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Web Service based Intelligent Search on Legal Documents

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Published:29 January 2019Publication History

ABSTRACT

Web services such as RESTful APIs provide high flexibility for knowledge base systems in different domains. To apply it to the legal aspect, we can obtain much data of the case law efficiently enabling us to relate one case to another easily and even to compare the details of a plenty of the case laws simultaneously. Having said that, to ensure the performance of the web services and the accuracy of the data sourcing from them is onerous in the consideration of the backend system for the web services and relevant search engine.

In this paper, we introduce a web service for the legal knowledge, LegalKB, enhancing a concept search, and implement a method, WMD ranking method, to speed up the data search through the enhanced system queries.

On top of this, we propose a method to automate the optimisation of the system parameters to ensure that the system queries run in the most optimal manner - integration of multiple machine learning methods into our web service to facilitate third-party applications to interface with our web service enlarging the knowledge base to a great extent.

References

  1. Eugene Agichtein and Luis Gravano. 2000. Snowball: Extracting Relations from Large Plain-text Collections. In Proceedings of the Fifth ACM Conference on Digital Libraries (DL '00). ACM, New York, NY, USA, 85--94. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007. DBpedia: A nucleus for a web of open data. The semantic web (2007), 722--735. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Cuong Xuan Chu, Niket Tandon, and Gerhard Weikum. 2017. Distilling Task Knowledge from How-To Communities. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 805--814. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Andrew M Dai, Christopher Olah, and Quoc V Le. 2015. Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998 (2015).Google ScholarGoogle Scholar
  5. Oren Etzioni, Michele Banko, Stephen Soderland, and Daniel S Weld. 2008. Open information extraction from the web. Commun. ACM 51, 12 (2008), 68--74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Oren Etzioni, Michael Cafarella, Doug Downey, Stanley Kok, Ana-Maria Popescu, Tal Shaked, Stephen Soderland, Daniel S. Weld, and Alexander Yates. 2004. Web-scale Information Extraction in KnowItAll: (Preliminary Results). In Proceedings of the 13th International Conference on World Wide Web (WWW '04). ACM, New York, NY, USA, 100--110. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Anthony Fader, Stephen Soderland, and Oren Etzioni. 2011. Identifying Relations for Open Information Extraction. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '11). Association for Computational Linguistics, Stroudsburg, PA, USA, 1535--1545. http://dl.acm.org/citation.cfm?id=2145432.2145596 Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Bettina Fazzinga, Giorgio Gianforme, Georg Gottlob, and Thomas Lukasiewicz. 2010. Semantic web search based on ontological conjunctive queries. Foundations of information and knowledge systems (2010), 153--172.Google ScholarGoogle Scholar
  9. Graham Greenleaf, Andrew Mowbray, and Philip Chung. 2011. AustLII: Thinking locally, acting globally. (2011).Google ScholarGoogle Scholar
  10. Ajinkya Kale, Thrivikrama Taula, Sanjika Hewavitharana, and Amit Srivastava. 2017. Towards Semantic Query Segmentation. arXiv preprint arXiv:1707.07835 (2017).Google ScholarGoogle Scholar
  11. Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger. 2015. From word embeddings to document distances. In International Conference on Machine Learning. 957--966. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Edward Loper and Steven Bird. 2002. NLTK: The natural language toolkit. In Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics-Volume 1. Association for Computational Linguistics, 63--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).Google ScholarGoogle Scholar
  14. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Seonwoo Min, Byunghan Lee, and Sungroh Yoon. 2017. Deep learning in Bioinformatics. Briefings in Bioinformatics 18, 5 (2017), 851--869.Google ScholarGoogle Scholar
  16. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. In Empirical Methods in Natural Language Processing (EMNLP). 1532--1543. http://www.aclweb.org/anthology/D14-1162Google ScholarGoogle Scholar
  17. Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: A Core of Semantic Knowledge. In Proceedings of the 16th International Conference on World Wide Web (WWW '07). ACM, New York, NY, USA, 697--706. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Niket Tandon, Gerard de Melo, Abir De, and Gerhard Weikum. 2015. Knowlywood: Mining activity knowledge from Hollywood narratives. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 223--232. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Alexander Yates, Michael Cafarella, Michele Banko, Oren Etzioni, Matthew Broadhead, and Stephen Soderland. 2007. TextRunner: open information extraction on the web. In Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations. Association for Computational Linguistics, 25--26. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Other conferences
      ACSW '19: Proceedings of the Australasian Computer Science Week Multiconference
      January 2019
      486 pages
      ISBN:9781450366038
      DOI:10.1145/3290688

      Copyright © 2019 ACM

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      Publication History

      • Published: 29 January 2019

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      • research-article
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      • Refereed limited

      Acceptance Rates

      ACSW '19 Paper Acceptance Rate61of141submissions,43%Overall Acceptance Rate61of141submissions,43%

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