Semantic Web Applications Research Articles

Theme Evolution of Researches on Knowledge Graphs Based on Visualization Analyses of Data

The knowledge graph, as a significant branch of Library and Information Science, has also become a vital part of Computer Science. In a bid to reveal the research trend and hot topics of the knowledge graph domain, this paper collected latest data about literature on knowledge graphs from SCI-Expanded and EI Compendex database. This paper took advantage of Citespace for visualization analyses of data, which discerned the research topics, hot issues and theme changes of the knowledge graph domain over time. Through analyses, we conclude that the knowledge graph has developed greatly in both Library and Information Science and Computer Science. In Library and Information Science domain, researches focus on the issues like the plotting of knowledge graphs and visualization of knowledge. In Computer Science domain, hot issues are the techniques of constructing knowledge graphs, which include entity recognition, knowledge integration, etc., and their applications in Semantic Web. Among all the research topics, the domain knowledge graph is one of the paramount research trends. Compared with previous researches, this paper keeps up with the times and can reveal the theme evolution of researches on knowledge graphs more precisely.

A review of the semantic web field

Tracing the triumphs and challenges of two decades of Semantic Web research and applications.

Building a New Semantic Social Network Using Semantic Web-Based Techniques

Most people are more or less related to the web by participating in a kind of social networking site. Semantic Web technology plays a crucial role in these sites as they contain an enormous amount of data about ‎persons, pages, events, places, corporations, etc. This research is a Semantic Web application designed to create a new ‎semantic social community called Socialpedia. It links the already existing social public information to the newly ‎public ones. This information is linked with different information on the web to construct a new immense ‎data container. The resulting data container can be processed using a variety of Semantic Web techniques to produce ‎machine-understandable content. This content shows the promise of using integrated data to improve Web search and ‎Web-scale data analysis, unlike conventional search engines or social ones. This community involves obtaining data ‎from traditional users known as contributors or participants, linking data from existing social networks, extracting ‎structured data in triples using predefined ontologies, and finally querying and inferring such data to obtain ‎meaningful pieces of information. Socailpedia supports all popular functionalities of social networking websites ‎besides the enhanced features of the Semantic Web, providing advanced semantic search that acts as a semantic ‎search engine.

Semantic Web Applications: Current Trends in Datasets, Tools and Technologies’ Development for Linked Open Data

Semantic Web Applications: Current Trends in Datasets, Tools and Technologies’ Development for Linked Open Data

An Art of Web Services using Web Mining and Semantic Web Techniques

Todays web browsers serve as an easy access to numerous sources of text and multimedia data. More than a billion pages are indexed by search engines, and finding the desired information is not an easiest task. Over the last decade, there is an explosive growth in the information available on the World Wide Web (WWW). The objective of this paper is to provide an outline of web mining, its various classifications, its subtasks, and to give a perspective to the research community about the potential of applying techniques to extract meaningful patterns. This paper also gives information in the area of web services, semantic web mining and comparison of traditional web applications and semantic web applications thereby providing the guidelines for future research in the area of web services using web mining and semantic web.

Designing and configuring context-aware semantic web applications

Context-aware services are attracting attention of world as the use of web services are rapidly growing. We designed an architecture of context-aware semantic web which provides on demand flexibility and scalability in extracting and mining the research papers from well-known digital libraries i.e. ACM, IEEE and SpringerLink. This paper proposes a context-aware administrations system, which supports programmed revelation and incorporation of setting dependent on Semantic Web administrations. This work has been done using the python programming language with a dedicated library for the semantic web analysis named as “Cubic-Web” on any defined dataset, in our case as we have used a dataset for extracting and studying several publications to measure the impact of context aware semantic web application on the results. We have found the average recall and averge accuracy for all the context aware research journals in our research work. Moreover, as this study is limited journal documents, other future studies can be approached by examining different types of publications using this advance research. An efficient system has been designed considering the parameters of research article meta-data to find out the papers from the web using semantic web technology. Parameters like year of publication, type of publication, number of contributors, evaluation methods and analysis method used in publication. All this data has been extracted using the designed context-aware semantic web technology.

Ontological methods and tools for semantic extension of the media WIKI technology

Practical aspects of ontological approach to organization of intelligent Wiki-based information resources (IR) are considered. We analyze the main features, capabilities and limitations of MediaWiki as a technological platform for development of the Web-based information resource and suggest main directions of its refinement. We propose an abstract model of MediaWiki architecture that formalizes relations between the main components of this software environment and analyze the ways of its semantic extensions based on ontological representation of domain knowledge. An original algorithm of semantic Wiki pages matching with domain ontology is developed. We propose an ontological model of IR that formalizes its knowledge base structure and explicitly performs main features of typical information objects (TIO) of this IR. Such TIOs depend on domain specifics and purposes of IR, therefore their development has to involve domain experts and knowledge engineers. Use of ontology corresponding to the set of Wiki pages (either with semantic markup or without it) provides new IR functions associated with semantic search and navigation. Other important aspect of intelligent Wiki resource development deals with adaptation of user interface to the specifics of IR: enabling various tools of navigation, visualization and content analysis by processing of TIO features enriches IR functionality, reduces access time to information and makes usage of IR more efficient. Developing additional MediaWiki functionality with new requests to the MediaWiki API using TIO templates, extends data analysis and integration capabilities, and offers different, user-focused, IR content views expands the possibilities of data integration and proposes various user-oriented representations of IR content. Wiki resource semantization allows the use knowledge acquired from such IR by external application, or example, by search engines for intelligent Web retrieval. Domain ontologies based on various subsets of the Wiki pages and generated by them thesauri can be used by various Semantic Web applications, both independently or in general technological chain for personified retrieval focused on individual users and their tasks. Approbation of this approach is demonstrated by MAIPS retrieval system. We consider the use semantic similarity of concepts represented by Wiki-pages of IR as an additional way of intelligent navigation between these pages. Such approach allows to group Wiki pages according to user interests by different aspects of their content and structure. Wiki ontologies are considered as the basis for estimation of semantic similarity between domain concepts pertinent to user task. Such elements of Wiki ontology as classes, property values of class instances and relations between them are used as parameters for the quantitative assessment of semantic similarity of Wiki pages. We propose to use local similarity and generate the sets of semantically similar concepts (SSC) that takes into account some subset of page properties and categories defined by user needs. Such sets of SSCs can be considered as user task thesauri for other applications. In addition, we propose to enrich the basic tools of MediaWiki used for access management to the IR content with specialized software code that performs content classification that take into consideration separate namespaces, categories, templates and semantic properties of TIO acquired from Wiki markup. We demonstrate the software implementation of proposed solutions by developing of portal version of the Great Ukrainian Encyclopedia (e-VUE) that contains heterogeneous multimedia content with complex structure. We analyze the specifics of e-VUE knowledge system and develop its formalized TIO representation based on Semantic Web technologies and ontological analysis. Ontological model of e-VUE and original methods of its processing used for this project extend the functionality of the portal in the area of search, navigation, integration and protection of content based on background domain knowledge. In addition, original user interface of e-VUE is developed with an allowance for Encyclopedia knowledge specifics, substantially differs from the standard Wiki, meets the requirements, goals and objectives of this IR and provides a lot of additional features.

Semantic Web Application in E-learning Using Protege based on Information Retrieval

Semantic Web Application in E-learning Using Protege based on Information Retrieval

Semantic Web in Disaster Management: A Systematic Literature Review

Disaster management requires extensive and various information. Semantic web offers a large potential to be utilized in the applications for supporting disaster management with its ability to integrate heterogeneous data from diverse sources. This study aims at investigating the extent to which semantic web is used in disaster management systems. The study conducted as a systematic literature review on 13 papers from academic databases that are leading in the area of computer science, i.e. ACM Digital Library, IEEE Explore and Science Direct. The result shows that the applications in the selected papers are mainly provided to institutions responsible for disaster management. Emergency relief is the phase that is supported by most applications. The study indicates that common geospatial ontology is necessary to facilitate the development of semantic web applications for disaster management. In the selected papers, the use of semantic web for data integration is done via an integration layer. This layer includes semantic processing to ensure that all data from multiple sources have proper semantics and to facilitate the application in querying the integrated information.

Using knowledge anchors to facilitate user exploration of data graphs

This paper investigates how to facilitate users’ exploration through data graphs. The prime focus is on knowledge utility, i.e. increasing a user’s domain knowledge while exploring a data graph, which is crucial in the vast number of user-facing semantic web applications where the users are not experts in the domain. We introduce a highly unique exploration support mechanism underpinned by the subsumption theory for meaningful learning. A core algorithmic component for operationalising the subsumption theory for meaningful learning is the automatic identification of knowledge anchors in a data graph (KADG). We present several metrics for identifying KADG which are evaluated against familiar concepts in human cognitive structures. The second key component is a subsumption algorithm that utilises KADG for generating exploration paths for knowledge expansion. The implementation of the algorithm is applied in the context of a Semantic data browser in a music domain. The resultant exploration paths are evaluated in a task-driven experimental user study compared to free data graph exploration. The findings show that exploration paths, based on subsumption and using knowledge anchors, lead to significantly higher increase in the users’ conceptual knowledge and better usability than free exploration of data graphs. The work opens a new avenue in semantic data exploration which investigates the link between learning and knowledge exploration. We provide the first framework that adopts educational theories to inform data graph exploration for knowledge expansion which extends the value of exploration and enables broader applications of data graphs in systems where the end users are not experts in the specific domain.

A New Semantic Resource Description Framework for Medical Diagnosis based on Gene Ontology

Semantic-Based medical informatics system is an interdisciplinary deflect of bioinformatics research, where a dealing to knowledge discovery of proper diagnosis of disease leads to more attention about health control before health complications. To do this work is in the aspects of various ways like preserving and accessing patient’s medical records for further information retrieval from pre-recorded datasets. Now some research has mostly used to medical metrics method combined with statistical information and social network analysis citation approach to analyze. Due to this limited citation information force to develop a new dimension of the semantic web-based medical diagnosis system. In this study the approach of implementing autonomic gene computing in medical informatics, in this framework a new semantic resource description network for medical diagnostics based on Gene Ontology, this way the diagnosis context capture its levels of symptoms. Every patient has their unique symptoms which are kept and fetched by semantic web applications, In this framework we used gene-based symptom indication for disease diagnosis, and merge with drug prescription leads to more suitable results that can be useful for physicians and medical practitioner, as to achieve efficient solutions in the new direction to make life saving analysis from the database. Being a very effective and enhanced web application tool can be applied in the medical field to make it useful for the society. The web interface for drugs prescription accepts the diseases and symptoms from the domain updated in the database and successfully displays prescribed drugs for each disease.

Semantic linkage of source content dynamically with virtual documents using Wikipedia in Hadoop

In recent years, the World Wide Web has developed enormously and become more multifaceted because of the rising number of users and emerging technologies. Web 2.0 has modified this by enabling the users to create and share the content and remain collaborative. As result of this there is enormous growth of web content. Hence relevant information retrieval became a challenging task. To handle this problem application of semantic web is very much essential. The proposed model, document-based content management system (DSCMS) extracts the meta-content of the link using Wiki application programming interface (API) and store it as repository based on mongo DB. The meta content matching is implemented by Jaccard similarity measure with alchemy API. Finally the exact source documents for our virtual document will be retrieved for future use. Graphical analysis with metrics recall and precision is calculated to prove the efficiency of the system compared to conventional systems.

Semantic linkage of source content dynamically with virtual documents using Wikipedia in Hadoop

In recent years, the World Wide Web has developed enormously and become more multifaceted because of the rising number of users and emerging technologies. Web 2.0 has modified this by enabling the users to create and share the content and remain collaborative. As result of this there is enormous growth of web content. Hence relevant information retrieval became a challenging task. To handle this problem application of semantic web is very much essential. The proposed model, document-based content management system (DSCMS) extracts the meta-content of the link using Wiki application programming interface (API) and store it as repository based on mongo DB. The meta content matching is implemented by Jaccard similarity measure with alchemy API. Finally the exact source documents for our virtual document will be retrieved for future use. Graphical analysis with metrics recall and precision is calculated to prove the efficiency of the system compared to conventional systems.

RDF graph validation using rule-based reasoning

The correct functioning of Semantic Web applications requires that given RDF graphs adhere to an expected shape. This shape depends on the RDF graph and the application’s supported entailments of that graph. During validation, RDF graphs are assessed against sets of constraints, and found violations help refining the RDF graphs. However, existing validation approaches cannot always explain the root causes of violations (inhibiting refinement), and cannot fully match the entailments supported during validation with those supported by the application. These approaches cannot accurately validate RDF graphs, or combine multiple systems, deteriorating the validator’s performance. In this paper, we present an alternative validation approach using rule-based reasoning, capable of fully customizing the used inferencing steps. We compare to existing approaches, and present a formal ground and practical implementation “Validatrr”, based on N3Logic and the EYE reasoner. Our approach – supporting an equivalent number of constraint types compared to the state of the art – better explains the root cause of the violations due to the reasoner’s generated logical proof, and returns an accurate number of violations due to the customizable inferencing rule set. Performance evaluation shows that Validatrr is performant for smaller datasets, and scales linearly w.r.t. the RDF graph size. The detailed root cause explanations can guide future validation report description specifications, and the fine-grained level of configuration can be employed to support different constraint languages. This foundation allows further research into handling recursion, validating RDF graphs based on their generation description, and providing automatic refinement suggestions.

Research on the Development Method of the Semantic Web Application Based on AllegroGraph

With the continuous development of semantic web technology, semantic web applications are getting more and more attention. However, the research on semantic web applications at home and abroad is still in the theoretical stage, lacking the specific methods of semantic web application development. Moreover, there are few semantic web products for users. In response to this problem, the advantages of AllegroGraph in semantic data storage and query as well as in the semantic web application development are introduced. Then the development framework of the semantic web application based on AllegroGraph and its implementation method are given. Specifically, taking information retrieval as an example, the construction process of the data layer, the logic layer and the presentation layer are described in detail. The experimental results show that the information retrieval method based on semantic, compared with that based on keyword, has a significant improvement in the recall rate and precision rate, which lays a foundation for the application of semantic web products in enterprises to a certain extent.

The Threat Detection Framework for Securing Semantic Web Services

Security is a necessary aspect of modern life for organizations and individuals who use the semantic web to provide various services. Semantic web applications are being used as a portal to communicate with back-end database systems and to support business processes. The confidential and personal information of any organization is stored on these systems. Access Control ensures that the requesting user has to meet certain criteria to access these systems. In most cases, it has been observed that access control only provides protection against external threats. There is no provision for detecting internal attacks. Therefore, there is a need for a mechanism that can be able to detect the malicious behaviour of previously authorized users. This paper proposes two algorithms to detect anomalous behaviour performed by the legitimate insider. During training phase, the first algorithm will create the query signature of each incoming query submitted by the legitimate insider. It also estimates the amount of data that can be extracted by the submitted query and includes in the query signature. The second algorithm will detect incongruous data extraction from the database by comparing the current query signature with the previous signature. If both signatures are identical, the query is considered safe for execution. Otherwise, the query will be considered as threat. In this paper, efforts are being made to give details of the security structure on the semantic web service.

Generating RDF resources from web open data portals

<span>Open data are available from various private and public institutions in different resource formats. There are already great number of open data that are published using open data portals, where datasets and resources are mainly presented in tabular or sheet formats. However, such formats have some barriers with application developments and web standards. One of the web recommenced standards for semantic web application is RDF. There are various research efforts have been focused on presenting open data in RDF formats. However, no framework has transformed tabular open data into RDFs considering the HTML tags and properties of the resources and datasets. Therefore, a methodology is required to generate RDF resources from this type of open data resources. This methodology applies data transformations of open data from a tabular format to RDF files for the Saudi Open Data Portal. The methodology successfully transforms open data resources in sheet format into RDF resources. Recommendations and future work are given to enhance the development of building open data.</span>

Fuzzy Role Based Access Control Design using Fuzzy Ontology

Fuzzy role-based access control (FRBAC) is essential for risk-based environments in many futuristic applications, even though role-based access control (RBAC) is the efficient and widely used access control model for enterprise applications. In FRBAC, authorization related information is vague. It brings the fuzziness in mapping among the components of FRBAC such as user, role and permission. Holding the fuzziness in FRBAC, it is challenging for the security engineer to verify the constraints and correctness of access control policy. On verifying the constraints and correctness of access control policy, knowledge representation techniques are much useful in practice. In this scenario, representing FRBAC using fuzzy ontology might be the right choice for semantic web application. The main objective of this article is to represent the access permissions of FRBAC using fuzzy ontology and verify whether the constraints of FRBAC are possible to get implemented in it or not.

An ontology‐based framework of assessment analytics for massive learning

AbstractAnalyzing learning traces is presently highly required in e‐learning environment. Several communities have been developed to address this need, such as those of Learning Analytics and Educational Data Mining. The main step of performing a learning analytics process is the educational data collection.Actually, learning environments such as Massive Open Online Course (MOOC) generate a big amount of educational data. They can be divided into assessment data, collaboration data, communication data, and so on. When we focus on assessment, we can launch a new source of data that can be analyzed and hence contribute to the improvement of learning analytics field.In this paper, we explore, investigate and compare the set of learning analytics models in the literature. Then, we study them from assessment point of view. The only current learning analytics model which can support tracking and modeling assessment data is the xAPI data model. For this reason, we study and investigate the xAPI specification from assessment point of view. Based on identified weaknesses of xAPI specification, we propose an enhancement of its data model. This is to support the assessment analytics effectively. We present an ontological model for assessment analytics inspired from the xAPI specification. To validate our approach, we focus on massive learning traces extracted from a real MOOC. Thus, we define and execute the set of proposed steps of preprocessing stage that extracts assessment data from whole learning data. Furthermore, we develop a java semantic web application to convert assessment data extracted to OWL file according to our proposed ontological model for assessment analytics.

A Review of Machine Learning Applications in Semantic Web

In recent years, the rapid growth of the semantic web has encouraged the use of knowledge graphs to store and exchange data among computers. The change in data storage and retrieval has imposed the need to integrate other techniques that interact with these data. Machine Learning (ML) is the most important fields of study that interacts with data, to extract the patterns and relations that can be used in future applications. However, these techniques are implemented to be used with structured data, where each data instance is characterized using a set of predefined features. Thus, to apply machine learning techniques to knowledge graphs, it is important to convert the formation of the information in these graphs. The knowledge graph represents a collection of interconnected descriptions of entities (objects, events, situations, or abstract concepts in the real world). This work reviews the knowledge graphs and how they are structured and stored. Then, the techniques being used to employ machine learning technique to improve the knowledge graphs are illustrated. Additionally, the integration of the information in the knowledge graphs, so that, they can be used in machine learning techniques are also reviewed. This review can provide a rigid overview for future researchers to provide more attention on the main drawbacks in the existing techniques. Moreover, the review shows that the existing integration approaches limit the performance of the machine learning techniques, as the knowledge extracted from the knowledge graph is trimmed to match the requirements of the ML technique. In conclusion, new machine learning techniques that can extract knowledge directly from the knowledge graph can provide significantly better performance. Keywords: Machine learning, Knowledge graph, Semantic web, Classification, Clustering DOI: 10.7176/JSTR/5-8-01