David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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Artificial Intelligence and Law 17 (2):101-124 (2009)
A variety of legal documents are increasingly being made available in electronic format. Automatic Information Search and Retrieval algorithms play a key role in enabling efficient access to such digitized documents. Although keyword-based search is the traditional method used for text retrieval, they perform poorly when literal term matching is done for query processing, due to synonymy and ambivalence of words. To overcome these drawbacks, an ontological framework to enhance the user’s query for retrieval of truly relevant legal judgments has been proposed in this paper. Ontologies ensure efficient retrieval by enabling inferences based on domain knowledge, which is gathered during the construction of the knowledge base. Empirical results demonstrate that ontology-based searches generate significantly better results than traditional search methods.
|Keywords||Information retrieval Knowledge base Legal ontology Query enhancement|
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Tho Thi Ngoc Le, Kiyoaki Shirai, Minh Le Nguyen & Akira Shimazu (2015). Extracting Indices From Japanese Legal Documents. Artificial Intelligence and Law 23 (4):315-344.
Anatoly P. Getman & Volodymyr V. Karasiuk (2014). A Crowdsourcing Approach to Building a Legal Ontology From Text. Artificial Intelligence and Law 22 (3):313-335.
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