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Adapting Semantic Spreading Activation to Entity Linking in Text

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Natural Language Processing and Information Systems (NLDB 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9612))

Abstract

The extraction and the disambiguation of knowledge guided by textual resources on the web is a crucial process to advance the Web of Linked Data. The goal of our work is to semantically enrich raw data by linking the mentions of named entities in the text to the corresponding known entities in knowledge bases. In our approach multiple aspects are considered: the prior knowledge of an entity in Wikipedia (i.e. the keyphraseness and commonness features that can be precomputed by crawling the Wikipedia dump), a set of features extracted from the input text and from the knowledge base, along with the correlation/relevancy among the resources in Linked Data. More precisely, this work explores the collective ranking approach formalized as a weighted graph model, in which the mentions in the input text and the candidate entities from knowledge bases are linked using the local compatibility and the global relatedness measures. Experiments on the datasets of the Open Knowledge Extraction (OKE) challenge with different configurations of our approach in each phase of the linking pipeline reveal its optimum mode. We investigate the notion of semantic relatedness between two entities represented as sets of neighbours in Linked Open Data that relies on an associative retrieval algorithm, with consideration of common neighbourhood. This measure improves the performance of prior link-based models and outperforms the explicit inter-link relevancy measure among entities (mostly Wikipedia-centric). Thus, our approach is resilient to non-existent or sparse links among related entities.

This work has been founded by the French ANR national grant (ANR-13-LAB2-0001).

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Notes

  1. 1.

    dbpedia.org; www.mpi-inf.mpg.de/yago/; www.freebase.com.

  2. 2.

    English Wikipedia dump downloaded on 2015/07/02.

  3. 3.

    https://lucene.apache.org/.

  4. 4.

    https://github.com/anuzzolese/oke-challenge.

  5. 5.

    http://dashboard.nlp2rdf.aksw.org/.

  6. 6.

    https://github.com/wikilinks/neleval/wiki.

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Author information

Authors and Affiliations

  1. INRIA Sophia Antipolis, Valbonne, France

    Farhad Nooralahzadeh & Fabien Gandon

  2. Viseo Research Center, Grenoble, France

    Cédric Lopez & Frédérique Segond

  3. University of Nice, Nice, France

    Elena Cabrio

Authors
  1. Farhad Nooralahzadeh
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  2. Cédric Lopez
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  3. Elena Cabrio
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  4. Fabien Gandon
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  5. Frédérique Segond
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Correspondence to Farhad Nooralahzadeh .

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Editors and Affiliations

  1. ConservatoireNational desArts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Salford, Salford, United Kingdom

    Farid Meziane

  3. University of Salford, Salford, United Kingdom

    Mohamad Saraee

  4. Oakland University, Rochester, Michigan, USA

    Vijayan Sugumaran

  5. University of Salford, Salford, United Kingdom

    Sunil Vadera

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Nooralahzadeh, F., Lopez, C., Cabrio, E., Gandon, F., Segond, F. (2016). Adapting Semantic Spreading Activation to Entity Linking in Text. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-41754-7_7

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