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Things and Strings: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence with Topic Modeling

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Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10024))

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Abstract

Place name disambiguation is the task of correctly identifying a place from a set of places sharing a common name. It contributes to tasks such as knowledge extraction, query answering, geographic information retrieval, and automatic tagging. Disambiguation quality relies on the ability to correctly identify and interpret contextual clues, complicating the task for short texts. Here we propose a novel approach to the disambiguation of place names from short texts that integrates two models: entity co-occurrence and topic modeling. The first model uses Linked Data to identify related entities to improve disambiguation quality. The second model uses topic modeling to differentiate places based on the terms used to describe them. We evaluate our approach using a corpus of short texts, determine the suitable weight between models, and demonstrate that a combined model outperforms benchmark systems such as DBpedia Spotlight and Open Calais in terms of F1-score and Mean Reciprocal Rank.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Washington.

  2. 2.

    http://dbpedia.org/resource/.

  3. 3.

    http://dbpedia.org/ontology/.

  4. 4.

    For example via dbr:FreedomWorks dbp:headquarters dbr:Washington,_D.C.

  5. 5.

    https://en.wikipedia.org/wiki/List_of_the_most_common_U.S._place_names.

  6. 6.

    https://datamarket.azure.com/dataset/bing/search.

  7. 7.

    https://github.com/dbpedia-spotlight/dbpedia-spotlight.

  8. 8.

    https://www.textrazor.com/.

  9. 9.

    http://www.opencalais.com/.

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Acknowledgement

The authors would like to acknowledge partial support by the National Science Foundation (NSF) under award 1440202 EarthCube Building Blocks: Collaborative Proposal: GeoLink Leveraging Semantics and Linked Data for Data Sharing and Discovery in the Geosciences.

Author information

Authors and Affiliations

  1. STKO Lab, University of California, Santa Barbara, USA

    Yiting Ju, Krzysztof Janowicz & Bo Yan

  2. Centre for eResearch, The University of Auckland, Berkeley, New Zealand

    Benjamin Adams

  3. Department of Geography, University of Tennessee, Knoxville, USA

    Yingjie Hu

  4. Department of Geographical Sciences, University of Maryland, College Park, USA

    Grant McKenzie

Authors
  1. Yiting Ju
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  2. Benjamin Adams
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  3. Krzysztof Janowicz
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  4. Yingjie Hu
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  5. Bo Yan
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  6. Grant McKenzie
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Corresponding author

Correspondence to Yiting Ju .

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Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Eva Blomqvist

  2. University of Bologna, Bologna, Italy

    Paolo Ciancarini

  3. University of Bologna, Bologna, Italy

    Francesco Poggi

  4. University of Bologna, Bologna, Italy

    Fabio Vitali

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Ju, Y., Adams, B., Janowicz, K., Hu, Y., Yan, B., McKenzie, G. (2016). Things and Strings: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence with Topic Modeling. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_23

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  • DOI: https://doi.org/10.1007/978-3-319-49004-5_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49003-8

  • Online ISBN: 978-3-319-49004-5

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