LiU Electronic Press
Download:
File size:
1207 kb
Format:
application/pdf
Author:
Dragisic, Zlatan (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab)
Title:
Semantic Matching for Stream Reasoning
Department:
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Publication type:
Student thesis
Language:
English
Level:
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Undergraduate subject:
Master's programme in Computer Science
Uppsok:
Technology
Pages:
110
Year of publ.:
2011
URI:
urn:nbn:se:liu:diva-71669
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71669
ISRN:
LIU-IDA/LITH-EX-A--11/041--SE
Subject category:
Computer Science
Keywords(en) :
Semantic Matching, Stream Reasoning, DyKnow, Semantic Web, OWL, ROS
Abstract(en) :

Autonomous system needs to do a great deal of reasoning during execution in order to provide timely reactions to changes in their environment. Data needed for this reasoning process is often provided through a number of sensors. One approach for this kind of reasoning is evaluation of temporal logical formulas through progression. To evaluate these formulas it is necessary to provide relevant data for each symbol in a formula. Mapping relevant data to symbols in a formula could be done manually, however as systems become more complex it is harder for a designer to explicitly state and maintain thismapping. Therefore, automatic support for mapping data from sensors to symbols would make system more flexible and easier to maintain.

DyKnow is a knowledge processing middleware which provides the support for processing data on different levels of abstractions. The output from the processing components in DyKnow is in the form of streams of information. In the case of DyKnow, reasoning over incrementally available data is done by progressing metric temporal logical formulas. A logical formula contains a number of symbols whose values over time must be collected and synchronized in order to determine the truth value of the formula. Mapping symbols in formula to relevant streams is done manually in DyKnow. The purpose of this matching is for each variable to find one or more streams whose content matches the intended meaning of the variable.

This thesis analyses and provides a solution to the process of semantic matching. The analysis is mostly focused on how the existing semantic technologies such as ontologies can be used in this process. The thesis also analyses how this process can be used for matching symbols in a formula to content of streams on distributed and heterogeneous platforms. Finally, the thesis presents an implementation in the Robot Operating System (ROS). The implementation is tested in two case studies which cover a scenario where there is only a single platform in a system and a scenario where there are multiple distributed heterogeneous platforms in a system.

The conclusions are that the semantic matching represents an important step towards fully automatized semantic-based stream reasoning. Our solution also shows that semantic technologies are suitable for establishing machine-readable domain models. The use of these technologies made the semantic matching domain and platform independent as all domain and platform specific knowledge is specified in ontologies. Moreover, semantic technologies provide support for integration of data from heterogeneous sources which makes it possible for platforms to use streams from distributed sources.

Presentation:
2011-10-03, Kurt Gödel, Linköping, 15:15 (English)
Supervisor:
Heintz, Fredrik (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab)
Examiner:
Heintz, Fredrik (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab)
Available from:
2011-11-15
Created:
2011-10-28
Last updated:
2011-11-15
Statistics:
213 hits
FILE INFORMATION
File size:
1207 kb
Mimetype:
application/pdf
Type:
fulltext
Statistics:
184 hits