Name
Abstract | ||
|---|---|---|
Vector embedding models have recently become popular for encoding both structured and unstructured data. In the context of knowledge graphs such models often serve as additional evidence supporting various tasks related to the knowledge base population: e.g., information extraction or link prediction to expand the original dataset. However, the embedding models themselves are often not used directly alongside structured data: they merely serve as additional evidence for structured knowledge extraction. In the metaphactory knowledge graph management platform, we use federated hybrid SPARQL queries for combining explicit information stated in the graph, implicit information from the associated embedding models, and information extracted using vector embeddings in a transparent way for the end user. In this paper we show how we integrated RDF data with vector space models to construct an augmented knowledge graph to be used in customer applications.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3184558.3191527 | WWW '18: The Web Conference 2018
Lyon
France
April, 2018 |
DocType | ISBN | Citations |
Conference | 978-1-4503-5640-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andriy Nikolov | 1 | 769 | 53.09 |
| Peter Haase | 2 | 1727 | 114.59 |
| Daniel M. Herzig | 3 | 149 | 9.58 |
| Johannes Trame | 4 | 46 | 5.34 |
| Artem Kozlov | 5 | 3 | 2.79 |