Knowledge Graph Inference for Spoken Dialog Systems

  • Yi Ma
  • Paul A. Crook
  • Ruhi Sarikaya
  • Eric Fosler-Lussier

Proceedings of 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015 |

Published by IEEE - Institute of Electrical and Electronics Engineers

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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.