Open-Ended Evolution and a Mechanism of Novelties in Web Services

Web services are analogous to living ecosystems in nature, in that they form an artificial ecosystem consisting of many tags and their associated media, such as photographs, movies, and web pages created by human users. In biological ecosystems, we view a tag as a species and a human as a hidden environmental resource. Our study examines the evolution of web services, in particular social tagging systems, with respect to the self-organization of new tags. The evolution of new combinations of tags is analyzed as an open-ended evolution (OEE) index. Tag meaning is computed by types of associated tags, including tags that vary their meanings temporally, which, we argue, are examples of OEE.

Enabling social WEB for IoT inducing ontologies from social tagging

<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>