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Embedding based Link Prediction for Knowledge Graph Completion

Biswas, Russa ORCID iD icon


Knowledge Graphs (KGs) have recently gained attention for representing knowledge about a particular domain. Since its advent, the Linked Open Data (LOD) cloud has constantly been growing containing many KGs about many different domains such as government, scholarly data, biomedical domain, etc. Apart from facilitating the inter-connectivity of datasets in the LOD cloud, KGs have been used in a variety of machine learning and Natural Language Processing (NLP) based applications. However, the information present in the KGs are sparse and are often incomplete. Predicting the missing links between the entities is necessary to overcome this issue. Moreover, in the LOD cloud, information about the same entities is available in multiple KGs in different forms. But the information that these entities are the same across KGs is missing. The main focus of this thesis is to do Knowledge Graph Completion by tackling the link prediction tasks within a KG as well as across different KGs. To do so, the latent representation of KGs in a low dimensional vector space has been exploited to predict the missing information in order to complete the KGs.

Preprint §
DOI: 10.5445/IR/1000134455
DOI: 10.1145/3340531.3418512
Zitationen: 4
Zitationen: 6
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 10.2020
Sprache Englisch
Identifikator ISBN: 978-1-4503-6859-9
KITopen-ID: 1000134455
Erschienen in Proceedings of the 29th ACM International Conference on Information & Knowledge Management
Veranstaltung 29th ACM International Conference on Information and Knowledge Management (2020), Online, 19.10.2020 – 23.10.2020
Verlag Association for Computing Machinery (ACM)
Seiten 3221–3224
Vorab online veröffentlicht am 19.10.2020
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Nachgewiesen in Scopus
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