Embedding based Link Prediction for Knowledge Graph Completion

Abstract

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.

Constructing Semantic Summaries Using Embeddings

“…For future work, our methodology will be expanded to include the evaluation of RDF2Vec alongside other embedders like TransE, TransR, RotatE, etc. [11,27]. Furthermore, we aim to investigate additional ML methodologies, such as techniques for learning to rank [28] and deep neural networks.…”

“…In graph-based knowledge representation, embeddings have already been widely used. For example, they have been used for predicting entity types [11], for entity classification [12], for question answering on top of KGs [13], and for selecting consistent subsets out of inconsistent ontologies [14]. In the summarization field, embeddings have also been used for generating summaries for specific entities in a KG [15], as well as for generating quotient summaries that are exploited for lossy query answering through similarity-embedding-based similarity searches [16].…”