Name
Abstract | ||
|---|---|---|
Commonsense knowledge is paramount to enable intelligent systems. Typically, it is characterized as being implicit and ambiguous, hindering thereby the automation of its acquisition. To address these challenges, this paper presents semantically enhanced models to enable reasoning through resolving part of commonsense ambiguity. The proposed models enhance in a knowledge graph embedding framework for knowledge base completion. Experimental results show the effectiveness of the new semantic models in commonsense reasoning. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICDMW.2018.00146 | 2018 18TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW) |
Keywords | DocType | Volume |
Knowledge graph embeddings, Commonsense | Conference | abs/1809.04708 |
ISSN | Citations | PageRank |
2375-9232 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ikhlas Alhussien | 1 | 0 | 0.34 |
| Erik Cambria | 2 | 3873 | 183.70 |
| Zhang NengSheng | 3 | 0 | 0.34 |