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.

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.

Model Comparison for Semantic Grouping 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.

Conjunctive Query Answering for the Description Logic SHIQ Artificial Intelligence

Conjunctive queries play an important role as an expressive query language for Description Logics (DLs). Although modern DLs usually provide for transitive roles, conjunctive query answering over DL knowledge bases is only poorly understood if transitive roles are admitted in the query. In this paper, we consider unions of conjunctive queries over knowledge bases formulated in the prominent DL SHIQ and allow transitive roles in both the query and the knowledge base. We show decidability of query answering in this setting and establish two tight complexity bounds: regarding combined complexity, we prove that there is a deterministic algorithm for query answering that needs time single exponential in the size of the KB and double exponential in the size of the query, which is optimal. Regarding data complexity, we prove containment in co-NP.