Name
Abstract | ||
|---|---|---|
We propose Inference Knowledge Graph, a novel approach of remapping existing, large scale, semantic knowledge graphs into Markov Random Fields in order to create user goal tracking models that could form part of a spoken dialog system. Since semantic knowledge graphs include both entities and their attributes, the proposed method merges the semantic dialog-state-tracking of attributes and the database lookup of entities that fulfill users' requests into one single unified step. Using a large semantic graph that contains all businesses in Bellevue, WA, extracted from Microsoft Satori, we demonstrate that the proposed approach can return significantly more relevant entities to the user than a baseline system using database lookup. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) | Knowledge graph, spoken dialog system, Markov Random Fields, linked big data |
Field | DocType | ISSN |
Semantic memory,Spoken dialog systems,Graph database,Inference,Computer science,Markov chain,Natural language processing,Artificial intelligence,Probabilistic logic,Graphical model,Semantics | Conference | 1520-6149 |
Citations | PageRank | References |
5 | 0.48 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Ma | 1 | 40 | 4.19 |
| Paul A. Crook | 2 | 12 | 2.58 |
| Ruhi Sarikaya | 3 | 698 | 64.49 |
| Eric Fosler-Lussier | 4 | 690 | 66.40 |