We represent knowledge as integrity constraints in a formalization of probabilistic spatio-temporal knowledge bases. We start by defining the syntax and semantics of a formalization called PST knowledge bases. This definition generalizes an earlier version, called SPOT, which is a declarative framework for the representation and processing of probabilistic spatio-temporal data where probability is represented as an interval because the exact value is unknown. We augment the previous definition by adding a type of non-atomic formula that expresses integrity constraints. The result is a highly expressive formalism for knowledge representation dealing with probabilistic spatio-temporal data. We obtain complexity results both for checking the consistency of PST knowledge bases and for answering queries in PST knowledge bases, and also specify tractable cases. All the domains in the PST framework are finite, but we extend our results also to arbitrarily large finite domains.
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.
This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.
Our Jijo-2 robot, whose purpose is to provide office services, such as answering queries about people's location, route guidance, and delivery tasks, is expected to conduct natural spoken conversation with the office dwellers. This paper describes dialogue technologies implemented on our Jijo-2 office robot, i.e. noise-free voice acquisition system by a microphone array, inference of under-specified referents and zero pronouns using the attentional states, and context-sensitive construction of semantic frames from fragmented utterances. The behavior of the dialogue system integrated with the sound source detection, navigation, and face recognition vision is demonstrated in real dialogue examples in a real office.