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Towards activity recognition using probabilistic description logics.
| Content Provider | CiteSeerX |
|---|---|
| Author | Helaoui, Rim Riboni, Daniele Niepert, Mathias Bettini, Claudio Stuckenschmidt, Heiner |
| Abstract | 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. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Probabilistic Description Logic Towards Activity Recognition Research Issue Different Degree Intelligent Environment Resides Ongoing Human Activity Decisive Aspect Ontology-based Activity Model Probabilistic Reasoning Initial Investigation Extensive Support Pervasive Context-aware Computing Temporal Reasoning Expressive Description Logic Computational Cost Major Challenge Heterogeneous Context Data Interoperable Multilevel Activity Representation Intelligent Environment Multilevel Activity Preliminary Investigation Log-linear Dl Unified Framework |
| Content Type | Text |
