Information Retrieval (IR) is concerned with the identification of documents in a collection that are relevant to a given information need, usually represented as a query containing terms or keywords, which are supposed to be a good description of what the user is looking for. IR systems may improve their effectiveness (i.e., increasing the number of relevant documents retrieved) by using a process of query expansion, which automatically adds new terms to the original query posed by an user. In this paper we develop a method of query expansion based on Bayesian networks. Using a learning algorithm, we construct a Bayesian network that represents some of the relationships among the terms appearing in a given document collection; this network is then used as a thesaurus (specific for that collection). We also report the results obtained by our method on three standard test collections.
Conversational interfaces with computers have been the talk of tech since the days of Star Trek. Mostly associated with voice response, frustrating experiences interacting with Siri, chatbots, or the interactive voice response (IVR) systems of call centers reveal what a long slog it's been for getting computers to understand natural language, regardless of whether it's in the form of voice or text. But it took the Amazon Echo's Alexa, which was designed as a conversational voice to Amazon's retail and entertainment services, to show that natural language interfaces could actually perform useful services. When we saw SAS founder Dr. James Goodnight demonstrate how Alexa could be used to query SAS Visual Analytics, we thought that was pretty cool. But when you look at this video, you'll realize that Alexa has only been taught a few things and has a long way to go before it will replace your keyboard or touchpad.
Causality has been recently introduced in databases, to model, characterize and possibly compute causes for query results (answers). Connections between query-answer causality, consistency-based diagnosis, database repairs (wrt. integrity constraint violations), abductive diagnosis and the view-update problem have been established. In this work we further investigate connections between query-answer causality and abductive diagnosis and the view-update problem. In this context, we also define and investigate the notion of query-answer causality in the presence of integrity constraints.
Answering queries posed over knowledge bases is a central problem in knowledge representation and database theory. In the database area, checking query containment is an important query optimization and schema integration technique. In knowledge representation it has been used for object classification, schema integration, service discovery, and more. In the presence of a knowledge base, the problem of query containment is strictly related to that of query answering; indeed, the two are reducible to each other; we focus on the latter, and our results immediately extend to the former.
The introduction of self-describing web services has opened up new avenues for the creation of information gathering agents, which are capable of discovering and employing such services at run time to answer user queries. It is desirable for such agents to not only build and execute a query plan, but also specify what information is not returned. In this paper we present a model for expressing the semantics of web services to provide information for such incompleteness analysis. The model relies on an external type system, which, in addition to types, specifies operations that can be performed on the types and properties of these operations. We also describe an algorithm for answering user queries in this model.