Title
Interfacing virtual agents with collaborative knowledge: open domain question answering using wikipedia-based topic models
Abstract
This paper is concerned with the use of conversational agents as an interaction paradigm for accessing open domain encyclopedic knowledge by means of Wikipedia. More precisely, we describe a dialog-based question answering system for German which utilizes Wikipedia-based topic models as a reference point for context detection and answer prediction. We investigate two different perspectives to the task of interfacing virtual agents with collaborative knowledge. First, we exploit the use of Wikipedia categories as a basis for identifying the broader topic of a spoken utterance. Second, we describe how to enhance the conversational behavior of the virtual agent by means of a Wikipedia-based question answering component which incorporates the question topic. At large, our approach identifies topic-related focus terms of a user's question, which are subsequently mapped onto a category taxonomy. Thus, we utilize the taxonomy as a reference point to derive topic labels for a user's question. The employed topic model is thereby based on explicitly given concepts as represented by the document and category structure of the Wikipedia knowledge base. Identified topic categories are subsequently combined with different linguistic filtering methods to improve answer candidate retrieval and reranking. Results show that the topic model approach contributes to an enhancement of the conversational behavior of virtual agents.
Year
DOI
Venue
2011
10.5591/978-1-57735-516-8/IJCAI11-317
IJCAI
Keywords
DocType
Citations 
virtual agent,identified topic category,broader topic,open domain question answering,topic model,topic label,reference point,question topic,wikipedia-based topic model,topic model approach,collaborative knowledge,conversational behavior
Conference
9
PageRank 
References 
Authors
0.57
20
3
Name
Order
Citations
PageRank
Ulli Waltinger16410.76
Alexa Breuing2142.44
ipke wachsmuth31053121.65