Sub-Symbolic Knowledge Representation for Evocative Chat-Bots
Giovanni Pilato, Agnese Augello, Giorgio Vassallo, Salvatore Gaglio · 2008
A sub-symbolic knowledge representation oriented to the enhancement of chat bot interaction is proposed. The result of the technique is the introduction of a semantic sub-symbolic layer to a traditional ontology-based knowledge representation. This layer is obtained mapping the ontology concepts into a semantic space built through Latent Semantic Analysis (LSA) technique and it is embedded into a conversational agent. This choice leads to a chat-bot with “evocative” capabilities whose knowledge representation framework is composed of two areas: the rational and the evocative one. As a standard ontology we have chosen the well-founded WordNet lexical dictionary, while as chat-bot the ALICE architecture. Experimental trials involving four lexical categories of WordNet have been conducted, and an example of interaction is shown at the end of the paper. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.