Semantic Web Perspective Research Articles

LPG-Based Knowledge Graphs: A Survey, a Proposal and Current Trends

A significant part of the current research in the field of Artificial Intelligence is devoted to knowledge bases. New techniques and methodologies are emerging every day for the storage, maintenance and reasoning over knowledge bases. Recently, the most common way of representing knowledge bases is by means of graph structures. More specifically, according to the Semantic Web perspective, many knowledge sources are in the form of a graph adopting the Resource Description Framework model. At the same time, graphs have also started to gain momentum as a model for databases. Graph DBMSs, such as Neo4j, adopt the Labeled Property Graph model. Many works tried to merge these two perspectives. In this paper, we will overview different proposals aimed at combining these two aspects, especially focusing on possibility for them to add reasoning capabilities. In doing this, we will show current trends, issues and possible solutions. In this context, we will describe our proposal and its novelties with respect to the current state of the art, highlighting its current status, potential, the methodology, and our prospect.

Transdisciplinary approach to archaeological investigations in a Semantic Web perspective

In recent years, the transdisciplinarity of archaeological studies has greatly increased because of the mature interactions between archaeologists and scientists from different disciplines (called “archaeometers”). A number of diverse scientific disciplines collaborate to get an objective account of the archaeological records. A large amount of digital data support the whole process, and there is a great value in keeping the coherence of information and knowledge, as contributed by each intervening discipline. During the years, a number of representation models have been developed to account for the recording of the archaeological process in data bases. Lately, some semantic models, compliant with the CRMarchaeo reference model, have been developed to account for linking the institutional forms with the formal knowledge concerning the archaeological excavations and the related findings. On the contrary, the archaeometric processes have not been addressed yet in the Semantic Web community and only an upper reference model, called CRMsci, accounts for the representation of the scientific investigations in general. This paper presents a modular computational ontology for the interlinked representation of all the facts related to the archaeological and archaeometric analyses and interpretations, also connected to the recording catalogues. The computational ontology is compliant with CIDOC-CRM reference models CRMarchaeo and CRMsci and introduces a number of novel classes and properties to merge the two worlds in a joint representation. The ontology is in use in “Beyond Archaeology”, a methodological project for the establishing of a transdisciplinary approach to archaeology and archaeometry, interlinked through a semantic model of processes and objects.

The support of constructs in thesaurus tools from a Semantic Web perspective: Framework to assess standard conformance

Thesauri are conceptual tools useful to achieve semantic interoperability and reusability, which are relevant goals in the Semantic Web. Thesaurus standards establish, among other issues, the constructs that can appear in a thesaurus. The ISO 25964 standard for thesauri, which supersedes ISO 2788, is the evolution of the ISO thesauri standard to a conceptual approach closer to the Semantic Web. However, it appeared when SKOS -the W3C Recommendation- was already consolidated as the standard for KOS (Knowledge Organization System) representation in the Semantic Web, including thesauri. The evolution from ISO 2788 to ISO 25964, and the relationships between constructs in ISO 2788/ISO 25964 and SKOS, are studied in this paper. From the analysis of this comparison, a methodological framework, that focuses on the construct support, is proposed to evaluate the conformance quality of thesaurus management tools. Target readers are professionals in charge of thesauri edition. A Semantic Web perspective is taken to characterize the effect that using SKOS to represent thesauri can have on the results of the assessment. A proof of concept for the model's feasibility was performed on two tools and the analysis of the results of this experimental validation is presented. The conclusions highlight the model's suitability for assessing conformance to the standards concerning support for thesaurus constructs.

Uma Linha de Produto de Software baseada na Web Semântica para Sistemas Tutores Inteligentes

This research describes an approach for design and development of Intelligent Tutoring Systems under the Semantic Web perspective, considering aspects of both effectiveness and large scale. It deals with a clearly interdisciplinary theme which combines characteristics of Artificial Intelligence, Semantic Web and Software Engineering, applied to this kind of educational system. Thus, this work proposes a study for the rapid development of Intelligent Tutoring Systems as part of Virtual Learning Environments for presencial or distance education, taking into account characteristics such as reuse and large scale development. In this sense, an assessment of the adequateness of current ITS approaches has been done, as well as the definition of an architecture containing the Intelligent Tutoring Systems essential components. The proposed architecture has been used to specify a product line that takes into consideration (i) the different user profiles, (ii) the nonpossession of advanced computing knowledge by the users (iii) the heterogeneity of knowledge domains that kind of systems will handle and (v) the constant change and evolution of the knowledge manipulated of this kind of systems. This way, satisfying the four mentioned considerations, a software product line based on the Semantic Web was developed and tested. The experiments carried out to evaluate the proposed approach, in two different domains, demonstrating its is effectivenees in terms of large scale, adaptivity and evolution.

A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements.