Information Retrieval from a Structured KnowledgeBase

Avani Chandurkar, Ajay Bansal

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    With the inception of World Wide Web, the amount of data present on the internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is also on exploring the use of a structured and annotated knowledge base as opposed to an unstructured knowledge base. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.

    Original languageEnglish (US)
    Title of host publicationProceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages407-412
    Number of pages6
    ISBN (Electronic)9781509048960
    DOIs
    StatePublished - Mar 29 2017
    Event11th IEEE International Conference on Semantic Computing, ICSC 2017 - San Diego, United States
    Duration: Jan 30 2017Feb 1 2017

    Other

    Other11th IEEE International Conference on Semantic Computing, ICSC 2017
    CountryUnited States
    CitySan Diego
    Period1/30/172/1/17

    Fingerprint

    Information retrieval
    Search engines
    Processing
    World Wide Web
    Internet
    Chemical analysis

    Keywords

    • Information Retrieval
    • Natural Language Processing
    • Question Answering System
    • Structured Semantic Data

    ASJC Scopus subject areas

    • Computer Science Applications
    • Information Systems
    • Computer Networks and Communications

    Cite this

    Chandurkar, A., & Bansal, A. (2017). Information Retrieval from a Structured KnowledgeBase. In Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017 (pp. 407-412). [7889571] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSC.2017.95

    Information Retrieval from a Structured KnowledgeBase. / Chandurkar, Avani; Bansal, Ajay.

    Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 407-412 7889571.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Chandurkar, A & Bansal, A 2017, Information Retrieval from a Structured KnowledgeBase. in Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017., 7889571, Institute of Electrical and Electronics Engineers Inc., pp. 407-412, 11th IEEE International Conference on Semantic Computing, ICSC 2017, San Diego, United States, 1/30/17. https://doi.org/10.1109/ICSC.2017.95
    Chandurkar A, Bansal A. Information Retrieval from a Structured KnowledgeBase. In Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 407-412. 7889571 https://doi.org/10.1109/ICSC.2017.95
    Chandurkar, Avani ; Bansal, Ajay. / Information Retrieval from a Structured KnowledgeBase. Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 407-412
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