As Semantic Web technology evolves many open areas emerge, which attract more research focus. In addition to quickly expanding Linked Open Data (LOD) cloud, various embeddable metadata formats (e.g. RDFa, microdata) are becoming more common. Corporations are already using existing Web of Data to create new technologies that were not possible before. Watson by IBM an artificial intelligence computer system capable of answering questions posed in natural language can be a great example. On the other hand, ubiquitous devices that have a large number of sensors and integrated devices are becoming increasingly powerful and fully featured computing platforms in our pockets and homes. For many people smartphones and tablet computers have already replaced traditional computers as their window to the Internet and to the Web. Hence, the management and presentation of information that is useful to a user is a main requirement for today’s smartphones. And it is becoming extremely important to provide access to the emerging Web of Data from the ubiquitous devices. In this thesis we investigate how ubiquitous devices can interact with the Semantic Web. We discovered that there are five different approaches for bringing the Semantic Web to ubiquitous devices. We have outlined and discussed in detail existing challenges in implementing this approaches in section 1.2. We have described a conceptual framework for ubiquitous semantic applications in chapter 4. We distinguish three client approaches for accessing semantic data using ubiquitous devices depending on how much of the semantic data processing is performed on the device itself (thin, hybrid and fat clients). These are discussed in chapter 5 along with the solution to every related challenge. Two provider approaches (fat and hybrid) can be distinguished for exposing data from ubiquitous devices on the Semantic Web. These are discussed in chapter 6 along with the solution to every related challenge. We conclude our work with a discussion on each of the contributions of the thesis and propose future work for each of the discussed approach in chapter 7.

The last couple of years have seen a fascinating evolution. While the early Web predominantly focused on human consumption of Web content, the widespread dissemination of social software and Web 2.0 technologies enabled new forms of collaborative content creation and problem solving. These new forms often utilize the principles of collective intelligence, a phenomenon that emerges from a group of people who either cooperate or compete with each other to create a result that is better or more intelligent than any individual result (Leimeister, 2010; Malone, Laubacher, & Dellarocas, 2010). Crowdsourcing has recently gained attention as one of the mechanisms that taps into the power of web-enabled collective intelligence (Howe, 2008). Brabham (2013) defines it as “an online, distributed problem-solving and production model that leverages the collective intelligence of online communities to serve specific organizational goals” (p. xix). Well-known examples of crowdsourcing platforms are Wikipedia, Amazon Mechanical Turk, or InnoCentive. Since the emergence of the term crowdsourcing in 2006, one popular misconception is that crowdsourcing relies largely on an amateur crowd rather than a pool of professional skilled workers (Brabham, 2013). As this might be true for low cognitive tasks, such as tagging a picture or rating a product, it is often not true for complex problem-solving and creative tasks, such as developing a new computer algorithm or creating an impressive product design. This raises the question of how to efficiently allocate an enterprise crowdsourcing task to appropriate members of the crowd. The sheer number of crowdsourcing tasks available at crowdsourcing intermediaries makes it especially challenging for workers to identify a task that matches their skills, experiences, and knowledge (Schall, 2012, p. 2). An explanation why the identification of appropriate expert knowledge plays a major role in crowdsourcing is partly given in Condorcet’s jury theorem (Sunstein, 2008, p. 25). The theorem states that if the average participant in a binary decision process is more likely to be correct than incorrect, then as the number of participants increases, the higher the probability is that the aggregate arrives at the right answer. When assuming that a suitable participant for a task is more likely to give a correct answer or solution than an improper one, efficient task recommendation becomes crucial to improve the aggregated results in crowdsourcing processes. Although some assumptions of the theorem, such as independent votes, binary decisions, and homogenous groups, are often unrealistic in practice, it illustrates the importance of an optimized task allocation and group formation that consider the task requirements and workers’ characteristics. Ontologies are widely applied to support semantic search and recommendation mechanisms (Middleton, De Roure, & Shadbolt, 2009). However, little research has investigated the potentials and the design of an ontology for the domain of enterprise crowdsourcing. The author of this thesis argues in favor of enhancing the automation and interoperability of an enterprise crowdsourcing environment with the introduction of a semantic vocabulary in form of an expressive but easy-to-use ontology. The deployment of a semantic vocabulary for enterprise crowdsourcing is likely to provide several technical and economic benefits for an enterprise. These benefits were the main drivers in efforts made during the research project of this thesis: 1. Task allocation: With the utilization of the semantics, requesters are able to form smaller task-specific crowds that perform tasks at lower costs and in less time than larger crowds. A standardized and controlled vocabulary allows requesters to communicate specific details about a crowdsourcing activity within a web page along with other existing displayed information. This has advantages for both contributors and requesters. On the one hand, contributors can easily and precisely search for tasks that correspond to their interests, experiences, skills, knowledge, and availability. On the other hand, crowdsourcing systems and intermediaries can proactively recommend crowdsourcing tasks to potential contributors (e.g., based on their social network profiles). 2. Quality control: Capturing and storing crowdsourcing data increases the overall transparency of the entire crowdsourcing activity and thus allows for a more sophisticated quality control. Requesters are able to check the consistency and receive appropriate support to verify and validate crowdsourcing data according to defined data types and value ranges. Before involving potential workers in a crowdsourcing task, requesters can also judge their trustworthiness based on previous accomplished tasks and hence improve the recruitment process. 3. Task definition: A standardized set of semantic entities supports the configuration of a crowdsourcing task. Requesters can evaluate historical crowdsourcing data to get suggestions for equal or similar crowdsourcing tasks, for example, which incentive or evaluation mechanism to use. They may also decrease their time to configure a crowdsourcing task by reusing well-established task specifications of a particular type. 4. Data integration and exchange: Applying a semantic vocabulary as a standard format for describing enterprise crowdsourcing activities allows not only crowdsourcing systems inside but also crowdsourcing intermediaries outside the company to extract crowdsourcing data from other business applications, such as project management, enterprise resource planning, or social software, and use it for further processing without retyping and copying the data. Additionally, enterprise or web search engines may exploit the structured data and provide enhanced search, browsing, and navigation capabilities, for example, clustering similar crowdsourcing tasks according to the required qualifications or the offered incentives.

The Semantic Web enables people and computers to interact and exchange information. Based on Semantic Web technologies, different machine learning applications have been designed. Particularly to emphasize is the possibility to create complex metadata descriptions for any problem domain, based on pre-defined ontologies. In this paper we evaluate the use of a semantic similarity measure based on pre-defined ontologies as an input for a classification analysis. A link prediction between actors of a social network is performed, which could serve as a recommendation system. We measure the prediction performance based on an ontology-based metadata modeling as well as a feature vector modeling. The findings demonstrate that the prediction accuracy based on ontology-based metadata is comparable to traditional approaches and shows that data mining using ontology-based metadata can be considered as a very promising approach.

No description available.

M. Tech. (Department of Information and Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology / The emergence of the Semantic Web has resulted in more and more large-scale ontologies being developed in real-world applications to represent and integrate knowledge and data in various domains. This has given rise to the problem of selection of the appropriate ontology for reuse, among the set of ontologies describing a domain. To address such problem, it is argued that the evaluation of the complexity of ontologies of a domain can assist in determining the suitable ontologies for the purpose of reuse. This study investigates existing metrics for measuring the design complexity of ontologies and implements these metrics in a framework that provides a stepwise process for evaluating the complexity of ontologies of a knowledge domain. The implementation of the framework goes through a certain number of phases including the: (1) download of 100 Biomedical ontologies from the BioPortal repository to constitute the dataset, (2) the design of a set of algorithms to compute the complexity metrics of the ontologies in the dataset including the depth of inheritance (DIP), size of the vocabulary (SOV), entropy of ontology graphs (EOG), average part length (APL) and average number of paths per class (ANP), the tree impurity (TIP), relationship richness (RR) and class richness (CR), (3) ranking of the ontologies in the dataset through the aggregation of their complexity metrics using 5 Multi-attributes Decision Making (MADM) methods, namely, Weighted Sum Method (WSM), Weighted Product Method (WPM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Weighted Linear Combination Ranking Technique (WLCRT) and Elimination and Choice Translating Reality (ELECTRE) and (4) validation of the framework through the summary of the results of the previous phases and analysis of their impact on the issues of selection and reuse of the biomedical ontologies in the dataset. The ranking results of the study constitute important guidelines for the selection and reuse of biomedical ontologies in the dataset. Although the proposed framework in this study has been applied in the biomedical domain, it could be applied in any other domain of Semantic Web to analyze the complexity of ontologies.

In this paper, we explore how ontological knowledge expressed via existential rules can be combined with possibilistic networks (i) to represent qualitative preferences along with domain knowledge, and (ii) to realize preference-based answering of conjunctive queries (CQs). We call these combinations ontological possibilistic networks (OP-nets). We define skyline and k-rank answers to CQs under preferences and provide complexity (including data tractability) results for deciding consistency and CQ skyline membership for OP-nets. We show that our formalism has a lower complexity than a similar existing formalism.

The Semantic Web and Linked Data movements with the aim of creating, publishing and interconnecting machine readable information have gained traction in the last years. However, the majority of information still is contained in and exchanged using unstructured documents, such as Web pages, text documents, images and videos. This can also not be expected to change, since text, images and videos are the natural way in which humans interact with information. Semantic structuring of content on the other hand provides a wide range of advantages compared to unstructured information. Semantically-enriched documents facilitate information search and retrieval, presentation, integration, reusability, interoperability and personalization. Looking at the life-cycle of semantic content on the Web of Data, we see quite some progress on the backend side in storing structured content or for linking data and schemata. Nevertheless, the currently least developed aspect of the semantic content life-cycle is from our point of view the user-friendly manual and semi-automatic creation of rich semantic content. In this thesis, we propose a semantics-based user interface model, which aims to reduce the complexity of underlying technologies for semantic enrichment of content by Web users. By surveying existing tools and approaches for semantic content authoring, we extracted a set of guidelines for designing efficient and effective semantic authoring user interfaces. We applied these guidelines to devise a semantics-based user interface model called WYSIWYM (What You See Is What You Mean) which enables integrated authoring, visualization and exploration of unstructured and (semi-)structured content. To assess the applicability of our proposed WYSIWYM model, we incorporated the model into four real-world use cases comprising two general and two domain-specific applications. These use cases address four aspects of the WYSIWYM implementation: 1) Its integration into existing user interfaces, 2) Utilizing it for lightweight text analytics to incentivize users, 3) Dealing with crowdsourcing of semi-structured e-learning content, 4) Incorporating it for authoring of semantic medical prescriptions.

The Semantic Web is the vision of the future Web. Its aim is to enable machines to process Web documents in a way that makes it possible for the computer software to "understand" the meaning of the document contents. Each document on the Semantic Web is to be enriched with meta-data that express the semantics of its contents. Many infrastructures, technologies and standards have been developed and have proven their theoretical use for the Semantic Web, yet very few applications have been created. Most of the current Semantic Web applications were developed for research purposes. This project investigates the major factors restricting the wide spread of Semantic Web applications. We identify the two most important requirements for a successful implementation as the automatic production of the semantically annotated document, and the creation and maintenance of semantic based knowledge base. This research proposes a framework for Semantic Web implementation based on context-oriented controlled automatic Annotation; for short, we called the framework the Semantic Web Implementation Framework (SWIF) and the system that implements this framework the Semantic Web Implementation System (SWIS). The proposed architecture provides for a Semantic Web implementation of stand-alone websites that automatically annotates Web pages before being uploaded to the Intranet or Internet, and maintains persistent storage of Resource Description Framework (RDF) data for both the domain memory, denoted by Control Knowledge, and the meta-data of the Web site¿s pages. We believe that the presented implementation of the major parts of SWIS introduce a competitive system with current state of art Annotation tools and knowledge management systems; this is because it handles input documents in the ii context in which they are created in addition to the automatic learning and verification of knowledge using only the available computerized corporate databases. In this work, we introduce the concept of Control Knowledge (CK) that represents the application¿s domain memory and use it to verify the extracted knowledge. Learning is based on the number of occurrences of the same piece of information in different documents. We introduce the concept of Verifiability in the context of Annotation by comparing the extracted text¿s meaning with the information in the CK and the use of the proposed database table Verifiability_Tab. We use the linguistic concept Thematic Role in investigating and identifying the correct meaning of words in text documents, this helps correct relation extraction. The verb lexicon used contains the argument structure of each verb together with the thematic structure of the arguments. We also introduce a new method to chunk conjoined statements and identify the missing subject of the produced clauses. We use the semantic class of verbs that relates a list of verbs to a single property in the ontology, which helps in disambiguating the verb in the input text to enable better information extraction and Annotation. Consequently we propose the following definition for the annotated document or what is sometimes called the ¿Intelligent Document¿ ¿The Intelligent Document is the document that clearly expresses its syntax and semantics for human use and software automation¿. This work introduces a promising improvement to the quality of the automatically generated annotated document and the quality of the automatically extracted information in the knowledge base. Our approach in the area of using Semantic Web iii technology opens new opportunities for diverse areas of applications. E-Learning applications can be greatly improved and become more effective.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / The web contains a massive amount of information and is continuously growing every day. Extracting information that is relevant to a user is an uphill task. Search engines such as Google TM, Yahoo! TM have made the task a lot easier and have indeed made people much more "smarter". However, most of the existing search engines still rely on the traditional keyword-based searching techniques i.e. returning documents that contain the keywords in the query. They do not take the associated semantics into consideration. To incorporate semantics into search, one could proceed in at least two ways. Firstly, we could plunge into the world of "Semantic Web", where the information is represented in formal formats such as RDF, N3 etc which can effectively capture the associated semantics in the documents. Secondly, we could try to explore a new semantic world in the existing structure of World Wide Web (WWW). While the first approach can be very effective when semantic information is available in RDF/N3 formats, for many web pages such information is not readily available. This is why we consider the second approach in this work. In this work, we attempt to capture the semantics associated with a query by rst extracting the concepts relevant to the query. For this purpose, we propose a novel Link Analysis based Concept Extractor (LACE) that extract the concepts associated with the query by exploiting the meta data of a web page. Next, we propose a method to determine relationships between a query and its extracted concepts. Finally, we show how LACE can be used to compute a statistical measure of semantic similarity between concepts. At each step, we evaluate our approach by comparison with other existing techniques (on benchmark data sets, when available) and show that our results are competitive with existing state of the art results or even outperform them.

Database integration is one of the research areas that have gained a lot of attention from researcher. It has the goal of representing the data from different database sources in one unified form. To reach database integration we have to face two obstacles. The first one is the distribution of data, and the second is the heterogeneity. The Web ensures addressing the distribution problem, and for the case of heterogeneity there are many approaches that can be used to solve the database integration problem, such as data warehouse and federated databases. The problem in these two approaches is the lack of semantics. Therefore, our approach exploits the Semantic Web methodology. The hybrid ontology method can be facilitated in solving the database integration problem. In this method two elements are available; the source (database) and the domain ontology, however, the local ontology is missing. In fact, to ensure the success of this method the local ontologies should be produced. Our approach obtains the semantics from the logical model of database to generate local ontology. Then, the validation and the enhancement can be acquired from the semantics obtained from the conceptual model of the database. Now, our approach can be applied in the generation phase and the validation-enrichment phase. In the generation phase in our approach, we utilise the reverse engineering techniques in order to catch the semantics hidden in the SQL language. Then, the approach reproduces the logical model of the database. Finally, our transformation system will be applied to generate an ontology. In our transformation system, all the concepts of classes, relationships and axioms will be generated. Firstly, the process of class creation contains many rules participating together to produce classes. Our unique rules succeeded in solving problems such as fragmentation and hierarchy. Also, our rules eliminate the superfluous classes of multi-valued attribute relation as well as taking care of neglected cases such as: relationships with additional attributes. The final class creation rule is for generic relation cases. The rules of the relationship between concepts are generated with eliminating the relationships between integrated concepts. Finally, there are many rules that consider the relationship and the attributes constraints which should be transformed to axioms in the ontological model. The formal rules of our approach are domain independent; also, it produces a generic ontology that is not restricted to a specific ontology language. The rules consider the gap between the database model and the ontological model. Therefore, some database constructs would not have an equivalent in the ontological model. The second phase consists of the validation and the enrichment processes. The best way to validate the transformation result is to facilitate the semantics obtained from the conceptual model of the database. In the validation phase, the domain expert captures the missing or the superfluous concepts (classes or relationships). In the enrichment phase, the generalisation method can be applied to classes that share common attributes. Also, the concepts of complex or composite attributes can be represented as classes. We implement the transformation system by a tool called SQL2OWL in order to show the correctness and the functionally of our approach. The evaluation of our system showed the success of our proposed approach. The evaluation goes through many techniques. Firstly, a comparative study is held between the results produced by our approach and the similar approaches. The second evaluation technique is the weighting score system which specify the criteria that affect the transformation system. The final evaluation technique is the score scheme. We consider the quality of the transformation system by applying the compliance measure in order to show the strength of our approach compared to the existing approaches. Finally the measures of success that our approach considered are the system scalability and the completeness.