Using Mapping Languages for Building Legal Knowledge Graphs from XML Files

TL;DR

This paper explores using mapping languages to construct RDF knowledge graphs from XML legal documents, aiming to enhance search accuracy and semantic understanding in legal information systems.

Contribution

It introduces a semantic model for legal documents and evaluates state-of-the-art mapping languages for building legal knowledge graphs from XML data.

Findings

Semantic model effectively represents legal documents

Mapping languages vary in suitability for legal data

Use case requirements guide language selection

Abstract

This paper presents our experience on building RDF knowledge graphs for an industrial use case in the legal domain. The information contained in legal information systems are often accessed through simple keyword interfaces and presented as a simple list of hits. In order to improve search accuracy one may avail of knowledge graphs, where the semantics of the data can be made explicit. Significant research effort has been invested in the area of building knowledge graphs from semi-structured text documents, such as XML, with the prevailing approach being the use of mapping languages. In this paper, we present a semantic model for representing legal documents together with an industrial use case. We also present a set of use case requirements based on the proposed semantic model, which are used to compare and discuss the use of state-of-the-art mapping languages for building knowledge graphs for legal data.