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Resource Description Framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the Query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. We propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.

Industry 4.0 is a technology-driven manufacturing process that heavily relies on technologies, such as the internet of things (IoT), cloud computing, web services, and big real-time data. Industry 4.0 has significant potential if the challenges currently being faced by introducing these technologies are effectively addressed. Some of these challenges consist of deficiencies in terms of interoperability and standardization. Semantic Web technologies can provide useful solutions for several problems in this new industrial era, such as systems integration and consistency checks of data processing and equipment assemblies and connections. This paper discusses what contribution the Semantic Web can make to Industry 4.0.

Using semantic web technologies is becoming an efficient way to overcome metadata storage and data integration problems in digital archives, thus enhancing the accuracy of the search process and leading to the retrieval of more relevant results. In this paper, the results of the implementation of the semantic layer of the Józef Piłsudski Institute of America digital archive are presented. In order to represent and integrate data about the archival collections housed by the institute, the authors developed arkivo, an ontology that accommodates the archival description of records but also provides a reference schema for publishing linked data. The authors describe the application of arkivo to the digitized archival collections of the institute, with emphasis on how these resources have been linked to external datasets in the linked data cloud. They also show the results of an experiment focused on the query answering task involving a state-of-the-art triple store system. The dataset related to the Piłsudski Institute archival collections has been made available for ontology benchmarking purposes.

The heterogeneity and the increasing amount of the news published on the web create challenges in accessing them. In the authors' previous studies, they introduced a semantic web-based sports news aggregation system called BKSport, which manages to generate metadata for every news item. Providing an intuitive and expressive way to retrieve information and exploiting the advantages of semantic search technique is within their consideration. In this paper, they propose a method to transform natural language questions into SPARQL queries, which could be applied to existing semantic data. This method is mainly based on the following tasks: the construction of a semantic model representing a question, detection of ontology vocabularies and knowledge base elements in question, and their mapping to generate a query. Experiments are performed on a set of questions belonging to various categories, and the results show that the proposed method provides high precision.

COVID-19 has affected people in nearly 180 countries worldwide. This paper presents a novel and improved Semantic Web-based approach for implementing the disease pattern of COVID-19. Semantics gives meaning to words and defines the purpose of words in a sentence. Previous ontology approaches revolved around syntactic methods. In this paper, semantics gives due priority to understand the nature and meaning of the underlying text. The proposed approach, FaD-CODS, focuses on a specific application of fake news detection. The formal definition is given by depiction of knowledge patterns using semantic reasoning. The proposed approach based on fake news detection uses description logic for semantic reasoning. FaD-CODS will affect decision making in medicine and healthcare. Further, the state-of-the-art method performs best for semantic text incorporated in the model. FaD-CODS used a reasoning tool, RACER, to check the consistency of the collected study. Further, the reasoning tool performance is critically analyzed to determine the conflicts between a myth and fact.

One of the critical challenges in health information systems is interoperability. The clinical, strategic, and operational decision depends on quality data and the ability to exchange data inter-systems and inter-health organizations. The Semantic Web plays a cornerstone role as a technology to enable functional and semantic interoperability without substantial changes in existing systems and to improve data sharing capabilities and, consequently, its quality and completeness. However, a common concern is data security. In this context, this research proposes a framework for securing health data, with a real case scenario focused on tuberculosis data exchange over the Semantic Web. The needs of security for the Semantic Web were satisfied, helping to build trust in the data and promoting its use in contexts that were not initially created to openly disseminate data without a significant technological apparatus.

The Semantic Web uses ontologies to cope with the data heterogeneity problem. However, ontologies become themselves heterogeneous; this heterogeneity may occur at the syntactic, terminological, conceptual, and semantic levels. To solve this problem, alignments between entities of ontologies must be identified. This process is called ontology matching. In this paper, the authors propose a new method to extract alignment with multiple cardinalities using integer linear programming techniques. The authors conducted a series of experiments and compared them with currently used methods. The obtained results show the efficiency of the proposed method.

In today's era, with the availability of a huge amount of dynamic information available in world wide web (WWW), it is complex for the user to retrieve or search the relevant information. One of the techniques used in information retrieval is clustering, and then the ranking of the web documents is done to provide user the information as per their query. In this paper, semantic similarity score of Semantic Web documents is computed by using the semantic-based similarity feature combining the latent semantic analysis (LSA) and latent relational analysis (LRA). The LSA and LRA help to determine the relevant concepts and relationships between the concepts which further correspond to the words and relationships between these words. The extracted interrelated concepts are represented by the graph further representing the semantic content of the web document. From this graph representation for each document, the HCS algorithm of clustering is used to extract the most connected subgraph for constructing the different number of clusters which is according to the information-theoretic approach. The web documents present in clusters in graphical form are ranked by using the text-rank method in combination with the proposed method. The experimental analysis is done by using the benchmark datasets OpinRank. The performance of the approach on ranking of web documents using semantic-based clustering has shown promising results.

Globally, aging is now a societal trend and challenge in many developed and developing countries. A key medical strategy that a fast-paced aging society must consider is the provision of quality long-term care (LTC) services. Even so, the lack of LTC caregivers is a persistent global problem. Herein, attention is called to the increasing need for identifying appropriate LTC caregivers and delivering client-specific LTC services to the elderly via emerging and integrative technologies. This paper argues for the use of an intelligent cloud computing long-term care platform (ICCLCP) that integrates statistical analysis, machine learning, and Semantic Web technologies into a cloud-computing environment to facilitate LTC services delivery. The Term frequency-inverse document frequency is a numerical statistic adopted to automatically assess the professionalism of each LTC caregiver's services. The machine learning method adopts naïve Bayes classifier to estimate the LTC services needed for the elderly. These two items of LTC information are integrated with the Semantic Web to provide an intelligent LTC framework. The deployed ICCLCP will then aid the elderly in the recommendation of LTC caregivers, thereby making the best use of available resources for LTC services.

Advances in semantic web technologies have rocketed the volume of linked data published on the web. In this regard, linked open data (LOD) has long been a topic of great interest in a wide range of fields (e.g. open government, business, culture, education, etc.). This article reports the results of a systematic literature review on LOD. 250 articles were reviewed for providing a general overview of the current applications, technologies, and methodologies for LOD. The main findings include: i) most of the studies conducted so far focus on the use of semantic web technologies and tools applied to contexts such as biology, social sciences, libraries, research, and education; ii) there is a lack of research with regard to a standardized methodology for managing LOD; and iii) a plenty of tools can be used for managing LOD, but most of them lack of user-friendly interfaces for querying datasets.

Over the last few decades, data has assumed a central role, becoming one of the most valuable items in society. The exponential increase of several dimensions of data, e.g. volume, velocity, variety, veracity, and value, has led the definition of novel methodologies and techniques to represent, manage, and analyse data. In this context, many efforts have been devoted in data reuse and integration processes based on the semantic web approach. According to this vision, people are encouraged to share their data using standard common formats to allow more accurate interconnection and integration processes. In this article, the authors propose an ontology matching framework using novel combinations of semantic matching techniques to find accurate mappings between formal ontologies schemas. Moreover, an upper-level ontology is used as a semantic bridge. An implementation of the proposed framework is able to retrieve, match, and align ontologies. The framework has been evaluated with the state-of-the-art ontologies in the domain of cultural heritage and its performances have been measured by means of standard measures.

Document Management Systems (DMS) are used for decades to store large amounts of information in textual form. Their technology paradigm is based on storing vast quantities of textual information enriched with metadata to support searchability. However, this exhibits limitations as it treats textual information as black box and is based exclusively on user-created metadata, a process that suffers from quality and completeness shortcomings. The use of knowledge graphs in DMS can substantially improve searchability, providing the ability to link data and enabling semantic searching. Recent approaches focus on either creating knowledge graphs from document collections or updating existing ones. In this paper, we introduce Doc2KG (Document-to-Knowledge-Graph), an intelligent framework that handles both creation and real-time updating of a knowledge graph, while also exploiting domain-specific ontology standards. We use DIAVGEIA (clarity), an award winning Greek open government portal, as our case-study and discuss new capabilities for the portal by implementing Doc2KG.

Software engineering employs different benchmarks for a software evaluation. This enables software developers to continuously improve their product. The same needs are intrinsic for software tools in the semantic web field. While there are many different benchmarks already available, there has not been their overview and categorization yet. This work provides such an overview and categorization of benchmarks specifically oriented on benchmarks where an ontology plays an important role. Benchmarks are naturally categorized in line with ontology tool categorization along with an indication which activities those benchmarks are deliberate and which are non-deliberative. Although the article itself can already navigate a reader to an adequate benchmark, we moreover automatically designed a flexible rule-based recommendation tool based on the analysis of existing benchmarks.

This article presents a methodology for the analysis of data on the Internet, combining techniques of Big Data analytics, NLP and semantic web in order to find knowledge about large amounts of information on the web. To test the effectiveness of the proposed method, webpages about cyberterrorism were analyzed as a case study. The procedure implemented a genetic strategy in parallel, which integrates (Crawler to locate and download information from the web; to retrieve the vocabulary, using techniques of NLP (tokenization, stop word, TF, TFIDF), methods of stemming and synonyms). For the pursuit of knowledge was built a dataset through the description of a linguistic corpus with semantic ontologies, considering the characteristics of cyber-terrorism, which was analyzed with the algorithms, Random Forests (parallel), Boosting, SVM, neural network, K-nn and Bayes. The results reveal a percentage of the 95.62% accuracy in the detection of the vocabulary of cyber-terrorism, which were approved through cross validation, reaching 576% time savings with parallel processing.

The research aims at design and develop a special semantic query engine “CityGML Spatial Semantic Web Client (CSSWC)” that facilitates ontology-based multicriteria queries on CityGML data in OGC standard. Presently, there is no spatial method, spatial information infrastructure or any tool to establish the spatial semantic relationship between the 3D city objects in CityGML model. The present work establishes the spatial and semantic relationships between the 3DCityObjects and facilitates ontology-driven spatial semantic query engine on 3D city objects, class with multiple attributes, spatial semantic relations like crosses, nearby, etc., with all other city objects. This is a novel and original work practically implemented generic product for any 3D CityGML model on the globe. A user-friendly form-based interface is designed to compose effective ontology based GeoSPARQL query. CSSWC enhances CityGML applications performance through effective and efficient querying system.

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.

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

The semantic web is a global initiative which employs ontologies to offer rich, semantic-based knowledge representation. Concepts in these ontologies are explored to find (dis)similarities between them using (dis)similarity measures. Despite the existence of numerous (dis)similarity measures, none have dynamically determined the quantum of information required to discover (dis)similarities between concepts. In this article, a new, efficient, feature-based semantic dissimilarity measure is proposed where the prime novelty lies in the dynamic selection of the semantic neighourhood (features) of the concepts. The neighbourhood is dynamically selected in accordance with the local density of the concept and the density of the ontology determined by the proposed density coefficient. Further, the proposed measure also scales down the dissimilarity value in accordance with the depth of the concept pair, using the novel Depth Coefficient.

Social tagging systems (STSs) allow collaborative users to share and annotate many types of resources with descriptive and semantically meaningful information in freely chosen text labels. STS provides three recommendations such as tag, item and user recommendations. Existing recommendation algorithms transform the three dimensional space of user, resource, and tag into two dimensions using pair relations in order to apply existing techniques. However, users may have different interests for an item, and items may have multiple facets. To circumvent this, a new system that models three types of entities user, tag and item in a STS as a 3-order tensor is proposed. The sparsity is reduced using stemming and predictions are made by applying latent semantic indexing using randomized singular value decomposition (RSVD). The proposal provides all the three recommendations using semantic web and shows notable improvements in terms of effectiveness through indices such as recall, precision, time and space.

The present society is considered an information society. A society where the creation, distribution, use, integration, and manipulation of digital information have become the most significant activity in all aspects. Information is producing from every sector of any society, which has resulted in an information explosion. Modern technologies are also having a huge impact. So managing this voluminous information is really a tough job. Again WWW has opened the door to connect anyone or anything within a fraction of a second. This study discussed the Semantic Web and linked data technologies and their effect and application to libraries for the handling of various types of resources.

The chapter examines the prospects and challenges of the application of Web 3.0 technologies as they relate to semantic web, federated search, mobile application, and their impact on library services. The principles, features, application, potentiality, and challenges of the technologies vis-à-vis library services form the broad objectives that guided the chapter. Following a brief retrospective review of the developments of web technologies, the chapter discusses Web 3.0 from the context of semantic web, cloud computing, federated search and virtual reference services. It broadens the prospects of Web 3.0 as it affects the provision of web-based services like its flexibility as solution to digital content volatility and ability to widen cloud-based services using open source electronic library software among others. Having expatiated the challenges Web 3.0 portends for web-based library services, the chapter concludes with the need for librarians and users to co-create value for participatory librarianship.

Nowadays, people are using lots of websites for searching and retrieving information. Most of the websites keep information in a simple format with all information simply linked with each other. Such type of information has less accuracy. So, there is utmost important to work on knowledge-based, information presentation. Hence, the advent of the semantic web called intelligent and meaningful web is a new trend in the area of web development. Ontology is a key term widely used in the development of the Semantic Web. It is an idea, which strongly focused on class, object, and relationship relatively than information. Protégé is a tool widely used for ontology development and customization. It has a user interface for ontology results visualization. It provides a view for a developer for a strong focus on creating knowledge rather than syntax. It provides the flexibility to add-on more additional features by the extendable plug-in. The purpose of this work is to develop a knowledge-based university system. Here, as an example of Indira Gandhi Delhi Technical University Delhi has been taken and created a university ontology using protégé tool. It also includes various aspects like classes, class hierarchy, superclass and subclass, and also created a subclass instance for designing class, class hierarchy, query searching, and retrieval process, and the result is demonstrated in graphical form.

One of the goals of data scientists and curators is to get information (contained in text) organized and integrated in a way that can be easily consumed by people and machines. A starting point for such a goal is to get a model to represent the information. This model should ease to obtain knowledge semantically (e.g., using reasoners and inferencing rules). In this sense, the Semantic Web is focused on representing the information through the Resource Description Framework (RDF) model, in which the triple (subject, predicate, object) is the basic unit of information. In this context, the natural language processing (NLP) field has been a cornerstone in the identification of elements that can be represented by triples of the Semantic Web. However, existing approaches for the representation of RDF triples from texts use diverse techniques and tasks for such purpose, which complicate the understanding of the process by non-expert users. This chapter aims to discuss the main concepts involved in the representation of the information through the Semantic Web and the NLP fields.