DBIS

Background

German law documents, comprising extensive case law and statutes, are inherently complex and interconnected. Navigating this vast corpus is challenging due to the unstructured nature of legal texts and the intricate relationships among legal entities, concepts, and cases. A knowledge graph provides a solution by structuring these documents into a network of interconnected legal concepts, enabling users to explore relationships, perform advanced searches, and analyze legal data more effectively.

Objectives

• Develop a legal ontology: Design an ontology that captures the unique aspects of German legal documents, covering entities such as laws, cases, parties, and legal concepts.
• Automate annotation and data extraction: Implement techniques for extracting and annotating legal entities and relationships automatically from text, leveraging natural language processing (NLP) and machine learning.
• Construct a knowledge graph: Transform annotated data into a knowledge graph that encodes the relationships among legal entities and provides an intuitive structure for querying and analysis.
• Enable semantic querying: Ensure that the knowledge graph supports semantic queries, allowing users to retrieve information about legal cases, concepts, and their relationships quickly.
• Visualize legal data: Provide visualization tools to help users explore the knowledge graph and understand the relationships within the legal domain.

Tasks

• Ontology design and development:
  • Research German legal structure and terminology.
  • Design an ontology that captures core entities and relationships within German law documents.
  • Define classes, properties, and relationships to structure the knowledge graph.
• Data collection and preprocessing:
  • Gather and preprocess legal documents, including case files, statutes, and legal references.
  • Develop methods for handling multilingual data and managing data consistency.
• Automated annotation and extraction:
  • Use NLP and machine learning to identify and annotate legal entities in text.
  • Implement entity recognition for legal terms, parties, case names, etc.
  • Establish entity linking techniques to identify relationships between cases, laws, and legal concepts.
• Knowledge graph construction:
  • Transform annotated data into a knowledge graph format (RDF or similar).
  • Ensure that the knowledge graph supports efficient storage, querying, and reasoning.
• System implementation and visualization:
  • Implement the knowledge graph in the format using technologies like SPARQL.
  • Develop a user-friendly interface for legal practitioners to explore the knowledge graph.
  • Incorporate visualization tools to illustrate the structure and relationships in the legal domain.