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Alignment of Biomedical Ontologies using Life Science Literature
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology. (IDA/ADIT)ORCID iD: 0000-0002-9084-0470
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
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2006 (English)In: Proceedings of the International Workshop on Knowledge Discovery in Life Science Literature / [ed] Eric G. Bremer, Springer Berlin/Heidelberg, 2006, 1-17 p.Chapter in book (Refereed)
Abstract [en]

This book constitutes the refereed proceedings of the International Workshop on Knowledge Discovery in Life Science Literature, KDLL 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006).

The 12 revised full papers presented together with two invited talks were carefully reviewed and selected for inclusion in the book. The papers cover all topics of knowledge discovery in life science data such as text mining, identification and retrieval of documents, passage retrieval, co-reference resolution, extraction of life science entities or relationships from large collections, automated characterization of biological, biomedical and biotechnological entities and processes, extraction and characterization of more complex patterns and interaction networks, automated generation of text summaries, automated construction, expansion and curation of ontologies for different domains, and construction of controlled vocabularies.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2006. 1-17 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 3886
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-14035DOI: 10.1007/11683568_1ISI: 000237198800001ISBN: 978-3-540-32809-4 (print)ISBN: 3-540-32809-2 (print)OAI: oai:DiVA.org:liu-14035DiVA: diva2:22497
Available from: 2006-09-28 Created: 2006-09-28 Last updated: 2016-12-06Bibliographically approved
In thesis
1. Aligning and Merging Biomedical Ontologies
Open this publication in new window or tab >>Aligning and Merging Biomedical Ontologies
2006 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Due to the explosion of the amount of biomedical data, knowledge and tools that are often publicly available over the Web, a number of difficulties are experienced by biomedical researchers. For instance, it is difficult to find, retrieve and integrate information that is relevant to their research tasks. Ontologies and the vision of a Semantic Web for life sciences alleviate these difficulties. In recent years many biomedical ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies they have to be aligned or merged. A number of systems have been developed for aligning and merging ontologies and various alignment strategies are used in these systems. However, there are no general methods to support building such tools, and there exist very few evaluations of these strategies. In this thesis we give an overview of the existing systems. We propose a general framework for aligning and merging ontologies. Most existing systems can be seen as instantiations of this framework. Further, we develop SAMBO (System for Aligning and Merging Biomedical Ontologies) according to this framework. We implement different alignment strategies and their combinations, and evaluate them in terms of quality and processing time within SAMBO. We also compare SAMBO with two other systems. The work in this thesis is a first step towards a general framework that can be used for comparative evaluations of alignment strategies and their combinations.

Place, publisher, year, edition, pages
Institutionen för datavetenskap, 2006. 14 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1225
Series
Keyword
Ontologies, Ontology engineering, Biomedical ontologies, Ontology alignment, Ontology alignment, Ontology merging
National Category
Computer and Information Science
Identifiers
urn:nbn:se:liu:diva-6201 (URN)91-85497-01-0 (ISBN)
Presentation
2006-01-23, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Note
Report code: LiU-Tek-Lic-2006:6.Available from: 2006-04-03 Created: 2006-04-03 Last updated: 2015-02-18
2. Integration of Biological Data
Open this publication in new window or tab >>Integration of Biological Data
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Data integration is an important procedure underlying many research tasks in the life sciences, as often multiple data sources have to be accessed to collect the relevant data. The data sources vary in content, data format, and access methods, which often vastly complicates the data retrieval process. As a result, the task of retrieving data requires a great deal of effort and expertise on the part of the user. To alleviate these difficulties, various information integration systems have been proposed in the area. However, a number of issues remain unsolved and new integration solutions are needed.

The work presented in this thesis considers data integration at three different levels. 1) Integration of biological data sources deals with integrating multiple data sources from an information integration system point of view. We study properties of biological data sources and existing integration systems. Based on the study, we formulate requirements for systems integrating biological data sources. Then, we define a query language that supports queries commonly used by biologists. Also, we propose a high-level architecture for an information integration system that meets a selected set of requirements and that supports the specified query language. 2) Integration of ontologies deals with finding overlapping information between ontologies. We develop and evaluate algorithms that use life science literature and take the structure of the ontologies into account. 3) Grouping of biological data entries deals with organizing data entries into groups based on the computation of similarity values between the data entries. We propose a method that covers the main steps and components involved in similarity-based grouping procedures. The applicability of the method is illustrated by a number of test cases. Further, we develop an environment that supports comparison and evaluation of different grouping strategies.

The work is supported by the implementation of: 1) a prototype for a system integrating biological data sources, called BioTRIFU, 2) algorithms for ontology alignment, and 3) an environment for evaluating strategies for similarity-based grouping of biological data, called KitEGA.

Place, publisher, year, edition, pages
Institutionen för datavetenskap, 2006. 20 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1035
Keyword
Datalogi, integration, grouping, databases, ontologies, biological data, ioinformatics, KitEGA, Datalogi
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-7484 (URN)91-85523-28-3 (ISBN)
Public defence
2006-09-25, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2006-09-28 Created: 2006-09-28 Last updated: 2017-08-15Bibliographically approved
3. Aligning Biomedical Ontologies
Open this publication in new window or tab >>Aligning Biomedical Ontologies
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The amount of biomedical information that is disseminated over the Web increases every day. This rich resource is used to find solutions to challenges across the life sciences. The Semantic Web for life sciences shows promise for effectively and efficiently locating, integrating, querying and inferring related information that is needed in daily biomedical research. One of the key technologies in the Semantic Web is ontologies, which furnish the semantics of the Semantic Web. A large number of biomedical ontologies have been developed. Many of these ontologies contain overlapping information, but it is unlikely that eventually there will be one single set of standard ontologies to which everyone will conform. Therefore, applications often need to deal with multiple overlapping ontologies, but the heterogeneity of ontologies hampers interoperability between different ontologies. Aligning ontologies, i.e. identifying relationships between different ontologies, aims to overcome this problem. A number of ontology alignment systems have been developed. In these systems various techniques and ideas have been proposed to facilitate identification of alignments between ontologies. However, there still is a range of issues to be addressed when we have alignment problems at hand. The work in this thesis contributes to three different aspects of identification of high quality alignments: 1) Ontology alignment strategies and systems. We surveyed the existing ontology alignment systems, and proposed a general ontology alignment framework. Most existing systems can be seen as instantiations of the framework. Also, we developed a system for aligning biomedical ontologies (SAMBO) according to this framework. We implemented various alignment strategies in the system. 2) Evaluation of ontology alignment strategies. We developed and implemented the KitAMO framework for comparative evaluation of different alignment strategies, and we evaluated different alignment strategies using the implementation. 3) Recommending optimal alignment strategies for different applications. We proposed a method for making recommendations.

Place, publisher, year, edition, pages
Institutionen för datavetenskap, 2007. 24 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1110
Keyword
ontologies, biomedical ontologies, aligning ontologies, semantic web, knowledge management
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-9487 (URN)978-91-85831-56-2 (ISBN)
Public defence
2007-09-03, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Supervisors
Available from: 2007-07-03 Created: 2007-07-03 Last updated: 2017-08-15

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Tan, HeJakoniené, VaidaLambrix, PatrickÅberg, JohanShahmehri, Nahid

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