most citedEntity Type Prediction in Knowledge Graphs using Embeddings

4 citations · 6 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI20201 cited

Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019

Nacira Abbas, Kholoud Alghamdi, Mortaza Alinam +71

One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is…

cs.CL20204 cited

Entity Type Prediction in Knowledge Graphs using Embeddings

Russa Biswas, Radina Sofronova, Mehwish Alam +1

Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized as the backbone of diverse applications in the field of data mining and information retrieval. Hence, t…

cs.CL2020

Is Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods

Russa Biswas, Mehwish Alam, Harald Sack

Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum…

cs.AI2019

A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?

Genet Asefa Gesese, Russa Biswas, Mehwish Alam +1

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…

cs.DB20191 cited

Linked Open Data Validity -- A Technical Report from ISWS 2018

Tayeb Abderrahmani Ghor, Esha Agrawal, Mehwish Alam +68

Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially a…