Research and Exploration on the Construction Method of Knowledge Graph of Water Field Based on Text

Abstract

With the development of the Internet age, collected data have become an important source of knowledge. The field of unstructured text contains many named entities, but includes very little detailed information about those entities. However, the Baidu encyclopedia website is a type of semistructured data that in many cases includes a detailed introduction of entities. By combining the advantages of these two kinds of data, we can enrich the knowledge base of a knowledge graph. This paper aims to extract semistructured data consisting of named entities starting from raw text data. On one hand, this paper extracts named entities with the help of the Harbin Institute of Technology model, parses semistructured content about the named entities using the Octopus tool, constructs a local ontology, and merges the ontology using Python's built-in difflib. SequenceMatcher tool and the Deckard similarity algorithm. On the other hand, we create an XPath-based wrapper to extract the attributes and attribute values of named entities from semistructured data. The experimental results show that this approach can extract information related to named entities from the Baidu encyclopedia automatically to supplement the knowledge base of a water domain knowledge graph. This article can also serve as a reference for constructing domain knowledge graphs in other fields.