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Call for Special Session Papers

Session papers will go through the normal process of submission through the EasyChair on-line submission system, where the authors need to indicate which session submitted to. The session organizers will handle the review process for each session paper. Session papers should have the same format as the regular papers and no more than 12 pages. All accepted special session papers will be published in the conference proceedings by Springer-Verlag in the Lecture Notes in Artificial Intelligence (LNAI) series.

Call for Organizers for the following new Special Sessions
(organizers are entitled to free registration for the conference)

• Data/information fusion for information retrieval
• Mining concept drift within financial time series data
• Knowledge processing in requirement engineering
• Discover knowledge from social networks
• DNA sequence analysis in bioinformatics
• Knowledge representation and reasoning under uncertainty in Ambient intelligence

Special Sessions

Theory and Practice of Ontology for the Semantic Web

The goal of this invited session is to bring together ontology researchers and practitioners discussing challenges encountered in the modeling and application of ontolgies and developing solutions and demonstrations.

We solicit high-quality papers on the following topics, but not limited to:

• Description logics for ontology
• Rules and inferences that beyond the capability of description logics for ontology
• Large scale ontology modeling and quality assurance
• Semantic integration for ontology alignment, merging and mapping
• Semantic Web annotation
• Database technologies for the Semantic Web
• Application of ontologies to support natural language processing, information retrieval, question-answering, information extraction, and data mining
• Biomedical ontologies
• Evaluation of and lessons learned in the modeling and application of ontologies

Please click here for more details about this session

Important Dates
Paper submission:                 30 March 2010
Author notification:                15 May 2010
Camera-ready submission:    30 May 2010

Special Session Organizers

Prof. Songmao Zhang, Academy of Mathematics and Systems Science,
Chinese Academy of Sciences, Beijing 100190, China (E-mail: smzhang@math.ac.cn)

Prof. Ying Jiang, Institute of Software,
Chinese Academy of Sciences, Beijing 100190, China (E-mail: jy@ios.ac.cn)

Application of Data Mining to Seismic Data Analysis for Earthquake Study

The aim of this invited special session is to establish a communicating platform for researchers and experts in the areas of earthquake science and intelligent data analysis, promote the development of data mining technology in earthquake science and foster new collaborations in these two fields.

We thus call for contributions on the following topics, but not limited by:

1. Methods and techniques for detecting concept drifting within sequential data that can be employed in earthquake research
2. Data fusion technologies for making use of multiple observing data sources for earthquake study
3. Effective methods for maintaining and accessing large seismic data archives
4. Intelligent data analysis methods for correlating seismic precursors to earthquakes
5. Intelligent systems for earthquake prediction
6. A broad spectrum of probabilistic or soft computing models for risk reduction that can be used to design effective mitigation strategies for communities

Please click here for more details about this session

Important Dates
Paper submission:                 30 March 2010
Author notification:                15 May 2010
Camera-ready submission:    30 May 2010

Special Session Organizer
Prof. Xueming Zhang, Institute of Earthquake Science,
China Earthquake Administration, Beijing, 100036, China (E-mail: zhangxm96@126.com)

Invited Talk

GQ Zhang, PhD
Professor, Department of Electrical Engineering and Computer Science
Division Chief, Medical Informatics
Case Western Reserve University
Cleveland, OH 44106, USA

Title: Large-scale, Exhaustive Lattice-based Structural Auditing of SNOMED CT

Abstract:
One criterion for the well-formedness of ontologies is that their hierarchical structure form a lattice. Formal Concept Analysis (FCA) has been used as a technique for assessing the quality of ontologies, but is not scalable to large ontologies such as SNOMED CT. We developed a methodology called Lattice-based Structural Auditing (LaSA), for auditing biomedical ontologies, implemented through automated SPARQL queries, in order to exhaustively identify all non-lattice pairs in SNOMED CT. The percentage of non-lattice pairs ranges from 0 to 1.66 among the 19 SNOMED CT hierarchies. Preliminary manual inspection of a limited portion of the >518K non-lattice pairs, among over 34 million candidate pairs, revealed inconsistent use of precoordination in SNOMED CT, but also a number of false positives. Our results are consistent with those based on FCA, with the advantage that the LaSA computational pipeline is scalable and applicable to ontological systems consisting mostly of taxonomic links. This work is based on collaboration with Olivier Bodenreider from the National Library of Medicine, Bethesda, USA.