Legal Artificial intelligence is a subfield of artificial intelligence (AI) which aims to apply AI techniques to deal with legal informatics problems and support original research on those problems. Current research on Legal Artificial intelligence mostly use legal texts as datasets. However, there are strict logical connections between law-specific words, phrases, concepts, actors in legal texts, which makes it complicated to apply existing research work on information retrieval and question answering to the legal domain. Fortunately, this problem can be relieved with the help of the techniques of knowledge graph. Knowledge graph, which belongs to the field of knowledge engineering, was proposed by Google in 2012 and has been applied in many applications, such as question answering and decision making.

Recently, many countries, such as China and USA, initiate projects on apply techniques of knowledge graph to legal domain. The major advantage of knowledge graph is its strong ability to describe data and connect data. Techniques of knowledge graph can help us to mine logical relationships of entities in legal texts. What's more, it has been shown that knowledge graph can be applied to many legal applications, such as similar case recommendation and question answering, and achieves a great success. Against this background, we organize a special track of KSEM which aims to attract submissions that are related to knowledge engineering and legal applications.