the chain extracting unit (102, 801), a detection result by the co-occurrence detecting unit (104, 802), and a co-occurrence ratio. The calculation result by the calculation unit (105, 804), the document stored in the document storage unit (103, 803), and the like may be included. In addition, the data may include processing data generated by the components of the information processing apparatus during the processing.

  In this case, a well-known communication network (for example, a communication bus) may be employed as a communication line that connects each component of the information processing apparatus. Further, the communication line connecting each component may be connected between each component by peer-to-peer.

  Further, the information processing apparatus described above may be configured by general-purpose hardware exemplified in FIG. 9 and various software programs (computer programs) executed by the hardware. In this case, the information processing apparatus may be configured by an arbitrary number of general-purpose hardware devices and software programs. That is, an individual hardware device may be assigned to each component configuring the information processing apparatus, and a plurality of components may be realized using a single hardware device.

  The arithmetic device 901 in FIG. 9 is an arithmetic processing device such as a general-purpose CPU (Central Processing Unit) or a microprocessor. The arithmetic device 901 may read various software programs stored in a nonvolatile storage device 903, which will be described later, into the storage device 902, and execute processing according to the software programs. In this case, the function of the component of the information processing apparatus in each of the above embodiments is realized using a software program executed by the arithmetic device 901.

  The storage device 902 is a memory device such as a RAM or a ROM that can be referred to from the arithmetic device 901, and stores software programs, various data, and the like. Note that the storage device 902 may be a volatile memory device or a nonvolatile memory device.

  The storage device 902 may temporarily store data held by the components of the information processing device. The data includes, for example, causal relationship information received by the input receiving unit 101, a chain of causal relationships extracted by the chain extracting unit (102, 801), a detection result by the co-occurrence detecting unit (104, 802), and a co-occurrence ratio. The calculation result by the calculation unit (105, 804), the document stored in the document storage unit (103, 803), and the like may be included. In addition, the data may include processing data generated by the components of the information processing apparatus during the processing.

  The nonvolatile storage device 903 is a nonvolatile storage device such as a magnetic disk drive or a semiconductor storage device using a flash memory. The nonvolatile storage device 903 can store various software programs, data, and the like. For example, various documents stored in the document storage unit (103, 803) may be stored in the nonvolatile storage device 903.

  The network interface 906 is an interface device that connects to a communication network. For example, a wired and wireless LAN connection interface device may be employed.

  The drive device 904 is, for example, a device that processes reading and writing of data with respect to a recording medium 905 described later.

  The recording medium 905 is an arbitrary recording medium capable of recording data, such as an optical disk, a magneto-optical disk, and a semiconductor flash memory.

  The input / output interface 907 is a device that controls input / output with an external device. For example, the input receiving unit 101 may receive input of causal relationship information from an input device (such as a keyboard) connected via the input / output interface 907. Further, the result output unit (106, 805) may output the calculation result of the causal relationship or the determination result of the consistency to the display device connected via the input / output interface 907.

  The information processing apparatus according to the present invention described with the above-described embodiments as an example, or a component thereof, for example, software capable of realizing the functions described in the above-described embodiments with respect to the hardware apparatus illustrated in FIG. -It may be realized by supplying a program. More specifically, for example, the present invention may be realized by causing the arithmetic device 901 to execute a software program supplied to the hardware device. In this case, an operating system running on the hardware device, database management software, network software, middleware such as a virtual environment platform, etc. may execute part of each process.

  In each embodiment described above, each unit illustrated in each drawing can be realized as a software module, which is a function (processing) unit of a software program executed by the hardware described above. However, the division of each software module shown in these drawings is a configuration for convenience of explanation, and various configurations can be assumed for implementation.

  For example, when each component of the information processing apparatus illustrated in FIGS. 2 and 8 is realized as a software module, these software modules are stored in the nonvolatile storage device 903. Then, when the arithmetic device 901 executes each process, these software modules are read out to the storage device 902.

  In addition, these software modules may be configured to transmit various data to each other by an appropriate method such as shared memory or inter-process communication. With such a configuration, these software modules are connected so as to communicate with each other.

  Further, the software program may be recorded on the recording medium 905. In this case, the software program may be configured to be stored in the nonvolatile storage device 903 through the drive device 904 as appropriate at the shipment stage or operation stage of the components of the information processing apparatus.

  In the above case, the method of supplying various software programs to the hardware is installed in the apparatus using an appropriate jig in the manufacturing stage before shipment or the maintenance stage after shipment. A method may be adopted. As a method for supplying various software programs, a general procedure may be adopted at present, such as a method of downloading from the outside via a communication line such as the Internet.

  In such a case, the present invention can be considered to be configured by a code that constitutes the software program or a computer-readable recording medium on which the code is recorded. In this case, the recording medium is not limited to a medium independent of the hardware device, but includes a recording medium in which a software program transmitted via a LAN or the Internet is downloaded and stored or temporarily stored.

  Further, the components of the information processing apparatus described above are configured by a virtualized environment in which the hardware device illustrated in FIG. 9 is virtualized and various software programs (computer programs) executed in the virtualized environment. May be. In this case, the components of the hardware device illustrated in FIG. 9 are provided as virtual devices in the virtual environment. In this case as well, the present invention can be realized with the same configuration as when the hardware device illustrated in FIG. 9 is configured as a physical device.

  In the above, this invention was demonstrated as an example applied to exemplary embodiment mentioned above. However, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to such embodiments. In such a case, new embodiments to which such changes or improvements are added can also be included in the technical scope of the present invention. Furthermore, the embodiments described above, or embodiments obtained by combining new embodiments with such changes or improvements may be included in the technical scope of the present invention. This is clear from the matters described in the claims.

  This application claims the priority on the basis of Japanese application Japanese Patent Application No. 2015-243101 for which it applied on December 14, 2015, and takes in those the indications of all here.

DESCRIPTION OF SYMBOLS 100 Information processing apparatus 101 Input reception part 102 Chain extraction part 103 Document storage part 104 Co-occurrence detection part 105 Co-occurrence ratio calculation part 106 Result output part 800 Information processing apparatus 801 Chain extraction part 802 Co-occurrence detection part 803 Document storage part 804 Co-occurrence Occurrence ratio calculation unit 805 Result output unit 901 Arithmetic device 902 Storage device 903 Non-volatile storage device 904 Drive device 905 Recording medium 906 Network interface 907 Input / output interface

Claims (9)

A chain extraction means for extracting a chain of causal relationships consisting of at least two causal relationships from a plurality of combinations of information representing the factors causing the causal relationships related to the events and information representing the resulting elements;
Document storage means capable of holding one or more documents;
Co-occurrence detecting means for detecting co-occurrence of the elements at both ends of the causal chain extracted by the chain extracting means in the document;
A co-occurrence ratio calculating means for calculating a ratio of co-occurrence related to the co-occurrence detected by the co-occurrence detecting means;
An information processing apparatus comprising: result output means for outputting output information including a result of calculation by the co-occurrence ratio calculation means. The elements at both ends of the chain of causality extracted by the chain extraction means are the element that causes the chain of causality and the element that results.
The co-occurrence detecting unit analyzes the document, and the causality detection range including at least a part of the document includes the causal element and the resulting element when the causal element is included. Detect co-occurrence of said elements at both ends of a chain of relationships;
The information processing apparatus according to claim 1, wherein the co-occurrence ratio calculation unit calculates a value of the co-occurrence ratio that represents the strength of the co-occurrence relationship related to the detected co-occurrence. The result output means adjusts the content of the output information according to the value of the co-occurrence ratio regarding the elements at both ends of the causal relationship chain calculated by the co-occurrence ratio calculation means. The information processing apparatus described in 1. The result output means, when the co-occurrence ratio value of the elements at both ends of the causal relation chain calculated by the co-occurrence ratio calculation means is less than or equal to a reference value, Outputting the output information including the result of the calculation for the element;
The information processing apparatus according to claim 3. The co-occurrence ratio calculation means includes
If the value of the co-occurrence ratio regarding the elements at both ends of the causal relationship chain is equal to or less than a reference value, the cause element in the causal relationship chain and the resulting element do not match And
The information processing according to claim 2, wherein when the co-occurrence ratio value is larger than a reference value, it is determined that the cause element in the causal relation chain and the resulting element match. apparatus. The co-occurrence ratio calculating means includes the number of detected elements as the cause, the number of detected elements as a result, and the number of co-occurrence of the element as a result and the element as a result in the co-occurrence detection range. The information processing apparatus according to claim 2, wherein the co-occurrence ratio value is calculated using The chain extraction means is the causal relationship information including a plurality of combinations of information representing the cause element and information representing the resulting element, and the first element is the cause of the causal relationship. There is a first causal relationship in which the second element is the element resulting in a causal relationship, the second element is the element causing the causal relationship, and a third element is the causal By extracting the second causal relationship that is the element resulting from the relationship, the causal relationship including the two causal relationships in which the first causal relationship is linked to the second causal relationship. The information processing apparatus according to any one of claims 1 to 6, wherein a chain is extracted. Extracting a chain of causal relationships consisting of at least two causal relationships from a plurality of combinations of information representing the factors that cause the causal relationships related to the events and information representing the resulting elements,
Detecting co-occurrence in one or more documents of the elements at both ends of the extracted causal chain;
Calculate the percentage of co-occurrence for the detected co-occurrence,
An information processing method for outputting output information including a result of the calculation. A process of extracting a chain of causal relationships composed of at least two causal relationships from a plurality of combinations of information representing the factors causing the causal relationships related to the events and information representing the resulting elements;
Processing for detecting co-occurrence in one or more documents of the elements at both ends of the extracted chain of causality,
A process of calculating a co-occurrence ratio for the detected co-occurrence;
A recording medium on which a computer program for causing a computer to execute processing for outputting output information including a result of the calculation is recorded.

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