Semantic Web Usage Research Articles

Semantic Web Usage in the Tourism Industry in Andalusia, Spain

Andalusia got the 4th place of tourists’ favorite destinations among 17 autonomous communities in Spain in September 2023 and almost returned to pre-pandemic normality regarding tourism. Although Spain is famous for its “sun and beach”, its tourist industry is facing new challenges in socio-economic, environmental and territorial issues; therefore, the role of artificial intelligence cannot be ignored in the evolution and transformation of the tourism business. AI algorithms reinforce decision-making capabilities through analysing real-time data. Effectiveness and efficiency of AI algorithms are dependent on well-structured, well-described and machine-readable meta data network which can be provided by Semantic Web standards. This article empirically study the current usage of Semantic Web formats, ontologies and touristic topics in 65 tourist-relevant web sites in Andalusia. Results show that just 57 % of examined sites used semantic markup with most of the semantic annotations (98.5%) made in three formats: Microdata, JSON-LD and RDFa. Furthermore, the Ontologies used in almost 90% of markups are ‘Schema.org’, ‘Data Vocabulary’ and ‘RDF’, but none of the special tourism ontologies appeared in examined tourism web sites. Therefore, Andalusia tourism websites should be enhanced with much more widespread Semantic Web in order to achieve a better performance in a future in which AI technologies will be widespread.

MPP-MLO: Multilevel Parallel Partitioning for Efficiently Matching Large Ontologies

The growing usage of Semantic Web has resulted in an increasing number, size and heterogeneity of ontologies on the web. Therefore, the necessity of ontology matching techniques, which could solve these issues, is highly required. Due to high computational requirements, scalability is always a major concern in ontology matching system. In this work, a partition-based ontology matching system is proposed, which deals with parallel partitioning of the ontologies at multilevel. At first level, the root based ontology partitioning is proposed. Matchable Sub-ontologies pair is generated using an efficient linguistic matcher (IEI-Sub) to uncover anchors and then based on maximum similarity value, pairs are generated. However, a distributed and parallel approach of MapReduce-based SEI-sub process has been proposed to efficiently handle the anchor discovery process which is highly time-consuming. In second level partitioning, an efficient approach is proposed to form non overlapping clusters. Extensive experimental evaluation is done by comparing existing approaches with the proposed approach, and the results shows that MPP-MLO turns out to be an efficient and scalable ontology matching system.

Survey on ontology Based Semantic Web Usage Mining for Enhanced Recommendation Model

Survey on ontology Based Semantic Web Usage Mining for Enhanced Recommendation Model

Web-Page Recommendation Based on Web Usage and Domain Knowledge

Web-page recommendation plays an important role in intelligent Web systems. Useful knowledge discovery from Web usage data and satisfactory knowledge representation for effective Web-page recommendations are crucial and challenging. This paper proposes a novel method to efficiently provide better Web-page recommendation through semantic-enhancement by integrating the domain and Web usage knowledge of a website. Two new models are proposed to represent the domain knowledge. The first model uses an ontology to represent the domain knowledge. The second model uses one automatically generated semantic network to represent domain terms, Web-pages, and the relations between them. Another new model, the conceptual prediction model, is proposed to automatically generate a semantic network of the semantic Web usage knowledge, which is the integration of domain knowledge and Web usage knowledge. A number of effective queries have been developed to query about these knowledge bases. Based on these queries, a set of recommendation strategies have been proposed to generate Web-page candidates. The recommendation results have been compared with the results obtained from an advanced existing Web Usage Mining (WUM) method. The experimental results demonstrate that the proposed method produces significantly higher performance than the WUM method.

Optimum Session Interval based on Particle Swarm Optimization for Generating Personalized Ontology

A semantic web usage mining method is suggested to identify the association between consumer emotions and buying behaviors by utilizing the web log data. Fuzzy logic is used to signify the temporal conception and resource attributes for the requested URLS of web access activities. From this, a Personal Web usage Ontology is created which facilitates semantic web applications. But the limitation is less efficient in terms of accuracy and user satisfaction. Thus an innovative technique which is called Optimum Session Interval based Particle Swarm Optimization (OSIPSO) is introduced. This technique is used to find the optimum session interval. The Particle swarm optimization has no overlapping and mutation computation and it is proficient in global search. Additionally, an associative classification is used to enhance the accuracy. Associative classification is a combination of associative rule mining and classification rule mining. An experimental result shows that the proposed work has a high accuracy and high efficient.

Ontology-based architecture with recommendation strategy in java tutoring system

The aim of Semantic Web is to provide distributed information with well-defined meaning, understandable for humans as well as machines. E-learning is an important domain which can be benefited from the Semantic Web technology. Ontologies, as a building structure of Semantic Web, will fundamentally change the way in which e-learning systems are constructed. The explicit conceptualization of system components in a form of ontology facilitates knowledge sharing, knowledge reuse, communication and collaboration among system components, and construction of intensive and expressive systems. In previous research, we implemented tutoring system named Protus (PRogramming TUtoring System) that is used for learning basic concepts of Java programming language. Protus uses principles of learner style identification and content recommendation for course personalization. The new version of the system called Protus 2.0, supported by several ontologies, as well as examples of its usage for performing personalization are presented in this paper. Architecture of new system extends the usage of Semantic Web concepts, where the representation of each Protus 2.0 component is made by a specific ontology, making possible a clear separation of the tutoring system components and explicit communication among them.

Comparative Analysis of Collaborative Filtering Technique

Today it is almost impossible to retrieve information with a keyword search when the information is spread over several pages. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Web personalization is the one application of semantic web usage mining. In this report we have explored comparison of various collaborative filtering techniques. Those techniques are memory based, model based and hybrid collaborative filtering. Our study shows that the performance of hybrid collaborative filtering technique is better than memory based and model based collaborative filtering technique. We have introduced normalization step, which will improve accuracy of traditional collaborative filtering techniques.

THE IMPACT OF APPLYING THE CONCEPT OF THE SEMANTIC WEB IN E-GOVERNMENT

Together with the development of science and technology demands of society as well as people's needs are changing. These changes affect all areas of human activity in particular those related to electronic business. One aspect of interaction that has been affected by these changes is the interaction within the government. It is precisely these requirements which imply the development of an E-government. This Web based technology which is being used by the government as a communication channel is becoming more and more advanced. The complexity and the amount of information included in the communication channels requires the application of a new business concept used by the E1 Lidija Paunovic is PhD student at Faculty of Organizational Sciences, University of Belgrade. She earned a scholarship from the Ministry of Education and Science, Republic of Serbia. Her current professional interests include ebusiness, Internet marketing and e-government. She can be reached at lidijapaunovic@elab.rs. 2 Konstantin Simic is teaching associate and MsC student at Faculty of Organizational Sciences, University of Belgrade. His current professional interests include internet technologies, cloud computing, e-government. he can be reached at kosta@elab.rs. 3 Jovana Dadic is teaching associate and BsC student at Faculty of Organizational Sciences, University of Belgrade. Her current professional interests include e-business, e-government, semantic web, data modeling. She can be reached at jovana@elab.rs. 4 Branislav Jovanic is Head of Laboratory for multidisciplinary research at Institute of Physics, University of Belgrade. His current professional research include experimental physics, computer simulation in science, scientific computing. He can be reached at brana@ipb.ac.rs. 5 Dusan Barac is assistant professor at Faculty of Organizational Sciences, University of Belgrade. His current professional interests include internet technologies, e-business, e-government, mobile business. He can be reached at dusan@elab.rs. government. It is in these systems that the Semantic Web found it's use. This paper explores the impact of innovative concepts of the Semantic Web in Egovernment systems. The importance of Semantic Web technology, in this context, is to provide a basis for effective and efficient exchange of knowledge and information in order to coordinate business processes. Research results show that the Semantic Web promotes and improves communication between citizens and the E-government through interoperability. It is the ability of heterogeneous systems to work together, so that the information could be shared without the requirement of additional operations so the two systems could communicate, which is an essential element of any E-government. The lack of interoperability is a source of unnecessary costs well as monopoly. On the other hand the results show that potential problems may arise due to application of services based on the Semantic Web, but also that these shortcomings can be regulated with further development of E-government. The results of this paper may form the basis for further development of E-government and its services. Guidelines are given for the enhancement of quality of the processes in the usage of Semantic Web in E-Government but also in the improvement of potential negative impacts the application of this concept may have on business.

Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis

The relationships between consumer emotions and their buying behaviors have been well documented. Technology-savvy consumers often use the web to find information on products and services before they commit to buying. We propose a semantic web usage mining approach for discovering periodic web access patterns from annotated web usage logs which incorporates information on consumer emotions and behaviors through self-reporting and behavioral tracking. We use fuzzy logic to represent real-life temporal concepts (e.g., morning) and requested resource attributes (ontological domain concepts for the requested URLs) of periodic pattern-based web access activities. These fuzzy temporal and resource representations, which contain both behavioral and emotional cues, are incorporated into a Personal Web Usage Lattice that models the user's web access activities. From this, we generate a Personal Web Usage Ontology written in OWL, which enables semantic web applications such as personalized web resources recommendation. Finally, we demonstrate the effectiveness of our approach by presenting experimental results in the context of personalized web resources recommendation with varying degrees of emotional influence. Emotional influence has been found to contribute positively to adaptation in personalized recommendation.

Semantic session analysis for Web usage mining

A semantic session analysis method partitioning Web usage logs is presented. Semantic Web usage log preparation model enhances usage logs with semantic. The Markov chain model based on ontology semantic measurement is used to identifying which active session a request should belong to. The competitive method is applied to determine the end of the sessions. Compared with other algorithms, more successful sessions are additionally detected by semantic outlier analysis.