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 dialogue-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 per- spectives 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.
The first time I met Alexa, the A.I. robot voice inside the wine-bottle-size speaker known as the Amazon Echo, I was at my friends' house, in rural New England. "Currently, it is seventy-five degrees," she told us, and assured us that it would not rain. This was a year ago, and I'd never encountered a talking speaker before. When I razzed my friend for his love of gadgetry, he showed me some of Alexa's other tricks: telling us the weather, keeping a shopping list, ordering products from Amazon. This summer, Alexa decided again and again who the tickle monster's next victim was, saying their children's adorable nicknames in her strange A.I. accent.
The term "machine learning" covers a grab bag of algorithms, techniques, and technology that are by now pretty much everywhere in modern life. However, machine intelligence has recently started to be used not just for identifying problems but to build better products. Amongst the first is the world's only beers brewed with the help of machine intelligence, which went on sale a few weeks ago. The machine learning algorithms uses a combination of reinforcement learning and bayesian optimisation to assist the brewer in deciding how to change the recipe of the beer, with the algorithms learning from experience and customer feedback. Perhaps the most obvious intrusion of machine learning into the physical world is the voice recognition that drives Apple's Siri, or Amazon's Alexa.
Domain ontologies contain information about the important concepts in a domain, the associated attributes and the relationships between various concepts. The manual creation of domain ontologies is an expensive and time consuming process. In this paper, we present an approach to the automatic extraction of domain ontologies from domainspecific text. This approach uses dependency relations between terms to represent text as a graph. The graph based ranking algorithm HITS is used to identify domain keywords and to structure them as concepts and attributes. Experiments on two different domains, digital cameras and wines, show that our method performs well in comparison to other approaches.
The ability to model preferences and exploit preferential information to assist users in searching for items has become an important issue in knowledge representation. Accurately eliciting preferences from the user in the form of a query can result in a coarse recommendation mechanism with numerous results returned. The problem lies in the user's knowledge concerning the items among which they are searching. Unless the user is a domain expert, their preferences are likely to be expressed in a vague manner and so vague results (in the form of numerous alternatives) are returned.