LEVERAGING TEXT MINING FOR THE DESIGN OF A LEGAL KNOWLEDGE MANAGEMENT SYSTEM

In today’s globalized world, companies are faced with numerous and continuously changing legal requirements. To ensure that these companies are compliant with legal regulations, law and consulting firms use open legal data published by governments worldwide. With this data pool growing rapidly, the complexity of legal research is strongly increasing. Despite this fact, only few research papers consider the application of information systems in the legal domain. Against this backdrop, we pro-pose a knowledge management (KM) system that aims at supporting legal research processes. To this end, we leverage the potentials of text mining techniques to extract valuable information from legal documents. This information is stored in a graph database, which enables us to capture the relation-ships between these documents and users of the system. These relationships and the information from the documents are then fed into a recommendation system which aims at facilitating knowledge transfer within companies. The prototypical implementation of the proposed KM system is based on 20,000 legal documents and is currently evaluated in cooperation with a Big 4 accounting company

A Simple Focused Crawler

A focused crawler may be described as a crawler which returns relevant web pages on a given topic in traversing the web. There are a number of issues related to existing focused crawlers, in particular the ability to ``tunnel'' through lowly ranked pages in the search path to highly ranked pages related to a topic which might re-occur further down the search path. We will introduce a simple focused crawler, which is described by two parameters, viz., degree of relatedness, and depth. Both provide an opportunity for the crawler to ``tunnel'' through lowly ranked pages. Results from initial experiments are promising and motivate for further research