CN111126608A - Knowledge representation method, knowledge representation device, electronic equipment and computer readable storage medium - Google Patents
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Abstract
The embodiment of the invention provides a knowledge representation method, a knowledge representation device, electronic equipment and a computer readable storage medium, wherein the knowledge representation method determines information constraint sets serving as basic units of a knowledge base based on information contents in a preset database, each information constraint set comprises a first information content and a second information content, and the conditional probability between the first information content and the second information content meets a preset requirement; and respectively carrying out qualitative analysis and quantitative analysis on the knowledge base based on the information constraint set to obtain a qualitative representation index and a quantitative representation index. Therefore, the knowledge in the knowledge base is expressed from the qualitative and quantitative aspects, and meanwhile, the method has the capability of describing the structured and unstructured knowledge qualitatively and quantitatively, and has the advantages of measurement and reasoning.
Description
Technical Field
The invention relates to the technical field of knowledge bases, in particular to a knowledge representation method, a knowledge representation device, electronic equipment and a computer readable storage medium.
Background
With the organic combination of Artificial Intelligence (AI) and Database (DB) computer technologies, the creation and development of knowledge bases has been facilitated. Currently, knowledge bases have been applied in various different fields. The knowledge representation is a key technology of the knowledge base.
With the continuous development of computer technology, the related technology provides a plurality of different knowledge representation methods from different application fields. However, the knowledge representation methods used in the prior art are not comprehensive enough, so that the representation description of knowledge stays at the level of word meaning and grammar, and a corresponding information measurement method system with higher levels (such as semantics and pragmatic information) is lacked.
Disclosure of Invention
In view of the above, the present invention provides a knowledge representation method, apparatus, electronic device and computer readable storage medium.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, an embodiment provides a knowledge representation method, where the knowledge representation method includes:
determining information constraint sets serving as basic units of a knowledge base based on information contents in a preset database, wherein each information constraint set comprises a first information content and a second information content, and the conditional probability between the first information content and the second information content meets a preset requirement;
and respectively carrying out qualitative analysis and quantitative analysis on the knowledge base based on the information constraint set to obtain a qualitative representation index and a quantitative representation index.
In an alternative embodiment, the step of performing a quantitative analysis of the knowledge base comprises:
and calculating corresponding mutual information quantity according to the information content in the knowledge base to serve as the quantitative representation index.
In an alternative embodiment, the step of performing a quantitative analysis of the knowledge base comprises:
and calculating the mutual singular quantity between the first information content and the second information content in each information constraint set according to the information content in the knowledge base to serve as the quantitative representation index.
In an alternative embodiment, the step of performing a qualitative analysis of the knowledge base comprises:
and performing qualitative analysis on the knowledge base based on the first information content and the second information content in the information constraint set.
In an alternative embodiment, the preset requirement is that the conditional probability is 1.
In a second aspect, embodiments provide a knowledge representation apparatus comprising:
the system comprises a determining module, a determining module and a judging module, wherein the determining module is used for determining information constraint sets serving as basic units of a knowledge base based on information contents in a preset database, each information constraint set comprises a first information content and a second information content, and the conditional probability between the first information content and the second information content meets a preset requirement;
and the analysis module is used for respectively carrying out qualitative analysis and quantitative analysis on the knowledge base based on the information constraint set so as to obtain a qualitative representation index and a quantitative representation index.
In an alternative embodiment, the analysis module is further configured to:
calculating corresponding mutual information quantity according to the information content in the knowledge base to serve as the quantitative representation index; alternatively, the first and second electrodes may be,
and calculating the mutual singular quantity between the first information content and the second information content in each information constraint set according to the information content in the knowledge base to serve as the quantitative representation index.
In an alternative embodiment, the analysis module is further configured to:
and performing qualitative analysis on the knowledge base based on the first information content and the second information content in the information constraint set.
In a third aspect, embodiments provide an electronic device comprising a processor and a memory, the memory storing machine executable instructions capable of being executed by the processor, the processor being capable of executing the machine executable instructions to implement the method of any one of the preceding embodiments.
In a fourth aspect, embodiments provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method according to any of the preceding embodiments.
The knowledge representation method, the knowledge representation device, the electronic equipment and the computer readable storage medium provided by the embodiment of the invention determine the information constraint set serving as the basic unit of the knowledge base based on the information content in the preset database. When knowledge is represented, an information constraint set form is adopted, each information constraint set comprises a first information content and a second information content, and the conditional probability between the first information content and the second information content meets a preset requirement. Therefore, the knowledge in the knowledge base can be qualitatively analyzed and quantitatively analyzed respectively based on the information constraint set to obtain the qualitative representation index and the quantitative representation index. Therefore, the method has the capability of describing structured knowledge and unstructured knowledge qualitatively and quantitatively at the same time, and has the advantages of measurement and reasoning.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 shows a schematic diagram of an electronic device provided by an embodiment of the present invention.
FIG. 2 is a flow chart illustrating the steps of a knowledge representation method provided by an embodiment of the present invention.
Fig. 3 is a flowchart illustrating sub-steps of step S102 in fig. 2.
Fig. 4 shows a schematic diagram of a knowledge representation apparatus provided by an embodiment of the invention.
Icon: 100-an electronic device; 110-a memory; 120-a processor; 130-a communication module; 200-a knowledge representation apparatus; 201-a determination module; 202-analysis module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The current method for realizing knowledge representation at home and abroad mainly comprises a first-order predicate logic representation method, a production formula rule representation method, a frame representation method, a semantic network representation method, an ontology-based representation method and the like. Of course, there are some methods, such as probability-based, fuzzy sets, fuzzy concept graphs, situational calculus, etc. methods for describing the uncertainty knowledge.
The first-order predicate logic representation method refers to various knowledge representation methods based on formal logic, uses logic formulas to describe objects, properties, conditions and relations, is one of the earliest and most extensive knowledge representation methods, and is mainly used for symbolizing logic demonstration in mathematics, and can prove that a new statement is derived from known correct statements in a mathematical deduction mode, so that the new statement can be judged to be correct. Although the first-order predicate logic representation method has the characteristics of simplicity, flexibility, easiness in implementation and the like, uncertainty knowledge and heuristic knowledge are difficult to express, and an inference process is long and low in efficiency.
The production rule representation is one of the commonly used knowledge representation methods, and is expressed in the form of "If-Then", i.e. the production rule, according to the causal relationship existing in large quantity among various knowledge in the human brain memory pattern. This form of rule captures the behavioral characteristics of human solvers to solve problems through a cyclic process of recognition-action. The production rule representation method has the characteristics of fixed representation format, simplicity and reasonability, and can represent the determined knowledge unit and the uncertain knowledge; the heuristic knowledge can be represented conveniently; both domain knowledge and meta knowledge can be represented. However, the generative rule representation also has the following disadvantages: 1. the knowledge format of the generated expression is rigid, so that the knowledge with stronger structural relation or hierarchical relation is difficult to express, and flexible explanation cannot be provided; 2. because the inference process of the production rule needs to continuously search and pattern match the condition parts of all the rules, the inference complexity is increased sharply along with the continuous accumulation of the production rule, and the inference efficiency of complex knowledge is insufficient; 3. the mutual conversion of qualitative and quantitative knowledge is difficult to realize due to the lack of the capability of quantitative description of knowledge.
The object-oriented knowledge representation method describes the entities of the objective world and the relation among the entities by packaging the objects, the knowledge represented by the objects is relatively close to the objective world and is easy to understand, the inheritability of the objects reduces the complexity of problem description and computational reasoning, and the redundancy on knowledge representation is greatly reduced; the ontology-based representation expresses and reflects the entities and the association between the entities through various knowledge representation elements (such as classes, slots, axioms, instances and the like) in order to describe the structure and the internal connection of the clear domain knowledge and better realize the sharing and reusing of the knowledge to a certain extent. However, these two knowledge representations also have some disadvantages: 1. the lack of quantitative description of knowledge representation, for example, the determination of the similarity of two ontologies, the judgment of whether the two ontologies are equivalent, and the lack of quantitative analysis of uniform qualitative analysis when discussing problems of ontology mapping, ontology integration, etc.; 2. the method can not introduce the limited domain knowledge and the non-limited domain knowledge for reasoning at the same time, has weak reasoning capability and lacks the capability of acquiring deep knowledge such as implicit information and the like.
In general, although many methods for knowledge representation focus on qualitative analysis of knowledge representation, such as structure, internal relation, and description method of knowledge, and some methods, such as the knowledge representation based on probability method, perform measurement of knowledge representation, the discussion level still remains in the word meaning and grammar layer, and the discussion and calculation method of semantic and pragmatic information is incomplete, and thus a complete measurement system is not formed. The prior knowledge representation technology has the main problems that: 1. based on classical logic, the description depth is not enough, and the qualitative description is not accurate; 2. quantitative descriptions have not yet formed a complete measurement system; 3. a knowledge representation approach that addresses both qualitative descriptive and quantitative measurement problems is lacking.
In order to solve the above problem, embodiments of the present invention provide a knowledge representation method, apparatus, electronic device, and computer-readable storage medium.
Fig. 1 is a block diagram of an electronic device. The electronic device includes a memory, a processor, and a communication module 130. The memory 110, the processor 120 and the communication module 130 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
The memory 110 is used to store programs or data. The Memory 110 may be, but is not limited to, a Random Access Memory 110 (RAM), a Read Only Memory 110 (ROM), a Programmable Read Only Memory 110 (PROM), an Erasable Read Only Memory 110 (EPROM), an electrically Erasable Read Only Memory 110 (EEPROM), and the like.
The processor 120 is used to read/write data or programs stored in the memory 110 and perform corresponding functions.
The communication module 130 is configured to establish a communication connection between the electronic device 100 and another communication terminal through the network, and to transmit and receive data through the network.
It should be understood that the structure shown in fig. 1 is only a schematic structural diagram of the electronic device 100, and the electronic device 100 may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
First embodiment
Referring to fig. 2, fig. 2 is a flow chart illustrating steps of a method for providing knowledge representation according to an embodiment of the present invention. As shown in fig. 2, the knowledge representation method may include the steps of:
step S101, determining an information constraint set as a basic unit of a knowledge base based on information content in a preset database.
In an embodiment of the present invention, each of the information constraint sets includes a first information content and a second information content. The first information content and the second information content are two information contents which are determined to have an association relationship from a database. The association between the above-mentioned information contents may be determined by a conditional probability between the information contents. Optionally, the conditional probability between each information content and other information contents in the database is sequentially obtained, and if the conditional probability obtained between the information content and one other information content meets a preset requirement, the information content is used as a first information content, and the other information content is used as a second information content, so as to form an information constraint set. That is, the conditional probability between the first information content and the second information content in each information constraint set satisfies the preset requirement. In other words, each constraint set indicates that the probability of the occurrence of the first information content meets the preset requirement when the corresponding second information content occurs. It is obvious that the same information content may be used as the first information content in one or more information constraint sets, and may also be used as the second information content in further information constraint sets.
In one embodiment, the predetermined requirement is that the conditional probability is 1. And constructing a knowledge base by taking the first information content and the second information content with the conditional probability of 1 as basic units.
And S102, respectively carrying out qualitative analysis and quantitative analysis on the knowledge base based on the information constraint set to obtain a qualitative representation index and a quantitative representation index.
In the embodiment of the present invention, as shown in fig. 3, the step S102 may include the following sub-steps:
and a substep S102-1 of performing qualitative analysis on the knowledge base based on the information constraint set in the knowledge base.
Alternatively, the qualitative analysis may be formally defining knowledge representation based on the information constraint set according to representation characteristics, description capacity and the like of different knowledge types in the knowledge base, and then substituting the knowledge representation into an information load model in the semantic information theory to describe the representation content of knowledge from the hierarchy of semantic information and pragmatic information.
In the embodiment of the present invention, a qualitative analysis may be performed on the knowledge base based on the first information content and the second information content in the information constraint set to obtain a qualitative representation index. It can be understood that the conditional probability between the first information content and the second information content is 1, which means that the second information content is known after the first information content is known; thus, if the second information content needs to be known, only the first information content needs to be queried. That is, the purpose of the qualitative analysis is to determine what the specific content of the knowledge representation is. Therefore, the qualitative representation index can be obtained by acquiring the number of the basic units in the knowledge base.
And a substep S102-2 of performing quantitative analysis on the knowledge base based on the information constraint set in the knowledge base.
The quantitative analysis may be a quantitative analysis method based on an information load relationship, for example, a measurement method based on an information load relationship logic expression, a relationship between a semantic information measurement and a knowledge representation measurement based on a Mutual singular quantity (Mutual singular) and a particle (particle); and constructing a knowledge measurement model suitable for structured knowledge and unstructured knowledge based on information flow logic and semantic information theory.
Optionally, the sub-step S102-2 may be to calculate a corresponding mutual information amount according to the information content in the knowledge base, so as to serve as the quantitative representation index. Specifically, based on the first information content and the second information content in the knowledge base, using the formula:
and calculating a quantitative representation index of the knowledge base. Wherein, the I represents a quantitative representation index, X represents second information content in all information constraint sets in the knowledge base, and X represents second information content; y represents the first information content in all the information constraint sets; y represents a first information content; p (x) represents the probability of occurrence of a second information content; p (y) represents the probability of occurrence of a first information content; p (xy) represents the probability that the first information content and the second information content occur simultaneously.
Optionally, the sub-step S102-2 may further calculate, according to information contents in a knowledge base, a mutual singular quantity between the first information content and the second information content in each information constraint set, so as to serve as the quantitative representation index. Specifically, according to the first information content and the second information content in each information constraint set, a formula is used:
MutualSurprisalα-β＝I(β)-Nβ(α)＝I(α)-Eα(β)，
and calculating the mutual singular quantity between the first information content and the second information content. Wherein, Mutual surgeryα-βRepresenting the amount of mutual singularity between the first information content and the second information content, β being the first information content, α being the second information content, wherein the second information contentAs an information source, the first information content may be an information payload, I (β), I (α) represent potentials of the first information content and the second information content, respectivelyα(β) representing a fiction (acquisition) from the second information content to the first information content, Nβ(α) representing noise from the first information content relative to the second information content.
| Second information content (information source) | First information content (information load) |
| α1 | β1 |
| α2 | β1 |
| α3 | β2 |
| α4 | β2 |
Then I (α)1)＝-log2(1/4)＝2bits，I(β1)＝1bits，Eα1(β1)＝I(α1|β1)＝1bits，Nβ1(α1)＝I(β1|α1)＝0。
And overlapping the mutual singular quantities corresponding to all the information constraint sets to obtain a deeper quantitative representation index based on particles (particles).
In the embodiment of the present invention, there is no certain sequence between the sub-step S102-1 and the sub-step S102-2.
In order to execute the corresponding steps in the above embodiments and various possible manners, an implementation manner of the knowledge representation apparatus 200 is given below, and optionally, the knowledge representation apparatus 200 may adopt the device structure of the electronic device 100 shown in fig. 1. Further, referring to fig. 4, fig. 4 is a functional block diagram of a knowledge representation apparatus 200 according to an embodiment of the present invention. It should be noted that the basic principle and the generated technical effect of the knowledge representation apparatus 200 provided by the present embodiment are the same as those of the above embodiments, and for the sake of brief description, no part of the present embodiment is mentioned, and corresponding contents in the above embodiments may be referred to. The knowledge representation apparatus 200 includes: a determination module 201 and an analysis module 202.
The determining module 201 is configured to determine, based on information contents in a preset database, information constraint sets serving as basic units of a knowledge base, where each information constraint set includes a first information content and a second information content, and a conditional probability between the first information content and the second information content meets a preset requirement.
And the analysis module 202 is configured to perform qualitative analysis and quantitative analysis on the knowledge base respectively based on the information constraint set to obtain a qualitative representation index and a quantitative representation index.
Optionally, the analysis module 202 is further configured to: and calculating corresponding mutual information quantity according to the information content in the knowledge base to serve as the quantitative representation index.
Optionally, the analysis module 202 is further configured to: and calculating the mutual singular quantity between the first information content and the second information content in each information constraint set according to the information content in the knowledge base to serve as the quantitative representation index.
Optionally, the analysis module 202 is further configured to: and performing qualitative analysis on the knowledge base based on the first information content and the second information content in the information constraint set.
Alternatively, the modules may be stored in the memory 110 shown in fig. 1 in the form of software or Firmware (Firmware) or be fixed in an Operating System (OS) of the electronic device 100, and may be executed by the processor 120 in fig. 1. Meanwhile, data, codes of programs, and the like required to execute the above-described modules may be stored in the memory 110.
In summary, the embodiments of the present invention provide a knowledge representation method, an apparatus, an electronic device, and a computer-readable storage medium, where the knowledge representation method determines information constraint sets serving as basic units of a knowledge base based on information contents in a preset database, where each information constraint set includes a first information content and a second information content, and a conditional probability between the first information content and the second information content meets a preset requirement; and respectively carrying out qualitative analysis and quantitative analysis on the knowledge base based on the information constraint set to obtain a qualitative representation index and a quantitative representation index. The method has the advantages of qualitative and quantitative description of structured and unstructured knowledge, measurable and reasonable capability, combines concepts such as information representation capability and information load relationship in semantic information theory, provides a new semantic database mixed knowledge representation form based on an information flow constraint set by using information flow logic, and improves the conventional knowledge representation method of structured and unstructured knowledge from the viewpoints of description accuracy (qualitative) and scalability (quantitative).
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A knowledge representation method, characterized in that the knowledge representation method comprises:
determining information constraint sets serving as basic units of a knowledge base based on information contents in a preset database, wherein each information constraint set comprises a first information content and a second information content, and the conditional probability between the first information content and the second information content meets a preset requirement;
and respectively carrying out qualitative analysis and quantitative analysis on the knowledge base based on the information constraint set to obtain a qualitative representation index and a quantitative representation index.
2. The method of knowledge representation of claim 1, wherein the step of quantitatively analyzing the knowledge base comprises:
and calculating corresponding mutual information quantity according to the information content in the knowledge base to serve as the quantitative representation index.
3. The method of knowledge representation of claim 1, wherein the step of quantitatively analyzing the knowledge base comprises:
and calculating the mutual singular quantity between the first information content and the second information content in each information constraint set according to the information content in the knowledge base to serve as the quantitative representation index.
4. The method of knowledge representation of claim 1, wherein the step of qualitatively analyzing the knowledge base comprises:
and performing qualitative analysis on the knowledge base based on the first information content and the second information content in the information constraint set.
5. The knowledge representation method according to any one of claims 1 to 4, wherein the preset requirement is that the conditional probability is 1.
6. A knowledge representation apparatus, wherein the knowledge representation apparatus comprises:
the system comprises a determining module, a determining module and a judging module, wherein the determining module is used for determining information constraint sets serving as basic units of a knowledge base based on information contents in a preset database, each information constraint set comprises a first information content and a second information content, and the conditional probability between the first information content and the second information content meets a preset requirement;
and the analysis module is used for respectively carrying out qualitative analysis and quantitative analysis on the knowledge base based on the information constraint set so as to obtain a qualitative representation index and a quantitative representation index.
7. The knowledge representation apparatus of claim 6, wherein the analysis module is further configured to:
calculating corresponding mutual information quantity according to the information content in the knowledge base to serve as the quantitative representation index; or
And calculating the mutual singular quantity between the first information content and the second information content in each information constraint set according to the information content in the knowledge base to serve as the quantitative representation index.
8. The knowledge representation apparatus of claim 6, wherein the analysis module is further configured to:
and performing qualitative analysis on the knowledge base based on the first information content and the second information content in the information constraint set.
9. An electronic device comprising a processor and a memory, the memory storing machine executable instructions executable by the processor to perform the method of any one of claims 1 to 5.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130066823A1 (en) * | 2005-03-30 | 2013-03-14 | Primal Fusion Inc. | Knowledge representation systems and methods incorporating customization |
| CN103500208A (en) * | 2013-09-30 | 2014-01-08 | 中国科学院自动化研究所 | Deep layer data processing method and system combined with knowledge base |
| US20140250047A1 (en) * | 2013-03-01 | 2014-09-04 | International Business Machines Corporation | Authoring system for bayesian networks automatically extracted from text |
| CN104516949A (en) * | 2014-12-10 | 2015-04-15 | 北京百度网讯科技有限公司 | Webpage data processing method and apparatus, query processing method and question-answering system |
| WO2017198198A1 (en) * | 2016-05-19 | 2017-11-23 | 中兴通讯股份有限公司 | Software compiling method and device |
-
2019
- 2019-12-11 CN CN201911268978.5A patent/CN111126608A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130066823A1 (en) * | 2005-03-30 | 2013-03-14 | Primal Fusion Inc. | Knowledge representation systems and methods incorporating customization |
| US20140250047A1 (en) * | 2013-03-01 | 2014-09-04 | International Business Machines Corporation | Authoring system for bayesian networks automatically extracted from text |
| CN103500208A (en) * | 2013-09-30 | 2014-01-08 | 中国科学院自动化研究所 | Deep layer data processing method and system combined with knowledge base |
| CN104516949A (en) * | 2014-12-10 | 2015-04-15 | 北京百度网讯科技有限公司 | Webpage data processing method and apparatus, query processing method and question-answering system |
| WO2017198198A1 (en) * | 2016-05-19 | 2017-11-23 | 中兴通讯股份有限公司 | Software compiling method and device |
Non-Patent Citations (4)
| Title |
|---|
| JUNKANG FENG: "\"Semantic Interoperability based on the Notion of "Information Carrying\"", pages 1 - 27 * |
| JUNKANG FENG: ""Semantic Interoperability based on the Notion of "Information Carrying"", pages 1 - 27 * |
| JUNKANG FENG: "Semantic Interoperability based on the Notion of "Information Carrying", 《HTTPS://WWW.RESEARCHGATE.NET/PUBLICATION/228827552》, pages 1 - 27 * |
| KAIBO XU 等: "Defining the notion of Information Content and reasoning about it in a database", 《KNOWLEDGE AND INFORMATION SYSTEMS》, no. 2009, pages 29 - 59 * |
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