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Knowledge Graph Based Question-Answering System for Effective Case Law Analysis

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Evolution in Computational Intelligence

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 267))

Abstract

Case Law Analysis is a critical step in legal research, as every lawyer has to acquire the skill set to read, understand and apply case laws to augment their arguments and pleadings. We found that the interpretations of case laws provided by existing prominent legal tools are not complete in terms of providing some of the essential details required for effective case law analysis. This paper overcomes the above-mentioned challenge by proposing a judicial knowledge graph based question-answer system. Representing the judicial data in the form of knowledge graph ultimately benefits the legal professionals in analyzing case laws. The proposed approach supports querying the judicial knowledge graph in natural language text and then returns the results directly in natural language. We model the task of answering input queries as a cypher query pattern selection problem. Experimental results are quite satisfying in terms of efficiency and effectiveness in comparison to existing knowledge graph based question answering systems. To the best of the authors’ knowledge, this work is the first to offer legal question answering based on knowledge graph.

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Notes

  1. 1.

    http://www.lexisnexis.com.

  2. 2.

    https://casetext.com/.

  3. 3.

    https://www.fastcase.com/.

  4. 4.

    https://rossintelligence.com/

  5. 5.

    https://legal.thomsonreuters.com/en/products/westlaw.

  6. 6.

    https://www.manupatrafast.com/.

  7. 7.

    http://www.olisindia.in/.

  8. 8.

    http://hphighcourt.nic.in

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Author information

Authors and Affiliations

  1. Text Analytics & NLP Lab, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

    Anu Thomas & S. Sangeetha

Authors
  1. Anu Thomas
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  2. S. Sangeetha
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Corresponding author

Correspondence to Anu Thomas .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  2. College of Computing, Michigan Technological University, Michigan, MI, USA

    Jinshan Tang

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India

    Suresh Chandra Satapathy

  4. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

  5. Department of Computer Science and Engineering, National Institute of Technology (NIT) Mizoram, Aizawl, India

    Ranjita Das

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Thomas, A., Sangeetha, S. (2022). Knowledge Graph Based Question-Answering System for Effective Case Law Analysis. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_27

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