<span>Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.</span><span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.</span>
Balancing control, usability and visibility of linked open government data to create public value
