Research And Application Of Question Answering System Based On Vertical Domain Knowledge Graph

With the rapid development of the Internet and artificial intelligence,it has entered the era of "knowledge interconnection",and the attention of the knowledge graph is gradually heating up.The knowledge graph is subdivided into a general domain knowledge graph and a vertical domain knowledge graph according to the knowledge categories it contains.This thesis focuses on the vertical domain knowledge graph construction and answer ranking research,aiming to introduce industry knowledge into the question and answer system,improve the user experience of the question and answer system,and provide ideas for implementing and optimizing the question and answer system based on the vertical domain knowledge graph.The main research content of this thesis includes three aspects:(1)Take “construction security” as an example to study the construction method of vertical domain knowledge graph.In view of the fact that there is currently lack open Chinese security knowledge graph in the field of construction,and the widely used ones are the general knowledge graph of encyclopedias.This thesis proposes a framework for constructing knowledge graph in the field of construction security.First,the terminology of the construction field is obtained through reptiles.Second,the CNN and RNN models are used to distinguish the texts in the field of construction security,and the Bi-LSTM + CRF model is used to complete the conventional entity extraction.The artificial professional entities are extracted by means of manual intervention and synonym expansion.Third,the relationship extraction is completed using dependency syntax analysis.Finally,the spliced triple data is imported into the open source database Neo4 j to complete the construction of the knowledge graph in the field of construction security,it can be used as a module with knowledge in the question and answer system.(2)In order to solve the problem that the information is lost and the efficiency is not high in the entity linking and the relation linking when processed independently,a joint entity relation linking algorithm based on the connection density is proposed.The algorithm introduces the concept of connection density,and calculates the probability that each candidate as the optimal candidate under the condition of existing entity relationship candidates,which solving the entity relation linking problem.(3)In order to optimize the answer sorting effect and improve the user experience,this thesis proposes an answer sorting learning algorithm that introduces subjective evaluation.Firstly,by constructing the answer to the crowdsourcing personnel,the subjective judgment of the human user is introduced into the answer sorting learning algorithm,and then the word vector is used to represent the answer,and the sorting learning algorithm is transformed into a binary-classification problem.Finally,the answer is sorted according to the quality score.The algorithm compares the classic pairwise learning to rank algorithm RankNet,and the results show that putting the subjective evaluation into experiment could improve the ranking effect.