Topic
Ontology-based data integration
About: Ontology-based data integration is a research topic. Over the lifetime, 11065 publications have been published within this topic receiving 216888 citations.
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TL;DR: The goal of this article is to survey several of the most outstanding methodologies, methods and techniques that have emerged in the last years, and present the most popular development environments, which can be utilized to carry out, or facilitate specific activities within the methodologies.
Abstract: Building ontologies in a collaborative and increasingly community-driven fashion has become a central paradigm of modern ontology engineering. This understanding of ontologies and ontology engineering processes is the result of intensive theoretical and empirical research within the Semantic Web community, supported by technology developments such as Web 2.0. Over 6 years after the publication of the first methodology for collaborative ontology engineering, it is generally acknowledged that, in order to be useful, but also economically feasible, ontologies should be developed and maintained in a community-driven manner, with the help of fully-fledged environments providing dedicated support for collaboration and user participation. Wikis, and similar communication and collaboration platforms enabling ontology stakeholders to exchange ideas and discuss modeling decisions are probably the most important technological components of such environments. In addition, process-driven methodologies assist the ontology engineering team throughout the ontology life cycle, and provide empirically grounded best practices and guidelines for optimizing ontology development results in real-world projects. The goal of this article is to analyze the state of the art in the field of collaborative ontology engineering. We will survey several of the most outstanding methodologies, methods and techniques that have emerged in the last years, and present the most popular development environments, which can be utilized to carry out, or facilitate specific activities within the methodologies. A discussion of the open issues identified concludes the survey and provides a roadmap for future research and development in this lively and promising field.
78 citations
TL;DR: A proposed semantic ETL framework applies semantics to various data fields and so allows richer data integration in the extract-transform-load process.
Abstract: Current tools that facilitate the extract-transform-load (ETL) process focus on ETL workflow, not on generating meaningful semantic relationships to integrate data from multiple, heterogeneous sources. A proposed semantic ETL framework applies semantics to various data fields and so allows richer data integration.
77 citations
TL;DR: This work uses ontologies to organize and describe the medical concepts of both the source system and the target system and demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it.
Abstract: Data from the electronic medical record comprise numerous structured but uncoded ele-ments, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of rele-vant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can then be used to automatically generate SQL code to perform the desired ETL procedures. This demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it.
77 citations
01 Jan 2014
TL;DR: Aber-OWL provides a framework for automatically accessing information that is annotated with ontologies or contains terms used to label classes in ontologies that enable ontology-based semantic access to biological data and literature.
Abstract: Background
Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of automated reasoning.
77 citations
Patent•
16 Mar 2005
TL;DR: In this paper, the authors present methods and systems for migrating a data integration facility such as an ETL job from a source data integration platform to a target data integration platforms, where the target job is adapted to perform substantially the same functions as the source job.
Abstract: Methods and systems are provided for migrating a data integration facility, such as an ETL job, from a source data integration platform to a target data integration platform. Certain embodiments involve externalizing a metadata representation of a source data integration job; parsing the metadata representation; importing the parsed metadata into a plurality of object representations of the source data integration job; generating an intermediate representation of the source data integration platform based on the plurality of object representations; and translating the intermediate representation to generate a target data integration job; wherein the target data integration job is adapted perform substantially the same functions as the source data integration job.
77 citations