Query Expansion in Description Logics and Carin Marie-Christine Rousset

AAAI Conferences

Given a knowledge base, expanding a query consists of determining all the ways of deriving it from atoms built on some distinguished predicates. In this paper, we address the problem of determining the expansions of a query in description logics and CARIN. Description Logics are logical formalisms for representing classes of objects (called concepts) and their relationships (expressed by binary relations called roles). Much of the research in description logics has concentrated on algorithms for checldng subsumption between concepts and satisfiability of knowledge bases (see e.g.

erating Interactive Explanations

AAAI Conferences

Existing approaches to text generation fail to consider how interactions with the user may be managed within a coherent explanation or description. This paper presents an approach to generating such interactive explanations based on two levels of discourse planning - content planning and dialogue planning. The system developed allows aspects of the changing context to be monitored with an explanation, and the developing explanation to depend on this changing context. Interruptions from the user are allowed and dealt with (and resumed from) within the context of that explanation.

Model Comparison for Semantic Grouping

arXiv.org Machine Learning

We introduce a probabilistic framework for quantifying the semantic similarity between two groups of embeddings. We formulate the task of semantic similarity as a model comparison task in which we contrast a generative model which jointly models two sentences versus one that does not. We illustrate how this framework can be used for the Semantic Textual Similarity tasks using clear assumptions about how the embeddings of words are generated. We apply model comparison that utilises information criteria to address some of the shortcomings of Bayesian model comparison, whilst still penalising model complexity. We achieve competitive results by applying the proposed framework with an appropriate choice of likelihood on the STS datasets.

Loop Restricted Existential Rules and First-order Rewritability for Query Answering

arXiv.org Artificial Intelligence

In ontology-based data access (OBDA), the classical database is enhanced with an ontology in the form of logical assertions generating new intensional knowledge. A powerful form of such logical assertions is the tuple-generating dependencies (TGDs), also called existential rules, where Horn rules are extended by allowing existential quantifiers to appear in the rule heads. In this paper we introduce a new language called loop restricted (LR) TGDs (existential rules), which are TGDs with certain restrictions on the loops embedded in the underlying rule set. We study the complexity of this new language. We show that the conjunctive query answering (CQA) under the LR TGDs is decid- able. In particular, we prove that this language satisfies the so-called bounded derivation-depth prop- erty (BDDP), which implies that the CQA is first-order rewritable, and its data complexity is in AC0 . We also prove that the combined complexity of the CQA is EXPTIME complete, while the language membership is PSPACE complete. Then we extend the LR TGDs language to the generalised loop restricted (GLR) TGDs language, and prove that this class of TGDs still remains to be first-order rewritable and properly contains most of other first-order rewritable TGDs classes discovered in the literature so far.