Name
Abstract | ||
|---|---|---|
Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic. This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatedness for topical coherence modeling. The DSRM is directly trained on large-scale KGs and it maps heterogeneous types of knowledge of an entity from KGs to numerical feature vectors in a latent space such that the distance between two semantically-related entities is minimized. Compared with the state-of-the-art relatedness approach proposed by (Milne and Witten, 2008a), the DSRM obtains 19.4% and 24.5% reductions in entity disambiguation errors on two publicly available datasets respectively. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Semantic similarity,Semantic memory,Knowledge graph,Feature vector,Descriptive knowledge,Computer science,Coherence (physics),Artificial intelligence,Natural language processing,Knowledge base,Machine learning,Deep neural networks |
DocType | Volume | Citations |
Journal | abs/1504.07678 | 17 |
PageRank | References | Authors |
0.64 | 34 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongzhao Huang | 1 | 126 | 6.64 |
| Larry Heck | 2 | 17 | 0.98 |
| Heng Ji | 3 | 1544 | 127.27 |