Details
Originalsprache | Englisch |
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Titel des Sammelwerks | The Semantic Web – ISWC 2020 - 19th International Semantic Web Conference, 2020, Proceedings |
Herausgeber/-innen | Jeff Z. Pan, Valentina Tamma, Claudia d’Amato, Krzysztof Janowicz, Bo Fu, Axel Polleres, Oshani Seneviratne, Lalana Kagal |
Seiten | 294-310 |
Seitenumfang | 17 |
Publikationsstatus | Veröffentlicht - 2020 |
Extern publiziert | Ja |
Publikationsreihe
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Band | 12506 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (elektronisch) | 1611-3349 |
Abstract
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
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The Semantic Web – ISWC 2020 - 19th International Semantic Web Conference, 2020, Proceedings. Hrsg. / Jeff Z. Pan; Valentina Tamma; Claudia d’Amato; Krzysztof Janowicz; Bo Fu; Axel Polleres; Oshani Seneviratne; Lalana Kagal. 2020. S. 294-310 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12506 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - KnowlyBERT
T2 - Hybrid Query Answering over Language Models and Knowledge Graphs
AU - Kalo, Jan-Christoph
AU - Fichtel, Leandra
AU - Ehler, Philipp
AU - Balke, Wolf-Tilo
PY - 2020
Y1 - 2020
N2 - Providing a plethora of entity-centric information, Knowledge Graphs have become a vital building block for a variety of intelligent applications. Indeed, modern knowledge graphs like Wikidata already capture several billions of RDF triples, yet they still lack a good coverage for most relations. On the other hand, recent developments in NLP research show that neural language models can easily be queried for relational knowledge without requiring massive amounts of training data. In this work, we leverage this idea by creating a hybrid query answering system on top of knowledge graphs in combination with the masked language model BERT to complete query results. We thus incorporate valuable structural and semantic information from knowledge graphs with textual knowledge from language models to achieve high precision query results. Standard techniques for dealing with incomplete knowledge graphs are either (1) relation extraction which requires massive amounts of training data or (2) knowledge graph embeddings which have problems to succeed beyond simple baseline datasets. Our hybrid system KnowlyBERT requires only small amounts of training data, while outperforming state-of-the-art techniques by boosting their precision by over 30% in our large Wikidata experiment.
AB - Providing a plethora of entity-centric information, Knowledge Graphs have become a vital building block for a variety of intelligent applications. Indeed, modern knowledge graphs like Wikidata already capture several billions of RDF triples, yet they still lack a good coverage for most relations. On the other hand, recent developments in NLP research show that neural language models can easily be queried for relational knowledge without requiring massive amounts of training data. In this work, we leverage this idea by creating a hybrid query answering system on top of knowledge graphs in combination with the masked language model BERT to complete query results. We thus incorporate valuable structural and semantic information from knowledge graphs with textual knowledge from language models to achieve high precision query results. Standard techniques for dealing with incomplete knowledge graphs are either (1) relation extraction which requires massive amounts of training data or (2) knowledge graph embeddings which have problems to succeed beyond simple baseline datasets. Our hybrid system KnowlyBERT requires only small amounts of training data, while outperforming state-of-the-art techniques by boosting their precision by over 30% in our large Wikidata experiment.
KW - Knowledge graphs
KW - Language models
KW - Query answering
UR - http://www.scopus.com/inward/record.url?scp=85096523582&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-62419-4_17
DO - 10.1007/978-3-030-62419-4_17
M3 - Conference contribution
SN - 9783030624187
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 294
EP - 310
BT - The Semantic Web – ISWC 2020 - 19th International Semantic Web Conference, 2020, Proceedings
A2 - Pan, Jeff Z.
A2 - Tamma, Valentina
A2 - d’Amato, Claudia
A2 - Janowicz, Krzysztof
A2 - Fu, Bo
A2 - Polleres, Axel
A2 - Seneviratne, Oshani
A2 - Kagal, Lalana
ER -