Merging and Ranking Answers in the Semantic Web: The Wisdom of Crowds

  • Authors:

  • Vanessa Lopez;Andriy Nikolov;Miriam Fernandez;Marta Sabou;Victoria Uren;Enrico Motta

  • Affiliations:

  • Knowledge Media Institute, The Open University, Walton Hall, United Kingdom MK76AA;Knowledge Media Institute, The Open University, Walton Hall, United Kingdom MK76AA;Knowledge Media Institute, The Open University, Walton Hall, United Kingdom MK76AA;Knowledge Media Institute, The Open University, Walton Hall, United Kingdom MK76AA;Knowledge Media Institute, The Open University, Walton Hall, United Kingdom MK76AA;Knowledge Media Institute, The Open University, Walton Hall, United Kingdom MK76AA

  • Venue:
  • ASWC '09 Proceedings of the 4th Asian Conference on The Semantic Web
  • Year:
  • 2009

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Abstract

In this paper we propose algorithms for combining and ranking answers from distributed heterogeneous data sources in the context of a multi-ontology Question Answering task. Our proposal includes a merging algorithm that aggregates, combines and filters ontology-based search results and three different ranking algorithms that sort the final answers according to different criteria such as popularity, confidence and semantic interpretation of results. An experimental evaluation on a large scale corpus indicates improvements in the quality of the search results with respect to a scenario where the merging and ranking algorithms were not applied. These collective methods for merging and ranking allow to answer questions that are distributed across ontologies, while at the same time, they can filter irrelevant answers, fuse similar answers together, and elicit the most accurate answer(s) to a question.