Research On Event Evolution Graph Construction In International Trade Of Agricultural Products

Agriculture is a deeply globalized industry.Agricultural products trade is related to national food security,economic security and political security,and it is an important part that affects national economic development.The changes of trade factors such as production,supply-demand relationship,production support,trade policy and related industries such as meteorology,logistics and services will have an impact on agricultural trade.Under the background of the Internet,it is difficult to obtain and transmit the value of data efficiently,so it is urgent to realize the orderly organization and presentation of agricultural product trade information.In this way,it can support event tracking and situation research and judgment,and then help trade and business decision-making and industrial services.The event map can intuitively describe the events in the real world,and intuitively display the characteristics of event knowledge from the dimensions of time,logic,and relationship.The event graph provides an important method for text data mining and application in the field of agricultural international trade.In this study,the complex agricultural field of agricultural product trade is selected to carry out the construction of the event map.The goal is to transform and visualize unstructured data in the field of agricultural trade.This research integrates multi-source unstructured data resources to build a corpus in the field of agricultural trade,and uses deep learning algorithms to build an event ontology model.Then this thesis conducts research on semi-automatic event knowledge extraction technology in the field of agricultural international trade.Finally,this thesis realizes knowledge structured storage and data visualization.It lays the foundation for subsequent intelligent applications.The main contents of the study include:Firstly,this thesis collects the text data in the field,and uses the crawler to collect the text related to agricultural products trade.The text is pre-processed as the original corpus of the study.Then,the existing corpus is clustered and analyzed by the hierarchical density clustering algorithm,and the types of agricultural products trade events are divided according to the results.Keywords in each category serve as trigger words for that type.As a result,a total of 9 firstlevel event classes and 28 second-level sub-event classes were summarized to realize the filling of the conceptual layer of the event ontology model;The next step is to use Hanlp to extract event triples as well as time and location attributes to fill the event layer of the ontology model.This not only constructs the event ontology in the field of agricultural international trade,but also constructs a set of event extraction task annotation corpus for agricultural trade;To supplement the data of the event graph,the text uses the SDP model to extract event relations,and uses the ERNIE-based sequence annotation model to extract event elements.Finally,this thesis realizes the automatic extraction of unstructured data.In the last step,the event data is applied to the Neo4 j graph database for entity storage and management,and the event correlation scene of agricultural product trade is visualized.