A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?
Knowledge Graphs (KGs) are composed of structured information about a particular domain in the form of entities and relations. In addition to the structured information KGs help in facilitating interconnectivity and interoperability between different resources represented in the Linked Data Cloud. KGs have been used in a variety of applications such as entity linking, question answering, recommender systems, etc. However, KG applications suffer from high computational and storage costs. Hence, there arises the necessity for a representation able to map the high dimensional KGs into low dimensional spaces, i.e., embedding space, preserving structural as well as relational information. This paper conducts a survey of KG embedding models which not only consider the structured information contained in the form of entities and relations in a KG but also the unstructured information represented as literals such as text, numerical values, images, etc. Along with a theoretical analysis and comparison of the methods proposed so far for generating KG embeddings with literals, an empirical evaluation of the different methods under identical settings has been performed for the general task of link prediction.
Code (0)
등록된 구현이 없습니다.
Tasks
Entity LinkingKnowledge Graph EmbeddingsKnowledge GraphsLink PredictionQuestion AnsweringRecommendation SystemsSimilar Papers 제목 키워드 기반
Incorporating Literals into Knowledge Graph Embeddings
Knowledge graphs, on top of entities and their relationships, contain other important elements: literals. Literals encode interesting properties (e.g. the height) of entities that are not captured by links between entiti…
Entity EmbeddingsKnowledge Graph EmbeddingsKnowledge GraphsLink PredictionUniversal Preprocessing Operators for Embedding Knowledge Graphs with Literals
Knowledge graph embeddings are dense numerical representations of entities in a knowledge graph (KG). While the majority of approaches concentrate only on relational information, i.e., relations between entities, fewer a…
Knowledge Graph EmbeddingsKnowledge GraphsInference over Unseen Entities, Relations and Literals on Knowledge Graphs
In recent years, knowledge graph embedding models have been successfully applied in the transductive setting to tackle various challenging tasks including link prediction, and query answering. Yet, the transductive setti…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink PredictionEnd-to-End Entity Classification on Multimodal Knowledge Graphs
End-to-end multimodal learning on knowledge graphs has been left largely unaddressed. Instead, most end-to-end models such as message passing networks learn solely from the relational information encoded in graphs' struc…
ClassificationGeneral ClassificationKnowledge GraphsNode ClassificationNumerical Literals in Link Prediction: A Critical Examination of Models and Datasets
Link Prediction(LP) is an essential task over Knowledge Graphs(KGs), traditionally focussed on using and predicting the relations between entities. Textual entity descriptions have already been shown to be valuable, but …
Knowledge GraphsLink Prediction