Ontology Construction for Polycystic Kidney Disease

Semantic Web Services are the key technology providing services for the users' convenience in the semantic web environment. Many companies in various fields are researching and developing languages for constructing Semantic Web Services such as DAML-S, WSDL, X-LANG and BPEL4WS. DAML-S is a method that accesses the existing Web Service method from a semantic web environment. However, the current Semantic Web Services Discovery System does not provide sufficient processing of quality factors (performance, accessibility, availability, reliability and transaction) nor reliable and accurate service results desired by the user through a UDDi search method. The present study analyzes the disadvantages of the current web services and suggests a Semantic Web Services Discovery System based on QoS applying semantic web technologies as a solution to the problems. The suggested system provides a UDDi and DAML-S based discovery engine to allow efficient web service discovery and composition.

Information and resources available on the Web are growing increasingly and web users need to have a common understanding of them. The Semantic Web whose most important role is to help machine to understand and analyze the existing data on the Web, has not been used commonly, yet. The foundation of the Semantic Web are ontologies. Ontologies play the main role in the exchange of information and development of the Lexical Web to the Semantic Web. Manual construction of ontologies is time-consuming, expensive, and dependent on the knowledge of domain engineers. Also, Ontologies that have been extracted automatically from corpus on the Web might have incomplete information. The main objective of this study is describing a method to improve and expand the information of the ontologies. Therefore, this study first discusses the automatic construction of prototype ontology in animals‟ domain from Wikipedia and then a method is presented to improve the built ontology. The proposed method of improving ontology expands ontology concepts through Bootstrapping methods using a set of concepts and relations in initial ontology and with the help of the Google search engine. A confidence measure was considered to choose the best option from the returned results by Google. Finally, the experiments showed the information that was obtained using the proposed method is twice more accurate than the information that was obtained at the stage of automatic construction of ontology from Wikipedia.

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.

According to the growth of the Web, the number of Web services is increased rapidly and efficient Web service discovery has become an important and challenging task. To overcome this issue, in this paper we proposed a Web service clustering method through calculating the semantic similarity of Web services using novel ontology learning method. The method uses terms similarity and terms specificity for ontology generation. Amount of domain specific information included in the term is identified as a specificity of the term. To calculate the similarity of Web services using generated ontology, this paper defines new logic based filters. If calculating similarity using the generated ontology failed, then applied information-retrieval-based methods. Empirical study of our novel approach has proved the effectiveness of clustering process. Further, experimental results show that our clustering approach works efficiently and performs better than existing approaches.

The explosive growth of textual data on the web coupled with the increase on demand for ontologies to promote the semantic web, have made the automatic ontology construction from the text a very promising research area. Ontology learning (OL) from text is a process that aims to (semi-) automatically extract and represent the knowledge from text in machine-readable form. Ontology is considered one of the main cornerstones of representing the knowledge in a more meaningful way on the semantic web. Usage of ontologies has proven to be beneficial and efficient in different applications (e.g. information retrieval, information extraction, and question answering). Nevertheless, manually construction of ontologies is time-consuming as well extremely laborious and costly process. In recent years, many approaches and systems that try to automate the construction of ontologies have been developed. This paper reviews various approaches, systems, and challenges of automatic ontology construction from the text. In addition, it also discusses ways the ontology construction process could be enhanced in the future by presenting techniques from shallow learning to deep learning (DL).

The Web, as we know it, is a collection of human readable pages that are virtually unintelligible to computer programs. For that reason, it has been called the “eyeball” Web. While the Web emerged as a World Wide repository of digitized information, by and large, the very same information is not available for automatic computation. In recent years two parallel efforts emerged that have the potential of overcoming this paradox: the first effort is the Semantic Web which provides the tools for the explicit markup of the content of Web pages; the second effort is the development of Web Services which results in a Web where programs act as independent agents to become the producers and consumers of information and enable automation of business transactions.In this talk I will focus on research that attempts to bridge the gap between the Web as we know it, the Semantic Web and Web services. Under this approach, I propose the vision of Web services as autonomous goal-directed agents which select other agents to interact with, and flexibly negotiate their interaction model, acting at times in client server mode, or at other times in peer to peer mode. The resulting Web services, that I call Autonomous Semantic Web services, utilize ontologies and semantically annotated Web pages to automate the fulfillment of tasks and transactions with other Web agents. In particular, Autonomous Semantic Web services use the Semantic Web to support capability based discovery and interoperation at run time. A first step towards this vision is the development of formal languages and inference mechanisms for representing and reasoning with core concepts of Web services. DAML-S (the Darpa Agent Markup Language for Services) is the first attempt to define such a language. I will give a brief overview of DAML-S and its relations with the Semantic Web and Web services. In addition, I will provide concrete examples of computational models of how DAML-S can be viewed as the first step in bridging the gap between the Semantic Web and current proposed industry standards for Web services. I will provide concrete examples of DAML-S in action, discuss challenges still open in the bridging between the Semantic Web and Web services and provide a roadmap to get us from today’s Web to the Web of autonomous Semantic Web services.KeywordsFormal LanguageCore ConceptDigitize InformationAutomatic ComputationBusiness TransactionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

One of the major unsolved problems in knowledge acquisition is reconciling knowledge originating from different sources. This paper proposes a technique for reconciling knowledge in two independent knowledge bases, describes a working program built to implement that technique, and discusses an exploratory study for validating the technique. The technique is based on the use of heuristics for identifying and resolving discrepancies between the knowledge bases. Each heuristic developed provides detection and resolution procedures for a distinct variety of discrepancy in the knowledge bases. Sample discrepancies include using synonyms for the same term, conflicting rules, and extra reasoning steps. Discrepancies are detected and resolved through the use of circumstantial evidence available from the knowledge bases themselves and by asking sharply focussed questions to the experts responsible for the knowledge bases. The technique was tested on two independently developed knowledge bases designed to aid novice statisticians in diagnosing problems in linear regression models. The heuristics located a significant number of the discrepancies between the knowledge bases and assisted the experts in creating a consensus knowledge base for diagnosing multicollinearity problems. We argue that the task of identifying discrepancies between independent bodies of knowledge is an inevitable part of any large knowledge acquisition effort. Hence the heuristics developed in this work are applicable even when knowledge acquisition is not done by reconciling two complete knowledge bases. We also suggest that our approach can be extended to other knowledge representations such as frames and database schemas, and speculate about its potential application to other domains involving the reconciliation of knowledge, such as requirements determination, negotiation, and design.

The semantic web and web service take ontology into usage to describe the important concepts and relations among them. But the construction of ontology from scratch is costly and difficult. In this paper an approach is proposed to construct OWL ontology from XML document with the help of entity-relation model, and this approach will alleviate the difficulties in ontology construction.

Web services – a new breed of self-contained, self-describing, modular applications published, located, and invoked across the Web – handle functions, from simple requests to complicated business processes. They are defined as network-based application components with a services-oriented architecture (SOA) using standard interface description languages and uniform communication protocols. SOA enables organizations to grasp and respond to changing trends and to adapt their business processes rapidly without major changes to the IT infrastructure. The Inaugural International Conference on Next-Generation Web Services Practices (NWeSP'05) attracted researchers who are also the world's most respected authorities on the semantic Web, Web-based services, and Web applications and services. NWeSP'05 was held in cooperation with the IEEE Computer Society Task Force on Electronic Commerce, the Technical Committee on Internet, and the Technical Committee on Scalable Computing. This special issue presents eight papers focused on different aspects of Web services and their applications. Papers were selected based on fundamental ideas and concepts rather than the thoroughness of techniques employed. Papers are organized as follows: <I>Taher et al.</I> present the first paper, on a Quality of Service Information and Computational framework (QoS-IC) supporting QoS-based service selection for SOA. The framework's functionality is expanded using a QoS constraints model that establishes an association relationship between different QoS properties and is used to govern QoS-based service selection in the underlying algorithm. Using a prototype implementation, the authors demonstrate how QoS constraints improve QoS-based service selection and save consumers valuable time. Due to the complex infrastructure of web applications, response times perceived by clients may be significantly longer than desired. To overcome some of the current problems, <I>Vilas et al.</I>, in the second paper, propose a cache-based extension of the architecture that enhances the current web services architecture, which is mainly based on program-logic or protocol-dependent optimization. In the third paper, Jo and Yoo present authorization for securing XML sources on the Web. One of the disadvantages of existing access control is that the DOM tree must be loaded into memory while all XML documents are parsed to generate the DOM tree, such that a lot of memory is used in repetitive search for tree to authorize access to all nodes in the DOM tree. The complex authorization evaluation process required thus lowers system performance. Existing access control fails to consider information structure and semantics sufficiently due to basic HTML limitations. The authors overcome some of these limitations in the proposed model. In the fourth paper, Jung and Cho propose a novel behavior-network-based method for Web service composition. The behavior network selects services automatically through internal and external links with environmental information from sensors and goals. An optimal service is selected at each step, resulting in a globally optimal service sequence for achieving preset goals. The authors detail experimental results for the proposed model by comparing them with rule-based system and user tests. <I>Kong et al.</I> present an efficient method in the fifth paper for merging heterogeneous ontologies – no ontology building standard currently exists – and the many ontology-building tools available are based on different ontology languages, mostly focusing on how to create, edit and infer the ontology efficiently. Even ontologies about the same domain differ because ontology experts hold different view points. For these reasons, interoperability between ontologies is very low. The authors propose merging heterogeneous domain ontologies by overcoming some of the above limitations. In the sixth paper, Chen and Che provide polynomial-time tree pattern query minimization algorithm whose efficiency stems from two key observations: (i) Inherent redundant "components" usually exist inside the rudimentary query provided by the user, and (ii) nonedundant nodes may become redundant when constraints such as co-occurrence and required child/descendant are given. They show that the algorithm obtained by first augmenting the input tree pattern using constraints, then applying minimization, invariably finds a unique minimal equivalent to the original query. Chen and Che present a polynomial-time algorithm for tree pattern query (TPQ) minimization without XML constraints in the seventh paper. The two-part algorithm is a dynamic programming strategy for finding all matching subtrees within a TPQ. The algorithm consists of one for subtree recognization and a second for subtree deletion. In the last paper, <I>Bagchi et al.</I> present the mobile distributed virtual memory (MDVM) concept and architecture for cellular networks containing server-groups (SG). They detail a two-round randomized distributed algorithm to elect a unique leader and co-leader of the SG that is free of any assumption about network topology, and buffer space limitations and is based on dynamically elected coordinators eliminating single points of failure. As guest editors, we thank all authors featured in this special issue for their contributions and the referees for critically evaluating the papers within the short time allotted. We sincerely believe that readers will share our enjoyment of this special issue and find the information it presents both timely and useful.

Efficient and fast service discovery becomes an extremely challenging task due to the proliferation and availability of functionally-similar web services. Service classification or service grouping is a popular and widely applied technique to classify services into several groups according to similarity, in order to ease up and expedite the discovery process. Existing research on web service classification uses several techniques, approaches and frameworks for web service classification. This study focused on a hybrid service classification approach based on a combination of ontology generation and machine learning algorithm, in order to gain more speed and accuracy during the classification process. Ontology generation is applied to capture the similarity between complicated words. Then, two machine learning classification algorithms, namely, Support Vector Machines (SVMs) and Naive Bayes (NB), were applied for classifying services according to their functionality. The experimental results showed significant improvement in terms of accuracy, precision and recall. The hybrid approach of ontology generation and NB algorithm achieved an accuracy of 94.50%, a precision of 93.00% and a recall of 95.00%. Therefore, a hybrid approach of ontology generation and NB has the potential to pave the way for efficient and accurate service classification and discovery.

OntoCase is a framework for semi-automatic pattern-based ontology construction. In this paper we focus on the retain and reuse phases, where an initial ontology is enriched based on content ontology design patterns (Content ODPs), and especially the implementation and evaluation of these phases. Applying Content ODPs within semi-automatic ontology construction, i.e. ontology learning (OL), is a novel approach. The main contributions of this paper are the methods for pattern ranking, selection, and integration, and the subsequent evaluation showing the characteristics of ontologies constructed automatically based on ODPs. We show that it is possible to improve the results of existing OL methods by selecting and reusing Content ODPs. OntoCase is able to introduce a general top structure into the ontologies, and by exploiting background knowledge the ontology is given a richer overall structure.KeywordsString MatchText CorpusOntology LearningOntology ConstructionOntology EngineerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Intelligent, autonomous agents are still not available in the Semantic Web at large scale today. Also the fields of Semantic Web and Multi-Agent Systems are not working together very closely although they could profit much from each other. Existing approaches merely use the Web as a transport layer and are not properly aligned to the architectural style of the Web. The Internet of Things, which would be very useful for agents to act upon Things in the real world, on the other side is very fragmented and not easily inter-operable. The Web of Things has emerged as an approach to use the (Semantic) Web as an application layer for Things. It is still unclear however, how agents on the WoT should look like. We propose that the Semantic Web is a suitable integration layer, both for agents and Things. We investigate how a Multi-Agent System in the Semantic Web of Things can be build by utilizing simple reflex agents and the communication paradigm of stigmergy. We map Things and artifacts to Web resources that are managed by a Web server and provide affordances to agents through hypermedia.KeywordsMulti-Agent SystemsWeb of ThingsSimple reflex agentsStigmergyRestHypermedia

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.

To realize the Semantic Web, it will be necessary to make existing database content available for emerging Semantic Web applications, such as web agents and services, which use ontologies to formally define the semantics of their data. Our research in the design and implementation of an ontology-based system, OntoGrate, addresses the critical and challenging problem of supporting human experts in multiple domains to interactively integrate information that is heterogenous in both structure and semantics. Databases, knowledge bases, the World Wide Web, and the emerging Semantic Web are some of the resources for which scalable integration remains a challenge. To integrate databases into the Semantic Web, we use Semantic Web ontologies to incorporate database schemas. An expressive first order ontology language, Web-PDDL, is used to define the structure, semantics, and mappings of data resources. A powerful inference engine, OntoEngine, can be used for query answering and data translation. In this paper, besides introducing new ideas in the OntoGrate system, we will elaborate on two case studies for which our system works well.