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Spreading Activation Way of Knowledge Integration

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Mining Intelligence and Knowledge Exploration (MIKE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9468))

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Abstract

Search and recommender systems benefit from effective integration of two different kinds of knowledge. The first is introspective knowledge, typically available in feature-theoretic representations of objects. The second is external knowledge, which could be obtained from how users rate (or annotate) items, or collaborate over a social network. This paper presents a spreading activation model that is aimed at a principled integration of these two sources of knowledge. In order to empirically evaluate our approach, we restrict the scope to text classification tasks, where we use the category knowledge of the labeled set of examples as an external knowledge source. Our experiments show a significantly improved classification effectiveness on hard datasets, where feature value representations, on their own, are inadequate in discriminating between classes.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, 600036, India

    Shubhranshu Shekhar, Sutanu Chakraborti & Deepak Khemani

Authors
  1. Shubhranshu Shekhar
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  2. Sutanu Chakraborti
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  3. Deepak Khemani
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Corresponding author

Correspondence to Shubhranshu Shekhar .

Editor information

Editors and Affiliations

  1. Norwegian Univ. of Science & Technology, Trondheim, Norway

    Rajendra Prasath

  2. Intl Inst of Info Tech Hyderabad, Hyderabad, India

    Anil Kumar Vuppala

  3. V.H.N.S.N.College (Autonomous), Virudhunagar, Tamil Nadu, India

    T. Kathirvalavakumar

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Shekhar, S., Chakraborti, S., Khemani, D. (2015). Spreading Activation Way of Knowledge Integration. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-26832-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26831-6

  • Online ISBN: 978-3-319-26832-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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