Merging and ranking answers in the semantic web: the wisdom of crowds

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

Research output: Chapter in Book/Published conference outputConference publication


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.
Original languageEnglish
Title of host publicationThe semantic web
Subtitle of host publicationfourth Asian conference, ASWC 2009, Shanghai, China, December 6-9, 2009. Proceedings
EditorsAsunción Gómez-Pérez, Yong Yu, Ying Ding
Place of PublicationBerlin (DE)
Number of pages18
Volume5926 LNCS
ISBN (Electronic)978-3-642-10871-6
ISBN (Print)978-3-642-10870-9
Publication statusPublished - 2009
Event4th Annual Asian Semantic Web Conference - Shanghai, China
Duration: 6 Dec 20099 Dec 2009

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th Annual Asian Semantic Web Conference
Abbreviated titleASWC 2009


  • question answering
  • ranking
  • merging
  • fusion
  • semantic web


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