Legal professionals often need to discover expert witnesses with specialized expertise and experience to give an expert opinion in a legal dispute resolution case. The common practice is that legal professionals use their personal networks and internet searches to discover and verify suitable expert witnesses. In addition they may use online systems such as directory services and social network sites (e.g. LinkedIn). However these systems describe experts using broad categories and shallow vocabularies making it difficult to identify expert witnesses for a specific domain such toy safety dispute cases. Keyword searches in these systems are usually based on conventional data models or unstructured text files. This means that although the search results have high recall, but low precision. This resulted in many irrelevant expert witnesses being identified. This thesis reports on the potential of using semantic web technology and social networking to better support expert witness discovery and improve the precision of the keyword search. The case study used in this research was from the toy safety disputes domain. The research was primarily advised by a barrister with good knowledge of this area of law. This thesis reports on a novel "semantically-enabled keyword search for expert witness discovery" that has been developed. A semantically enriched expert witness information knowledge base has been built to enhance the expert witness profile for use within the social network. The semantic data model enabled the information about expert witnesses to be stored and retrieved with higher precision and recall. Unfortunately formal semantic query languages (such as SPARQL) used to search the knowledge base require the user to understand the ontology and master the syntax. For this reason, a prototype "Semantic and Keyword interface engine" (SKengine) was developed. The SKengine automatically generates and selects a set of SPARQL queries derived from the user-input keywords. It then extracts the possible meanings of the keywords from the domain specific knowledge base, then generates and selects the SP ARQL query that best fitted the keywords entered by the user. Finally the generated SPARQL query is executed to retrieve the selected expert witness information from the knowledge base. The result of the semantic query is then returned to the user. To generate the SPARQL query the SKengine used a novel "fix-root query graph construction" algorithm. This was demonstrated to be sufficient for the discovery of expert witnesses. The algorithm avoids generating query trees with irrelevant roots that are not involved with expert witness discovery. The experimental results showed that the prototype has significantly improved the precision and relevance of the query results. In addition, evaluation was conducted to understand time performance of SKengine.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:588742 |
| Date | January 2012 |
| Creators | Sitthisarn, Siraya |
| Publisher | University of Leeds |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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