Abstract
Semantic Sensor Web enhances raw sensor data with spatial, temporal, and thematic annotations to enable high-level reasoning. In this paper, we explore how abductive reasoning framework can benefit formalization and interpretation of sensor data to garner situation awareness. Specifically, we show how abductive logic programming techniques, in conjunction with symbolic knowledge rules, can be used to detect inconsistent sensor data and to generate human accessible description of the state of the world from consistent subset of the sensor data. We also show how trust/belief information can be incorporated into the interpreter to enhance reliability. For concreteness, we formalize Weather domain and develop a meta-interpreter in Prolog to explain Weather data. This preliminary work illustrates synthesis of high-level, reliable information for situation awareness by querying low-level sensor data.
| Original language | American English |
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| Pages | 111-118 |
| Number of pages | 8 |
| DOIs | |
| State | Published - May 1 2009 |
| Event | 2009 International Symposium on Collaborative Technologies and Systems: May 18-22, 2009, Baltimore, Maryland, USA - Duration: May 1 2009 → … |
Conference
| Conference | 2009 International Symposium on Collaborative Technologies and Systems: May 18-22, 2009, Baltimore, Maryland, USA |
|---|---|
| Period | 5/1/09 → … |
ASJC Scopus Subject Areas
- Computer Networks and Communications
- Hardware and Architecture
- Human-Computer Interaction
Keywords
- Abductive Reasoning
- Inconsistency
- Meta-Interpreter
- SSW
- Semantic Sensor Web
- Situation Awareness
- Trust
- Knowledge base
- Trust/belief
Disciplines
- Bioinformatics
- Communication
- Communication Technology and New Media
- Computer Sciences
- Databases and Information Systems
- Life Sciences
- OS and Networks
- Physical Sciences and Mathematics
- Science and Technology Studies
- Social and Behavioral Sciences