HAWK - hybrid question answering using linked data
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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The Semantic Web: Latest Advances and New Domains - 12th European Semantic Web Conference, ESWC 2015, Proceedings. Hrsg. / Fabien Gandon; Harald Sack; Antoine Zimmermann; Marta Sabou; Claudia d’Amato; Philippe Cudré-Mauroux. Springer Nature Switzerland AG, 2015. S. 353-368 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9088).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - HAWK - hybrid question answering using linked data
AU - Usbeck, Ricardo
AU - Ngomo, Axel Cyrille Ngonga
AU - Bühmann, Lorenz
AU - Unger, Christina
N1 - Conference code: 12
PY - 2015/1/1
Y1 - 2015/1/1
N2 - The decentral architecture behind the Web has led to pieces of information being distributed across data sources with varying structure. Hence, answering complex questions often requires combining information from structured and unstructured data sources. We present HAWK, a novel entity search approach for Hybrid Question Answering based on combining Linked Data and textual data. The approach uses predicate argument representations of questions to derive equivalent combinations of SPARQL query fragments and text queries. These are executed so as to integrate the results of the text queries into SPARQL and thus generate a formal interpretation of the query. We present a thorough evaluation of the framework, including an analysis of the influence of entity annotation tools on the generation process of the hybrid queries and a study of the overall accuracy of the system. Our results show that HAWK achieves 0.68 respectively 0.61 F-measure within the training respectively test phases on the Question Answering over Linked Data (QALD-4) hybrid query benchmark.
AB - The decentral architecture behind the Web has led to pieces of information being distributed across data sources with varying structure. Hence, answering complex questions often requires combining information from structured and unstructured data sources. We present HAWK, a novel entity search approach for Hybrid Question Answering based on combining Linked Data and textual data. The approach uses predicate argument representations of questions to derive equivalent combinations of SPARQL query fragments and text queries. These are executed so as to integrate the results of the text queries into SPARQL and thus generate a formal interpretation of the query. We present a thorough evaluation of the framework, including an analysis of the influence of entity annotation tools on the generation process of the hybrid queries and a study of the overall accuracy of the system. Our results show that HAWK achieves 0.68 respectively 0.61 F-measure within the training respectively test phases on the Question Answering over Linked Data (QALD-4) hybrid query benchmark.
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=84937407730&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/c03135ec-f5c6-3f8e-b1a3-5d8cd6c954f8/
U2 - 10.1007/978-3-319-18818-8_22
DO - 10.1007/978-3-319-18818-8_22
M3 - Article in conference proceedings
AN - SCOPUS:84937407730
SN - 978-3-319-18817-1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 353
EP - 368
BT - The Semantic Web
A2 - Gandon, Fabien
A2 - Sack, Harald
A2 - Zimmermann, Antoine
A2 - Sabou, Marta
A2 - d’Amato, Claudia
A2 - Cudré-Mauroux, Philippe
PB - Springer Nature Switzerland AG
T2 - 12th European Semantic Web Conference - ESWC 2015
Y2 - 31 May 2015 through 4 June 2015
ER -