Semantic technology for open data publishing

After years of focus on technologies for big data storing and processing, many observers are pointing out that making sense of big data cannot be done without suitable tools for conceptualizing, preparing, and integrating data (see http://www.dbta.com/). Research in the last years has shown that taking into account the semantics of data is crucial for devising powerful data integration solutions. In this work we focus on a specific paradigm for semantic data integration, named "Ontology-Based Data Access" (OBDA), proposed in [1-4]. According to such paradigm, the client of the information system is freed from being aware of how data and processes are structured in concrete resources (databases, software programs, services, etc.), and interacts with the system by expressing her queries and goals in terms of a conceptual representation of the domain of interest, called ontology. More precisely, a system realizing the vision of OBDA is constituted by three components: The ontology, whose goal is to provide a formal, clean and high level representation of the domain of interest, and constitutes the component with which the clients of the system (both humans and software programs) interact. fiedata source layer, representing the existing data sources in the information system, which are managed by the processes and services operating on their data. e mapping between the two layers, which is an explicit representation of the relationship between the data sources and the ontology, and is used to translate the operations on the ontology (e.g., query answering) in terms of concrete actions on the data sources.

A semantic framework for web-based accommodation information integration

University of Technology, Sydney. Faculty of Engineering and Information Technology.With the tremendous growth of the Web, a broad spectrum of accommodation information is to be found on the Internet. In order to adequately support information users in collecting and sharing information online, it is important to create an effective information integration solution, and to provide integrated access to the vast numbers of online information sources. In addition to the problem of distributed information sources, information users also need to cope with the heterogeneous nature of the online information sources, where individual information sources are stored and presented following their own structures and formats. In this thesis, we explore some of the challenges in the field of information integration, and propose solutions to some of the arising challenges. We focus on the utilization of ontology for integrating heterogeneous, structured and semi-structured information sources, where instance level data are stored and presented according to meta-data level schemas. In particular, we looked at XML-based data that is stored according to XML schemas. In a first step towards a large-scale information integration solution, we propose a semantic integration framework. The proposed framework solves the problem of information integration on three levels: the data level, process level and architecture level. On the data level, we leverage the benefit of ontology, and use ontology as a mediator for enabling semantic interoperability among heterogeneous data sources. On the process level, we alter the process of information integration, and propose a three step integration process named as the publish-combine-use mechanism. The primary goal is to distribute the efforts of collecting and integrating information sources to various types of end users. In the proposed approach, information providers have more control over their own data sources, as data sources are able to join and leave the information sharing network according to their own preferences. On the architecture level, we combine the flexibility offered by the emerging distributed P2P approach with the query processing capability provided by the centralized approach. The joint architecture is similar to the structure of the online accommodation industry. This thesis also demonstrates the practical applicability of the proposed semantic integration framework by implementing a prototype system. The prototype system named the "accommodation hub" is specifically developed for integrating online accommodation information in the large, distributed, heterogeneous online environment. The proposed semantic integration solution and the implemented prototype system are evaluated to provide a measure of the system performance and usage. Results show that the proposed solution delivers better performance with respect to some of the evaluation criteria than some related approaches in information integration

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

The Requirements for Ontologies in Medical Data Integration: A Case Study

Evidence-based medicine is critically dependent on three sources of information: a medical knowledge base, the patients medical record and knowledge of available resources, including where appropriate, clinical protocols. Patient data is often scattered in a variety of databases and may, in a distributed model, be held across several disparate repositories. Consequently addressing the needs of an evidence-based medicine community presents issues of biomedical data integration, clinical interpretation and knowledge management. This paper outlines how the Health-e-Child project has approached the challenge of requirements specification for (bio-) medical data integration, from the level of cellular data, through disease to that of patient and population. The approach is illuminated through the requirements elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database Engineering & Applications Symposium (Ideas2007). Banff, Canada September 200

A Query Integrator and Manager for the Query Web

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions

Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)

Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data