Applications of Ontologies and Text Mining in the Biomedical Domain

Ontologies represent domain knowledge that improves user interaction and interoperability between applications. In addition, ontologies deliver precious input to text mining techniques in the biomedical domain, which might improve the performance in different text mining tasks. This chapter will explore on the mutual benefits for ontologies and text mining techniques. Ontology development is a time consuming task. Most efforts are spent in the acquisition of terms that represent concepts in real life. This process can use the existing scientific literature and the World Wide Web. The identification of concept labels, i.e. terms, from these sources using text mining solutions improves ontology development since the literature resources make reference to existing terms and concepts. Furthermore, automatic text processing techniques profit from ontological resources in different tasks, for example in the disambiguation of terms and the enrichment of terminological resources for the text mining solution. One of the most important text mining tasks that exploits ontological resources consists of the mapping of concepts to terms in textual sources (e.g. named entity recognition, semantic indexing) and the expansion of queries in information retrieval.

Semantic Enrichment of Web Service Architecture

The SOA: Service Oriented Architecture is a paradigm which allows the unification in the approaches of integration of the information systems. This data integration of shared semantic description of handled by the services. This integration of the data is more flexible considering the limited number of the concepts used by the services. Therefore, architecture is suggested in order to reduce domain ontologies development and integration complexity. It allows also finding and automatic invocation of the services. Ontologies are integrated without doing major changes in operating mode of web services like HTTP, SOAP. This chapter presents an architecture which is a step towards its automation through the semantic web services without redefining the information system completely.

Ontology Learning from Thesauri

Ontology learning is the term used to encompass methods and techniques employed for the (semi-)automatic processing of knowledge resources that facilitate the acquisition of knowledge during ontology construction. This chapter focuses on ontology learning techniques using thesauri as input sources. Thesauri are one of the most promising sources for the creation of domain ontologies thanks to the richness of term definitions, the existence of a priori relationships between terms, and the consensus provided by their extensive use in the library context. Apart from reviewing the state of the art, this chapter shows how ontology learning techniques can be applied in the urban domain for the development of domain ontologies.

Ontology Based Multimedia Indexing

The chapter goal is to provide responses to the following question: how the ontologies could be used in order to index and manage the multimedia collections? Alongside with reviewing the main standard formats, vocabularies and ontology categories developed especially for multimedia content description, the chapter emphasis the existing techniques for acquiring ontology-based indexing. Since a fully automatic such technique is not possible yet, the chapter also proposes a solution for indexing a multimedia collection by combining technologies from both semantic Web and multimedia indexation domains. The solution considers the management of multimedia metadata based on two correlated dictionaries: a metadata dictionary centralizes the multimedia metadata obtained through an automatic indexation process, while the visual concepts dictionary identifies the list of visual objects contained in multimedia documents and considered in the ontology-based annotation process. This approach facilitates as well the multimedia retrieval process.

Reusing the Inter-Organizational Knowledge to Support Organizational Knowledge Management Process

Various types of Knowledge Management approaches have been developed that only focus on managing organizational knowledge. These approaches are inadequate because employees often need to access knowledge from external knowledge sources in order to complete their works. Therefore, a new inter-organizational Knowledge Management practice is required to enhance knowledge sharing across organizational boundaries in their business networks. In this chapter, an ontology-based Inter-organizational knowledge Network that incorporates ontology mediation is developed so that heterogeneity of knowledge semantic in the ontologies could be reconciled. The reconciled inter-organizational knowledge could be reused to support organizational Knowledge Management process semi- or automatically. The authors also investigate the application of ontology mediation that provides mechanisms of reconciling inter-organizational knowledge in the network.

Approaches for Semantic Association Mining and Hidden Entities Extraction in Knowledge Base

Due to the rapidly increasing use of information and communications technology, Semantic Web technology is being increasingly applied in a large spectrum of applications in which domain knowledge is represented by means of an ontology in order to support reasoning performed by a machine. A semantic association (SA) is a set of relationships between two entities in knowledge base represented as graph paths consisting of a sequence of links. Because the number of relationships between entities in a knowledge base might be much greater than the number of entities, it is recommended to develop tools and invent methods to discover new unexpected links and relevant semantic associations in the large store of the preliminary extracted semantic association. Semantic association mining is a rapidly growing field of research, which studies these issues in order to create efficient methods and tools to help us filter the overwhelming flow of information and extract the knowledge that reflect the user need. The authors present, in this work, an approach which allows the extraction of association rules (SWARM: Semantic Web Association Rule Mining) from a structured semantic association store. Then, present a new method which allows the discovery of relevant semantic associations between a preliminary extracted SA and predefined features, specified by user, with the use of Hyperclique Pattern (HP) approach. In addition, the authors present an approach which allows the extraction of hidden entities in knowledge base. The experimental results applied to synthetic and real world data show the benefit of the proposed methods and demonstrate their promising effectiveness.

Ontology Theory, Management and Design

Ontologies now play an important role in providing a commonly agreed understanding of a domain and in developing knowledge-based systems. They intend to capture the intrinsic conceptual and semantic structure of a specific domain. Many methodologies, tools and languages are already available to help anthologies’ designers and users. However, a number of questions remain open: what ontology development methodology provides the best guidance to model a given problem, what steps to be performed in order to develop an ontology? which techniques are appropriate for each step? how ontology’ lifecycle steps are upheld by the software tools? how to maintain an ontology and to evolve it in a consistent way? how to adapt an ontology to a given context? To provide answers to these questions, the authors review in this chapter the main methodologies, tools and languages for building, updating and representing ontologies that have been reported in literature.

Ontology Evolution

Ontologies evolve continuously throughout their lifecycle to respond to different change requirements. Several problems emanate from ontology evolution: capturing change requirements, change representation, change impact analysis and resolution, change validation, change traceability, change propagation to dependant artifacts, versioning, etc. The purpose of this chapter is to gather research and current developments to manage ontology evolution. The authors highlight ontology evolution issues and present a state-of-the-art of ontology evolution approach by describing issues raised and the ontology model considered (ontology representation language), and also the ontology engineering tools supporting ontology evolution and maintenance. Furthermore, they sum up the state-of-the-art review by a comparative study based on general characteristics, evolution functionalities supported, and specificities of the existing ontology evolution approaches. At the end of the chapter, the authors discuss future and emerging trends.

An Algebra of Ontology Properties for Service Discovery and Composition in Semantic Web

The authors address in this chapter the problem of the automated discovery and composition of Web Services. Now, Service-oriented computing is emerging as a new and promising paradigm. However, selection and composition of Services to achieve an expected goal remain purely manual and time consuming tasks. Basing our approach on domain concept definitions thanks to an Ontology, the authors develop here an algebraic approach that enables to express formal definitions of Web Service semantics as well as user information needs. Both are captured by the means of algebraic expressions of ontology properties. They present an algorithm that generates efficient orchestration plans, with characteristics of optimality regarding Quality of Service. The approach has been validated by a prototype and an evaluation in the case of an Health Information System.

Large Scale Matching Issues and Advances

Nowadays, the Information technology domains (semantic web, E-business, digital libraries, life science, etc) abound with a large variety of data (e.g. DB schemas, XML schemas, ontologies) and bring up a hard problem: the semantic heterogeneity. Matching techniques are called to overcome this challenge and attempts to align these data. In this chapter, the authors are interested in studying large scale matching approaches. They survey the techniques of large scale matching, when a large number of schemas/ontologies and attributes are involved. They attempt to cover a variety of techniques for schema matching called Pair-wise and Holistic, as well as a set of useful optimization techniques. They compare the different existing schema/ontology matching tools. One can acknowledge that this domain is on top of effervescence and large scale matching needs many more advances. Then the authors provide conclusions concerning important open issues and potential synergies of the technologies presented.