CN110866122A - Method and device for mapping entity words based on knowledge graph - Google Patents
Method and device for mapping entity words based on knowledge graph Download PDFInfo
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Abstract
The invention discloses a method and a device for mapping entity words based on a knowledge graph. The semantic-based entity word similarity calculation is used for solving the problem that after a named entity recognition algorithm recognizes an entity word, the entity word is mapped with the existing knowledge map entity word.
Description
Technical Field
The embodiment of the invention relates to the technical field of natural language processing, in particular to a method and a device for mapping entity words based on a knowledge graph.
Background
In a natural language processing task, particularly in a process of processing a business in a vertical field, for example, an intelligent customer service system of a household appliance environment is built, a user often uses words close to a general domain to describe the problem of a professional field through self understanding in a conversation process with a robot (for example, description of spoken language of the user includes that an air conditioner screen of the user suddenly flashes suddenly and actually corresponds to entities in the professional field, namely 'air conditioner', 'display screen' and 'indicator lamp flashing', and another example, description of natural language of the user includes that the air conditioner only blows but not cold and actually corresponds to entities in the professional field, namely 'air conditioner', 'no' and 'refrigeration'). The system understands that the intention of the user is to acquire keyword information in a rule-based or named entity identification-based mode, and then perform business processing and reply generation based on the keywords. After the key entities in the natural language expression of the user are identified, how the expression entities based on the user approximate domain are aligned with the professional entities in the field is a problem that key information processing needs to be solved and optimized urgently at present.
Disclosure of Invention
The embodiment of the invention provides a knowledge graph-based entity word mapping method and device, which are used for establishing a quantitative similarity measurement standard for the expression of a user in a nearly extensive domain and a professional domain entity so as to find a professional entity closest to a user keyword, thereby realizing the problem of mapping the entity and the conventional knowledge graph entity.
In a first aspect, an embodiment of the present invention provides a method for mapping entity words based on a knowledge graph, including:
constructing a semantic knowledge graph according to the explanation of the words in the Chinese language;
constructing a service knowledge graph according to entity attributes of system functions of the equipment;
and establishing a mapping relation between the entity words of the semantic knowledge graph and the entity words in the service knowledge graph in a word entity distance calculation mode.
In the technical scheme, the problem that after the entity word is identified by the named entity identification algorithm, the entity word is mapped with the existing knowledge map entity word is solved through semantic-based entity word similarity calculation.
Optionally, the constructing a semantic knowledge graph according to the explanation of the words in the chinese language includes:
based on the explanation of the words in Chinese language, a semantic knowledge graph with three types of entity relations of paraphrase words, near-meaning words and related words is established for each entity word.
Optionally, the constructing a service knowledge graph according to the entity attribute of the system function of the device includes:
acquiring functional words of fault entities provided by a user;
analyzing the system function of the fault entity related equipment to obtain the entity attribute of the system function of the equipment;
and establishing a service knowledge map for the functional words according to the entity attributes of the system functions of the equipment.
Optionally, the establishing a mapping relationship between the entity words of the semantic knowledge graph and the entity words in the service knowledge graph by a word entity distance calculation method includes:
if the current entity word is not in the service knowledge graph in the semantic knowledge graph, calculating the distance between the current entity word and each functional word in the service knowledge graph, and establishing a mapping relation between the functional word with the shortest distance and the current entity word;
if the current entity word is not in the semantic knowledge graph in the service knowledge graph, segmenting the current entity word; and performing similarity calculation on the current entity word and each word after word segmentation, and establishing a mapping relation between the word with the highest similarity and the current entity word.
In a second aspect, an embodiment of the present invention provides an apparatus for mapping entity words based on a knowledge graph, including:
the construction unit is used for constructing the semantic knowledge graph according to the explanation of the Chinese language to the words; constructing a service knowledge graph according to entity attributes of system functions of the equipment;
and the processing unit is used for establishing the mapping relation between the entity words of the semantic knowledge graph and the entity words in the service knowledge graph in a word entity distance calculation mode.
Optionally, the building unit is specifically configured to:
based on the explanation of the words in Chinese language, a semantic knowledge graph with three types of entity relations of paraphrase words, near-meaning words and related words is established for each entity word.
Optionally, the building unit is specifically configured to:
acquiring functional words of fault entities provided by a user;
analyzing the system function of the fault entity related equipment to obtain the entity attribute of the system function of the equipment;
and establishing a service knowledge map for the functional words according to the entity attributes of the system functions of the equipment.
Optionally, the processing unit is specifically configured to:
if the current entity word is not in the service knowledge graph in the semantic knowledge graph, calculating the distance between the current entity word and each functional word in the service knowledge graph, and establishing a mapping relation between the functional word with the shortest distance and the current entity word;
if the current entity word is not in the semantic knowledge graph in the service knowledge graph, segmenting the current entity word; and performing similarity calculation on the current entity word and each word after word segmentation, and establishing a mapping relation between the word with the highest similarity and the current entity word.
In a third aspect, an embodiment of the present invention further provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the method for mapping the entity words based on the knowledge graph according to the obtained program.
In a fourth aspect, the embodiments of the present invention also provide a computer-readable non-volatile storage medium, which includes computer-readable instructions, when the computer reads and executes the computer-readable instructions, cause the computer to perform the above method for mapping knowledge-graph-based entity words.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for mapping entity words based on a knowledge-graph according to an embodiment of the present invention;
FIG. 3 is a diagram of an semantic knowledge graph according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a business knowledge graph according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an apparatus for mapping entity words based on a knowledge graph according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 illustrates an exemplary system architecture to which embodiments of the present invention may be applied, which may be a server 100, where the server 100 may include a processor 110, a communication interface 120, and a memory 130.
The communication interface 120 is used for the smart device to perform communication, receive and transmit information transmitted by the smart device, and implement communication.
The processor 110 is a control center of the server 100, connects various parts of the entire server 100 using various interfaces and routes, performs various functions of the server 100 and processes data by operating or executing software programs and/or modules stored in the memory 130 and calling data stored in the memory 130. Alternatively, processor 110 may include one or more processing units.
The memory 130 may be used to store software programs and modules, and the processor 110 executes various functional applications and data processing by operating the software programs and modules stored in the memory 130. The memory 130 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to a business process, and the like. Further, the memory 130 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
It should be noted that the structure shown in fig. 1 is only an example, and the embodiment of the present invention is not limited thereto.
Based on the above description, fig. 2 shows in detail a flow of a method for mapping an entity word based on a knowledge-graph according to an embodiment of the present invention, where the flow may be performed by a device for mapping an entity word based on a knowledge-graph, and the device may be located in the server 100 shown in fig. 1, or may be the server 100.
As shown in fig. 2, the process specifically includes:
The method is mainly based on the explanation of Chinese language to words and phrases, and establishes an semantic knowledge graph with three types of entity relations of paraphrase words, near-meaning words and related words for each entity word.
As shown in fig. 3, each word entity represents a word, and three types of entity relationships of paraphrase words, similar words and related words are set together. Paraphrasing words: based on the explanation of Chinese language to words, the range is a domain range, and can reflect the application scene of one word with multiple meanings; the similar meaning word: the words are similar; related words: the words related to the words cover all Chinese fields and can reflect a word polysemous application scene.
Specifically, a functional word of a faulty entity provided by a user needs to be acquired first, then the faulty entity is analyzed with respect to the system function of the device to obtain an entity attribute of the system function of the device, and finally a service knowledge map is established for the functional word according to the entity attribute of the system function of the device.
In an air conditioner fault interaction scene, a series of business processes such as inquiry of accompanying phenomena, interpretation of fault phenomena, guidance of misoperation and the like need to be carried out on equipment according to fault phenomena described by a user, so in a fault reasoning process based on a knowledge graph, reasoning calculation needs to be carried out according to fault entities (actually, phenomenon entities) provided by the user, entity attributes shown in table 1 are defined according to product system functions, and a business knowledge graph shown in fig. 4 is constructed.
TABLE 1
| Attribute name | Examples of such applications are |
| Device name | Air conditioner |
| Device name | Condenser |
| Function word | Refrigeration system |
| Operation word | Cleaning of |
| Phenomenon of failure | Internal machine leakage |
| Cause of failure | Filth blockage of condensation pipe |
| Misoperation | Mode setting error |
| Pattern word | Pendulum wind |
| Negative word | Is not limited to |
| Pseudonyms | Buzzing |
| Weighing word | Power consumption |
| Address word | Qingdao city |
| Telephone set | 138****6700 |
| Brand | Kelong |
| Functional consumable | Freon |
And if the current entity word is not in the service knowledge map in the semantic knowledge map, calculating the distance between the current entity word and each functional word in the service knowledge map, and establishing a mapping relation between the functional word with the shortest distance and the current entity word.
If the current entity word is not in the semantic knowledge graph in the service knowledge graph, segmenting the current entity word; and performing similarity calculation on the current entity word and each word after word segmentation, and establishing a mapping relation between the word with the highest similarity and the current entity word.
The distance calculation mode of the two word entities a and b is as follows:
DIS _ hanyu (a, b) ═ α (one of synonyms, paraphrases and related words overlaps by 1)/3+ β ((a, b synonym, paraphrase, number of related word co-occurring words)/(sum of a, b synonym, paraphrase and related word number)), where α + β ═ 1.
Suppose that the key entity word extracted by the named entity algorithm entity of "my air conditioner is only blowing air but not cooling" input by the user is:
air-conditioning- "equipment" entity;
not- "negative" entity;
cool- "functional word" entity.
If the current entity word current _ entry is "cool" in the semantic knowledge graph:
the functional word "cool" is not in the business knowledge map, but in the semantic knowledge map. Therefore, the entity word with the shortest distance between current _ entry "cool" and the "function word" bussiness _ entry "in the service knowledge graph needs to be searched, that is, the distance between current _ entry" cool "and all the" function words "in the service knowledge graph is calculated, and the" function word "with the shortest distance is selected as the near-meaning entity word of current _ entry" cool ".
The method for calculating the similarity between the entity words in the semantic knowledge graph and the entity words in the service knowledge graph is described by taking the refrigeration entity words in the service graph as an example:
if the refrigeration is in the semantic knowledge graph, directly calculating DIS _ hanyu (cooling and refrigeration) in the semantic knowledge graph;
if the refrigeration is not in the semantic knowledge graph, carrying out jieba search engine mode word segmentation on the refrigeration, segmenting words into a finer granularity set cut _ result [ "send", "cool" ], and then calculating the similarity with the cut _ result, wherein the calculation method comprises the following steps:
and (3) carrying out similarity calculation on the current entity word current _ entry: "cool" and each element item-i in the cut _ result:
similar(current_entity,cut_result)
＝min(DIS_hanyu(current_entity,item-i)*parm_len)
wherein the content of the first and second substances,
if the current entity word current _ entry is not in the semantic knowledge graph, performing jieba search engine mode word segmentation on the current _ entry, segmenting the word into a finer granularity set client _ entry _ cut, calculating the similarity of each element in the client _ entry _ cut set, and selecting the entity word in the service knowledge graph corresponding to the entity word of the semantic knowledge graph with the shortest distance as a result.
The embodiment shows that the semantic knowledge graph is constructed according to the explanation of the words in the Chinese language, the business knowledge graph is constructed according to the entity attributes of the system functions of the equipment, and the mapping relation between the entity words of the semantic knowledge graph and the entity words in the business knowledge graph is established in a word entity distance calculation mode. The semantic-based entity word similarity calculation is used for solving the problem that after a named entity recognition algorithm recognizes an entity word, the entity word is mapped with the existing knowledge map entity word.
Based on the same technical concept, fig. 5 exemplarily shows a structure of an apparatus for mapping entity words based on a knowledge graph, which may perform a process of mapping entity words based on a knowledge graph, and the apparatus may be located in the server 100 shown in fig. 1, or the server 100.
As shown in fig. 5, the apparatus specifically includes:
the construction unit 501 is used for constructing a semantic knowledge graph according to the explanation of the words in the Chinese language; constructing a service knowledge graph according to entity attributes of system functions of the equipment;
the processing unit 502 is configured to establish a mapping relationship between entity words in the semantic knowledge graph and entity words in the business knowledge graph in a word entity distance calculation manner.
Optionally, the constructing unit 501 is specifically configured to:
based on the explanation of the words in Chinese language, a semantic knowledge graph with three types of entity relations of paraphrase words, near-meaning words and related words is established for each entity word.
Optionally, the constructing unit 501 is specifically configured to:
acquiring functional words of fault entities provided by a user;
analyzing the system function of the fault entity related equipment to obtain the entity attribute of the system function of the equipment;
and establishing a service knowledge map for the functional words according to the entity attributes of the system functions of the equipment.
Optionally, the processing unit 502 is specifically configured to:
if the current entity word is not in the service knowledge graph in the semantic knowledge graph, calculating the distance between the current entity word and each functional word in the service knowledge graph, and establishing a mapping relation between the functional word with the shortest distance and the current entity word;
if the current entity word is not in the semantic knowledge graph in the service knowledge graph, segmenting the current entity word; and performing similarity calculation on the current entity word and each word after word segmentation, and establishing a mapping relation between the word with the highest similarity and the current entity word.
Based on the same technical concept, an embodiment of the present invention further provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the method for mapping the entity words based on the knowledge graph according to the obtained program.
Based on the same technical concept, embodiments of the present invention also provide a computer-readable non-volatile storage medium, which includes computer-readable instructions, and when the computer-readable instructions are read and executed by a computer, the computer is enabled to perform the above-mentioned method for mapping entity words based on a knowledge graph.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for mapping entity words based on a knowledge graph is characterized by comprising the following steps:
constructing a semantic knowledge graph according to the explanation of the words in the Chinese language;
constructing a service knowledge graph according to entity attributes of system functions of the equipment;
and establishing a mapping relation between the entity words of the semantic knowledge graph and the entity words in the service knowledge graph in a word entity distance calculation mode.
2. The method of claim 1, wherein constructing a semantic knowledge graph from interpretations of words in a Chinese language comprises:
based on the explanation of the words in Chinese language, a semantic knowledge graph with three types of entity relations of paraphrase words, near-meaning words and related words is established for each entity word.
3. The method of claim 1, wherein constructing a business knowledge graph from entity attributes of system functions of a device comprises:
acquiring functional words of fault entities provided by a user;
analyzing the system function of the fault entity related equipment to obtain the entity attribute of the system function of the equipment;
and establishing a service knowledge map for the functional words according to the entity attributes of the system functions of the equipment.
4. The method of claim 1, wherein the establishing a mapping relationship between entity words of the semantic knowledge graph and entity words in the business knowledge graph by means of distance calculation of word entities comprises:
if the current entity word is not in the service knowledge graph in the semantic knowledge graph, calculating the distance between the current entity word and each functional word in the service knowledge graph, and establishing a mapping relation between the functional word with the shortest distance and the current entity word;
if the current entity word is not in the semantic knowledge graph in the service knowledge graph, segmenting the current entity word; and performing similarity calculation on the current entity word and each word after word segmentation, and establishing a mapping relation between the word with the highest similarity and the current entity word.
5. An apparatus for mapping entity words based on a knowledge graph, comprising:
the construction unit is used for constructing the semantic knowledge graph according to the explanation of the Chinese language to the words; constructing a service knowledge graph according to entity attributes of system functions of the equipment;
and the processing unit is used for establishing the mapping relation between the entity words of the semantic knowledge graph and the entity words in the service knowledge graph in a word entity distance calculation mode.
6. The apparatus of claim 5, wherein the construction unit is specifically configured to:
based on the explanation of the words in Chinese language, a semantic knowledge graph with three types of entity relations of paraphrase words, near-meaning words and related words is established for each entity word.
7. The apparatus of claim 5, wherein the construction unit is specifically configured to:
acquiring functional words of fault entities provided by a user;
analyzing the system function of the fault entity related equipment to obtain the entity attribute of the system function of the equipment;
and establishing a service knowledge map for the functional words according to the entity attributes of the system functions of the equipment.
8. The apparatus as claimed in claim 5, wherein said processing unit is specifically configured to:
if the current entity word is not in the service knowledge graph in the semantic knowledge graph, calculating the distance between the current entity word and each functional word in the service knowledge graph, and establishing a mapping relation between the functional word with the shortest distance and the current entity word;
if the current entity word is not in the semantic knowledge graph in the service knowledge graph, segmenting the current entity word; and performing similarity calculation on the current entity word and each word after word segmentation, and establishing a mapping relation between the word with the highest similarity and the current entity word.
9. A computing device, comprising:
a memory for storing program instructions;
a processor for calling program instructions stored in said memory to execute the method of any one of claims 1 to 4 in accordance with the obtained program.
10. A computer-readable non-transitory storage medium including computer-readable instructions which, when read and executed by a computer, cause the computer to perform the method of any one of claims 1 to 4.
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| CN112434200B (en) * | 2020-11-30 | 2024-06-04 | 北京思特奇信息技术股份有限公司 | Data display method and system and electronic equipment |
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