Discovering Research Hypotheses in Social Science Using Knowledge Graph Embeddings

Autor: de Haan, R., Tiddi, I., Beek, W., Verborgh, Ruben, Hose, Katja, Paulheim, Heiko, Champin, Pierre-Antoine, Maleshkova, Maria, Corcho, Oscar, Ristoski, Petar, Alam, Mehwish
Přispěvatelé: Verborgh, Ruben, Hose, Katja, Paulheim, Heiko, Champin, Pierre-Antoine, Maleshkova, Maria, Corcho, Oscar, Ristoski, Petar, Alam, Mehwish, Artificial intelligence, Network Institute, Artificial Intelligence (section level)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: de Haan, R, Tiddi, I & Beek, W 2021, Discovering Research Hypotheses in Social Science Using Knowledge Graph Embeddings . in R Verborgh, K Hose, H Paulheim, P-A Champin, M Maleshkova, O Corcho, P Ristoski & M Alam (eds), The Semantic Web : 18th International Conference, ESWC 2021, Virtual Event, June 6–10, 2021, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12731 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 477-494, 18th European Semantic Web Conference, ESWC 2021, Virtual, Online, 6/06/21 . https://doi.org/10.1007/978-3-030-77385-4_28
The Semantic Web ISBN: 9783030773847
ESWC
The Semantic Web: 18th International Conference, ESWC 2021, Virtual Event, June 6–10, 2021, Proceedings, 477-494
STARTPAGE=477;ENDPAGE=494;TITLE=The Semantic Web
Popis: In an era of ever-increasing scientific publications available, scientists struggle to keep pace with the literature, interpret research results and identify new research hypotheses to falsify. This is particularly in fields such as the social sciences, where automated support for scientific discovery is still widely unavailable and unimplemented. In this work, we introduce an automated system that supports social scientists in identifying new research hypotheses. With the idea that knowledge graphs help modeling domain-specific information, and that machine learning can be used to identify the most relevant facts therein, we frame the problem of hypothesis discovery as a link prediction task, where the ComplEx model is used to predict new relationships between entities of a knowledge graph representing scientific papers and their experimental details. The final output consists in fully formulated hypotheses including the newly discovered triples (hypothesis statement), along with supporting statements from the knowledge graph (hypothesis evidence and hypothesis history). A quantitative and qualitative evaluation is carried using experts in the field. Encouraging results show that a simple combination of machine learning and knowledge graph methods can serve as a basis for automated scientific discovery.
Databáze: OpenAIRE
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