Ontology Extraction Using Views for Semantic Web

The emergence of Semantic Web (SW) and the related technologies promise to make the web a meaningful experience. However, high level modeling, design and querying techniques proves to be a challenging task for organizations that are hoping to utilize the SW paradigm for their industrial applications. To address one such issue, in this paper, we propose an abstract view model with conceptual extensions for the SW. First we outline the view model, its properties and some modeling issues with the help of an industrial case study example. Then, we provide some discussions on constructing such views (at the conceptual level) using a set of operators. Later we provide a brief discussion on how such this view model can utilized in the MOVE [1] system, to design and construct materialized Ontology views to support Ontology extraction.KeywordsSemantic Web (SW)view modelsOntology viewsObject-Oriented conceptual models (OOCM)conceptual viewsOntology extraction

Frame-based ontological view for semantic integration

In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data format for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly important role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of the Semantic Web's data structure. We discuss some of the particular aspects of learning that we expect will be of relevance for the Semantic Web such as scalability, missing and contradicting data, and the potential to integrate ontological background knowledge. In addition we review some of the work on the learning of ontologies and on the population of ontologies, mostly in the context of textual data.

Semantic integration is crucial for successful collaboration between heterogeneous information systems in an open environment. Traditional ontology-driven approaches rely on the availability of explicit ontologies. However, in many domains, this prerequisite cannot be met. In order to address this issue, this paper investigates the theoretical foundation of ontologies and extends the traditional ontology concept to an ontological view concept. To explicitly and formally specify the ontological views, a Frame-based Ontological view Specification Language (FOSL) is proposed. This language is based on the Frame knowledge representation paradigm and uses XML as the encoding. A prototype environment that supports semantic integration based on ontological views specified with FOSL is introduced.

The Semantic Web offers significant capabilities that transform the current Web into a global knowledge base including various cross-linked multimedia content with formal descriptions of its semantics understandable to humans and processable by computers. Content on the Semantic Web can be subject to reasoning and queries with standardized languages, methods and tools, which opens new opportunities for collaborative creation, use and exploration of web repositories. However, these opportunities have not been exploited so far by the available 3D formats and modeling tools, which limits the possibilities of search and reuse of 3D content as part of the Semantic Web. This work contributes a semantic development pipeline of the X3D Ontology, with corresponding conversion of X3D models into triple forms suitable for formal query. The ontology design reflects experience accompanying the development of the Extensible 3D (X3D) Graphics International Standard, in particular, the X3D Unified Object Model (X3DUOM). This approach combines semantic and syntactic elements of X3D models and metadata to support integration with the Semantic Web. The pipeline enables automatic generation of the X3D Ontology, thereby providing an up-to-date 3D representation with semantics during X3D specification development. By extending commonplace model conversions from other formats to X3D, the ontology presents the potential to enable integration of most forms of 3D content with the Semantic Web.

One fundamental issue for semantic web application is how to define the mapping between relational model and RDF model, so that the legacy data in relational databases can be integrated into semantic web. In this paper, we propose an view-based approach to mediate relational schema using RDF-based ontology. We formally define the Ontology View, and precisely define the semantics of answering queries using ontology view. With our approach, we highlight the important role played by RDF Blank Node in defining semantic mappings and representing the incomplete part of relational schema.

Ontology provides a shared and reusable piece of knowledge about a specific domain, and has been applied in many fields, such as semantic Web, e-commerce and information retrieval, etc. However, building ontology by hand is a very hard and error-prone task. Learning ontology from existing resources is a good solution. Because relational database is widely used for storing data and OWL is the latest standard recommended by W3C, this paper proposes an approach of learning OWL ontology from data in relational database. Compared with existing methods, the approach can acquire ontology from relational database automatically by using a group of learning rules instead of using a middle model. In addition, it can obtain OWL ontology, including the classes, properties, properties characteristics, cardinality and instances, while none of existing methods can acquire all of them. The proposed learning rules have been proven to be correct by practice.

Most of the challenges faced when building the Semantic Web require a substantial amount of human labor and intelligence. Despite significant advancement in ontology learning and human language technology, the tasks of ontology construction, semantic annotation, and establishing alignments between multiple ontologies remain highly dependent on human intelligence. This means that individuals need to contribute time and sometimes other resources. Unfortunately, we observe a serious lack of user involvement in the aforementioned tasks, which may be due to the absence of motivations for people who contribute. As a novel solution, we (1) propose to masquerade the core tasks of weaving the Semantic Web behind online, multi-player game scenarios, in order to create proper incentives for human users to get involved. Doing so, we adopt the findings from the already famous “games with a purpose” by von Ahn, who has shown that presenting a useful task, which requires human intelligence, in the form of an online game can motivate a large amount of people to work heavily on this task, and this for free. Then, we (2) describe our generic OntoGame platform, and (3) several gaming scenarios for various tasks plus our respective prototypes. Based on the analysis of user data and interviews with players, we provide preliminary evidence that users (4) enjoy the games and are willing to dedicate their time to those games, (5) are able to produce high-quality conceptual choices. Eventually we show how users entertaining themselves by online games can unknowingly help weave and maintain the Semantic Web.

This work introduces a formal framework for the social acquisition of ontologies which are constructed dynamically from overhearing the possibly conflicting symbolic interaction of autonomous information sources, and an approach to the pragmatics of communicated ontological axioms. Technically, the framework is based on distributed variants of description logic for the formal contextualization of statements w.r.t. their respective provenance, speaker's attitude, addressees, and subjective degree of confidence. Doing so, our approach demarcates from the dominating more or less informal approaches to context and provenance representation on the semantic web, and carefully distinguishes between communication attitudes such as public assertion and intention exhibited on the (semantic) web on the one hand, and mental attitudes such as private belief on the other. Furthermore, our framework provides formal means for the probabilistic fusion of controversial opinions, and presents a semantics of information publishing acts. Our approach provides an incomplex and genuinely social approach to knowledge acquisition and representation, and is thus expected to be widely applicable in fields such as the semantic web and social software, and possibly also in other distributed environments such as P2P systems.

The Web contains a lot of information but computers can't do very much with it. We have access to and a need for a lot of information (thanks to pervasive information devices such as mobile phones, PDAs, smart devices, TV, radio...) but we don't have time to find and make associations between information every time we want to. Given the need for quick, intuitive access to information, multimedia presentation is an effective form of communication - different media act as a better means to communicate dependant on the context, and synchronization (organisation of media in space and in time) can express non- verbally relationships between the content (e.g. linear presentation to represent a timeline of events). Appropriately delivering content in the form of synchronized multimedia is a complex task which needs to meet the requirements of the user, device, and actual context. This proves to be too complex to be fulfilled by manually authoring for all possibilities. This has lead to efforts in developing systems for (semi-)automatic multimedia generation. Knowledge representation techniques developed in earlier AI work are being applied to the World Wide Web. Through standards such as RDF and OWL, the so-called "Semantic Web" offers the potential of a large scale distributed knowledge repository built upon the existing Web infrastructure. This makes powerful amounts of knowledge available to Web-based applications as the basis for a new paradigm of Web-oriented services which are intelligent, dynamic, flexible and user-centred. Such services could find the information we need and present it to us in an effective manner. The current situation of Web content discovery and delivery displays significant disorder and inflexibility: natural language is problematically ambiguous and Web content is only adaptive to a limited extent. Using the Semantic Web, intelligent Web clients will be able to deliver the correct information every time by unambiguously understanding the meaning of queries and reasoning on them (through the knowledge available to them on the Web) to come to an answer. Likewise, Web resources will be described with metadata that supports their selection, adaptation and presentation to the needs of the client. Such a paradigm shift in Web-based systems (from content to knowledge-centred) requires a similar paradigm shift in system models, architectures, implementations and operations. This is equally the case for multimedia presentation systems. We propose a framework for a new type of multimedia presentation system in which the multimedia generation process is tightly integrated with the distributed knowledge of the Semantic Web. Unlike previous work in which knowledge is narrowly defined and used by the system, this approach is built with the scale and dynamics of the Web in mind. We name this framework SWeMPs - a Semantic Web-enabled Multimedia Presentation System. The key results of our research are the conceptual model for a multimedia presentation system and the generic rule base which interacts with that model (through querying, reasoning and deriving new knowledge) to realise individual multimedia generation tasks. These form the basis for an implementation of a multimedia presentation system which is fundamentally different from existing multimedia presentation systems in that it operates at the logical or conceptual level rather than the data or syntactic level. This reflects the reality that what users seek is information (knowledge about concepts) and effective communication of that information (respecting the relationships between concepts). It leverages the potential of the Semantic Web as a distributed, large scale, accessible repository of knowledge about the world of the user just as systems today use the Web as a repository of content. The implementation of this proposal serves as a 'proof of concept' and as a basis for the evaluation of the new approach through use cases.

The semantic web promises automates invocation, discovery and composition of web services by enhancing services with semantic descriptions. This paper describes a model driven approach to facilitate the construction of OWL-S specifications. The methodology is divided into three main steps. In the first step we reverse engineered WSDL documents into UML profile models that enable the use of high-level graphical models as an integration platform for semantic web services. In the second step, suitable domain ontologies are used for the semantic annotation of the UML models. Finally, in the third step a conversion tool will generate automatically the OWL-S description from these UML models. The UML profile provides flexibility as it can expresses multiple semantic web service concepts.

Ontologies and knowledge bases are fundamental cornerstones in semantic search systems based on which efficient web services and sophisticated search mechanisms are designed. High quality domain ontologies are essential for successful employment of semantic Web services. However, acquisition of ontologies is difficult and costly, thus hindering the development of this field. In this work, the knowledge acquisition problem in the semantic Web is cast as the task of ontology construction. We construct an ontology for “polycystic kidney disease” from domain text by a pattern based method which requires minimum human intervention. We also create ontology manually to present the evaluation results of the constructed ontology.KeywordsOntologyontology constructionpatternsconcept extraction

Currently search engines are using keyword based approach which becomes problematic when the user is not aware about the way to write query such that desirable results only appear because for that he must know the semantic concepts that are used in that particular domain in which he is interested. To overcome this problem, we introduced the concept of Semantic Web which is an extension of the current Web that allows the meaning of information to be precisely described in terms of well-defined vocabularies that are understood by people and computers. Ontology is one of the fundamental ingredients used in the semantic web infrastructure. This paper focuses on the problem of internet users who most of the time looks for the good hotels available beforehand whenever they plan any trip. At times they go for image search option to have a view of hotels in a particular location. But problem is user gets confused whether search results displayed online by currently available search engines are reliable or not. This paper aims to implement a tool which is based on Semantic Web for searching the images of hotels and displaying the results. Semantic web refines the search in such a way that only relevant images are returned. The crux is to develop ontology for various hotels along with their locations and the facilities they provide. User enters his query from an interface and corresponding SPARQL query is generated. This query with the help of JENA API is searched in the data present in Ontology.

In the last decade, ontologies have become a hot issue in the conceptual modeling community, which is chiefly due to the emergence of new application areas such as Electronic Commerce and the Semantic Web. The implementation of large-scale information systems, as well as their semantic interoperability, seem to be only tractable on the basis of ontologies as abtract domain models.

Existing item bank systems present a variety of assessments for data management and integration with individual learning evaluation systems. However, as the data in these established item bank systems do not include semantics, the bank systems cannot analyze implications and perform accurate searches such as for synonymous words. Therefore, both learners and teachers can access only simple text data using the item bank system and often waste time checking unnecessary search results and extracting information from data repeatedly. Moreover, since there is no clear definition of the relationship between items and teachers or learners, or between data and units, it is difficult to use and share extra-item information. In order to solve these problems, this research describes the definition of conception and organization by constructing the Ontology of an E-Item Bank system using OWL, which is a form of semantic web technology. Furthermore, on the basis of Ontology, OWL metadata, individuals, are built, semantic factors are then extracted using OWLJessKB. We do not only use this extracted semantic factors as a fact, but also domain rules using SWRL for the JESS inferencing engine. As such, it is possible to make inferences and provide reasoning in web data structures. As a result, it is possible to combine all the data of the E-Item bank system and facilitate understanding of the meanings of concepts by the computer. In addition, a search service with inference can be applied to education and can lead to cooperative study between teachers and students.