# Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning
## 2 Related Work
### Knowledge Graph  
Knowledge graphs can be divided into universal knowledge graphs and industry knowledge graphs. The universal knowledge graph is based on common knowledge and emphasizes the breadth of knowledge. The industry knowledge graph is based on industry-specific data and emphasizes the depth of knowledge. In the universal knowledge graph, Freebase, Wikidata, DBpedia, and YAGO are representative examples. DBpedia is a multilanguage comprehensive knowledge base that was created by researchers from the University of Leipzig and the University of Mannheim in Germany and is at the core of the LOD (linking open data) project [33]. DBpedia extracts structured information from a multilingual Wikipedia and publishes it as linked data on the Internet for online web applications, social networking sites, and other online knowledge bases [34]. YAGO is a comprehensive knowledge base that was built by researchers from the Max Planck institute (MPI) in Germany. YAGO integrates Wikipedia, WordNet, GeoNames and other data sources, and integrates the classification system in Wikipedia with that in WordNet to build a complex hierarchy of categories. Freebase knowledge base was originally created by Metaweb and later acquired by Google [35]. Freebase knowledge base has become an important part of the Google knowledge graph. The data in Freebase is mainly constructed by humans, while the other data are mainly from Wikipedia, IMDB, Flickr and other websites or corpora. Wikidata are a collaborative knowledge base that was designed to support Wikipedia, Wikimedia Commons, and other Wikimedia projects. It is the central repository for structured data in Wikipedia, Wikivoyage, and Wikisource and is free to use [36]. The data in Wikidata are primarily stored as documents and currently contain over 17 million documents. Most universal knowledge graphs are constructed to obtain knowledge from semistructured or structured web pages. In terms of processing semistructured data, the main task is to learn the extraction rules of semistructured data through wrappers. Because semistructured data have a large number of repetitive structures, a small amount of annotation data can allow the machine to learn certain rules and then use the rules to extract the same type of data in the whole site. The construction of an industry knowledge graph is different from the construction of a universal knowledge graph. At present, there is little research on the industry knowledge map and is limited to a few fields. Due to the complex data structure, most of it is unstructured data, which makes the construction of an industry knowledge graph more challenging. An industry knowledge graph can also be called a vertical knowledge graph. The description target of this kind of knowledge graph is the specific industry domain, which usually relies on the data of a specific industry to build, so its description scope is very limited. In the automotive industry, there is no corresponding knowledge graph. In this article, we will crawl the unstructured data related to the automotive field from the vehicle websites and BBS, and extract the structured knowledge from the unstructured data by employing the method of the bidirectional GRU (gate recurrent unit) combined with an attention mechanism. We construct the knowledge graph of the automotive industry based on the structured knowledge. The construction of the knowledge graph is divided into two main parts: entity extraction and relationship extraction. Entity extraction is also known as named entity recognition (NER) [37] and refers to automatic recognition of named entities from the data set. In this experiment, we automatically extract specific automobile names from unstructured texts, such as \"Chevrolet\" and \"Ford\". After entity extraction of the text corpus, we obtain a series of discrete namedentities. To obtain semantic information, we also need to extract the relationship between entities from the relevant corpus and form a network knowledge structure by connecting the entities through the relationship.