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/Report/Conference proceedingConference contribution

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.
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)
PublisherSpringer
Pages135-152
Number of pages18
Volume5926 LNCS
ISBN (Electronic)978-3-642-10871-6
ISBN (Print)978-3-642-10870-9
DOIs
Publication statusPublished - 2009
Event4th Annual Asian Semantic Web Conference - Shanghai, China
Duration: 6 Dec 20099 Dec 2009

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume5926
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th Annual Asian Semantic Web Conference
Abbreviated titleASWC 2009
CountryChina
CityShanghai
Period6/12/099/12/09

Fingerprint

Semantic Web
Merging
Ranking
Ontology
Electric fuses
Filter
Question Answering
Semantics
Experimental Evaluation
Sort
Confidence
Scenarios

Keywords

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

Cite this

Lopez, V., Nikolov, A., Fernandez, M., Sabou, M., Uren, V., & Motta, E. (2009). Merging and ranking answers in the semantic web: the wisdom of crowds. In A. Gómez-Pérez, Y. Yu, & Y. Ding (Eds.), The semantic web: fourth Asian conference, ASWC 2009, Shanghai, China, December 6-9, 2009. Proceedings (Vol. 5926 LNCS, pp. 135-152). (Lecture notes in computer science; Vol. 5926). Berlin (DE): Springer. https://doi.org/10.1007/978-3-642-10871-6_10
Lopez, Vanessa ; Nikolov, Andriy ; Fernandez, Miriam ; Sabou, Marta ; Uren, Victoria ; Motta, Enrico. / Merging and ranking answers in the semantic web : the wisdom of crowds. The semantic web: fourth Asian conference, ASWC 2009, Shanghai, China, December 6-9, 2009. Proceedings. editor / Asunción Gómez-Pérez ; Yong Yu ; Ying Ding. Vol. 5926 LNCS Berlin (DE) : Springer, 2009. pp. 135-152 (Lecture notes in computer science).
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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.",
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Lopez, V, Nikolov, A, Fernandez, M, Sabou, M, Uren, V & Motta, E 2009, Merging and ranking answers in the semantic web: the wisdom of crowds. in A Gómez-Pérez, Y Yu & Y Ding (eds), The semantic web: fourth Asian conference, ASWC 2009, Shanghai, China, December 6-9, 2009. Proceedings. vol. 5926 LNCS, Lecture notes in computer science, vol. 5926, Springer, Berlin (DE), pp. 135-152, 4th Annual Asian Semantic Web Conference, Shanghai, China, 6/12/09. https://doi.org/10.1007/978-3-642-10871-6_10

Merging and ranking answers in the semantic web : the wisdom of crowds. / Lopez, Vanessa; Nikolov, Andriy; Fernandez, Miriam; Sabou, Marta; Uren, Victoria; Motta, Enrico.

The semantic web: fourth Asian conference, ASWC 2009, Shanghai, China, December 6-9, 2009. Proceedings. ed. / Asunción Gómez-Pérez; Yong Yu; Ying Ding. Vol. 5926 LNCS Berlin (DE) : Springer, 2009. p. 135-152 (Lecture notes in computer science; Vol. 5926).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Merging and ranking answers in the semantic web

T2 - the wisdom of crowds

AU - Lopez, Vanessa

AU - Nikolov, Andriy

AU - Fernandez, Miriam

AU - Sabou, Marta

AU - Uren, Victoria

AU - Motta, Enrico

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - Lecture notes in computer science

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BT - The semantic web

A2 - Gómez-Pérez, Asunción

A2 - Yu, Yong

A2 - Ding, Ying

PB - Springer

CY - Berlin (DE)

ER -

Lopez V, Nikolov A, Fernandez M, Sabou M, Uren V, Motta E. Merging and ranking answers in the semantic web: the wisdom of crowds. In Gómez-Pérez A, Yu Y, Ding Y, editors, The semantic web: fourth Asian conference, ASWC 2009, Shanghai, China, December 6-9, 2009. Proceedings. Vol. 5926 LNCS. Berlin (DE): Springer. 2009. p. 135-152. (Lecture notes in computer science). https://doi.org/10.1007/978-3-642-10871-6_10