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 are 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.

In this paper we show how the joint use of metamodeling and ontologies allows to describe domain knowledge for a complex domain. Ontologies are used as stabilized descriptions of the business domain while metamodels allow a fine description of the domain (to be constructed in the initial phases of modeling). We propose to use an ontology for anticipated categorization, i.e., as a “natural ” complement of the formal system which is induced by the metamodel. 1

The Semantic Web of the future will be characterized by using a very large number of ontologies embedded in ontology networks. It is important to provide strong methodological support for collaborative and context-sensitive development of networks of ontologies. This methodological support includes the identification and definition of which activities should be carried out when ontology networks are collaboratively built. In this paper we present the consensus reaching process followed within the NeOn consortium for the identification and definition of the activities involved in the ontology network development process. The consensus reaching process here presented produces as a result the NeOn Glossary of Activities. This work was conceived due to the lack of standardization in the Ontology Engineering terminology, which clearly contrasts with the Software Engineering field. Our future aim is to standardize the NeOn Glossary of Activities. 1.

The goal of recent research projects on integrating Web databases has been to enable uniform access to the large amount of data behind query interfaces. Among the tasks addressed are: source discovery, query interface extraction, schema matching, etc. There are also a number of tasks that are commonly ignored or assumed to be apriori solved either manually or by some oracle. These tasks include (1) finding the set of stop words and (2) handling occurrences of “semantic enrichment words ” within labels. These two subproblems have a direct impact on determining the synonymy and hyponymy relationships between labels. In (1), a word like “from ” is a stop word in general but it is a content word in domains such as Airline and Real Estate. We formulate the stop word problem, prove its complexity and provide an approximation algorithm. In (2), we study the impact of words like AND and OR on establishing semantic relationships between labels (e.g. “departure date and time ” is a hypernym of “departure date”). In addition, we develop a theoretical framework to differentiate synonymy relationship from hyponymy relationship among labels involving multiple words. We scrutinize its strength and limitations both analytically and experimentally. We use real data from the Web in our experiments. We analyze over 2300 labels of 220 user interfaces in 9 distinct domains.

Homogenization has been the most widely applied approach to create common semantic spaces when it is necessary to overcome the limitations of sharing knowledge posed by semantically heterogeneous concept structures. Ontology

Taxonomies are widely used to classify information, and multiple (possibly competing) taxonomies often exist for the same domain. Given a set of correspondences between two taxonomies, it is often necessary to “merge ” the taxonomies, thereby creating a unified taxonomy (e.g., that can then be used by data integration and discovery applications). We present an algorithm for merging taxonomies that have been related using articulations given as RCC-5 constraints. Two taxa N and M can be related using (disjunctions of) the five base relations in RCC-5: N ≡ M; N � M; N �; N ⊕ M (partial overlap of N and M); and N! M (disjointness: N ∩ M = ∅). RCC-5 is increasingly being adopted by scientists to specify mappings between large species taxonomies. We discuss the properties of the proposed merge algorithm and evaluate our approach using real-world biological taxonomies. Categories and Subject Descriptors