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Link Prediction in Knowledge Graphs with Numeric Triples Using Clustering

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dc.contributor.author Bayrak, Betül
dc.contributor.author Choupani, Roya
dc.contributor.author Doğdu, Erdoğan
dc.date.accessioned 2022-06-14T10:13:59Z
dc.date.available 2022-06-14T10:13:59Z
dc.date.issued 2020
dc.identifier.citation Bayrak, Betül; Choupani, Roya; Doğdu, Erdoğan (2020). "Link Prediction in Knowledge Graphs with Numeric Triples Using Clustering", Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, 10 December 2020 through 13 December 2020, pp, 4492-4498. tr_TR
dc.identifier.isbn 9781728162515
dc.identifier.uri http://hdl.handle.net/20.500.12416/5610
dc.description.abstract Knowledge graphs (KG) include large amounts of structured data in many different domains. Knowledge or information is captured by entities and relationships between them in KG. One of the open problems in knowledge graphs area is "link prediction", that is predicting new relationships or links between the given existing entities in KG. A recent approach in graph-based learning problems is "graph embedding", in which graphs are represented as low-dimensional vectors. Then, it is easier to make link predictions using these vector representations. We also use graph embedding for graph representations. A sub-problem of link prediction in KG is the link prediction in the presence of literal values, and specifically numeric values, on the receiving end of links. This is a harder problem because of the numeric literal values taking arbitrary values. For such entries link prediction models cannot work, because numeric entities are not embedded in the vector space. There are several studies in this area, but they are all complex approaches. In this study, we propose a novel approach for link prediction in KG in the presence of numerical values. To overcome the embedding problem of numeric values, we used a clustering approach for clustering these numerical values in a knowledge graph and then used the clusters for performing link prediction. Then we clustered the numerical values to enhance the prediction rates and evaluated our method on a part of Freebase knowledge graph, which includes entities, relations, and numerical literals. Test results show that a considerable increase in link prediction rate can be achieved in comparison to previous studies. © 2020 IEEE. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1109/BigData50022.2020.9378475 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Knowledge Graph Embedding tr_TR
dc.subject Knowledge Graphs tr_TR
dc.subject Link Prediction tr_TR
dc.title Link Prediction in Knowledge Graphs with Numeric Triples Using Clustering tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 tr_TR
dc.identifier.startpage 4492 tr_TR
dc.identifier.endpage 4498 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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