EP1690219A2 - Machine d'inference - Google Patents

Machine d'inference

Info

Publication number
EP1690219A2
EP1690219A2 EP04803492A EP04803492A EP1690219A2 EP 1690219 A2 EP1690219 A2 EP 1690219A2 EP 04803492 A EP04803492 A EP 04803492A EP 04803492 A EP04803492 A EP 04803492A EP 1690219 A2 EP1690219 A2 EP 1690219A2
Authority
EP
European Patent Office
Prior art keywords
rules
data
inference
processing system
data processing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP04803492A
Other languages
German (de)
English (en)
Inventor
Jürgen ANGELE
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
SEMEO SERVICES GMBH
Original Assignee
ontoprise GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by ontoprise GmbH filed Critical ontoprise GmbH
Publication of EP1690219A2 publication Critical patent/EP1690219A2/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/045—Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence

Definitions

  • Inference machines are able to answer questions or find new information by logical closing.
  • BESTATIGUNGSKOPIE The object of the invention is to make working with inference machines more user-friendly.
  • An inference machine is usually based on a data processing system or a computer system with means for storing data.
  • the data processing system has an interrogation unit for determining output variables with access to the stored data.
  • the data are assigned to predefined classes which are part of at least one stored class structure which forms an object model. So-called ontologies are often used as the object model.
  • the object model and is preferably implemented in the form of a hierarchical structure of the classes.
  • the classes of a certain level of the hierarchical structure are assigned to exactly one class of the higher level.
  • the class structure can also be implemented in other ways, for example as an acyclic graph, in which multiple inheritance can also be permitted.
  • the classes are assigned attributes that can be passed on within a class structure. Attributes are characteristics of a class. For example, the Person class can have the Hair Color attribute. This attribute is used for different specific people (called “instances”). Different values (called “attribute values”) assigned, e.g. B. brown, blonde, black, etc.
  • the query unit contains an inference machine or inference unit, by means of which rules can be evaluated.
  • classes, attributes, synonyms, relations, that is, relationships between elements or assignments are briefly referred to as all that makes up the
  • the rules are i. d.
  • An important property of a declarative system of rules is that the results of an evaluation do not depend on the order in which the rules are defined.
  • the rules allow e.g. B. Finding information that was not explicitly described by the search terms.
  • the inference unit even enables - by / linking individual statements to generate new information that was not explicitly included in the data, but can only be inferred from the data.
  • Query terms can be entered as input variables via the query unit, which are not only formed by the stored data, but can also be formed by the classes or attributes of the class structure.
  • Query terms can be entered as input variables via the query unit, which are not only formed by the stored data, but can also be formed by the classes or attributes of the class structure.
  • a question: "What articles did Mr. Mustermann write?" - Mustermann would be a specific name from the data - are also questions of the type: "What are the names of all employees of company X?” possible.
  • a search term would not be based on the specific name of an employee, but on the values of the "hatName" attribute of all instances of the employees who are related to the instance X from the class of the companies, namely in the relationship "is employed".
  • abraham one.
  • sarah oman. isaac: man [fatherls-> abraham; motherIs-> sarah].
  • ishmael man [fatherls-> abraham; motherIs-> hagar: woman].
  • jacob man [£ atherIs -.> isaac; motherIs-> rebekah. :: woman] •. esau: man [fatherls-> isaac; motherIs-> rebekah].
  • F-logic is obviously also suitable as a language to formulate a class structure.
  • the ontology languages RDF, DAML + OIL and OWL are preferably used to define class structures.
  • a query takes the form of a rule, but without the rule header on the left.
  • the answer is:
  • Such systems are e.g. B. from DE 101 03 845 AI known
  • the data processing system comprises at least one information generating unit and / or storage unit for the storage and / or generation of a data file ⁇ forming data.
  • Information such. B. via sensors, the Internet, sensors connected to the Internet or other input options.
  • data come e.g. B. Operating states of machines in question, further
  • the data processing system has at least one computer unit for generating an object model, which consists of a class structure with classes and, as a rule, associated attributes.
  • the data can be structured using the object model. • .
  • the object model includes a declarative system of rules.
  • the data processing system has an input / output unit for entering a query and for outputting answers to the query. Furthermore, an inference unit in which the rules are evaluated in order to generate a response to a query.
  • An evaluation unit is assigned to the inference unit, it being possible to read into these inference protocols via instantiations of rules.
  • explanations about the evaluation of rules are generated in the evaluation unit.
  • a user receives a documentation of a query he has carried out, as to how this query was processed in order to generate a specific output size on it. The logical steps, the derivation to answer the question is shown.
  • the user thus has a possibility of checking whether the answer to a query has come about.
  • the user can use the explanations to check the relevance and credibility, ie generally the quality of an output variable generated in the data processing system.
  • Another advantage for the user is that the user can use the explanations given to formulate subsequent queries more precisely. This creates a data processing system with significantly improved search options.
  • the explanations are generated in an evaluation unit, which preferably has an inference unit.
  • the explanations are generated by evaluating predefined explanation rules.
  • Inference protocols which contain the facts (instances) that were used to process a query in the instantiation of rules in the inference unit of the query unit, serve as input variables for this.
  • an explanation can be generated for each inference step defined by a rule, which is carried out to process a query in the inference unit of the query unit, and output as an output variable.
  • the explanations are generated in each case • ' , ••, by means of explanatory rules, which are in: .tder -Leinferenzunheit der Aus. 0 value unit can be processed.
  • further explanation rules are provided in order to make a choice between different explanation options.
  • the issuance of redundant explanations can be avoided with such explanation rules.
  • external data are included to generate the explanations, as a result of which the meaningfulness of the explanations is considerably increased. This data can be structured particularly advantageously in at least one object model.
  • An example of such external data are user profiles of users, which the data processing system use purposes. By evaluating the user profiles, explanations are generated which are optimally adapted to the respective user, in particular his level of knowledge, training and the like.
  • the object of the invention is further achieved by a method. Individual process steps are described in more detail below. The steps do not necessarily have to be carried out in the order specified, and the method to be described can also have further steps not mentioned.
  • a database is generated and / or saved.
  • an object model is created, consisting of a class structure and a declarative system of rules.
  • the data can be structured using the object model.
  • the rules link elements of the class structure and / or data.
  • a query is entered.
  • An inference unit evaluates the query by evaluating the rules.
  • inference protocols are generated in an evaluation unit assigned to the inference unit about instantiations of the rules that occurred during the evaluation.
  • explanations about the evaluation of the rules are generated in the evaluation unit.
  • the scope of the invention includes a computer program which, when running on a computer or on a plurality of computers in a computer network, executes the method according to the invention in one of its configurations.
  • the scope of the invention includes a computer program with program code means in order to carry out the method according to the invention in one of its configurations if the program is executed on one computer or on a plurality of computers in a computer network.
  • the program code means can be stored on a computer-readable data carrier.
  • the scope of the invention includes a data carrier on which a data structure is stored which, after loading into a working and / or main memory of a computer or a plurality of computers in a computer network, can carry out the method according to the invention in one of its configurations.
  • a computer program product stored on a machine-readable carrier, the program code means for carrying out the inventive method in egg ⁇ - -ner perform its embodiments when> that program on a computer or on a plurality of computers of a Computer network is running.
  • a computer program product is understood to mean the program as a tradable product. In principle, it can be in any form, for example on paper or a computer-readable data carrier, and can in particular be distributed over a data transmission network.
  • the scope of the invention includes a modulated data signal which contains instructions for executing the method according to the invention in one of its configurations, which can be executed by a computer system or by a plurality of computers in a computer network.
  • a modulated data signal which contains instructions for executing the method according to the invention in one of its configurations, which can be executed by a computer system or by a plurality of computers in a computer network.
  • a computer system Both a stand-alone computer and a network of computers, for example an in-house, closed network, or computers that are connected to one another via the Internet.
  • the computer system can be implemented by a client-server constellation, parts of the invention running on the server and others on a client.
  • FIG. 1 is a schematic representation of an embodiment of the data processing system according to the invention.
  • FIG. 2 shows an example of a class structure for the data processing system according to FIG. 1;
  • Fig. 3 is a schematic representation of the process flow.
  • FIG. 1 shows an exemplary embodiment of the data processing system 1 according to the invention.
  • the data processing system 1 has a storage unit 2, on which an inventory of data is stored.
  • the storage unit 2 is formed by a database system, a file system (ie by a quantity of files stored on a computer) or the like.
  • a database system ie by a quantity of files stored on a computer
  • several database systems possibly integrated on different computer systems, can also be provided.
  • a memory 3 is assigned to the storage unit 2, via which the data stored in the storage unit 2 are accessed.
  • a computer unit 4 is assigned to this server 3.
  • the computing unit 4 consists of a processor system or the like. In the simplest case, the computing unit 4 is formed by a personal computer or a workstation.
  • the computer unit 4 has a first software module 5, by means of which an object model forming an ontology can be generated.
  • the data of the storage system can be structured in a class structure using the object model.
  • the software module 5 is connected to an interrogation unit 6, which also consists of a software module.
  • a first interference unit 7 is implemented in the interrogation unit 6.
  • the computer unit 4 according to FIG. 1 further comprises an input / output unit 8 via which information can be input into the computer unit 4 or output from the computer unit 4.
  • the input / output unit 8 essentially consists of a
  • the interrogation unit 6 is connected via an interface module 9 to an evaluation unit 10, in which a second inference unit 11 is implemented. Furthermore, the evaluation unit 10 has a software module 12, by means of which a further object model can be generated. With this object model, external data can be structured. Access to the external Data is sent via server 3 or suitable interface components.
  • the stock of data stored in the storage unit 2 is structured by means of the object model generated with the software module 5.
  • Such an object model generally represents an ontology and has a structure of classes, wherein the structure can be designed as a hierarchical structure. In hierarchical structures, classes of a given level are assigned to exactly one class of a higher level. Only single inheritance is permitted. In general, the class structure can also be designed as an acyclic graph, in which multiple inheritance is permitted.
  • FIG. 2 shows an example of two such hierarchical class structures, each of which forms an object model.
  • the first object model contains a class "publications”, which are assigned as a subclass “lectures” and "documents”.
  • the second object model contains a class "people”, which are assigned as subclasses "self-employed” and “employees”.
  • the subclass "employees” are assigned as further subclasses "technical employees” and "commercial employees”.
  • Certain attributes are assigned to each class in a hierarchical class structure.
  • An attribute that is assigned to a class such as the "People" class is passed on to the subclasses that are subordinate to the class.
  • Such an attribute can, for example, be a Be a company location.
  • This attribute is inherited within the class structure, in this example to the subordinate classes “self-employed” and “employees” as well as the subclasses "commercial employees” and "technical employees” assigned to this class.
  • queries are entered by a user as input variables via the input / output unit 8 of the computer unit 4.
  • the processing of the queries takes place in the query unit 6, in particular in the inference unit 7 implemented there. Suitable rules for processing a query are searched and evaluated there.
  • the rules in the inference unit 7 contain logical links between classes and / or attributes and / or data of the data stock. Since the rules are a declarative system, the order in which the rules are defined is irrelevant. In the inference unit 7, the one
  • the rules are preferably written in the rule languages F-Logic, OWL, TRIPLE, SWRL or RULEML, the class structure of the ontology preferably being designed as an F-Logic, RDF (S) or OWL model.
  • a rule that can be used to answer this query can be formulated as follows: "If a person writes a document and this document is about a given subject, then that person has knowledge of the subject.” F-Logic could express this rule as follows (see.,. ' .Ss ⁇ ⁇ below):,
  • the ontology itself is defined in this first section.
  • the data contains documents with two relevant attributes: the author and the area of knowledge.
  • This variable substitution provides the result of our query.
  • the result is preferably output via the input / output unit 8.
  • the query was processed by an instantiation of the rule, ie the rule are considered as concrete values, so-called instances (facts)., 'Die.
  • an explanation is generated in the evaluation unit 10, which is output as a further output variable via the input / output unit 8.
  • an inference protocol is read into the inference unit 11 of the evaluation unit 10 via the interface module 9.
  • the inference log generally contains an indexing, which indicates which rules were processed during a query.
  • the inference log also contains the instances (facts) of the respective rules for processing the query.
  • the person, document and topic form variables to which the instances of the inference protocol have been assigned to process the explanation rules.
  • explanations can be generated in such a way that for each rule that is processed in the inference unit 7, an explanation is generated in the inference unit 11. This case represents the greatest possible level of detail in the generation of explanations.
  • explanation rules can be used to avoid redundant explanations.
  • the acidity of two substances can be determined by determining the pH values, which are calculated by the amounts of the respective substances dissolved in aqueous solutions.
  • a qualitative determination can be made, for example by determining the number of oxygen atoms in the formulas of these substances.
  • Both calculation methods can be output in the form of explanations from the inner unit 11 as output variables for the description of the substances.
  • only one of the two explanations can be selected on the basis of a relevance check, so that the output variables no longer have redundant explanations. Since the evaluation unit 10 has an interface to external data, these external data can also be included as defined external knowledge in the generation of explanations. In the exemplary embodiment according to FIG.
  • the evaluation unit 10 has, in addition to the inference unit 11, the software module 12, by means of which at least one object model for structuring external data is generated.
  • the mode of operation of the inference unit 11 and the software module 12 is analogous to the inference unit 7 and the associated software module 5.
  • the user profiles are preferably stored as data files in the computer unit 4 and are structured with an object model generated in the software module 12.
  • the explanations generated in the inference unit 11 can be adapted to the level of knowledge of the respective user.
  • the explanations explain chemical processes, the explanations can be made on a qualitative level if the user is a student or a pupil. If, on the other hand, the user is a chemist with a doctorate, the explanations can also include quantitative calculations.
  • Figure 3 shows a schematic representation of the process flow in the data processing system.
  • a database is generated in step 300 and / or the database is stored 302.
  • an object mode is generated which consists of a declarative; System of rules and a class structure.
  • the data is structured using the object model.
  • the rules link elements of the class structure and / or data.
  • a query 306 is entered via an input device.
  • the query 308 is evaluated in an inference unit by evaluating the rules.
  • the output 312 of the results of the evaluation 308 takes place via an output unit.
  • an evaluation unit which is assigned to the inference unit, reads 316 inference protocols via instantiations of the rules that occurred during the evaluation.
  • explanations about the evaluation of the rules are generated in the evaluation unit in step 318. Via an output In step 320, the generated explanations are output.

Abstract

L'invention concerne une machine d'inférence destinée à répondre à des questions concernant des données prédéfinies au moyen d'une ontologie servant à structurer les données, et un système déclaratif de règles, reproduisant des connaissances supplémentaires. Lors de l'évaluation de la requête, des protocoles d'inférence concernant l'instanciation des règles apparues pendant l'évaluation sont lus dans une unité d'évaluation affectée à l'unité d'inférence. Des explications concernant l'évaluation des règles sont produites dans l'unité d'évaluation en fonction des protocoles d'inférence. Ainsi, la déduction logique des réponses devient transparente. Le système est capable de répondre à des questions techniques et de réaliser et expliquer des réflexions techniques.
EP04803492A 2003-12-03 2004-12-03 Machine d'inference Withdrawn EP1690219A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE10356399A DE10356399B4 (de) 2003-12-03 2003-12-03 Datenverarbeitungssystem
PCT/EP2004/013769 WO2005055134A2 (fr) 2003-12-03 2004-12-03 Machine d'inference

Publications (1)

Publication Number Publication Date
EP1690219A2 true EP1690219A2 (fr) 2006-08-16

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US (1) US7333970B2 (fr)
EP (1) EP1690219A2 (fr)
CN (1) CN100565570C (fr)
DE (1) DE10356399B4 (fr)
WO (1) WO2005055134A2 (fr)

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Publication number Publication date
US7333970B2 (en) 2008-02-19
DE10356399B4 (de) 2006-06-14
WO2005055134A3 (fr) 2006-06-08
WO2005055134A2 (fr) 2005-06-16
CN100565570C (zh) 2009-12-02
CN1902647A (zh) 2007-01-24
US20070011125A1 (en) 2007-01-11
DE10356399A1 (de) 2005-07-14

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