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

an approach for semantic integration of heterogeneous data sources

Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semi-structured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (Data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view

An Ontology-Based Data Integration System for Data and Multimedia Sources

Data integration is the problem of combining data residing at distributed heterogeneous sources, including multimedia sources, and providing the user with a unified view of these data. Ontology based Data Integration involves the use of ontology(s) to effectively combine data and information from multiple heterogeneous sources [16]. Ontologies, with respect to the integration of data sources, can be used for the identification and association of semantically correspond- ing information concepts, i.e. for the definition of semantic mappings among concepts of the information sources. MOMIS is a Data Integration System which performs in-formation extraction and integration from both structured and semi- structured data sources [6]. In [5] MOMIS was extended to manage “traditional” and “multimedia” data sources at the same time. STASIS is a comprehensive application suite which allows enterprises to simplify the mapping process between data schemas based on semantics [1]. Moreover, in STASIS, a general framework to perform Ontology-driven Semantic Mapping has been pro-posed [7]. This paper describes the early effort to combine the MOMIS and the STASIS frameworks in order to obtain an effective approach for Ontology-Based Data Integration for data and multimedia sources

Knowledge Integration to Overcome Ontological Heterogeneity: Challenges from Financial Information Systems

The shift towards global networking brings with it many opportunities and challenges. In this paper, we discuss key technologies in achieving global semantic interoperability among heterogeneous information systems, including both traditional and web data sources. In particular, we focus on the importance of this capability and technologies we have designed to overcome ontological heterogeneity, a common type of disparity in financial information systems. Our approach to representing and reasoning with ontological heterogeneities in data sources is an extension of the Context Interchange (COIN) framework, a mediator-based approach for achieving semantic interoperability among heterogeneous sources and receivers. We also analyze the issue of ontological heterogeneity in the context of source-selection, and offer a declarative solution that combines symbolic solvers and mixed integer programming techniques in a constraint logic-programming framework. Finally, we discuss how these techniques can be coupled with emerging Semantic Web related technologies and standards such as Web-Services, DAML+OIL, and RuleML, to offer scalable solutions for global semantic interoperability. We believe that the synergy of database integration and Semantic Web research can make significant contributions to the financial knowledge integration problem, which has implications in financial services, and many other e-business tasks.Singapore-MIT Alliance (SMA

The mediated data integration (MeDInt) : An approach to the integration of database and legacy systems

The information required for decision making by executives in organizations is normally scattered across disparate data sources including databases and legacy systems. To gain a competitive advantage, it is extremely important for executives to be able to obtain one unique view of information in an accurate and timely manner. To do this, it is necessary to interoperate multiple data sources, which differ structurally and semantically. Particular problems occur when applying traditional integration approaches, for example, the global schema needs to be recreated when the component schema has been modified. This research investigates the following heterogeneities between heterogeneous data sources: Data Model Heterogeneities, Schematic Heterogeneities and Semantic Heterogeneities. The problems of existing integration approaches are reviewed and solved by introducing and designing a new integration approach to logically interoperate heterogeneous data sources and to resolve three previously classified heterogeneities. The research attempts to reduce the complexity of the integration process by maximising the degree of automation. Mediation and wrapping techniques are employed in this research. The Mediated Data Integration (MeDint) architecture has been introduced to integrate heterogeneous data sources. Three major elements, the MeDint Mediator, wrappers, and the Mediated Data Model (MDM) play important roles in the integration of heterogeneous data sources. The MeDint Mediator acts as an intermediate layer transforming queries to sub-queries, resolving conflicts, and consolidating conflict-resolved results. Wrappers serve as translators between the MeDint Mediator and data sources. Both the mediator and wrappers arc well-supported by MDM, a semantically-rich data model which can describe or represent heterogeneous data schematically and semantically. Some organisational information systems have been tested and evaluated using the MeDint architecture. The results have addressed all the research questions regarding the interoperability of heterogeneous data sources. In addition, the results also confirm that the Me Dint architecture is able to provide integration that is transparent to users and that the schema evolution does not affect the integration

The CIDOC CRM, an Ontological Approach to Schema Heterogeneity

The CIDOC Conceptual Reference Model (CRM), now ISO/CD21127, is a core ontology that aims at enabling information exchange and integration between heterogeneous sources of cultural heritage information, archives and libraries. It provides semantic definitions and clarifications needed to transform disparate, heterogeneous information sources into a coherent global resource, be it within a larger institution, in intranets or on the Internet. It is argued that such an ontology is property-centric, compact and highly generic, in contrast to terminological systems. The presentation will demonstrate how such a well-crafted core ontology can help to achieve a very high precision of schema integration at reasonable cost in a huge, diverse domain. It is further argued that such ontologies are widely reusable and adaptable to other domains which makes their development cost effective

Semantic Integration of Coastal Buoys Data using SPARQL

Currently, the data provided by the heterogeneous buoy sensors/networks (e.g. National Data Buoy center (NDBC), Gulf Of Maine Ocean Observing System (GoMoos) etc. is not amenable to the development of integrated systems due to conflicts in the data representation at syntactic and structural levels. With the rapid increase in the amount of information, the integration of heterogeneous resources is an important issue and requires integrative technologies such as semantic web. In distributed data dissemination system, normally querying on single database will not provide relevant information and requires querying across interrelated data sources to retrieve holistic information. In this thesis we develop system for integrating two different Resource Description Framework (RDF) data sources through intelligent querying using Simple Protocol and RDF Query Language (SPARQL). We use Semantic Web application framework from AllegroGraph that provides functionality for developing triple store for the ontological representations, forming federated stores and querying it through SPARQL

Ontology-based data integration methods: a framework for comparison

A data integration system provides a uniform interface to distributed and heterogeneous sources. These sources can be databases as well as unstructured information such as files, HTML pages, etc. One of the most important problems within data integration is the semantic heterogeneity, which analyzes the meaning of terms included in the different information sources. This survey describes seven systems and three proposals for ontology -based data integration. An important feature is that all of them use, in some way, ontologies as the way to solve problems about semantic heterogeneity. In this paper, we show similarities and differences among the systems by providing a framework for comparison and classification.Keywords: Data Integration, Ontology, Semantic Heterogeneity