Semantic Web technology, ontologies, and semantically described web services have now demonstrated their maturity and usefulness in a number of applications. However, their wide-scale adoption is hampered by the relatively high adoption costs, mainly through the need of employing specialist knowledge engineers, the manual ontology creation effort for the domain experts, and the need to deal with diverse, heterogeneous legacy data and software.
The migration problem is particularly difficult in legacy applications, which tend to be:
- Built with languages and data models that are now out-dated
- Badly structured and hard to maintain
- Badly documented and understood
- Difficult to integrate with each other and with new systems
- Need for migration towards Web 2.0 applications and services
The goal of TAO is to make transitioning existing 'legacy' applications to ontologies fast and effective, thus allowing companies to:
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Build a reusable transitioning process;
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Minimise consulting time during migration and integration;
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Minimize costs;
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Reduce integration overheads and limit risk.
We created an open source infrastructure to aid transitioning of legacy applications to ontologies, through automatic ontology bootstrapping, semantic content augmentation, and generation of semantic web service descriptions. The work is grounded in the TAO transitioning methodology and the tools are integrated into the TAO Suite. In this way, TAO enables a much larger group of companies to exploit semantics without having to re-implement their applications.
The results are validated in two high-profile case studies: a comprehensive open source platform (with thousands of users) and a data-intensive business process application (managing a multi-million business).
Project objectives
The process of transforming monolithic, legacy applications into semantic ones requires new research in several areas, which constitute the three RTD objectives of TAO:
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Bootstrapping via semi-automatic acquisition of domain ontologies (Objective 1 in the figure below). The main research objective of TAO is to identify the major characteristics of the data sources required for learning domain ontologies suitable for annotation of legacy content and services; choose the right extraction methods; and support human corrections of automatically learned domain ontologies as part of the application transitioning process.
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Augmentation and integration of legacy content (databases and documents used by applications) relative to the domain ontologies to enable ontology-based information access (Objective 2 in the figure below). A new heterogeneous knowledge store was developed to support scalability and heterogeneity. Efficient support for a combination of structured/semantic queries and keyword search (IR-style) queries is now in place, as are a number of content augmentation tools which enrich legacy data with semantics.
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Transitioning Methodology and Infrastructure (Objective 3 in the figure below). TAO provides an innovative infrastructure for transitioning legacy applications to semantic- and service-based ones via semi-automatic bootstrapping.
TAO tackles several major bottlenecks of knowledge technologies in the areas of semi-automatic creation of ontologies; automated methods for metadata creation and augmentation of legacy content; and distributed heterogeneous repositories.
The project builds on and enhances research and technology from diverse areas into an infrastructure for transitioning legacy systems.
To access latest project presentation click here
If you want to know more about TAO Project contact Kalina Bontcheva
