An Ontology-Based Framework for Heterogeneous Data Sources Integration

Ontologies have been extensively used to model domain-specific knowledge. The main reason for this success is due to their capability to be at the “semantic” level, away from data structures and implementation strategies. In addition, ontology formalisms have allowed certain kinds of reasoning to be automated within a reasonable time complexity. Due to ontology data independence and automated reasoning, ontologies are well suited for integrating heterogeneous databases, enabling interoperability among isparate systems, and specifying interfaces to independent, knowledge-based services

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

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

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

KA-SB: from data integration to large scale reasoning

<p>Abstract</p> <p>Background</p> <p>The analysis of information in the biological domain is usually focused on the analysis of data from single on-line data sources. Unfortunately, studying a biological process requires having access to disperse, heterogeneous, autonomous data sources. In this context, an analysis of the information is not possible without the integration of such data.</p> <p>Methods</p> <p>KA-SB is a querying and analysis system for final users based on combining a data integration solution with a reasoner. Thus, the tool has been created with a process divided into two steps: 1) KOMF, the Khaos Ontology-based Mediator Framework, is used to retrieve information from heterogeneous and distributed databases; 2) the integrated information is crystallized in a (persistent and high performance) reasoner (DBOWL). This information could be further analyzed later (by means of querying and reasoning).</p> <p>Results</p> <p>In this paper we present a novel system that combines the use of a mediation system with the reasoning capabilities of a large scale reasoner to provide a way of finding new knowledge and of analyzing the integrated information from different databases, which is retrieved as a set of ontology instances. This tool uses a graphical query interface to build user queries easily, which shows a graphical representation of the ontology and allows users o build queries by clicking on the ontology concepts.</p> <p>Conclusion</p> <p>These kinds of systems (based on KOMF) will provide users with very large amounts of information (interpreted as ontology instances once retrieved), which cannot be managed using traditional main memory-based reasoners. We propose a process for creating persistent and scalable knowledgebases from sets of OWL instances obtained by integrating heterogeneous data sources with KOMF. This process has been applied to develop a demo tool <url>http://khaos.uma.es/KA-SB</url>, which uses the BioPax Level 3 ontology as the integration schema, and integrates UNIPROT, KEGG, CHEBI, BRENDA and SABIORK databases.</p

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

OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia

<p>Abstract</p> <p>Background</p> <p>The OpenTox Framework, developed by the partners in the OpenTox project (<url>http://www.opentox.org</url>), aims at providing a unified access to toxicity data, predictive models and validation procedures. Interoperability of resources is achieved using a common information model, based on the OpenTox ontologies, describing predictive algorithms, models and toxicity data. As toxicological data may come from different, heterogeneous sources, a deployed ontology, unifying the terminology and the resources, is critical for the rational and reliable organization of the data, and its automatic processing.</p> <p>Results</p> <p>The following related ontologies have been developed for OpenTox: a) Toxicological ontology – listing the toxicological endpoints; b) Organs system and Effects ontology – addressing organs, targets/examinations and effects observed in <it>in vivo</it> studies; c) ToxML ontology – representing semi-automatic conversion of the ToxML schema; d) OpenTox ontology– representation of OpenTox framework components: chemical compounds, datasets, types of algorithms, models and validation web services; e) ToxLink–ToxCast assays ontology and f) OpenToxipedia community knowledge resource on toxicology terminology.</p> <p>OpenTox components are made available through standardized REST web services, where every compound, data set, and predictive method has a unique resolvable address (URI), used to retrieve its Resource Description Framework (RDF) representation, or to initiate the associated calculations and generate new RDF-based resources.</p> <p>The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications, tailored to solving different problems by end users (e.g. toxicologists).</p> <p>Availability</p> <p>The OpenTox toxicological ontology projects may be accessed via the OpenTox ontology development page <url>http://www.opentox.org/dev/ontology</url>; the OpenTox ontology is available as OWL at <url>http://opentox.org/api/1 1/opentox.owl</url>, the ToxML - OWL conversion utility is an open source resource available at <url>http://ambit.svn.sourceforge.net/viewvc/ambit/branches/toxml-utils/</url></p

Enabling semantic queries across federated bioinformatics databases

MOTIVATION: Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases. RESULTS: We introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: (i) Bgee, a gene expression relational database; (ii) Orthologous Matrix (OMA), a Hierarchical Data Format 5 orthology DS; and (iii) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialized RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface

Disaster Data Management in Cloud Environments

Facilitating decision-making in a vital discipline such as disaster management requires information gathering, sharing, and integration on a global scale and across governments, industries, communities, and academia. A large quantity of immensely heterogeneous disaster-related data is available; however, current data management solutions offer few or no integration capabilities and limited potential for collaboration. Moreover, recent advances in cloud computing, Big Data, and NoSQL have opened the door for new solutions in disaster data management. In this thesis, a Knowledge as a Service (KaaS) framework is proposed for disaster cloud data management (Disaster-CDM) with the objectives of 1) facilitating information gathering and sharing, 2) storing large amounts of disaster-related data from diverse sources, and 3) facilitating search and supporting interoperability and integration. Data are stored in a cloud environment taking advantage of NoSQL data stores. The proposed framework is generic, but this thesis focuses on the disaster management domain and data formats commonly present in that domain, i.e., file-style formats such as PDF, text, MS Office files, and images. The framework component responsible for addressing simulation models is SimOnto. SimOnto, as proposed in this work, transforms domain simulation models into an ontology-based representation with the goal of facilitating integration with other data sources, supporting simulation model querying, and enabling rule and constraint validation. Two case studies presented in this thesis illustrate the use of Disaster-CDM on the data collected during the Disaster Response Network Enabled Platform (DR-NEP) project. The first case study demonstrates Disaster-CDM integration capabilities by full-text search and querying services. In contrast to direct full-text search, Disaster-CDM full-text search also includes simulation model files as well as text contained in image files. Moreover, Disaster-CDM provides querying capabilities and this case study demonstrates how file-style data can be queried by taking advantage of a NoSQL document data store. The second case study focuses on simulation models and uses SimOnto to transform proprietary simulation models into ontology-based models which are then stored in a graph database. This case study demonstrates Disaster-CDM benefits by showing how simulation models can be queried and how model compliance with rules and constraints can be validated

Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting

In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).Comment: 5 pages, 3 figures, In Proceedings of the Doctoral Symposium of the 16th International Middleware Conference (Middleware Doct Symposium 2015), Ivan Beschastnikh and Wouter Joosen (Eds.). ACM, New York, NY, US