Ontology-based composition and matching for dynamic cloud service coordination

Recent cross-organisational software service offerings, such as cloud computing, create higher integration needs. In particular, services are combined through brokers and mediators, solutions to allow individual services to collaborate and their interaction to be coordinated are required. The need to address dynamic management - caused by cloud and on-demand environments - can be addressed through service coordination based on ontology-based composition and matching techniques. Our solution to composition and matching utilises a service coordination space that acts as a passive infrastructure for collaboration where users submit requests that are then selected and taken on by providers. We discuss the information models and the coordination principles of such a collaboration environment in terms of an ontology and its underlying description logics. We provide ontology-based solutions for structural composition of descriptions and matching between requested and provided services

Un modello ontologico per l'integrazione delle informazioni del patrimonio culturale: Cidoc-crm

L’articolo intende presentare l’ontologia formale CIDOC-Conceptual Reference Model, utilizzata per rendere scambiabili e integrabili le informazioni e la documentazione in fonti eterogenee del patrimonio culturale, e da anni impostasi come ontologia di riferimento per il settore. Si analizzano in particolare i fondamenti logici sui quali essa si basa, entrando nel merito della Description Logics, e l’impostazione teoretica che soggiace alla modellazione dello schema. Si presenta la struttura delle relazioni (proprietà), vero fulcro dell’ontologia, e i meccanismi proposti per la integrazione delle informazioni culturali. Infine, il lavoro offre una breve rassegna, non esaustiva, dei sistemi informativi che oggi adottano l’ontologia CIDOC-CRM, da CLAROS al portale CulturaItalia.The article aims at presenting the formal ontology CIDOC-Conceptual Reference Model, used for allowing information and documentation exchange and integration into the diverse sources of cultural heritage, and which is the leading ontology in the field since years. The ontology logical foundations are examined, entering into the details of the Description Logics, and also the theoretical configuration underlying the schema modelling. The relation (properties) structure - which is the ontology core - is presented along with the mechanisms proposed for the integration of cultural heritage information. In the end, this essays offers a brief and non-exhaustive overview of the information systems actually adopting the CIDOC-CRM ontology, from CLAROS to CulturaItalia

The Form of Organization for Small Business

Matching and integrating ontologies has been a desirable technique in areas such as data fusion, knowledge integration, the Semantic Web and the development of advanced services in distributed system. Unfortunately, the heterogeneities of ontologies cause big obstacles in the development of this technique. This licentiate thesis describes an approach to tackle the problem of ontology integration using description logics and production rules, both on a syntactic level and on a semantic level. Concepts in ontologies are matched and integrated to generate ontology intersections. Context is extracted and rules for handling heterogeneous ontology reasoning with contexts are developed. Ontologies are integrated by two processes. The first integration is to generate an ontology intersection from two OWL ontologies. The result is an ontology intersection, which is an independent ontology containing non-contradictory assertions based on the original ontologies. The second integration is carried out by rules that extract context, such as ontology content and ontology description data, e.g. time and ontology creator. The integration is designed for conceptual ontology integration. The information of instances isn't considered, neither in the integrating process nor in the integrating results. An ontology reasoner is used in the integration process for non-violation check of two OWL ontologies and a rule engine for handling conflicts according to production rules. The ontology reasoner checks the satisfiability of concepts with the help of anchors, i.e. synonyms and string-identical entities; production rules are applied to integrate ontologies, with the constraint that the original ontologies should not be violated. The second integration process is carried out with production rules with context data of the ontologies. Ontology reasoning, in a repository, is conducted within the boundary of each ontology. Nonetheless, with context rules, reasoning is carried out across ontologies. The contents of an ontology provide context for its defined entities and are extracted to provide context with the help of an ontology reasoner. Metadata of ontologies are criteria that are useful for describing ontologies. Rules using context, also called context rules, are developed and in-built in the repository. New rules can also be added. The scientific contribution of the thesis is the suggested approach applying semantic based techniques to provide a complementary method for ontology matching and integrating semantically. With the illustration of the ontology integration process and the context rules and a few manually integrated ontology results, the approach shows the potential to help to develop advanced knowledge-based services.QC 20130201</p

The Distributed Ontology Language (DOL): Use Cases, Syntax, and Extensibility

The Distributed Ontology Language (DOL) is currently being standardized within the OntoIOp (Ontology Integration and Interoperability) activity of ISO/TC 37/SC 3. It aims at providing a unified framework for (1) ontologies formalized in heterogeneous logics, (2) modular ontologies, (3) links between ontologies, and (4) annotation of ontologies. This paper presents the current state of DOL's standardization. It focuses on use cases where distributed ontologies enable interoperability and reusability. We demonstrate relevant features of the DOL syntax and semantics and explain how these integrate into existing knowledge engineering environments.Comment: Terminology and Knowledge Engineering Conference (TKE) 2012-06-20 to 2012-06-21 Madrid, Spai

Ontology-Based Data Access and Integration

An ontology-based data integration (OBDI) system is an information management system consisting of three components: an ontology, a set of data sources, and the mapping between the two. The ontology is a conceptual, formal description of the domain of interest to a given organization (or a community of users), expressed in terms of relevant concepts, attributes of concepts, relationships between concepts, and logical assertions characterizing the domain knowledge. The data sources are the repositories accessible by the organization where data concerning the domain are stored. In the general case, such repositories are numerous, heterogeneous, each one managed and maintained independently from the others. The mapping is a precise specification of the correspondence between the data contained in the data sources and the elements of the ontology. The main purpose of an OBDI system is to allow information consumers to query the data using the elements in the ontology as predicates. In the special case where the organization manages a single data source, the term ontology-based data access (ODBA) system is used

On the similarity relation within fuzzy ontology components

Ontology reuse is an important research issue. Ontology merging, integration, mapping, alignment and versioning are some of its subprocesses. A considerable research work has been conducted on them. One common issue to these subprocesses is the problem of defining similarity relations among ontologies components. Crisp ontologies become less suitable in all domains in which the concepts to be represented have vague, uncertain and imprecise definitions. Fuzzy ontologies are developed to cope with these aspects. They are equally concerned with the problem of ontology reuse. Defining similarity relations within fuzzy context may be realized basing on the linguistic similarity among ontologies components or may be deduced from their intentional definitions. The latter approach needs to be dealt with differently in crisp and fuzzy ontologies. This is the scope of this paper.ou