vantages
The framework’s most important challenge is to be able to coherently control the underlined technologies and manage the domain requirements. This however is un- surprising since Web 3.0 is by definition a web of collaborating technologies and disciplines. Any holistic framework proposed for Web 3.0 has to take notice of that fact and incorporate its key technologies and players.
Being ontology-driven, Web3.OWL’s major advantage in this regard is that it re- lies on standard grounds for representing terms and concepts, and classifying them according to their expressivity and features, thereby supporting easy transmission and interpretation of data for various applications.
Accordingly, a basic framework prerequisite is the ontology-modeling know-how and the maintenance of the modeled knowledge in the extended SMOF metamodel. Any deficiency at this level will lead to serious implications at later stages of the process.
Thus, having described Web3.OWL and examined its features, an evaluation of its challenging aspects reveals the following considerations:
• An evident challenge for an ontology-driven framework is to master the methods and best practices of ontology modeling and management. Having established the ontology as an interoperability-enabler across the multiple framework com- ponents and disciplines, it is essential to set up techniques to manage its com- plexity, size and constraints. The expressiveness management approach with projection and modularization based on rigorous logical formalisms plays an important role at this level.
• The framework’s exploitation of other disciplines, including data mining, NLP and NLG techniques makes it apt to inherit these disciplines’ accuracy and am- biguity constraints. The semantic tagging layer overcomes these limitations; this layer depends on the taggers’ willingness to cooperate. The Web 2.0 community promotes high expectations, based on evidence, as to the gradual increase of this cooperation willingness.
• A very specialized level of expertise and know-how in ontology and metadata modeling is required; such expertise cannot be easily made available. This is a known drawback of MDA. While there exists an exhaustive reliance on model- ing and correctness of critical metadata and configurations (for semantics and scenarios), errors and inconsistencies at their level have important effects on the whole flow.
There is also a reliance on the expertise of the knowledge engineer to deal with and correct any potential inconsistencies in metadata and ontological informa- tion.
• Web3.OWL’s full implementation brings in several complications, especially if all possible configurations and possibilities are to be made available. The incom- pleteness in the metamodeling requirements prevent the adaptation of transfor- mations that should be made available through the standardized components. The project could also be strongly boosted by a certain level of industrial sup- port, or by particular Web 2.0 sites pushing generated tag recommendations in
the framework.
• While Web3.OWL benefits from the enhancements and novelties of the Seman- tic Web and Description Logics, it is also constrained by limitations in the areas that are least subject to the efficient advances (for instance OWL Full support; querying facilities for certain complex roles and axioms; etc.).
Again at this stage, the underlined projection facilities and algorithms by mod- ularizing and/or trimming down the knowledge look for the appropriate least depreciated subsets, indirectly overcoming the aforementioned constraints. In parallel, the framework suggests methods to make use of extra expressiveness in other contextual scenarios.
While the field is in constant progress with an evolutionary research effort, the continuously positive impacts will automatically be reflected in the framework’s collaboration platform. This was the main aim behind its standard-based con- ception which included extended means and methods for interoperability. For example, if new or variations of the existing OWL profiles are proposed along with applicable reasoners that are specifically designed to efficiently pro- cess them, the existent metamodel will allow its integration in the framework. Similarly to how the current fragments are identified and projected, the data in the meta-semantics will allow the extraction of other potential fragments, to their associated most suitable reasoners, use cases and so on.
As another example, if a new SN ontology proves its usefulness and prolifer- ation on the net, the means to integrate, describe and map it to the existing ontologies in the repository are all available.
On the other hand, apart from the already underlined Web3.OWL characteristics, it is worth re-emphasizing its underlying principle:
A knowledge base for reliable sociomedical modeled facts enriched with semanti- cally engineered social data. This data consists in populated individuals describing
the community, and the categorization of resources according to specific ontological axioms.
As the KB Web 3.0 data is made readily accessible for further extensive reasoning and analysis, its reached outcomes surpass by far the sum of its social and semantic data components. The whole process typically leads to significant services, recommender and decision support systems.
Taking into consideration the applicable involved reasoning, the opportunity of iden- tifying, creating and expanding social and semantic networks is presented.
Implemented algorithms allow opinion mining, detection of ties and similarities be- tween people, leading to connections via shared interests or any possible common ground areas.
Social Networks can be deduced through the users’ joint actions and interactions, their created, commented upon, linked to, or similarly annotated contents.
Many aspects of the conclusions and findings are thus related to the concept of “object-centered sociality”, which connects people via the common interests asso- ciated with their occupations, hobbies, jobs, etc.
We can further highlight the following analogous potentials and benefits of the conceptual framework. They serve the purposes of recommender and decision-support systems:
• User profiling, clustering and segmentation based on certain traits and criteria, all of which are endeavors considered closely related to opinion mining and sentiment analysis undertakings
• Tracking processes to identify a user’s Web history from different Web 2.0 plat- forms, outlining this user’s general overall contributions to the Web and report- ing their different activities, goals and problems
• Improved quality of the search process, with ego-centric algorithms and searches to identify a key user’s associated or closely related nodes, as well as community
3.7
Conclusion
This chapter was dedicated for the holistic proposed framework, pointing out its global flow’s details on the one hand, and its most impacting principles and compo- nents on the other.
Its role was to present the framework, i.e. the basic conceptional structure. It highlighted the big impact of the metamodel and the knowledge base, and the role of the ontology driving the rest of the components. It did not tackle implementation- related aspects and detailed use case examples; these will be left to Chapter 6.
The next two chapters will reveal a more analytic approach to present the aspects considered at the heart of the framework’s originality.
Chapter 4
Expressiveness Management
Approach
4.1
Introduction
When one of the main purposes of Web3.OWL is to promote the usage of high ex- pressiveness and go beyond available RDF-based efforts, it is essential for it to adopt a particular approach to handle this expressiveness in such a way that it does not eventually become an obstructing factor of the framework, due to the tradeoff be- tween performance and expressiveness.
This chapter makes a particular emphasis on the ontology-driven suggested method to model, arrange and manage the expressive knowledge. It starts with a recapitu- lation of the state of the art exploited and possibly exploitable efforts (Section 4.2), and in Section 4.3, it states the expressiveness drawbacks due to which the proposed framework calls for an approach to manage expressiveness. Following that, in Sec- tion 4.4, it revisits the already introduced SMOF-based extensions to develop the packages related to this chapter, and the way they handle particular expressiveness elements. Section 4.5 encapsulates the whole to then reveal and explain the algorithm that first processes the sublanguage projected ontology, and accordingly assesses the deprecated and depreciated knowledge. At the end, Sections 4.6 and 4.7 wrap up the