He Tan

SAMBO-A system for aligning and merging biomedical ontologies

Due to the recent explosion of the amount of on-line accessible biomedical data and tools, finding and retrieving the relevant information is not an easy task. The vision of a Semantic Web for life sciences alleviates these difficulties. A key technology for the Semantic Web is ontologies. 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. In this paper we propose a framework for aligning and merging ontologies. Further, we developed a system for aligning and merging biomedical ontologies (SAMBO) based on this framework. The framework is also a first step towards a general framework that can be used for comparative evaluations of alignment strategies and their combinations. In this paper we evaluated different strategies and their combinations in terms of quality and processing time and compared SAMBO with two other systems.

A tool for evaluating ontology alignment strategies

Ontologies are an important technology for the Semantic Web. In different areas ontologies have already been developed and many of these ontologies contain overlapping information. Often we would therefore want to be able to use multiple ontologies. To obtain good results, we need to find the relationships between terms in the different ontologies, i.e. we need to align them. Currently, there exist a number of systems that support users in aligning ontologies, but not many comparative evaluations have been performed and there exists little support to perform such evaluations. However, the study of the properties, the evaluation and comparison of the alignment strategies and their combinations, would give us valuable insight in how the strategies could be used in the best way. In this paper we propose the KitAMO framework for comparative evaluation of ontology alignment strategies and their combinations and present our current implementation. We evaluate the implementation with respect to performance. We also illustrate how the system can be used to evaluate and compare alignment strategies and their combinations in terms of performance and quality of the proposed alignments. Further, we show how the results can be analyzed to obtain deeper insights into the properties of the strategies.

Alignment of biomedical ontologies using life science literature

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. In this paper we propose strategies for aligning ontologies based on life science literature. We propose a basic algorithm as well as extensions that take the structure of the ontologies into account. We evaluate the strategies and compare them with strategies implemented in the alignment system SAMBO. We also evaluate the combination of the proposed strategies and the SAMBO strategies.

A method for recommending ontology alignment strategies

In different areas ontologies have been developed and many of these ontologies contain overlapping information. Often we would therefore want to be able to use multiple ontologies. To obtain good results, we need to find the relationships between terms in the different ontologies, i.e. we need to align them. Currently, there already exist a number of different alignment strategies. However, it is usually difficult for a user that needs to align two ontologies to decide which of the different available strategies are the most suitable. In this paper we propose a method that provides recommendations on alignment strategies for a given alignment problem. The method is based on the evaluation of the different available alignment strategies on several small selected pieces from the ontologies, and uses the evaluation results to provide recommendations. In the paper we give the basic steps of the method, and then illustrate and discuss the method in the setting of an alignment problem with two well-known biomedical ontologies. We also experiment with different implementations of the steps in the method.

Repairing the Missing is-a Structure of Ontologies

Developing ontologies is not an easy task and often the resulting ontologies are not consistent or complete. Such ontologies, although often useful, also lead to problems when used in semantically-enabled applications. Wrong conclusions may be derived or valid conclusions may be missed. To deal with this problem we may want to repair the ontologies. Up to date most work has been performed on finding and repairing the semantic defects such as unsatisfiable concepts and inconsistent ontologies. In this paper we tackle the problem of repairing modeling defects and in particular, the repairing of structural relations (is-a hierarchy) in the ontologies. We study the case where missing is-a relations are given. We define the notion of a structural repair and develop algorithms to compute repairing actions that would allow deriving the missing is-a relations in the repaired ontology. Further, we define preferences between repairs. We also look at how we can use external knowledge to recommend repairing actions to a domain expert. Further, we discuss an implemented prototype and its use as well as an experiment using the ontologies of the Anatomy track of the Ontology Alignment Evaluation Initiative.

Selecting an Ontology for Biomedical Text Mining

Text mining for biomedicine requires a significant amount of domain knowledge. Much of this information is contained in biomedical ontologies. Developers of text mining applications often look for appropriate ontologies that can be integrated into their systems, rather than develop new ontologies from scratch. However, there is often a lack of documentation of the qualities of the ontologies. A number of methodologies for evaluating ontologies have been developed, but it is difficult for users by using these methods to select an ontology. In this paper, we propose a framework for selecting the most appropriate ontology for a particular text mining application. The framework comprises three components, each of which considers different aspects of requirements of text mining applications on ontologies. We also present an experiment based on the framework choosing an ontology for a gene normalization system.

Structure-Based Filtering for Ontology Alignment

Ontologies are an important technology for the semantic Web and many ontologies have already been developed. Many ontologies also contain overlapping information and to be able to use them together effectively, we need to align them. Some of the current alignment techniques use information about the structure of the ontologies, but they have not produced good results in evaluations. We propose an approach where, in contrast to the other approaches, structural information is used as a filtering method in the alignment process. We evaluate the approach in terms of quality and performance

Information integration in bioinformatics with ontologies and standards

New experimental methods allow researchers within molecular and systems biology to rapidly generate larger and larger amounts of data. This data is often made publicly available on the Internet and although this data is extremely useful, we are not using its full capacity. One important reason is that we still lack good ways to connect or integrate information from different resources. One kind of resource is the over 1000 data sources freely available on the Web. As most data sources are developed and maintained independently, they are highly heterogeneous. Information is also updated frequently. Other kinds of resources that are not so well-known or commonly used yet are the ontologies and the standards. Ontologies aim to define a common terminology for a domain of interest. Standards provide a way to exchange data between data sources and tools, even if the internal representations of the data in the resources and tools are different. In this chapter we argue that ontological knowledge and standards should be used for integration of data. We describe properties of the different types of data sources, ontological knowledge and standards that are available on the Web and discuss how this knowledge can be used to support integrated access to multiple biological data sources. Further, we present an integration approach that combines the identified ontological knowledge and standards with traditional information integration techniques. Current integration approaches only cover parts of the suggested approach. We also discuss the components in the model on which much recent work has been done in more detail: ontology-based data source integration, ontology alignment and integration using standards. Although many of our discussions in this chapter are general we exemplify mainly using work done within the REWERSE working group on Adding Semantics to the Bioinformatics Web.

A framework for aligning ontologies

Ontologies are an important technology for the Semantic Web. In different areas ontologies have already been developed and many of these ontologies contain overlapping information. Often we would therefore want to be able to use multiple ontologies and thus the ontologies need to be aligned. Currently, there exist a number of systems that support users in aligning ontologies, but not many comparative evaluations have been performed. In this paper we present a general framework for aligning ontologies where different alignment strategies can be combined. Further, we exemplify the use of the framework by describing a system (SAMBO) that is developed according to this framework. Within this system we have implemented some already existing alignment algorithms as well as some new algorithms. We also show how the framework can be used to experiment with combinations of strategies. This is a first step towards defining a framework that can be used for comparative evaluations of alignment strategies. For our tests we used several well-known bio-ontologies.

Ontology Alignment and Merging

In recent years many biomedical ontologies, including anatomy ontologies, have been developed. Many of these ontologies contain overlapping information and often we would want to be able to use multiple ontologies. This requires finding the relationships between terms in the different ontologies, i.e. we need to align them. Sometimes we also want to merge ontologies into a new one.