Logical formula generation device, logical formula generation method, and program

Abstract

A logical formula generation device 100 according to the present invention comprises: a planning means 121 for, on the basis of background knowledge information indicating a rule of inference, input information to be subjected to an inference process by the rule of inference, an inference schema indicating an inference method in the inference process, and a concept schema indicating a concept to be addressed in the inference process, generating a logical conversion protocol for converting the background knowledge information and the input information to a logical formula; and a conversion means 122 for converting the background knowledge information and the input information to a logical formula on the basis of the generated logical conversion protocol.

Description

Logical formula generation device, logical formula generation method, and program

[0001] The present invention relates to a logical expression generating device, a logical expression generating method, and a program.

[0002] Deductive reasoning is an inference method that receives as input a logical formula (Proposition) that represents premise information and a logical formula (Background knowledge) that represents inference rules, and outputs a logical formula (Consequence) that is derived from the input information using the inference rules.

[0003] Abduction (Abductive reasoning) is an inference method that receives a logical formula (Observation) that represents observed information as input, and background knowledge, and outputs a logical formula (Hypothesis) that derives the input information as a conclusion using inference rules.

[0004] Although deductive inference and abductive inference are theoretically different modes of logical inference, they are the same in that they receive input information and inference rules and output inference results, and can be interpreted as essentially the same when implemented on a computer. Therefore, models based on deductive inference or abductive inference are collectively called logical inference models, and software programs that implement computational processing based on logical inference models on a computer are called logical inference engines.

[0005] Non-Patent Document 1 discloses a method for implementing weighted abduction, which is one type of abduction, on a computer. Non-Patent Document 2 discloses a method for implementing Markov Logic Network, which is one type of deductive inference, on a computer.

[0006] Naoya Inoue and Kentaro Inui. ILP-based Reasoning for Weighted Abduction. In Proceedings of AAAI Workshop on Plan, Activity and Intent Recognition, pp. 25-32, August 2011. Richardson, Matt and Domingos, Pedro (2006). Markov Logic Networks. Machine Learning, 62, 107-136, 2006.

[0007] Systems based on logical inference models require input information and inference rules expressed as logical expressions. The appropriate logical expressions for the target domain knowledge and inputs vary depending on factors such as the logical inference model, the logical inference engine used, and the intended behavior of inference. Therefore, building a practical application system based on a logical inference model requires the work of experts with extensive knowledge of logical inference engines, which poses a major problem in terms of the cost of building such systems for practical applications.

[0008] The above problem can be broken down into several issues. First, the task requires a long time spent by personnel with deep knowledge of the logical inference engine, resulting in high human and economic costs. Second, if a logical expression for a specific state is modified during the above-mentioned review process, all of the corresponding expressions contained in the background knowledge information and input information must be manually revised, further increasing the time cost of the review process. In other words, when knowledge and information are stored as logical expressions, it is not obvious which parts of the logical expressions used are structures based on semantic requirements, which parts are structures based on computational efficiency requirements, and which parts are structures based on requirements related to inference behavior. This means that rewriting the logical expressions cannot be performed automatically. Thus, there is a problem in that the construction and maintenance of logical inference systems requires a lot of human effort.

[0009] Therefore, an object of the present disclosure is to solve the above-mentioned problem of the amount of man-hours required to build and maintain a logical inference system.

[0010] A logical formula generation device according to one embodiment of the present disclosure comprises: a planning means for generating a logical conversion protocol for converting the background knowledge information and the input information into a logical formula based on background knowledge information representing inference rules, input information to be inferred by the inference rules, an inference schema representing an inference method in the inference process, and a conceptual schema representing concepts handled in the inference process; and a conversion means for converting the background knowledge information and the input information into a logical formula based on the generated logical conversion protocol.

[0011] Furthermore, a logical formula generation method according to one embodiment of the present disclosure is configured to generate a logical conversion protocol for converting the background knowledge information and the input information into a logical formula based on background knowledge information representing inference rules, input information to be inferred using the inference rules, an inference schema representing an inference method used in the inference process, and a conceptual schema representing concepts used in the inference process, and to convert the background knowledge information and the input information into a logical formula based on the generated logical conversion protocol.

[0012] Furthermore, a program that is one form of the present disclosure is configured to generate a logic conversion protocol for converting the background knowledge information and the input information into a logical formula based on background knowledge information that represents inference rules, input information to be inferred using the inference rules, an inference schema that represents an inference method in the inference processing, and a conceptual schema that represents concepts handled in the inference processing, and to cause a computer to execute a process of converting the background knowledge information and the input information into a logical formula based on the generated logic conversion protocol.

[0013] With the above-described configuration, the present disclosure can reduce the amount of man-hours required to build and maintain a logical inference system.

[0014] FIG. 1 is a block diagram showing the configuration of a logical formula generation device according to a first embodiment of the present disclosure. FIG. 2 is a flowchart showing the operation of the logical formula generation device disclosed in FIG. 1. FIG. 3 is a block diagram showing the configuration of a logical formula generation device according to a second embodiment of the present disclosure. FIG. 4 is a flowchart showing the operation of the logical formula generation device disclosed in FIG. 3. FIG. 5 is a block diagram showing the hardware configuration of a logical formula generation device according to a third embodiment of the present disclosure. FIG. 6 is a block diagram showing the configuration of a logical formula generation device according to the third embodiment of the present disclosure.

[0015] First Embodiment A first embodiment of the present disclosure will be described with reference to Figures 1 and 2. Figure 1 is a diagram for explaining the configuration of a logical expression generation device, and Figure 2 is a diagram for explaining the processing operation of the logical expression generation device.

[0016] The logical formula generation device 10 in this embodiment is a device that converts the inference rules and input information described in a schema that defines an inference method to be realized in a target domain, based on some description format other than logical formulas, into logical formulas so that the expected inference method is satisfied, and outputs the converted information. The configuration and operation of the logical formula generation device will be described below.

[0017] The logical formula generation device 10 is composed of one or more information processing devices each including an arithmetic unit and a storage device. As shown in FIG. 1 , the logical inference device 10 includes a schema acquisition unit 11, an information acquisition unit 12, a planning unit 13, a conversion unit 14, and an output unit 15. The functions of the schema acquisition unit 11, the information acquisition unit 12, the planning unit 13, the conversion unit 14, and the output unit 15 can be realized by the arithmetic unit executing a program for realizing each function stored in the storage device. The logical formula generation device 10 also includes an inference schema storage unit 16, a conceptual schema storage unit 17, a background knowledge information storage unit 18, and an input information storage unit 19. The inference schema storage unit 16, the conceptual schema storage unit 17, the background knowledge information storage unit 18, and the input information storage unit 19 are each composed of a storage device.

[0018] The schema acquisition unit 11 acquires an inference schema representing the definition of an inference method to be implemented from the inference schema storage unit 16 (step S1). The inference schema is information describing the definition of the inference method to be implemented by the user in some format. The definition of the inference method includes information such as "what logical inference model is it based on?" and "what logical inference engine is used." Information regarding the content of the inference to be implemented described in the inference schema also includes the following information using "concepts," which will be described later. For example, this is a constraint imposed on each concept in the inference method, such as "a certain concept is always included in the observed information," "a certain concept is never included in the observed information," or "a hypothesis always contains exactly one logical formula based on a certain concept." For example, in a medical task, this is information desired as the output of the inference method, such as "to predict whether a specific symptom is present" or "to derive the most appropriate combination of pathologies for the observed information." The format used to describe the inference schema may be some kind of text format intended for human writing, or it may be written as binary information intended for computer management.

[0019] The schema acquisition unit 11 also acquires from the conceptual schema storage unit 17 a conceptual schema representing the definitions of concepts to be treated as components of input information and inference rules for the inference method to be implemented (step S1). A conceptual schema is information that describes, in some format, the definitions of concepts to be treated as components of input information and inference rules for the inference method to be implemented by the user. The concept definition includes information necessary to express the concept as a logical formula, such as the name and components of the concept. For example, the "information for expressing the concept as a logical formula" in a conceptual schema mainly consists of two types of information: "components constituting the concept" and "logical properties possessed by each component." As a specific example, if the domain of the target of logical inference is medicine, concepts such as "pneumonia" and "patient" may be considered. As an example, the concept "pneumonia" can be defined as follows: - The components constituting "pneumonia" are "patient," "severity," and "symptom." - The following logical properties hold for the component "patient." - One "pneumonia" always has exactly one "patient." - Different "pneumonias" cannot have the same "patients." - The following logical properties hold for the component "severity." - "Severity" is expressed as an integer value. - One "pneumonia" always has exactly one "severity." - The following logical properties hold for the component "symptoms." - One "pneumonia" can have any number of "symptoms." It is also possible for a concept to have other concepts as its components. The format used to describe the conceptual schema may be some kind of text format intended for human description, or it may be written as binary information intended for computer management. The text format used to describe the conceptual schema may be an existing ontology description language such as OWL (Ontology Web Language).

[0020] The information acquisition unit 12 acquires background knowledge information, in which inference rules are expressed in some formal language, from the background knowledge information storage unit 18 (step S2). The background knowledge information is information in which a set of inference rules (background knowledge), in which the consequent is true if the antecedent is true, is expressed in some formal language. As an example, the background technology information is information that "if a person has pneumonia with a severity level of 3, he or she will develop sepsis." In this case, the concept "sepsis" has only "patient" as a component. The background knowledge information is expressed as some character string that can be interpreted by an information processing device, such as "pneumonia 3: $1 => sepsis: $1." Here, a description representing a variable such as $1 is used to indicate that the different concepts "pneumonia" and "sepsis" share the same entity as the component "patient."

[0021] The information acquisition unit 12 also acquires input information, which expresses observed facts in some formal language, from the input information storage unit 19 (step S2). The input information is information to be inferred using inference rules. While any format may be used for the description of the background knowledge information and input information, it is preferable for the language to be in a format based on concepts defined by a conceptual schema, that is, a language with the expressive ability to describe such concepts. As an example, assume that the input information is "Pneumonia 3: Yamada Taro: Cough." This represents "patient = Yamada Taro, severity = 3, symptom = cough" for the concept "pneumonia."

[0022] The information acquisition unit 12 may acquire either or all of the background knowledge information and the input information by reading them from a storage device built into the logical formula generation device or from an external storage device. The information acquisition unit 12 may also acquire either or all of the background knowledge information and the input information by receiving them from another device via a communication unit. The information acquisition unit 12 may also generate either or all of the background knowledge information and the input information in response to a user's input operation performed via an arbitrary input device such as a mouse or a touch panel, and acquire the generated information.

[0023] The planning unit 13 (planning means) generates a logic conversion protocol, which is a method for converting the background knowledge information and the input information into a logical formula, by referring to the inference schema, conceptual schema, background knowledge information, and input information (step S3). The logic conversion protocol is information describing, in any format, a method (conversion rule) for converting the background knowledge information and the input information into a logical formula. When generating the logic conversion protocol, the frequency of occurrence of each concept included in the conceptual schema in the input information and inference rules, the logical inference engine to be used, the desired inference behavior, and other factors are taken into consideration. Any method may be used to generate the logic conversion protocol based on these factors. For example, pairs of conditions for the inference schema, conceptual schema, background knowledge information, and input information, and conversion rules for converting the components of the background knowledge information and input information into a logical formula, may be prepared in advance, and an expert system may be constructed that outputs a logic conversion protocol using each schema and each piece of information as input. Alternatively, for example, the structural features of each schema and each piece of information may be mapped to a vector space, and a logic conversion protocol may be determined and generated based on statistical analysis. In other words, a logic conversion protocol for a logical formula can be determined by combining the inference schema and conceptual schema described above. For example, in the above-mentioned conceptual schema, "severity" in the definition of the concept "pneumonia" is defined as "a numerical value." However, if the inference engine specified in the inference schema does not have the functionality to specify a numerical value as a logical argument, it is necessary to formulate a logical expression that satisfies such behavior within the scope of the engine's functionality, and such a logical conversion protocol will be created.

[0024] The conversion unit 14 (conversion means) converts the background knowledge information and the input information into logical expressions (step S4) based on the logical conversion protocol generated by the planning unit 13. That is, the conversion unit 14 converts the background knowledge information and the input information into logical expressions using the logical conversion protocol including conversion rules for converting the components of the background knowledge information and the input information into logical expressions.

[0025] Here, we will provide an example of the logic conversion protocol described above and an example of converting input information into a logical formula. A logic conversion protocol is basically composed of information indicating which components of each concept correspond to which arguments of which logical formulas. For example, the following logic conversion protocol is generated for the above-mentioned concept of "pneumonia." - Pneumonia (r, p, x) - The first argument r is a reference logical variable that refers to pneumonia itself. - The second argument p is a logical variable that refers to the affected individual. - The third argument x is a logical variable that indicates the severity. - Symptoms for a given pneumonia r are described as different logical formulas that share the same variables. For example, "Pneumonia (R, John, 2) ∧ Cough (R) ∧ Sputum (R)" represents that "John has pneumonia R, which has symptoms of cough and sputum." Regarding the logical conversion of input information, a logical formula can be obtained by simply applying the concepts described in some format to the above correspondence relationship. The procedure is as follows: (1) Obtain the input information "Pneumonia 3: Yamada Taro: Cough." (2) The input information string is interpreted as a concept description. It is interpreted as "patient = Yamada Taro, severity = 3, symptom = cough" for the concept "pneumonia". (3) It is converted into a logical formula using a logical conversion protocol. The logical formula "pneumonia (R1, Yamada Taro, 3) ∧ cough (R1)" is obtained.

[0026] Next, an example of the above-mentioned logic conversion protocol and an example of converting background knowledge information into a logical formula are given below. In this case, a logic conversion protocol is generated as a rule for converting the content "If the antecedent is true, the consequent is true" into a logical formula "If the antecedent is true => the consequent is true." The above-mentioned background knowledge information "If you have pneumonia with a severity of 3, you will develop sepsis" is expressed as a character string that can be interpreted by an information processing device, such as "Pneumonia 3: $1 => Sepsis: $1." Each of these character strings is further interpreted as a concept description, and the following conceptual entities are obtained, for example: - Concept "pneumonia" of the component "patient = $1, severity = 3" - Concept "sepsis" of the component "patient = $1" These conceptual entities are converted into logical formulas by the logic conversion protocol, and ultimately, the following logical formula is obtained as an inference rule using the above-mentioned input information. Logical formula: pneumonia (r, x1, 3) => sepsis (x1)

[0027] The output unit 15 outputs the logical formula generated by the conversion unit 14 as input information and inference rules (step S5). For example, the output unit 15 may display the logical formula on a display panel, or may store the logical formula in a recording medium (not shown). For example, the output unit 15 may output the logical formula to another device via an input / output interface or a communication interface.

[0028] According to the above configuration, the logical formula generation device automatically generates and outputs appropriate logical expressions for input information and background knowledge information so as to realize the inference method defined by the inference schema and conceptual schema. This improves the human efficiency of building a logical inference system, reduces the amount of human labor required, and reduces the knowledge and skills required for the work, compared to manually designing and writing logical expressions for input information and background knowledge information. Furthermore, knowledge and information can be stored separately from logical expressions, and appropriate logical expressions can be automatically generated as needed.

[0029] Second Embodiment A second embodiment of the present disclosure will be described with reference to Fig. 3 and Fig. 4. Fig. 3 is a diagram for explaining the configuration of a logical expression generation device, and Fig. 4 is a diagram for explaining the processing operation of the logical expression generation device. Note that, here, components having the same functions as those described in the first embodiment are assigned the same reference numerals, and their description will be omitted as appropriate.

[0030] 3, the logical expression generation device 10 of this embodiment includes an execution unit 21 and an amount conversion unit 22 in addition to the configuration of the first embodiment. The functions of the execution unit 21 and the inverse conversion unit 22 can be realized by the calculation device executing a program for realizing each function stored in the storage device.

[0031] The execution unit 21 (execution means) obtains the result of logical inference performed by the logical inference engine specified by the inference schema using the results of converting the background knowledge information and the input information into logical formulas generated by the conversion unit 14 as input (step S6). The inference result is the result of execution of the logical inference engine using the results of converting the background knowledge information and the input information into logical formulas as input, which is expressed as one or more logical formulas.

[0032] The inverse conversion unit 22 (inverse conversion means) converts the inference results expressed in logical formulas into expressions using vocabulary on the conceptual schema based on the logical conversion protocol obtained by the planning unit 13 (step S7). That is, the inverse conversion unit 22 converts the inference results expressed in logical formulas into expressions using vocabulary on the conceptual schema by utilizing the correspondence between the components of each concept and the arguments of each logical formula, which represent the background knowledge information and conversion rules for converting the components of the input information into logical formulas in the logical conversion protocol. For example, the logical formula "pneumonia (R, John, 2) ∧ cough (R) ∧ sputum (R)" is an entity of the concept "pneumonia," and its components can be inversely converted into "patient = John, severity = 2, symptoms = cough & sputum." In addition, background knowledge information expressed as a logical formula can also be reverse-converted in the same way. For example, the logical formula "pneumonia (r, x1, 3) => sepsis (x1)" can be converted into an expression such as "pneumonia 3: $1 => sepsis: $1," that is, "a patient suffering from pneumonia of severity 3 will develop sepsis."

[0033] Unlike the first embodiment, the output unit 15 outputs the inference result expressed using the vocabulary in the conceptual schema generated by the inverse conversion unit 22 (step S8). For example, the output unit 15 may display the inference result on a display panel, or may store the inference result in a recording medium (not shown). For example, the output unit 18 may output the inference result to another device via an input / output interface or a communication interface.

[0034] According to the above configuration, the logical inference device automatically generates appropriate logical expressions for input information and background knowledge information based on the inference method defined by the inference schema and conceptual schema, and outputs the inference results for the generated expressions using the vocabulary in the conceptual schema. This improves the human efficiency involved in building a logical inference system compared to manually designing and writing logical expressions for the input information and background knowledge information. In addition, because the logical expressions are hidden from input and output, even people without a background in logical inference can build a logical inference system and interpret the system output.

[0035] <Embodiment 3> Next, a third embodiment of the present disclosure will be described with reference to Figures 5 and 6. Figures 5 and 6 are block diagrams showing the configuration of a logical expression generation device in embodiment 3. Note that this embodiment shows an outline of the configuration of the logical expression generation device described in the above embodiments.

[0036] First, the hardware configuration of the logical expression generation device 100 of this embodiment will be described with reference to Fig. 5. The logical expression generation device 100 is configured as a general information processing device, and is equipped with the following hardware configuration, for example: CPU (Central Processing Unit) 101 (arithmetic unit); ROM (Read Only Memory) 102 (storage device); RAM (Random Access Memory) 103 (storage device); programs 104 loaded into RAM 103; storage device 105 storing programs 104; drive device 106 for reading and writing data from and to a storage medium 110 external to the information processing device; communication interface 107 for connecting to a communication network 111 external to the information processing device; input / output interface 108 for inputting and outputting data; and bus 109 for connecting the various components.

[0037] The logical expression generation device 100 can be equipped with the planning means 121 and conversion means 122 shown in Fig. 6 by having the CPU 101 acquire and execute the program group 104. The program group 104 is, for example, stored in advance in the storage device 105 or the ROM 102, and is loaded into the RAM 103 and executed by the CPU 101 as needed. The program group 104 may be supplied to the CPU 101 via the communication network 111, or may be stored in advance in the storage medium 110, and the drive device 106 may read out the programs and supply them to the CPU 101. However, the planning means 121 and conversion means 122 described above may be constructed using dedicated electronic circuits for realizing such means.

[0038] 5 shows an example of the hardware configuration of the information processing device that is the logical expression generation device 100, and the hardware configuration of the information processing device is not limited to the above-described case. For example, the information processing device may be configured with a part of the above-described configuration, such as excluding the drive device 106. Furthermore, instead of the CPU described above, the information processing device may use a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an MPU (Micro Processing Unit), an FPU (Floating Point Number Processing Unit), a PPU (Physics Processing Unit), a TPU (Tensor Processing Unit), a quantum processor, a microcontroller, or a combination thereof.

[0039] The planning unit 121 generates a logic conversion protocol for converting the background knowledge information and input information into a logical formula based on background knowledge information representing inference rules, input information to be inferred using the inference rules, an inference schema representing the inference method used in the inference process, and a conceptual schema representing the concepts used in the inference process. The inference schema contains information such as the logical inference model and the logical inference engine to be used. The conceptual schema defines the concepts to be used as components of the inference rules and input information, and includes information necessary to express the concepts as logical formulas, such as the names and components of the concepts. The planning unit 121 then generates the logic conversion protocol by applying preset conversion rules corresponding to the conditions satisfied by each schema and each piece of information.

[0040] The conversion means 122 converts the background knowledge information and input information into a logical formula based on the generated logical conversion protocol.

[0041] With the above-described configuration, the present disclosure can reduce the amount of man-hours required to build and maintain a logical inference system.

[0042] The above-described program can be stored and supplied to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program may also be supplied to a computer by various types of transitory computer-readable media. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer-readable media can be supplied to a computer via wired communication paths such as electric wires and optical fibers, or via wireless communication paths.

[0043] Although the present disclosure has been described above with reference to the above-described embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that are understandable to those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, at least one or more of the functions of the planning unit 121 and the conversion unit 122 described above may be executed by an information processing device installed and connected anywhere on a network, i.e., may be executed by so-called cloud computing.

[0044] <Supplementary Notes> Some or all of the above embodiments can also be described as in the following supplementary notes. Below, an outline of the configurations of a logical formula generation device, a logical formula generation method, and a program according to the present invention will be described. However, the present invention is not limited to the following configurations. (Supplementary Note 1) A logical formula generation device comprising: a planning means for generating a logic conversion protocol for converting background knowledge information and input information into a logical formula based on background knowledge information representing inference rules, input information to be inferred according to the inference rules, an inference schema representing an inference method in the inference processing, and a conceptual schema representing concepts handled in the inference processing; and a conversion means for converting the background knowledge information and the input information into a logical formula based on the generated logic conversion protocol. (Supplementary Note 2) A logical formula generation device according to Supplementary Note 1, wherein the planning means generates the logic conversion protocol in which conversion rules for converting components of the background knowledge information and the input information into a logical formula are defined. (Supplementary Note 3) The logical formula generation device according to Supplementary Note 2, wherein the planning means generates the logical conversion protocol in which the conversion rules corresponding to conditions satisfied by the background knowledge information, the input information, the inference schema, and the conceptual schema are defined. (Supplementary Note 4) The logical formula generation device according to Supplementary Note 1, wherein the background knowledge information and the input information are described in a language of a format set based on the concepts represented in the conceptual schema. (Supplementary Note 5) The logical formula generation device according to Supplementary Note 1, comprising: an execution means for executing the logical formula in the inference engine based on the inference method represented in the inference schema, using the inference formula as input to an inference engine; and an inverse conversion means for converting an inference result of a logical formula expression obtained by execution of the logical formula into an expression corresponding to the concept represented in the conceptual schema based on the logical conversion protocol.(Supplementary Note 6) A logical formula generation method comprising: generating a logic conversion protocol for converting background knowledge information and input information into a logical formula based on background knowledge information representing inference rules, input information to be inferred by the inference rules, an inference schema representing an inference method in the inference process, and a conceptual schema representing concepts handled in the inference process, and converting the background knowledge information and the input information into a logical formula based on the generated logic conversion protocol. (Supplementary Note 7) A logical formula generation method according to Supplementary Note 6, further comprising: generating the logic conversion protocol in which conversion rules for converting components of the background knowledge information and the input information into a logical formula are defined. (Supplementary Note 8) A logical formula generation method according to Supplementary Note 7, further comprising: generating the logic conversion protocol in which conversion rules corresponding to conditions satisfied by the background knowledge information, the input information, the inference schema, and the conceptual schema are defined. (Supplementary Note 9) The logical formula generation method according to Supplementary Note 6, wherein the background knowledge information and the input information are described in a language with a format set based on the concepts represented in the conceptual schema. (Supplementary Note 10) The logical formula generation method according to Supplementary Note 6, wherein the inference formula is used as input to an inference engine, and the logical formula is executed in the inference engine based on the inference method represented in the inference schema, and an inference result of a logical formula expression obtained by executing the logical formula is converted into an expression corresponding to the concept represented in the conceptual schema based on the logic conversion protocol. (Supplementary Note 11) A computer-readable storage medium storing a program for causing a computer to execute the following processes: generating a logic conversion protocol for converting the background knowledge information and the input information into a logical formula based on background knowledge information representing inference rules, input information to be inferred by the inference rules, an inference schema representing the inference method in the inference processing, and a conceptual schema representing the concepts handled in the inference processing, and converting the background knowledge information and the input information into a logical formula based on the generated logic conversion protocol.

[0045] REFERENCE SIGNS LIST 10 Logical formula generation device 11 Schema acquisition unit 12 Information acquisition unit 13 Planning unit 14 Conversion unit 15 Output unit 16 Inference schema storage unit 17 Conceptual schema storage unit 18 Background knowledge information storage unit 19 Input information storage unit 21 Execution unit 22 Inverse conversion unit 100 Logical formula generation device 101 CPU 102 ROM 103 RAM 104 Program group 105 Storage device 106 Drive device 107 Communication interface 108 Input / output interface 109 Bus 110 Storage medium 111 Communication network 121 Planning means 122 Conversion means

Claims

1. a planning means for generating a logic conversion protocol for converting the background knowledge information and the input information into a logical formula based on background knowledge information representing inference rules, input information to be inferred according to the inference rules, an inference schema representing an inference method in the inference process, and a conceptual schema representing a concept to be handled in the inference process; a conversion means for converting the background knowledge information and the input information into a logical formula based on the generated logical conversion protocol; A logical expression generating device comprising:

2. 2. The logical expression generating device according to claim 1, the planning means generates the logic conversion protocol in which conversion rules are defined for converting components of the background knowledge information and the input information into a logical formula. A logical formula generator.

3. 3. The logical expression generating device according to claim 2, the planning means generates the logical conversion protocol in which the conversion rules corresponding to conditions satisfied by the background knowledge information, the input information, the inference schema, and the conceptual schema are defined. A logical formula generator.

4. 2. The logical expression generating device according to claim 1, the background knowledge information and the input information are described in a language of a format set based on the concepts represented in the conceptual schema; A logical formula generator.

5. 2. The logical expression generating device according to claim 1, an execution means for executing the logical formula in the inference engine based on the inference scheme represented by the inference schema, using the inference formula as an input to the inference engine; an inverse conversion means for converting an inference result of a logical expression obtained by executing the logical expression into an expression corresponding to the concept represented in the conceptual schema based on the logical conversion protocol; A logical expression generating device comprising:

6. generating a logic conversion protocol for converting the background knowledge information and the input information into a logical formula based on background knowledge information representing inference rules, input information to be inferred according to the inference rules, an inference schema representing an inference method in the inference process, and a conceptual schema representing a concept to be handled in the inference process; converting the background knowledge information and the input information into a logical formula based on the generated logical conversion protocol; How to generate logical expressions.

7. 7. A method for generating a logical expression according to claim 6, comprising the steps of: generating the logic conversion protocol in which a conversion rule is defined for converting the components of the background knowledge information and the input information into a logical expression; How to generate logical expressions.

8. 7. A method for generating a logical expression according to claim 6, comprising the steps of: the background knowledge information and the input information are described in a language of a format set based on the concepts represented in the conceptual schema; How to generate logical expressions.

9. 7. A method for generating a logical expression according to claim 6, comprising the steps of: Executing the logical formula in the inference engine based on the inference scheme represented by the inference schema, using the inference formula as an input to the inference engine; converting an inference result of the logical expression obtained by executing the logical expression into an expression corresponding to the concept represented in the conceptual schema based on the logical conversion protocol; How to generate logical expressions.

10. generating a logic conversion protocol for converting the background knowledge information and the input information into a logical formula based on background knowledge information representing inference rules, input information to be inferred according to the inference rules, an inference schema representing an inference method in the inference process, and a conceptual schema representing a concept to be handled in the inference process; converting the background knowledge information and the input information into a logical formula based on the generated logical conversion protocol; A program that causes a computer to execute a process.