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Found 5 papers, 2 papers with code
RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphs
In this work, a novel Relation Aware Inductive Link preDiction (RAILD) is proposed for KG completion which learns representations for both unseen entities and unseen relations.
A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals
These efforts to standardize these data and make them accessible have also led to many challenges such as exploration of scholarly articles, ambiguous authors, etc.
Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019
Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything.
Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction
Most Knowledge Graphs (KGs) contain textual descriptions of entities in various natural languages.
A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?
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.
