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Leveraging literals for knowledge graph embeddings

Genet Asefa Gesese
2021
These datasets could be used for evaluating both kind of KG Embeddings, those using literals and those which do not include literals.  ...  Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entity recognition, entity linking, question answering.  ...  Mehwish Alam for their invaluable mentoring and support.  ... 
doi:10.5445/ir/1000141527 fatcat:pzp3nljrwfae5dfqk4mjq4xjl4

Leveraging Literals for Knowledge Graph Embeddings

Genet Asefa Gesese, Technische Informationsbibliothek (TIB), Valentina Tamma, Miriam Fernandez, María Poveda-Villalón
2022
These datasets could be used for evaluating both kind of KG Embeddings, those using literals and those which do not include literals.  ...  Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entity recognition, entity linking, question answering.  ...  Mehwish Alam for their invaluable mentoring and support.  ... 
doi:10.34657/7998 fatcat:ezcn4karyvhexf64m3ucwyxppa

Structure and Learning (Dagstuhl Seminar 21362)

Tiansi Dong, Achim Rettinger, Jie Tang, Barbara Tversky, Frank van Harmelen
2022
Integrating symbolic and numeric inference was set as one of the next open AI problems at the Townhall meeting "A 20 Year Roadmap for AI" at AAAI 2019.  ...  Joint deductive and inductive reasoning benchmarks  ...  LiterallyWikidata has been prepared with the main focus on providing benchmark datasets for multimodal KG Embedding (KGE) models, specifically for models using numeric and/or text literals.  ... 
doi:10.4230/dagrep.11.8.11 fatcat:jmwpccjj6bdnveruyrh4qgfzhu