Law Text Clustering Based on Referential Relations

This paper proposes a new method to cluster law texts based on referential relation of laws. We extract law entities (an entity represents a law) and their referential relation from law texts. Then SimRank algorithm is applied to calculate law entity’s similarity through referential relation and law clustering is carried out based on the SimRank similarity. This is the first time to apply SimRank algorithm in the domain of Law and use it to carry out text clustering. Prototype and experiments show that our solution is feasible. We also publish the extracted data as Linked Law Data with RDF data model, which forms the first open semantic web database in Law domain. Linked Law Data enables user to access law data with rich data links and query web data by application interface of Semantic Web.