CN113517042A - Construction method, construction system and storage medium of chemical safety knowledge graph - Google Patents
Construction method, construction system and storage medium of chemical safety knowledge graph Download PDFInfo
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- 239000000126 substance Substances 0.000 title claims abstract description 133
- 238000010276 construction Methods 0.000 title claims abstract description 25
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- 238000000034 method Methods 0.000 claims description 28
- 150000005829 chemical entities Chemical class 0.000 claims description 21
- 238000009835 boiling Methods 0.000 claims description 5
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 231100000279 safety data Toxicity 0.000 abstract description 3
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 34
- 235000013399 edible fruits Nutrition 0.000 description 24
- 238000006243 chemical reaction Methods 0.000 description 6
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- 125000004122 cyclic group Chemical group 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 239000002994 raw material Substances 0.000 description 3
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Abstract
The invention relates to the field of chemical safety, in particular to a construction method, a construction system and a storage medium of a chemical safety knowledge graph, wherein the construction method of the chemical safety knowledge graph comprises the following steps: s1: collecting data in a chemical safety field database, and constructing a chemical safety knowledge dictionary; s2: acquiring multi-source heterogeneous data in the field of chemical safety to obtain a data source required by a chemical safety knowledge graph; s3: extracting entities and attributes and attribute values of entities from the data source based on the chemical safety knowledge dictionary; s4: analyzing and establishing the incidence relation among different sporocarp; s5: and establishing a chemical safety knowledge graph according to the incidence relation. The invention provides a convenient and fast inquiry platform of chemical safety data for the public and enterprises, and simultaneously assists chemical safety practitioners to carry out more targeted supervision on the chemical safety.
Description
Technical Field
The invention relates to the field of chemical safety, in particular to a construction method, a construction system and a storage medium of a chemical safety knowledge graph.
Background
The knowledge map is a map structure database, and is characterized in that data of different sources, different types and different structures are fused, and after an ontology is extracted, the data are associated into a map through the relationship between the ontology. The essence of the method is to systematize and relate data in the field and then realize visualization in a graphic mode. The knowledge graph can be used for presenting knowledge resources, mining, analyzing, constructing and displaying the association relation between knowledge. At present, the application of the knowledge graph is mainly focused on the fields of search engines and intelligent questions and answers, and the application in the professional field is less.
In the field of chemical safety, various chemical types, various chemical reactions, various chemical management methods, large and extensive chemical enterprises and frequent chemical accidents, and the traditional relational database is inconvenient for the fusion and expansion of the content of a chemical safety knowledge system and cannot provide high-quality data support for chemical safety employees. Therefore, a chemical safety knowledge map is constructed, multi-source heterogeneous data is integrated, and the relationship among the data is deeply mined to play an important role.
Disclosure of Invention
The invention aims to overcome the problem that a chemical safety knowledge graph is lacked in the prior art, and provides a method for constructing a chemical safety knowledge graph in a first aspect.
The invention provides a construction system of a chemical safety knowledge graph.
A third aspect of the invention provides a storage medium.
In order to achieve the above object, the present invention provides a method for constructing a chemical safety knowledge graph, comprising the following steps:
s1: collecting data in a chemical safety field database, and constructing a chemical safety knowledge dictionary;
s2: acquiring multi-source heterogeneous data in the field of chemical safety to obtain a data source required by a chemical safety knowledge graph;
s3: extracting entities and attributes and attribute values of the entities from the data source based on the chemical safety knowledge dictionary, wherein the entities comprise a plurality of sub-entities;
s4: analyzing and establishing the incidence relation among different sporocarp;
s5: and establishing a chemical safety knowledge graph according to the incidence relation.
And under the preferable condition, carrying out data cleaning, data deduplication and data annotation on the obtained multi-source heterogeneous data, and carrying out classification, hierarchy and multi-dimensional processing on the multi-source heterogeneous data to obtain the construction data.
Preferably, in step S2, the obtaining route of the multi-source heterogeneous data includes: acquiring data of an existing chemical safety database; acquiring literature data in the field of chemical safety; and acquiring related webpage content in the chemical safety field.
Preferably, in step S3, the method for extracting the entity is: and performing character string recognition on the data in the data source based on the chemical safety knowledge dictionary, and taking the recognized vocabulary as an entity.
Preferably, in step S3, the entities include chemical entities, business entities, major hazard entities, and accident entities.
Preferably, in step S3, the attributes of the chemical sub-entity include: chemical Chinese name, CAS number, boiling point, etc.; and/or
The attributes of the corporate sub-entity include: business name, legal person, address, etc.; and/or
Attributes of the significant-risk-source sub-entities include: major hazard source name, major hazard source grade, R value, etc.; and/or
The attributes of the incident sub-entity include: accident name, accident occurrence time, accident location, etc.
Preferably, in step S4, the association relationship includes: the correlation relationship between the chemical entity and the enterprise entity, the correlation relationship between the chemical entity and the major hazard source entity, the correlation relationship between the chemical entity and the accident entity, the correlation relationship between the enterprise entity and the major hazard source entity, and the correlation relationship between the enterprise entity and the accident entity.
Under the preferable conditions, in step S5, the method for establishing the chemical safety knowledge map is as follows: and (4) constructing a knowledge graph by using the relation data between the entities obtained in the step (S4), taking each entity as a node in the knowledge graph, taking the incidence relation between the sub-entities as the edge of the knowledge graph, and storing knowledge represented by chemical sub-entity nodes, enterprise sub-entity nodes, major hazard source sub-entity nodes, accident sub-entity nodes and the relation edges thereof through extensible markup language (XML) to obtain the chemical safety knowledge graph.
The second aspect of the invention provides a system for constructing a knowledge graph in the field of chemical safety, which comprises a processor, wherein the processor is used for executing the construction method.
A third aspect of the invention provides a storage medium having stored thereon instructions for reading by a machine to cause the machine to perform the method of construction.
Through the technical scheme, the invention has the following technical effects:
according to the invention, through constructing the chemical safety knowledge map, multi-source heterogeneous data is combed, barriers among different databases are opened, and a convenient and fast inquiry platform of chemical safety data is provided for the public and enterprises, so that practitioners can find and explore deeper association relations among chemicals, enterprises, major hazard sources and accidents, and further, the chemical safety practitioners are assisted to carry out more targeted supervision on the chemical safety, and the method has a wide application prospect.
Drawings
FIG. 1 is a flow diagram of a method of constructing a chemical safety knowledge-graph according to one embodiment of the present invention;
fig. 2 is a block diagram of a construction system of a chemical safety knowledge graph according to an embodiment of the present invention.
Detailed Description
The endpoints of the ranges and any values disclosed herein are not limited to the precise range or value, and such ranges or values should be understood to encompass values close to those ranges or values. For ranges of values, between the endpoints of each of the ranges and the individual points, and between the individual points may be combined with each other to give one or more new ranges of values, and these ranges of values should be considered as specifically disclosed herein.
Fig. 1 is a flowchart of a method for constructing a chemical safety knowledge-graph according to an embodiment of the present invention, and as shown in fig. 1, the present invention provides a method for constructing a chemical safety knowledge-graph, including the steps of:
s1: collecting data in a chemical safety field database, and constructing a chemical safety knowledge dictionary;
s2: acquiring multi-source heterogeneous data in the field of chemical safety to obtain a data source required by a chemical safety knowledge graph;
s3: extracting entities and attributes and attribute values of the entities from the data source based on the chemical safety knowledge dictionary, wherein the entities comprise a plurality of sub-entities;
s4: analyzing and establishing the incidence relation among different sporocarp;
s5: and establishing a chemical safety knowledge graph according to the incidence relation.
In step S2, the obtaining approach of the multi-source heterogeneous data includes: acquiring data of an existing chemical safety database; acquiring literature data in the field of chemical safety; the method comprises the steps of obtaining webpage content related to the chemical safety field, for example, obtaining data sources from news websites, government announcements, laws and regulations, industrial and commercial websites, chemical safety data websites, encyclopedia websites and the like.
In the present invention, the data source may be unstructured text data, such as legal documents, accident news, etc., or may be in the form of a list, such as a chemical catalog, an enterprise catalog, etc.
Under the preferable condition, the method carries out data cleaning, data deduplication and data annotation on the obtained multi-source heterogeneous data, and carries out processing treatment on the multi-source heterogeneous data in a classified, hierarchical and multi-dimensional mode to obtain the construction data.
In step S3 of the present invention, the method for extracting entities includes: and performing character string recognition on the data in the data source based on the chemical safety knowledge dictionary, and taking the recognized vocabulary as an entity. In the present invention, the entities include chemical entity, enterprise entity, major hazard entity and accident entity, and the attributes of the chemical entity include: chemical Chinese name, CAS number, boiling point, etc.; the attributes of the corporate sub-entity include: business name, legal person, address, etc.; attributes of the significant-risk-source sub-entities include: major hazard source name, major hazard source grade, R value, etc.; the attributes of the incident sub-entity include: accident name, accident occurrence time, accident location, etc.
In order to obtain richer and more accurate chemical safety knowledge graph construction data, it is further preferable that in step S3, the entity and the attribute value of the entity are cyclically extracted from the data source, where the cyclic extraction includes cyclic identification of existing data and cyclic identification of real-time data, and the real-time data refers to data updated in real time, such as networks, periodicals, and the like.
The method for circularly identifying the existing data comprises the following steps: and striping the data to be identified, and extracting the entity and the attribute of the entity one by one for each striped data. For example, in a data source composed of 100 pieces of data, the attribute triple (ethanol, alias, alcohol) should be extracted, and the entity and the attribute value thereof should be extracted from the first piece of data to the 100 th piece of data one by one, that is, although the entity "ethanol" is extracted from the 1 st piece of data, the attribute triple (ethanol, alias, alcohol) is also extracted from the 100 th piece of data, in the first 2 to 99 pieces of data, all the attributes related to the keyword "alcohol" can be extracted only through cycle identification, and the end point of the cycle identification is that each piece of data is completely extracted.
The method for circularly identifying the real-time data comprises the following steps: and (4) carrying out targeted periodic crawling on real-time data (network data or periodical data). For example: for the entity of 'enterprise A', the system periodically uses the enterprise name of the enterprise A as a key word to perform crawling work, when a certain accident happens to the enterprise, the system crawls accident data and extracts an accident entity B. By crawling and supplementing regularly, a chemical safety knowledge mapping system can only become a system with growth.
Since different classes of sub-entities have different attributes, one attribute of a sub-entity having uniqueness can be merged, de-duplicated and aligned. For example, for chemical fruiting bodies, the attribute "CAS number" has the characteristic of uniform format and uniqueness. Therefore, after extracting the "attribute-attribute value" from the chemical fruit body, the "CAS number" is used as the chemical fruit body identification attribute, and filtering, deduplication, and alignment are performed. On the basis, the Chinese name is used as a key attribute, and the entities without the identification attribute are filtered and aligned.
For example, a chemical fruit body one { CAS No.: 64-17-5; chinese name: ethanol }, chemical fruit body { CAS No.: 64-17-5; chinese name: anhydrous alcohol }, chemical fruiting body { chinese name: ethanol; boiling point: 78.3 deg.C. Firstly, filtering the fruit body, putting a chemical fruit body I and a chemical fruit body II with an identification attribute (CAS number) into a chemical fruit body library as two entities, and putting the chemical fruit body III into a library to be identified as the chemical fruit body III without the identification attribute of 'CAS number' (the identification attribute value can also be considered as empty); and secondly, comparing the other chemical entity bodies in the entity library with the chemical entity body I as a reference. Since the second chemical fruit body has the same identification attribute value (64-17-5) as the first chemical fruit body, the first chemical fruit body and the second chemical fruit body are combined to obtain a fourth chemical fruit body { CAS No.: 64-17-5; chinese name: ethanol, absolute alcohol }. And putting the chemical fruit body four into the entity library, and deleting the chemical fruit body one and the chemical fruit body two at the same time. And finally, taking the key attribute 'Chinese name' as a reference, comparing the chemical fruit body three with the entities in the chemical fruit body library, and combining the chemical fruit body three with the chemical fruit body four to obtain a chemical fruit body { CAS number: 64-17-5; chinese name: ethanol, absolute alcohol; boiling point: 78.3 deg.C.
In the field of chemical safety, there is no correlation between fruit bodies of the same class. Therefore, in step S4 of the present invention, the association refers to a relationship between entities of different categories, and further, the association includes: the correlation relationship between the chemical entity and the enterprise entity, the correlation relationship between the chemical entity and the major hazard source entity, the correlation relationship between the chemical entity and the accident entity, the correlation relationship between the enterprise entity and the major hazard source entity, and the correlation relationship between the enterprise entity and the accident entity. For example, the association relationship between the chemical entity and the enterprise entity is divided into three types: firstly, importing chemicals (simply called import) into enterprises; secondly, enterprises use the chemical as a production raw material (which is simply called as a raw material), and thirdly, enterprises produce the chemical (which is simply called as a product).
In step S5 of the present invention, the method for establishing the chemical safety knowledge graph includes: constructing a knowledge graph by using the relation data between the entities obtained in the step S4; and storing knowledge represented by chemical sub-entity nodes, enterprise sub-entity nodes, major hazard source sub-entity nodes, accident sub-entity nodes and relationship edges thereof through extensible markup language XML to obtain the chemical safety knowledge graph.
In a preferred embodiment of the present invention, in constructing the visual chemical safety knowledge map, the connecting line between two points may be replaced by an arrow in order to distinguish different correlations between the same entities. For example, in representing the relationship between the chemical entity and the business entity, the import is represented by pointing the chemical entity to the business entity; the raw material is expressed by directing the chemical sporocarp to the enterprise sporocarp; the product is expressed by directing the enterprise sporocarp to the chemical sporocarp; the method for representing the incidence relation between the chemical sporocarp and the major hazard source sporocarp is that the chemical sporocarp points to the major hazard source sporocarp; the expression method of the correlation between the chemical fruiting body and the accident fruiting body is that the chemical fruiting body points to the accident fruiting body; the expression method of the correlation between the major hazard source sporocarp and the enterprise sporocarp is that the major hazard source sporocarp points to the enterprise sporocarp; the relationship between the business entity and the accident entity is expressed by pointing the business entity to the accident entity.
Fig. 2 is a block diagram of a system for constructing a chemical safety knowledge-graph according to another embodiment of the present invention, and as shown in fig. 2, the present invention further provides a system for constructing a chemical safety domain knowledge-graph, which includes a processor for executing the construction method.
More specifically, the construction system includes:
a chemical safety knowledge dictionary construction unit 10, configured to collect data in a chemical safety domain database, and construct a chemical safety knowledge dictionary;
the data set construction unit 20 is used for acquiring multi-source heterogeneous data in the chemical safety field to obtain data sources required for constructing a chemical safety knowledge graph;
an entity recognition unit 30 for performing entity recognition on the data source according to the chemical safety knowledge dictionary;
a reaction relation establishing unit 40, configured to establish a reaction relation for each entity formed by the entity identifying unit 30;
the chemical safety knowledge graph construction unit 50 is used for constructing a chemical reaction knowledge graph according to the reaction relation among the entities;
specifically, the entity identifying unit 30 includes:
a chemical entity identification unit 31, configured to perform entity identification on the chemicals in the data set to obtain a chemical entity;
an enterprise entity identification unit 32, configured to perform entity identification on the enterprises in the data set to obtain a chemical entity;
and a major hazard source sub-entity identifying unit 33, configured to perform entity identification on the major hazard source reaction in the data set, so as to obtain a major hazard source sub-entity.
And an accident sub-entity identifying unit 34, configured to perform entity identification on the accident in the data set to obtain an accident sub-entity.
The invention also provides a storage medium storing instructions for reading by a machine to cause the machine to perform the construction method.
The preferred embodiments of the present invention have been described above in detail, but the present invention is not limited thereto. Within the scope of the technical idea of the invention, many simple modifications can be made to the technical solution of the invention, including combinations of various technical features in any other suitable way, and these simple modifications and combinations should also be regarded as the disclosure of the invention, and all fall within the scope of the invention.
Claims (11)
1. A construction method of a chemical safety knowledge graph is characterized by comprising the following steps:
s1: collecting data in a chemical safety field database, and constructing a chemical safety knowledge dictionary;
s2: acquiring multi-source heterogeneous data in the field of chemical safety to obtain a data source required by a chemical safety knowledge graph;
s3: extracting entities and attributes and attribute values of the entities from the data source based on the chemical safety knowledge dictionary, wherein the entities comprise a plurality of sub-entities;
s4: analyzing and establishing the incidence relation among different sporocarp;
s5: and establishing a chemical safety knowledge graph according to the incidence relation.
2. The building method according to claim 1, wherein the step S2 includes: and carrying out data cleaning, data deduplication and data annotation on the obtained multi-source heterogeneous data, and carrying out processing of classification, hierarchy and multiple dimensions on the multi-source heterogeneous data to obtain the constructed data.
3. The construction method according to claim 1 or 2, wherein in step S2, the obtaining route of the multi-source heterogeneous data includes: acquiring data of an existing chemical safety database; acquiring literature data in the field of chemical safety; and acquiring related webpage content in the chemical safety field.
4. The building method according to claim 1, wherein in step S3, the method for extracting the entity is: and performing character string recognition on the data in the data source based on the chemical safety knowledge dictionary, and taking the recognized vocabulary as an entity.
5. The method of claim 4, wherein in step S3, the entities include chemical entities, business entities, major hazard entities, and accident entities.
6. The constructing method according to claim 5, wherein in step S3, the attributes of the chemical sub-entity include: chemical Chinese name, CAS number, boiling point, etc.; and/or
The attributes of the corporate sub-entity include: business name, legal person, address, etc.; and/or
Attributes of the significant-risk-source sub-entities include: major hazard source name, major hazard source grade, R value, etc.; and/or
The attributes of the incident sub-entity include: accident name, accident occurrence time, accident location, etc.
7. The building method according to claim 1, wherein the step S3 further includes: and filtering, de-duplicating and aligning the attribute and the attribute value of the entity.
8. The building method according to claim 7, wherein in step S4, the association relationship includes: the correlation relationship between the chemical entity and the enterprise entity, the correlation relationship between the chemical entity and the major hazard source entity, the correlation relationship between the chemical entity and the accident entity, the correlation relationship between the enterprise entity and the major hazard source entity, and the correlation relationship between the enterprise entity and the accident entity.
9. The constructing method according to claim 1, wherein in step S5, the chemical safety knowledge map is established by: and (4) constructing a knowledge graph by using the relation data between the entities obtained in the step (S4), taking each entity as a node in the knowledge graph, taking the incidence relation between the sub-entities as the edge of the knowledge graph, and storing knowledge represented by chemical sub-entity nodes, enterprise sub-entity nodes, major hazard source sub-entity nodes, accident sub-entity nodes and the relation edges thereof through extensible markup language (XML) to obtain the chemical safety knowledge graph.
10. A system for building a knowledge-graph of the safety domain of a chemical, the system comprising a processor configured to perform the method of building of any one of claims 1 to 9.
11. A storage medium storing instructions for reading by a machine to cause the machine to perform a construction method according to any one of claims 1 to 9.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019023982A1 (en) * | 2017-08-02 | 2019-02-07 | Accenture Global Solutions Limited | Multi-dimensional industrial knowledge graph |
| CN110633364A (en) * | 2019-09-23 | 2019-12-31 | 中国农业大学 | Graph database-based food safety knowledge graph construction method and display mode |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019023982A1 (en) * | 2017-08-02 | 2019-02-07 | Accenture Global Solutions Limited | Multi-dimensional industrial knowledge graph |
| CN110633364A (en) * | 2019-09-23 | 2019-12-31 | 中国农业大学 | Graph database-based food safety knowledge graph construction method and display mode |
Non-Patent Citations (1)
| Title |
|---|
| 刘宝等: ""基于自然语言处理(NLP)技术建立化学品危险评估知识图谱的研究"", 《计算机与应用化学》, vol. 35, no. 7, 28 July 2018 (2018-07-28), pages 605 - 610 * |
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