CN113032493A - Food safety emergency disposal flow knowledge graph construction method - Google Patents
Food safety emergency disposal flow knowledge graph construction method Download PDFInfo
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- CN113032493A CN113032493A CN201911230990.7A CN201911230990A CN113032493A CN 113032493 A CN113032493 A CN 113032493A CN 201911230990 A CN201911230990 A CN 201911230990A CN 113032493 A CN113032493 A CN 113032493A
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- 238000010276 construction Methods 0.000 title claims description 5
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- 238000012800 visualization Methods 0.000 claims description 4
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- 238000012545 processing Methods 0.000 description 6
- 229920000877 Melamine resin Polymers 0.000 description 5
- JDSHMPZPIAZGSV-UHFFFAOYSA-N melamine Chemical compound NC1=NC(N)=NC(N)=N1 JDSHMPZPIAZGSV-UHFFFAOYSA-N 0.000 description 5
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- 235000013336 milk Nutrition 0.000 description 2
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- 239000002574 poison Substances 0.000 description 2
- 231100000614 poison Toxicity 0.000 description 2
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- 238000009395 breeding Methods 0.000 description 1
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Abstract
The invention relates to a method for constructing a knowledge graph of a food safety emergency disposal process, which is mainly technically characterized by comprising the following steps of: based on the field of food safety, an emergency disposal process is constructed by using a knowledge graph technology; attribute characteristics and a relation structure of the food safety case are described in a triple form, a food safety emergency disposal flow model is constructed, and knowledge representation of the food safety case is realized; the model carries out hierarchical description on emergency tasks of emergency processes, so that the structure is clearer, and the graph database is adopted to graphically express emergency disposal processes, so that the emergency disposal processes are more visual and easy to understand. The method is reasonable in design, and for sudden and high-complexity food safety events, the structure of the treatment process is clearer due to the model constructed based on the knowledge graph, the intuitiveness of the treatment process is improved due to graphical description, and the treatment process is easy to understand.
Description
Technical Field
The invention belongs to the field of food safety, and particularly relates to a method for constructing a knowledge graph of a food safety emergency disposal process.
Background
The food safety problem is a common public health safety problem and is one of the key factors threatening the physical health of human beings. The sudden food safety event causes great loss to lives and properties of people of all countries, and also seriously influences the social and economic development and the orderly stability of the countries. At present, in the aspect of food safety emergency research, most of the research focuses on emergency measures, emergency plans and the like, the research on emergency disposal processes is less, and the modeling research is much less and less. The emergency processing flow modeling means that the factors of the emergency processing flow and the mutual relations thereof are generally expressed through a formal model, and meanwhile, the flow performance, the elements embodying the flow and the mutual relations are described, so that the emergency processing flow is analyzed, controlled and optimized.
At present, a plurality of process modeling methods exist, and a Petri network becomes one of mainstream methods for dynamic discrete system modeling by virtue of excellent control selection capability and strict mathematical definition. However, there are some disadvantages, such as complex models, unreflected conditions for triggering the execution of the emergency task, and unsupportable to construct a large-scale model, and in order to effectively solve the above problems, more advanced Petri nets, such as colored Petri nets, hierarchical Petri nets, temporal Petri nets, generalized stochastic Petri nets, etc., are developed to create a disposal flow model. UML (active sheet) is a unified modeling language that describes workflows in a system. Baucicm et al model the process using an object-oriented modeling method, and complete and demonstrate the single-step operation of the transaction process through a UML activity diagram. Hoogendoom et al adopts a time series track Language (TTL) based method to perform modeling, and simultaneously adopts a proper method to expand the TTL based method, so that an emergency treatment organization structure system and the dynamic characteristics of the emergency treatment organization structure system can be expressed in detail by using the TTL Language. An EPC (event-driven process chain) diagram represents a flow structure in a transaction process using various kinds of symbols, but the definition of semantics and syntax is not clear at all. The method is characterized in that a plurality of expression modes of the plan are researched by people of the same peak, A.M. Mulvehill, Canos and the like, multimedia information (mainly comprising pictures, sounds, texts and images) is discussed, documents, videos, voices, animations, 3D simulations and the like are fused, the emergency plan of the underground traffic of a large city is described, the emergency response process is described into a graph similar to a workflow, and the emergency plan is expressed in a multimedia mode. And in order to solve the problem that information in different fields cannot be shared, the Yuan-Peak and the like utilize the ABC event ontology model to express the plan in a formalized mode, and the semantic sharing of the information in the fields is realized.
The research provides a beneficial idea for the process modeling of emergency disposal, but has certain limitations, such as low modeling efficiency, neglect of the relation between the flows and the like. Although the methods can describe the dynamic development process of the emergency process, the tasks and the logical relationship between the tasks are mainly depicted in a formalized mode, and information transmission between department organizations at different levels is omitted in the process of executing the tasks. The knowledge graph is a huge relationship network essentially, and stores numerous and disorderly data in a structured database, so that the knowledge is more complete, and meanwhile, entities and implicit relationships in information can be found, and higher-quality search is provided.
Disclosure of Invention
The invention aims to provide a method for constructing a knowledge graph of a food safety emergency disposal process, which hierarchically describes emergency tasks in an emergency process, so that the structure is clearer, and the emergency disposal process is graphically expressed and is easy to understand.
In order to achieve the purpose, the technical scheme of the invention is as follows: a method for constructing a knowledge graph of a food safety emergency disposal process comprises the following steps:
step A: typical food safety case data are collected from various large portal websites;
and B: extracting knowledge, extracting relation and extracting attribute by using a knowledge graph technology, and expressing the extracted knowledge by using an RDF (resource Description framework) resource Description framework;
and C: importing all triple knowledge into a Neo4J graphic database for storage;
step D: after the data import work is finished, the knowledge graph can be operated by using Cypher language, and visualization can also be carried out.
Drawings
FIG. 1 is a diagram of the knowledge-graph technical architecture of the method of the present invention; the construction of the knowledge graph mainly comprises the following steps: data extraction, information acquisition, knowledge fusion and knowledge processing.
FIG. 2 is a simplified graphical model example of the method of the present invention; there are three entity nodes in the graph, including: two case name nodes and one event type node. The relationships between nodes are represented by arrows, with the text on the nodes and relationship edges representing attributes.
FIG. 3 is an example of a single entity in the graph of the present invention; a single node only contains attribute information of the entity itself and does not contain relationship information between the entity and other entities.
FIG. 4 is a single node relationship diagram of the present invention; the graph shows the semantic relationship between a single node (melamine event) and other partial node entities in the knowledge graph of the food safety emergency treatment process.
FIG. 5 is a partial node relationship diagram of the present invention; this figure shows a richer set of relationships between the entities. Due to the limited space of the visualization, the diagram only shows a portion of the entities and the relationships between the entities.
FIG. 6 is a diagram illustrating an example of the contents of a node according to the present invention; the case entity node contains the following attributes: time of occurrence of the event, food name, drug name, web site, case name, and ID, etc.
FIG. 7 is a relational attribute storage diagram of the present invention; the type of the relationship can be viewed as a table name in a relational database, and the relationship can be retrieved according to the type. When storing the relationship, the attributes of the relationship are also required to be stored in the graph database. Attributes of a relationship include action time, action implementer, and action implementer.
Detailed Description
The above features and advantages of the present invention are described in further detail below with reference to the accompanying drawings.
FIG. 1 is a technical architecture diagram of a knowledge graph. As shown in fig. 1, the method comprises the steps of:
data acquisition refers to the acquisition of valuable data from structured, semi-structured, and unstructured data. The information acquisition comprises the following steps: entity extraction, relationship extraction, and attribute extraction. Knowledge fusion includes coreference resolution and entity disambiguation. The information acquisition, knowledge fusion and knowledge processing are the core of knowledge graph construction.
Fig. 2 is a relatively simple model diagram. Specifically, as shown in fig. 2:
there are three entity nodes in the graph, including: two case name nodes and one event type node. The relationships between nodes are represented by arrows, with the text on the nodes and relationship edges representing attributes. For example, the case name node in the lower left corner has two attributes of "milk powder food and melamine poison", indicating that the node represents the event that the food name is milk powder and the poison is melamine. The node on the left has an arrow pointing to the middle "food additive" node, the name of which is "case type", indicating that the case type for the relationship between the two is "melamine event" is "food additive". The graph database can intuitively express the relationship between the entities in a formalization mode, so that the user can be helped to understand knowledge more deeply.
Figure 3 is an example of a single entity in the graph of the present invention. Specifically, as shown in fig. 3:
after the data import work is finished, the knowledge graph can be operated by using Cypher language, and visualization can also be carried out. The figure shows an example of an entity node in a knowledge graph of food safety emergency disposal flow, and a single node only contains attribute information of the entity and does not contain relationship information between the entity and other entities.
FIG. 4 is a single node relationship diagram of the present invention. Specifically, as shown in fig. 4:
this entity relationship graph is developed centered around the "melamine event" case name, and the dimension is one. The method comprises the relationship between case events and event occurrence places, the relationship between case events and pathogenic degree, the relationship between case events and occurrence links, the relationship between case events and event types and the relationship between case events and disposal processes, wherein orange nodes represent case name entities, reddish nodes represent pathogenic degree entities, green nodes represent occurrence link entities, sky blue nodes represent event category entities, and deep blue nodes represent emergency disposal process entities. In the visualized map, information of entity attributes and relationship attributes is not displayed.
FIG. 5 is a partial node relationship diagram of the present invention. Specifically, as shown in fig. 5:
the number of the displayed entities can be controlled through Cypher language, the size, the color and the like of the entity icon can be controlled, and the attributes of the entities can be seen when the entities are clicked.
FIG. 6 is a diagram illustrating an example of the contents of a node in accordance with the present invention. Specifically, as shown in fig. 6:
the labels of the nodes can be regarded as table names of the relational database, the nodes can be searched according to the labels, and the labels in the invention mainly comprise types such as case names, categories, links, pathogenic degrees, event levels and the like. The case type nodes comprise attributes such as food additives, non-edible substances, forbidden pesticides and veterinary drugs, microorganisms, adulteration, quality indexes and the like; the nodes of the link type comprise attributes of planting, breeding, processing, packaging, storing, transporting, selling, consuming and the like.
Fig. 7 is a relationship attribute storage diagram of the present invention. Specifically, as shown in fig. 7:
the type of the relationship can be viewed as a table name in a relational database, and the relationship can be retrieved according to the type. When storing the relationship, the attributes of the relationship are also required to be stored in the graph database. Attributes of a relationship include action time, action implementer, and action implementer.
Claims (2)
1. A food safety emergency disposal flow knowledge graph construction method is characterized by comprising the following steps:
step 1: typical food safety case data are collected from various large portal websites;
step 2: extracting knowledge, extracting relation and extracting attribute by using a knowledge graph technology, and expressing the extracted knowledge by using an RDF (resource Description framework) resource Description framework;
and step 3: importing all triple knowledge into a Neo4J graphic database for storage;
and 4, step 4: after the data import work is finished, the knowledge graph can be operated by using Cypher language, and visualization can also be carried out.
2. The method for constructing the knowledge graph of the food safety emergency disposal process according to claim 1, wherein: the method includes the steps that representative food safety cases are collected from related websites, a food safety emergency disposal flow model is constructed by means of a knowledge graph technology, emergency tasks in the emergency process are described hierarchically, the structure is clearer, and the emergency disposal flow is graphically expressed and easy to understand.
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| CN201911230990.7A CN113032493A (en) | 2019-12-05 | 2019-12-05 | Food safety emergency disposal flow knowledge graph construction method |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113722430A (en) * | 2021-08-23 | 2021-11-30 | 北京工业大学 | Multi-mode man-machine interaction method and system, equipment and medium for food safety |
| CN113886716A (en) * | 2021-11-30 | 2022-01-04 | 国家食品安全风险评估中心 | Emergency disposal recommendation method and system for food safety emergencies |
-
2019
- 2019-12-05 CN CN201911230990.7A patent/CN113032493A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113722430A (en) * | 2021-08-23 | 2021-11-30 | 北京工业大学 | Multi-mode man-machine interaction method and system, equipment and medium for food safety |
| CN113886716A (en) * | 2021-11-30 | 2022-01-04 | 国家食品安全风险评估中心 | Emergency disposal recommendation method and system for food safety emergencies |
| CN113886716B (en) * | 2021-11-30 | 2022-04-05 | 国家食品安全风险评估中心 | Emergency disposal recommendation method and system for food safety emergencies |
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Application publication date: 20210625 |