Paper: Knowledge Graph and Text Jointly Embedding

ACL ID D14-1167
Title Knowledge Graph and Text Jointly Embedding
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014

We examine the embedding approach to reason new relational facts from a large- scale knowledge graph and a text corpus. We propose a novel method of jointly em- bedding entities and words into the same continuous vector space. The embedding process attempts to preserve the relations between entities in the knowledge graph and the concurrences of words in the text corpus. Entity names and Wikipedia an- chors are utilized to align the embeddings of entities and words in the same space. Large scale experiments on Freebase and a Wikipedia/NY Times corpus show that jointly embedding brings promising improvement in the accuracy of predicting facts, compared to separately embedding knowledge graphs and text. Particularly, jointly embedding enables the prediction of facts containing entities out o...