Embedding based Link Prediction for Knowledge Graph Completion
Buchtitel: CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
Verlag: Association for Computing Machinery
Organisation: 29th ACM International Conference on Information & Knowledge Management
Knowledge Graphs (KGs) have recently gained attention for rep- resenting 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 govern- ment, 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 Pro- cessing (NLP) based applications. However, the information present in the KGs are sparse and are often incomplete. Predicting the miss- ing 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 fo- cus 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.
Download: Media:2020_Biswas_CIKM DC_Embedding based Link Prediction for Knowledge Graph Completion.pdf
DOI Link: https://doi.org/10.1145/3340531.3418512