Collaborating Authors

Description Logic: Overviews

Towards Activity Recognition Using Probabilistic Description Logics

AAAI Conferences

A major challenge of pervasive context-aware computing and intelligent environments resides in the acquisition and modelling of rich and heterogeneous context data. Decisive aspects of this information are the ongoing human activities at different degrees of granularity. We conjecture that ontology-based activity models are key to support interoperable multilevel activity representation and recognition. In this paper, we report on an initial investigation about the application of probabilistic description logics (DLs) to a framework for the recognition of multilevel activities in intelligent environments. In particular, being based on Log-linear DLs, our approach leverages the potential of highly expressive description logics with probabilistic reasoning in one unified framework. While we believe that this approach is very promising, our preliminary investigation suggests that challenging research issues remain open, including extensive support for temporal reasoning, and optimizations to reduce the computational cost.

Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic Approach Artificial Intelligence

Matchmaking arises when supply and demand meet in an electronic marketplace, or when agents search for a web service to perform some task, or even when recruiting agencies match curricula and job profiles. In such open environments, the objective of a matchmaking process is to discover best available offers to a given request. We address the problem of matchmaking from a knowledge representation perspective, with a formalization based on Description Logics. We devise Concept Abduction and Concept Contraction as non-monotonic inferences in Description Logics suitable for modeling matchmaking in a logical framework, and prove some related complexity results. We also present reasonable algorithms for semantic matchmaking based on the devised inferences, and prove that they obey to some commonsense properties. Finally, we report on the implementation of the proposed matchmaking framework, which has been used both as a mediator in e-marketplaces and for semantic web services discovery.

A Survey on how Description Logic Ontologies Benefit from Formal Concept Analysis Artificial Intelligence

Although the notion of a concept as a collection of objects sharing certain properties, and the notion of a conceptual hierarchy are fundamental to both Formal Concept Analysis and Description Logics, the ways concepts are described and obtained differ significantly between these two research areas. Despite these differences, there have been several attempts to bridge the gap between these two formalisms, and attempts to apply methods from one field in the other. The present work aims to give an overview on the research done in combining Description Logics and Formal Concept Analysis.

Applications and Extensions of PTIME Description Logics with Functional Constraints

AAAI Conferences

We review and extend earlier work on the logic CFD, a description logic that allows terminological cycles with universal restrictions over functional roles. In particular, we consider the problem of reasoning about concept subsumption and the problem of computing certain answers for a family of attribute-connected conjunctive queries, showing that both problems are in PTIME. We then consider the effect on the complexity of these problems after adding a concept constructor that expresses concept union, or after adding a concept constructor for the bottom class. Finally, we show that adding both constructors makes both problems EXPTIME-complete.

Description Logics and Planning

AI Magazine

This article surveys previous work on combining planning techniques with expressive representations of knowledge in description logics to reason about tasks, plans, and goals. Description logics can reason about the logical definition of a class and automatically infer class-subclass subsumption relations as well as classify instances into classes based on their definitions. Descriptions of actions, plans, and goals can be exploited during plan generation, plan recognition, or plan evaluation. These techniques should be of interest to planning practitioners working on knowledge-rich application domains. Another emerging use of these techniques is the semantic web, where current ontology languages based on description logics need to be extended to reason about goals and capabilities for web services and agents.