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Entity Markup for Knowledge Base Population

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Big Data Analytics (BDA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10721))

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Abstract

Entities (e.g. people, places, products) exist in various heterogeneous sources, such as Wikipedia, web page, and social media. Entity markup, like entity extraction, coreference resolution, and entity disambiguation, is the essential means for adding semantic value to unstructured web contents and this way enabling the linkage between unstructured and structured data and knowledge collections. A major challenge in this endeavor lies in the ambiguity of the digital contents, with context-dependent semantic and dynamic. In this paper, I introduce the main challenges of coreference resolution and named entity disambiguation. Especially, I propose practical strategies to improve entity markup. Furthermore, experimental studies are conducted to fulfill named entity disambiguation in combination with the optimized entity extraction and coreference resolution. The main goal of this paper is to analyze the significant challenges of entity markup and present insights on the proposed entity markup framework for knowledge base population. The preliminary experimental results prove the significance of improving entity markup.

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Notes

  1. 1.

    http://en.wikipedia.org/wiki/Albertina_Walker.

  2. 2.

    http://acronyms.silmaril.ie/.

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Acknowledgment

Many thanks Johannes Hoffart and Gerhard Weikum for some discussions relevant with this paper. Thanks Stephan Seufert for his original version of random walk codes.

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Authors and Affiliations

  1. Department of Computing Science, Umeå University, Umeå, Sweden

    Lili Jiang

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  1. Lili Jiang
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Correspondence to Lili Jiang .

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Editors and Affiliations

  1. International Institute of Information Technology, Hyderabad, India

    P. Krishna Reddy

  2. Rajiv Gandhi Education City, Sonepat, India

    Ashish Sureka

  3. University of Texas at Arlington, Arlington, Texas, USA

    Sharma Chakravarthy

  4. University of Aizu, Aizu-Wakamatsu, Japan

    Subhash Bhalla

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Jiang, L. (2017). Entity Markup for Knowledge Base Population. In: Reddy, P., Sureka, A., Chakravarthy, S., Bhalla, S. (eds) Big Data Analytics. BDA 2017. Lecture Notes in Computer Science(), vol 10721. Springer, Cham. https://doi.org/10.1007/978-3-319-72413-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-72413-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72412-6

  • Online ISBN: 978-3-319-72413-3

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