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Today's factory planning is characterized by a multitude of different IT systems. This results in data silos that lead to problems with the provision of information within factory planning. This paper addresses this problem by integrating data from different data silos into a knowledge graph based on the Semantic Web Stack. To improve the information provision a function is developed, which automatically identifies data changes and makes the data available in line with the demands of the planners on the basis of a role-based concept. The developed concept is implemented and validated in the factory planning of a german automobile manufacturer.

In this paper we demonstrate the feasibility of IoT deployment for noise level measurement to time-limited and high-intense, high-volume data, events. Through an iterative process, a prototype solution were designed and implemented in a real-time, privacy-compliant IoT sensor system under tight constraints concerning budget and development time. Our sensor system enables festival management to easily track, document and further, by applying real time big data analytics to the harvested information, have fact-full insights generated for decision making in terms of resolving noise disturbances. The whole approach was demonstrated by the use of lightweight Internet of Things architecture demonstrating how web technologies can be used throughout the technology stack in and IoT big data analytics case.

Linked Data provides a natural platform for taxonomy management, and when used in concert with the International Image Interoperability Framework (IIIF) for accessing graphical image resources, provides an architecture that is suited to an image annotation application. The combination of Linked Data and IIIF specifications can be used to develop extensible discovery tools based on the Semantic Web Stack.

The Sensor Web is evolving into a complex information space, where large volumes of sensor observation data are often consumed by complex applications. Provenance has become an important issue in the Sensor Web, since it allows applications to answer “what”, “when”, “where”, “who”, “why”, and “how” queries related to observations and consumption processes, which helps determine the usability and reliability of data products. This paper investigates characteristics and requirements of provenance in the Sensor Web and proposes an interoperable approach to building a provenance model for the Sensor Web. Our provenance model extends the W3C PROV Data Model with Sensor Web domain vocabularies. It is developed using Semantic Web technologies and thus allows provenance information of sensor observations to be exposed in the Web of Data using the Linked Data approach. A use case illustrates the applicability of the approach.

It is known that the representation and utilization of real-world dynamic knowledge are increasingly important to open and dynamic computing environments, but traditional semantic web ontology and current existing context-aware systems are still unsuitable to process real-world dynamic knowledge. To solve this problem, by virtue of the advantage of Pragmatic Web in dynamic knowledge expressing and processing, a Pragmatic Web oriented Context-Aware System (called PWCAS) is proposed, and its architectural framework is designed, which not only can capture users real intention by inter-agents semantic negotiation and preliminarily can achieve the personalized user-centric services.

Semantic search is becoming a more independent search engine, which is capable of analyzing independently, understanding the essential needs and providing accurate answers. In this work, the basis of analyzing the content framework puts forward the theory on the basis of knowledge graph. In the semantic web, search engines use a knowledge graph to identify the query involved in the entity and its attributes, giving a precise answer by efficient web search. Based on the semantic web, we proposed the K-means algorithm that applied to construct knowledge graph, will be sorted in knowledge bases by classifying similarity word, building Uygur's knowledge graph architecture, and then, take the built knowledge graph to show the semantic searching.

With the continuous development of society and the improvement of Internet technology, people are getting more and more dissatisfied with the current exploration of unknown world, and increasingly rigorous on the search engine requirements. As a result, semantic search technology came into being. This article mainly through the knowledge graph and semantic search technology to understand the function of this technology, as well as its limitations, improvement strategies, and the future direction of development.

In order to build a domain-specific knowledge hub for learning on the web, the web resources crawled by generic search engines will need to be sifted and sorted before use. We propose a re-ranking algorithm that recognizes the highly domain relevant web data to feed in the domain knowledge learning hub. The algorithm studies the structure and semantics of the domain ontology (graph) and constructs computational relations among nodes. Through mining matching terms between ontology dictionary and the textual content (text, metadata) of the retrieved documents crawled by some credited web search engines, we calculate three-dimensional information scores - distance, direction, and attributes of each document and subsequently re-rank the retrieved documents to provide learners with more meaningful knowledge in the domain space they embrace.

Web-based learning environments such as the Go-Lab portal facilitate the structuring and scaffolding of inquiry processes and enable learners to create various types of knowledge artefacts (texts, concept maps, hypotheses). In this context, we have introduced semantic technologies to extract key concepts from heterogeneous learner-generated artefacts. Based on this approach, we propose an algorithm and a software application that forms heterogeneous groups. Semantic group formation creates groupings based on semantic diversity representing students' complementarity in knowledge. Finally, we compare this approach to a prior score-based approach.

Websites increasingly embed semantic data for search engine optimization. The most common ontology for semantic data, schema.org, is supported by all major search engines and describes over 500 data types, including calendar events, recipes, products, and TV shows. As of today, users wishing to pass this data to their favorite applications, e.g., their calendars, cookbooks, price comparison applications or even smart devices such as TV receivers, rely on cumbersome and error-prone workarounds such as reentering the data or a series of copy and paste operations. In this paper, we present Semantic Data Mediator (SDM), an approach that allows the easy transfer of semantic data to a multitude of services, ranging from web services to applications installed on different devices. SDM extracts semantic data from the currently displayed web page on the client-side, offers suitable services to the user, and by the press of a button, forwards this data to the desired service while doing all the necessary data conversion and service interface adaptation in between. To realize this, we built a reusable repository of service descriptions, data converters, and service adapters, which can be extended by the crowd. Our approach for linking services to websites relies solely on semantic data and does not require any additional support by either website or service developers. We have fully implemented our approach and present a real-world case study demonstrating its feasibility and usefulness.

Today, modern IT-systems are often an interplay of third-party web services. Developers in their role as requesters integrate existing services of different providers into new IT-systems. Providers use frameworks like Open API to create syntactic service specifications from which requesters generate code to integrate services. Proper service discovery is crucial to identify usable services in the growing plethora of third-party services. Most advanced service discovery approaches rely on semantic specifications, e.g., OWL-S. While semantic specifications are crucial for a precise discovery, syntactical specification are needed for service invocation. To close the gap between semantic and syntactic specifications, service grounding establishes links between the semantic and syntactic specifications. However, for a large number of web services still no semantic specification or grounding exists. In this paper, we present an approach that semi-automates the semantic specification of web services for service providers and additionally helps service requesters to leverage semantic web services. Our approach enables a higher degree of automation than other approaches. This includes the creation of semantic specifications and service groundings for service providers as well as the integration of services for requesters by using our code generator. As proof-of-concept, we provide a case study, where we derive a sophisticated semantic OWL-S specification from a syntactic Open API specification.

RESTful Web APIs have no description files like WSDL in traditional Web service. Although some REST API definition models have been arising recently, there is still lacking in structured description format for existing large mounts of Web APIs. Almost all Web APIs are documented in semi-structured web pages, and these documentation formats are various for different sites. It's hard for machine to read the semantics of Web APIs. In this paper, we have proposed a novel hierarchical recurrent neural network to convert REST API documentation to structured machine-readable description format -- the Swagger REST API specification. The network extracts the Swagger defined attributes of a REST API from HTML web pages without any feature engineering. With the extracted API specifications, we built an API repository to index, search and compose Web APIs. Experiment showed that the hierarchical RNN model performed well even with only a few training samples.

Semantic Web approaches are often used for Web service description, modeling, semantics discovery, capabilities matching, etc. However, as the primary querying tool for Semantic Web, SPARQL is yet to be deeply explored to support Semantic Web service composition. Therefore the description, modeling and composition of Semantic Web services are usually two-tier. This paper extends SPARQL to support path query, so that SPARQL is used in our service composition framework which finds the top-k shortest data flows that satisfy the user constraints. Experiment results on real-world Web service datasets exhibit its applicable performance compared with service compositions using other SPARQL engines/extensions.

As the Internet of Things evolves and matures, the number of connected devices and the amount of generated data grows exponentially. Integrative standards and API design patterns are required to deal with this fast growth, while easing machine to machine communication and promoting ubiquitous computing. This paper discusses the W3C Web of Things model that is currently in the process of standardization, and presents our overview and implementation of this model. FCT (Foundation for Science and Technology) from Portugal [UID/MULTI/00631/2013] info:eu-repo/semantics/publishedVersion

The Semantic Web of Things (SWoT) merges the Internet of Things with knowledge representation and reasoning techniques borrowed from the Semantic Web, in order to improve resource management and discovery. This paper proposes a SWoT framework in Wireless Sensor Networks (WSNs) enabling cooperative discovery of sensors and actuators. A backward-compatible extension of the Constrained Application Protocol (CoAP) makes possible to use semantic matchmaking via non-standard reasoning to better characterize the resource discovery. The framework also integrates nimble data stream mining to detect and annotate high-level events through raw data gathered from the environment. A cooperative environmental monitoring case study in Hybrid Sensor and Vehicular Networks (HSVN) is presented together with experiments on a real testbed to assess feasibility and benefits of proposal.

In this work we analyze the role of ontologies in the Semantic Web and the impact of the growth of open and linked data in the ontology engineering domain. Basically, we present a synthesis of the knowledge about ontologies in the field of Web Science, highlighting its role in the Semantic Web, emphasizing those that are being developed to support publishing processes of linked data in the Web. Additionally, we present a synthesis of the types of ontologies that are being created, published and used, as support for the publication of linked data, as well as relating the so-called light and heavy ontologies with the two basic functions of ontologies in the Semantic Web. Finally, we present and discuss a proposal for a conceptual framework for collaborative development of reference models for the publication and consumption of open and linked data on the Web.

The Web has become a necessary resource of daily use, the benefits it offers, being a source of knowledge, and collaboration giving rise to new initiatives such as linked data, whose purpose is link the data scattered through the Web through of semantic relationships between them. The propose of this article is show the improving, consumption, and visualization of linked data in the Web, in such a way that any user with or without any knowledge on the subject, can explore semantic databases, through the construction of software capable of providing a search engine for information related to a natural language search term that is converted to SPARQL query, in the same context the visualization of results by means of a graph, for which a research, and analysis of existing applications is carried out. In addition, semantic repositories contributed to the proposal, and for the develop of software.

Web services have become an industrial standard offering interoperability among various platforms. With their proliferation, it is becoming increasingly difficult to find a web service that satisfies one's requirements. Yet, discovering the most relevant ones is a key solution to benefit from the large number of web services available nowadays on the Internet. This paper presents a practical tool - called rMatcher - to measure the similarity of semantic web services in order to suggest to its user the best ones meeting his requirements. The tool has been developed and experimented on semantic web services described using the OWL-S language.

The purpose of this research is to infer the effect of web elements on web visual design. First, semantic differential Technique is applied to inquire the influence of web elements on website banner and content section and then we advance AE analysis of visual components properties of cognitive. At last, the statistical analysis is used to suggest web design reference. The paper presents a user interface design approach for web producers to lessen the process of figure out target audience perception and advocate an optimal decision making for the visual of website template design.

The development and the advances of the World Wide Web from its first inception resulted in numerous diverse amounts of data and webpages on the web which motivates to greater demand for managing this data in an effective manner. Therefore, novel tools and techniques are required to effectively manage these data. Such tools are assumed to support interoperability and warehousing between the multiple data sources and extracting information from the different trusted databases on the web. Subsequently, the web is evolving into what is now called the Semantic Web aims to alleviate users from the burden of integrating different information sources as well as to perform searches. However, one needs to examine the trust issue of the searching results from any Semantic Web application. This paper investigates trust policies and mechanisms in semantic web applications and introduces an intermediary step to classify the relevant web services via intelligent model based on NN.

IoT Data and smart data is the most important key in research on particulate domain. This paper we present a semantic data model for environment system by propose a light-weight semantic ontology approach for represent IoT data with Semantic Web. By using a cross-domain knowledge by extend SSN ontology combine with other domain specific to mapping sensor streaming data and annotation sensor.

In the article the methods, algorithms and architecture of software system to identify non-relevant and wrong information for online website of organization using the web semantic and content analysis are considered.

Semantic web services represent the potential of the web and they have significant impact on the discovery process. Due to the high proliferation of web services, selecting the best web services from functional equivalent service providers have become a real challenge when a large number of services have been published in a registry. If these services have been functionally-equivalent, it is difficult for service requester to choose which one to be invoked. So the quality of the service plays a crucial role and it becomes a very important factor in discovery and selection of these candidates services to best meet users requirement. In this paper, a QOS method is designed and implemented to support web services of non-functional aspect. The proposed method is based on OWL-S expansion and adding needed information for acquiring non-functional parameters and it construct a better QoS metrics model. Furthermore, the experimental results show that the proposed method improve the accuracy of the discovery system.

As more and more learners are opting for onlinelearning, e-learning industry is working on improving learningexperience of online user by providing relevant content and lotof additional references. Since online learners mostly prefervideo tutorials, identifying major topics and subtopics coveredin video tutorial is a big challenge. Recently, for efficientknowledge sharing and interoperability over web lot ofattention is given to semantic web. In this paper, we propose asemantic web-based framework for automatic topicidentification from video tutorials in order to identify theconcepts and their associated semantically relevant resources. Our framework identifies relevant topic using disambiguationin e-learning resource which helps learners in more focused study.

Due to unprecedented growth of information on the Web and lack of structure in many Web sites, it became real challenge to the Web users to find relevant information. To solve this problem, Personalization is considered as a popular solution to customize the World Wide Web environment toward the user's preferences. Recent study shows that Web Usage Mining techniques play an important role in designing Web page recommendation systems. However the present conventional content-based recommender systems, built using the Web Usage Mining process, do not take Semantic Knowledge into pattern discovery and recommendation process. Recent studies show that integrating domain knowledge in the form of ontology into Web Usage Mining process can enhance the quality of the discovered usage patterns. Our work aims to incorporate semantics knowledge in all the phases of Web Usage mining process. CloSpan, a state-of-the-art algorithm for Sequential Pattern mining is applied over the Semantic space to generate frequent Sequential Patterns. The generated semantically enriched patterns are fed to Web page Recommendation model in offline phase. Experimental results shown are promising and showed a significant improvement on the quality of the recommendations.

Semantic similarity is an essential part for question answering, it is used various fields such as Artificial Intelligence, Natural Language Processing, information retrieval, Document Retrieval and Automatic evaluations. This paper mainly focuses on similarity measure based on the posted query, and finding the appropriate meaning between the words. Accessing an accurate answer from the web document is challenging task. The proposed approach is used to analyze and measuring the similarity between the words. It presents the Web And semantic knowledge-Driven automatic question answering system (WAD). It encompasses three phases to enhance the performance of QA system using the web as well as the semantic knowledge. Initially, the WAD approach determines the user query, query expansion technique and entity linking method. The ontology based information is used in WAD to rank the answers and experimental results provide the result with high accuracy than the baseline method.

The relative influence of Semantic Web on Artificial Intelligence and vice versa is ever increasing. However Artificial Intelligence and Semantic Web applications and research are going in different tangents as mentioned by the pioneers of Semantic Web. The real benefit would be on mixing semantic data for use in Artificially Intelligent apps so that reasoning can be done on open datasets for different entities. One way to do reasoning between entities would be by finding commonalities. that purpose we have created EnSense (Enhanced Sensing) — A web application that can find commonalities between two RDF objects using Semantic Web. We have devised an algorithm that could perform the task with greater speed, better number of relationships and quality as opposed to its closely related works. Our results have proven an 85% decrease in time taken and have 145% increase in the number of commonalities found. We provide a visual representation of the commonalities based on an interactive graph to the user.

The present paper proposes a dynamic semantic search interface for discovering First World War events. A semi-automated knowledge retrieval algorithm was designed to automatically extract historical events from unstructured scholar texts and define their ontology-based semantic relevance, as well as, their spatial and temporal information. The interface is called "Events Discovery Assistant" and is able to improvise with the continuously growing historical contents. Based on semantic web technologies, the interface allow users to query events related to First World War specific (ontology-based) topic(s) and explore their evolved spatial and temporal patterns.

This paper is aimed to provide a brief description about the working and the processes involved with the system. And it is also focusing on the challenges during the deployment of ontology for university examination system. The development of semantic web also enhanced the creation and publishing of ontology and relationships in almost all domains. The ontology developed in this paper using protege can be extended to model and create data and metadata elements which are helpful for developing university examination applications and for knowledge representation in semantic web.

The Semantic Web (SW) is a significant advancement in the field of Internet technologies and an uncharted territory as far as security is concerned. In this paper, we investigate and assess the impact of known attacks of SPARQL/SPARUL injections on Semantic Web applications developed in PHP. We highlight future challenges of developing robust Semantic Web applications using PHP. Our results demonstrate and quantify impacts on Confidentiality, Integrity and Availability (CIA) breaches of data in Semantic Web applications. Our recommendations are targeted to PHP developers, to encourage them to integrate security as early in their design and coding practice as possible.

Concepts in web ontologies help machines to understand data through the meanings they hold. Furthermore, learning contexts and topics of web documents also have helped in better semantic-oriented structuring and retrieval of data on the web. In this short paper we present a novel approach for domain-independent open learning of domain concepts, context and topic of any given web document. Our approach is based on a computational version of the Construction-Integration (CI) model of text comprehension. Our proposed system mimics the way humans learn the meanings of textual units and identify domain concepts, contexts and topics in the form of semantic networks. We apply our system on a number of web documents with a range of topics and domains. The resulting semantic networks provide a quantitative and qualitative insights into the nature of the given web documents.

The Linked Data paradigm is a common standard initiated to complement the general architecture of the semantic web and create a single space containing data that is machine-readable and connected to related data. Figures, however, show that one of the main sources of semi-structured data providers, Web APIs, continued to grow even after the creation of the Linked Data concept. Given that data sources with significant value are still in a semi-structured format, it is essential to bridge between the two data models, so that the full potential of the semantic web can be realised. This paper presents SimiMatch, an approach for schema matching between semi-structured and Linked Data. It contributes towards a virtual integration system that will be able to provide transparent access to heterogeneous and autonomous sources. It addresses the challenge of sustaining the continuous changes of the web of data via semantic similarity measurement.

This paper describes the specification, modelling and design of an ontology-based news authoring environment for the Semantic Web, that takes into account the construction and use of an ontology of the Zika disease. CMSs are being adapted in order to receive semantic features, such as automatic generations of keywords, semantic annotation and tagging, content reviewing etc. We present here the infrastructure designed to foster research on semantic CMSs as well as semantic web technologies that can be integrated into an ontology-based news authoring environment.

The development of semantic web technologies assures a new incentive for researches over software engineering. Although the basic concepts of semantic web have a widespread tradition over engineering sector, it moreover becomes difficult for software engineers to look into the diversity of ontology-provided approaches. A realistic scenario Zoo Management is taken based on which an ontology is generated to provide more semantic information. We illustrate how these techniques can be put into practice using the modern Semantic Web development tool Protege, and indicate future possibilities.

This article introduces the Open Semantic Framework (OSF), a one-stop shop for creating and deploying semantic applications as well as their lifecycle management. The authors discuss how the OSF supports knowledge acquisition into semantic knowledge models via novel interface technologies, manages these models internally within core ontologies and pluggable knowledge packs, and provides moderated access to them via a REST API and prefabricated SPARQL queries. As an example of OSF's usage in a practical scenario, the authors show how it can increase worker safety in industrial settings by mitigating workplace safety hazards through the automatic insertion of safety-relevant actions directly in the workflow, based on a model of applicable workplace safety law and regulation. By facilitating the creation, integration, and provisioning of knowledge, the OSF represents an important step on the way to integrating knowledge models worldwide, enabling semantic applications globally to "stand on the shoulders of giants."

The semantic web is evolving and helping users to find, share, understand and combine the information and be processed by the automated tools. With the traditional web, it is a time-consuming and tedious task to find the necessary information. By using the semantic web, we obtain information in a way that is more efficient and fast and avoid all the unnecessary data. Semantic web helps to make intelligent decisions by understanding the meaning of data. Educational institutions have the requirement of keeping track of resources like students, faculty, visitors etc. In our work, we have recorded the information of various resources of the institution in Web Ontology Language (OWL) using Protege tool. Then the SPARQL Query is used to acquire useful information. This helps to keep track of the various required resources. The developed ontology can be extended and reused by other institutions based on the need.

The Sharing Economy with the central theme of “access over ownership” gains popularity and is receiving a lot of coverage by the media. Companies like UBER, AIRBNB and BLABLACAR are valued in the billions and enable individuals to act as private taxi drivers, rent out their apartments or participate in ridesharing. Besides those prominent companies, there are platforms that facilitate the sharing of tools, miscellaneous types of equipment, 3D printers and even pets. A peer searching for a specific resource to borrow needs to at first identify relevant websites and then look through various offers to find an appropriate one. This laborious process can be eased by making use of Semantic Web technologies. Using the Design Science Research Methodology (DSRM), we synthesize the characteristics of various exemplary Sharing Economy websites with regard to the description of resources and services that can be shared including the transaction details and other relevant components. Following up, core concepts serving as a foundation for the conceptualization and the design of a description language in the form of an ontology are derived. The description language is based on Semantic Web technologies to enable efficient semantic search and matchmaking using established standards like RDF(S), OWL, and SPARQL. The underlying idea is to abstract from proprietary implementations in order to provide a common ground for service description in the domain of the Sharing Economy. Thus, a search against multiple websites could be facilitated to improve the discoverability and comparability of individual offers on different websites.

With web services growing and accumulating in application area, service discovery has become a hot issue for service composition and service management. Service clustering provides a promising way to split the whole searching space into small regions so as to minimize the discovery time effectively. However, semantic information is a critical element during the whole disposing process. Current industrialized Web Service Description Language (WSDL) does not contain enough information for service description. Therefore, a service clustering method has been proposed, which enhances original WSDL document with semantic information by means of Linked Open Data (LOD). Experiment based on real service data has been performed, and comparison with similar methods has also been provided to demonstrate the effectiveness of the method. It is shown that utilizing semantic information from LOD enhances the precision of service clustering. And it forms a sound base for further comprehensive processing with semantic information.

Day by day, the amount of digital data grows exponentially. Furthermore, these data come from heterogeneous content and structure resources. Both factors are the major handicap that limits the correct functioning of information systems. Being on the same wavelength, researchers have fixed as a challenge to reach targeted information retrieval which allows an easy access to the desired resource then return relevant and correct outcomes corresponding to a specific request. Therefore, semantic annotation is chosen as a paramount solution that may handle all these requirements, while ensuring a good understanding of document content then exploit and share it readily afterward. In this paper, we treat semantic annotation of documents using multi-ontologies, by stressing the effect of that on the relevance and the quality of semantic annotation results. We present a survey of different multi-ontologies based semantic annotation approaches and conclude with a synthesis of the study made.

In the construction of the semantic web, scientists use to annotate the existing web to improve the precision in handling documents for applications. The rapid growing of the web make impossible doing this manually. Many annotation techniques are used to resolve the first and easiest problem of information search which is finding documents containing the searched data. In this work we proposed a deep annotation model for locating and extracting the more exact parts of the documents that correspond to the responses of the request. This work extends SHIRI1 which is an ontology-based system for integration of semi-structured documents related to a specific domain. The ontology is described by a set of concepts, relations and their properties. It also contains a lexical part. It relies on an automatic, unsupervised and ontology-driven approach for extraction, alignment and querying for semantic annotation of tagged elements of documents. In this paper we focus on two major improvements: (1) we apply statistical techniques to purge extracted terms and named entities and (2) we annotate documents parts with one metadata. Experiments on real datasets will show that these improvements increase greatly the recall and the returned answers are effectively more precise and ranked according to their precision.

Traditional Information Retrieval (IR) methods were initially used for searching and ranking web pages on the Web. These methods were progressively modified to exploit the peculiarities of the Web including the use of the hyperlinked structure of the Web for relevance ranking. These Web IR techniques, however, are also being applied for searching and ranking to other forms of text collections which are not inherently web documents. Books (especially in PDF form) are by nature different from web pages because they lack an explicit hypertextual structure and therefore cannot be accurately and precisely searched and ranked using traditional approaches. Books contain a highly structured content with implicit logical connections among different parts of the same book as well as to related content in other books. These book structural semantics and logical connections could be discovered and used to establish a web of books where the logical concepts, images, figures, tables, and other parts are linked with each other thus resulting in a semantic graph, which could then be exploited by a semantic book search engine for more precise and accurate indexing, searching, ranking and recommendations. Based on this hypothesis, the paper outlines a high-level architecture for one of the possible implementations of a semantic book search engine, identifies all the potential areas of research for future researchers, and reports on our work in progress in the form of the proposed model for the purpose. The proposed architecture, if implemented in its true sense, has the potential to better serve the needs of all the stakeholders including authors, publishers, readers, and librarians.

A Semantic Web of Things (SWoT) brings together the Semantic Web and the Web of Things (WoT), associating semantically annotated information to web-enabled physical devices, services and their data, towards seamless data integration and better understanding of real-world information. A missing element in order to realize SWoT is a standardized, scalable and flexible way to globally discover in (near) real time web-connected embedded devices, as well as their semantic data. To address this gap, we propose WOT Semantic Search Engine (WOTS2E), which is a search engine for the SWoT, based on web crawling, being able to discover Linked Data endpoints and, through them, WoT-enabled devices and their services. In this paper, we describe the design, development and implementation of WOTS2E, as well as an evaluation procedure showing its operation and performance across the web.

Accelerated growth of the Web has made difficult retrieving information although the current search engines algorithms implemented are very sophisticated. This paper proposes a methodology that makes use of Semantic Web technologies to build search engine for a given context. The Semantic Web is made of a set of technologies, tools and standards that form the basis of an infrastructure to support the vision of a Web associated with meaning. The proposed methodology was test in commercial contexts that provide some kind of product or service, such as restaurants and hotels. The information, which is used as a starting point, is extracted from a Web site with thematic information or directory, from which the selected ontology is populated, this being built from scratch or by adapting some already existing ontology, creating a semantic database; finally, a search application that searches and displays the information found with the specific criteria. The results show that a search engine that uses Semantic Web technologies can achieve good results compared with traditional search engines.

In this paper we give details about the role of ontologies in Semantic Web and tools required for constructing Semantic Web applications. The Protege tool for ontology development and knowledge acquisition can be adapted for editing models in different Semantic Web languages. In recent experiments to prototype the Semantic Web, members of different communities with different backgrounds have created a multitudinous of languages for representing ontologies and instance data on the Web. Typically, a Semantic Web language for describing ontologies and instance data contains a hierarchical description of important conceptual thing in a domain. Individuals in the domain are instances of these classes, and properties of each classes describes the various features and attributes of the concept. Logical statements describe relations among concepts. Protege adopt the new world of the Semantic Web reusing its user interface, internal representation, and framework also it is useful in conceptual modeling with the new Semantic Web languages.

The Semantic Web propels ubiquitous computing by driving the evolution of Web technologies for meaningful user-friendly access. It allows users to apply its potential to find, share and combine information. Ontology based annotations can be used in the Semantic Web to provide better support for organizing knowledge exchange between users. We address this issue to extract meta knowledge from the Web, regardless of devices (e.g., desktops, tablets). Accessing and reusing RDF data from many sources of varying credibility and authority is done through the Semantic Web. This makes the tracking, representation and utilization of various meta knowledge aspects such as reliability, provenance and timestamps extremely important. In this paper, we present an ontology based approach deployed to extract meta knowledge with the following tasks: (1) annotate and extract Wikipedia contents (2) create RDF metadata using the annotations (3) query metadata from the RDF files to obtain the required knowledge. Our general purpose is to link documents to Wikipedia fed to DBpedia / Cyc for extracting the shared lightweight ontology and utilize it for SPARQL querying of documents encompassing meta knowledge. In our approach and experiments we focus on academic scenarios. This knowledge extraction process is useful in developing AI tools and mobile apps where semantics is critical. This work enhances ubiquitous computing by providing efficient and accurate Web access for knowledge extraction on many platforms including desktop computers and mobile devices.

With the exponential rise in the amount of information in the World Wide Web, there is a need for a much efficient algorithm for Web Search. The traditional keyword matching as well as the standard statistical techniques is insufficient as the Web Pages they recommend are not highly relevant to the query. With the growth in Semantic Web, an algorithm which semantically computes the most relevant Web Pages is required. In this paper, a methodology which computes the semantic heterogeneity between the keywords, content words and query words for web page recommendation is incorporated. A Differential Adaptive PMI Algorithm is formulated for with varied thresholds for recommending the Web Pages based on the input query. The proposed methodology yields an accuracy of 0.87 which is much better than the existing strategies.

Web page recommendation system is one kind of the recommendation system based on the user's current web navigation behavior that automatically recommends web pages in which a particular user is most interesting. For effective web page recommendations, it is very crucial and difficult to find the useful and sufficient knowledge from web usage data. This paper discusses taxonomy of recommendation system techniques and some of open challenges and issues in development of recommendation systems. The paper has performed the case studies of some of main web page recommendation frameworks based on semantic web usage mining.

Internet communication and collaboration tools are widely used on the Internet to express opinions and describe views on various subjects. The main goal of Argument Web is related to the definition of an infrastructure to represent correctly the arguments of the posted messages and enable these to relate to its various sources. When integrated with the initiative of open and connected databases, the Argument Web has the potential to increase the quality of collaborative discussions on the Internet and to encourage their analysis. This paper describes a model for the creation of such applications, based on the Argument Interchange Format and the use of Semantic Web.

The Semantic Web is a compilation of various technologies designed to work together. Research on Semantic Web proposes to advance individual web services or technologies to integrate them to promote rapid development of semantic web services as flexible changes are occurring. Diverse methods are considered in order to construct systematic composition of services. The intent of paper is to differentiate various web service based composition approaches like discovery, selection and ontology mapping at end user level to provide high level web services. The integration of semantics in web service cycle addresses the variety of critical issues in reuse, integration and scalability. The different approaches are comparable on the basis of three parameters costs, computational overhead, accuracy and performance index. The computed results are shown in graphical form.

The reality of Internet of Things (IoT), with its growing number of devices and their diversity is challenging current approaches and technologies for a smarter integration of their data, applications and services. While the Web is seen as a convenient platform for integrating things, the Semantic Web can further improve its capacity to understand things' data and facilitate their interoperability. In this paper we present an overview of some of the Semantic Web technologies used in IoT systems, as well as some of the well accepted ontologies used to develop applications and services for the IoT. We finally present the Semantic Web Stack for the Internet of Things pointing out some of its shortcomings in the development of an IoT application or service.