Semantic Web Ontologies Research Articles

A proposed paradigm for smart learning environment based on semantic web

The current approaches of e-learning face challenges, in isolation of learners from learning process, and shortage of learning process quality. The researchers mentioned that the next generation of e-learning is e-learning ecosystem. E-learning ecosystem has many advantages, in which, learners form groups, collaborate with each other and with educators, and content designed for interaction. E-learning ecosystem faces some issues. It applies teacher-student model, in which, fixed learning pathway is considered suitable for all learners. Consequently, learners are presented with limited personalized materials. E-learning ecosystem needs to merge the personalization's concept. Semantic web ontology based personalization of learning environment plays a leading role to build smart e-learning ecosystem.This paper previews a detailed study which addresses research papers that apply ontology within learning environment. Most of these studies focus on personalizing e-learning by providing learners with suitable learning objects, ignoring the other learning process components. This paper proposes and implements framework for smart e-learning ecosystem using ontology and SWRL. A new direction is proposed. This direction fosters the creation of a separate four ontologies for the personalized full learning package which is composed of learner model and all the learning process components (learning objects, learning activities and teaching methods).

POEM: Practical ontology engineering model for semantic web ontologies

AbstractSundown of the twentieth century saw the emergence of the World Wide Web. A decade later, semantic web (SW) envisioned enriching of web-accessible information and services with machine-processable semantics to solve the problems of information management and sharing which are aroused by the success of the web. However, success of the SW largely depends on the availability of formal ontologies for representing and structuring information in different domains. To facilitate web ontology engineering (OE), methodologies are proposed by the researchers since years. However, OE particularly web OE is still immature and none of the methodologies is standardized for web OE projects as yet. This paper presents an evolutionary SW ontology engineering methodology called POEM. POEM presents a complete methodology comprising of various phases for performing conceptualization, designing, implementation, evaluation, and project management tasks. POEM exploits and incorporates the large experiences from the widel...

$$\uptau $$ τ OWL: A Systematic Approach to Temporal Versioning of Semantic Web Ontologies

The W3C OWL 2 recommendation is an ontology language for the Semantic Web. It allows defining both schema (i.e., entities, axioms, and expressions) and instances (i.e., individuals) of ontologies. However, OWL 2 lacks explicit support for time-varying schema or for time-varying instances. Hence, knowledge engineers or maintainers of semantics-based Web resources have to use ad hoc techniques to specify OWL 2 time-varying ontologies. In this paper, for a disciplined and systematic approach to the temporal management of Semantic Web ontologies, we propose the adoption of a framework called temporal OWL 2 (\(\uptau \)OWL), which is inspired by the \(\uptau \)XSchema framework defined for XML data. In a way similar to what happens in \(\uptau \)XSchema, \(\uptau \)OWL allows creating a temporal OWL 2 ontology from a conventional (i.e., non-temporal) OWL 2 ontology and a set of logical and physical annotations. Logical annotations identify which elements of the ontology can vary over time; physical annotations specify how the time-varying aspects are represented in the OWL 2 document. Using annotations to integrate temporal aspects in the traditional Semantic Web, our framework (1) guarantees logical and physical data independence for temporal schemas and (2) provides a low-impact solution, since it requires neither modifications of existing Semantic Web ontologies, nor extensions to the OWL 2 recommendation and Semantic Web standards. Moreover, since the conventional schema and annotation documents could evolve over time to respond to new applications’ requirements, \(\uptau \)OWL supports temporal schema versioning by allowing changing these components and by keeping track of their evolution through the conventional schema versions and annotation document versions, respectively. Two complete sets of operations are proposed for changing the conventional schema and annotation documents; to complete the figure, a set of operations is also introduced for updating temporal schema which must be changed consequently each time one of the mentioned components evolves over time. To show the feasibility of our approach, a prototype tool, named \(\uptau \)OWL-Manager, is presented.

From hyperlinks to Semantic Web properties using Open Knowledge Extraction

Open information extraction approaches are useful but insufficient alone for populating the Web with machine read- able information as their results are not directly linkable to, and immediately reusable from, other Linked Data sources. This work proposes a novel paradigm, named Open Knowledge Extraction, and its implementation (Legalo) that performs unsuper- vised, open domain, and abstractive knowledge extraction from text for producing machine readable information. The imple- mented method is based on the hypothesis that hyperlinks (either created by humans or knowledge extraction tools) provide a pragmatic trace of semantic relations between two entities, and that such semantic relations, their subjects and objects, can be revealed by processing their linguistic traces (i.e. the sentences that embed the hyperlinks) and formalised as Semantic Web triples and ontology axioms. Experimental evaluations conducted on validated text extracted from Wikipedia pages, with the help of crowdsourcing, confirm this hypothesis showing high performances. A demo is available at http://wit.istc.cnr.it/stlab-tools/ legalo.

Service Level Agreement Mapping and Provider Selection Using Annotated Semantic Information to WS-Agreement

AbstractDue to the increasing number of service agreements between service providers and users and the lack of standard in agreement specifications in service computing environments, there is need for an efficient service mapping and provider selection approach that focuses on Service Level Agreement (SLA) documents. This paper proposes a novel SLA mapping and provider selection approach with annotated ontological semantic information to WS-Agreement and advanced mapping rules using semantic web rule language (SWRL) and web ontology language (OWL) ontologies. The experimental data shows that our proposed approach is superior to other proficient approaches, because it can properly exploit ontological semantic technology in service specification and matchmaking.

An Automated Approach for Digital Forensic Analysis of Heterogeneous Big Data

The major challenges with big data examination and analysis are volume, complex interdependence across content, and heterogeneity. The examination and analysis phases are considered essential to a digital forensics process. However, traditional techniques for the forensic investigation use one or more forensic tools to examine and analyse each resource. In addition, when multiple resources are included in one case, there is an inability to cross-correlate findings which often leads to inefficiencies in processing and identifying evidence. Furthermore, most current forensics tools cannot cope with large volumes of data. This paper develops a novel framework for digital forensic analysis of heterogeneous big data. The framework mainly focuses upon the investigations of three core issues: data volume, heterogeneous data and the investigators cognitive load in understanding the relationships between artefacts. The proposed approach focuses upon the use of metadata to solve the data volume problem, semantic web ontologies to solve the heterogeneous data sources and artificial intelligence models to support the automated identification and correlation of artefacts to reduce the burden placed upon the investigator to understand the nature and relationship of the artefacts.

A Framework for Anomaly Diagnosis in Smart Homes Based on Ontology

Smart homes are pervasive environments to enhance the comfort, the security, the safety and the energy consumption of the residence. An ambient intelligence system uses information of devices to represent the context of the home and its residents. Based on a context database, this system infer the daily life activities of the resident. Hence, abnormal behavior or chronic disease can be detected by the system. Due to the complexity of these systems, a large variety of anomalies may occur and disrupt the functioning of critical and essential applications. To detect anomalies and take appropriate measures, an anomaly management system has to be integrated in the overall architecture. In this paper, we propose an anomaly management framework for smart homes. This framework eases the work of designers in the conception of anomaly detection modules and processes to respond to an anomaly appropriately. Our framework can be used in all heterogeneous environments such as smart home because it uses Semantic Web ontologies to represent anomaly information. Our framework can be useful to detect hardware, software, network, operator and context faults. To test the efficiency of our anomaly management framework, we integrate it in the universAAL middleware. Based on a reasoner, our framework can easily infer some context anomalies and take appropriate measures to restore the system in a full functioning state.

E-Tendering: Modeling of a multi-agents system integrating the concepts of ontology and big data

<p>This work deals with the modeling of a computer system dedicated to e-procurement specifically e-tendering. For the establishment of this system, semantic web ontologies, multi-agent systems and big data were used. To have a modular system, pledge of reliability, of speed and potential scalability, multi-agents systems have been used. The agents of the system communicate and collaborate throughout the tendering process to perform tasks such as the generation of tender documents, bids evaluation, contract signature and monitoring of the realization of the contract. The knowledge engineering through the semantic web ontologies was used in order to have a complete semantic description of the shared information and foster good communication between the different agents of the system. Finally, to store and take advantage of structured and unstructured data generated by the different sources of data relating to the governance of public procurement, Big Data technologies were integrated.</p><p>The objective of this work is to establish an efficient computer system capable of solving all or part of the problems relating to the award of public contracts.</p><p> </p><p> </p><p>Keywords: e-procurement, e-tendering, software engineering, big data, multi-agents system, semantic web, ontology, artificial intelligence;</p>

Semantic representation of scientific literature: bringing claims, contributions and named entities onto the Linked Open Data cloud

Motivation.Finding relevant scientific literature is one of the essential tasks researchers are facing on a daily basis. Digital libraries and web information retrieval techniques provide rapid access to a vast amount of scientific literature. However, no further automated support is available that would enable fine-grained access to the knowledge ‘stored’ in these documents. The emerging domain ofSemantic Publishingaims at making scientific knowledge accessible to both humans and machines, by adding semantic annotations to content, such as a publication’s contributions, methods, or application domains. However, despite the promises of better knowledge access, the manual annotation of existing research literature is prohibitively expensive for wide-spread adoption. We argue that a novel combination of three distinct methods can significantly advance this vision in a fully-automated way: (i) Natural Language Processing (NLP) forRhetorical Entity(RE) detection; (ii)Named Entity(NE) recognition based on the Linked Open Data (LOD) cloud; and (iii) automatic knowledge base construction for both NEs and REs using semantic web ontologies that interconnect entities in documents with the machine-readable LOD cloud.Results.We present a complete workflow to transform scientific literature into a semantic knowledge base, based on the W3C standards RDF and RDFS. A text mining pipeline, implemented based on the GATE framework, automatically extracts rhetorical entities of typeClaimsandContributionsfrom full-text scientific literature. These REs are further enriched with named entities, represented as URIs to the linked open data cloud, by integrating the DBpedia Spotlight tool into our workflow. Text mining results are stored in a knowledge base through a flexible export process that provides for a dynamic mapping of semantic annotations to LOD vocabularies through rules stored in the knowledge base. We created a gold standard corpus from computer science conference proceedings and journal articles, whereClaimandContributionsentences are manually annotated with their respective types using LOD URIs. The performance of the RE detection phase is evaluated against this corpus, where it achieves an averageF-measure of 0.73. We further demonstrate a number of semantic queries that show how the generated knowledge base can provide support for numerous use cases in managing scientific literature.Availability.All software presented in this paper is available under open source licenses athttp://www.semanticsoftware.info/semantic-scientific-literature-peerj-2015-supplements. Development releases of individual components are additionally available on our GitHub page athttps://github.com/SemanticSoftwareLab.

Ontology Partitioning: Clustering Based Approach

The semantic web goal is to share and integrate data across different domains and organizations. The knowledge representations of semantic data are made possible by ontology. As the usage of semantic web increases, construction of the semantic web ontologies is also increased. Moreover, due to the monolithic nature of the ontology various semantic web operations like query answering, data sharing, data matching, data reuse and data integration become more complicated as the size of ontology increases. Partitioning the ontology is the key solution to handle this scalability issue. In this work, we propose a revision and an enhancement of K-means clustering algorithm based on a new semantic similarity measure for partitioning given ontology into high quality modules. The results show that our approach produces meaningful clusters than the traditional algorithm of K-means.

ENanoMapper: harnessing ontologies to enable data integration for nanomaterial risk assessment.

Engineered nanomaterials (ENMs) are being developed to meet specific application needs in diverse domains across the engineering and biomedical sciences (e.g. drug delivery). However, accompanying the exciting proliferation of novel nanomaterials is a challenging race to understand and predict their possibly detrimental effects on human health and the environment. The eNanoMapper project (www.enanomapper.net) is creating a pan-European computational infrastructure for toxicological data management for ENMs, based on semantic web standards and ontologies. Here, we describe the development of the eNanoMapper ontology based on adopting and extending existing ontologies of relevance for the nanosafety domain. The resulting eNanoMapper ontology is available at http://purl.enanomapper.net/onto/enanomapper.owl. We aim to make the re-use of external ontology content seamless and thus we have developed a library to automate the extraction of subsets of ontology content and the assembly of the subsets into an integrated whole. The library is available (open source) at http://github.com/enanomapper/slimmer/. Finally, we give a comprehensive survey of the domain content and identify gap areas. ENM safety is at the boundary between engineering and the life sciences, and at the boundary between molecular granularity and bulk granularity. This creates challenges for the definition of key entities in the domain, which we also discuss.

Mapping of Semantic Web Ontology in User Query System

main purpose of the Semantic Web promises a kind Machine Intelligence, which can support a verity of user tasks like efficiency of search engine or Question Answering (QA). Ontologies are needed for realization of the semantic web, which in turn depends on the ability of system to identify and take advantage of relationships that exist within ontologies. There are huge numbers of ontologies present on the web they need to be integrated for data integration. These ontologies are having different in representation, quality of data and larger sizes of ontologies, this lead to be problem during ontology mapping, on analyzing these problems and to introduce Multiagent mapping system. Main aim is to achieve heterogeneous data integration through semantic mapping of ontologies. this paper provide a mapping framework for Multiagent ontology having heterogeneous data in Semantic web and develop a question answering system from developed framework of ontology's and improve performance by adding semantic relation interpreter which improves response time.

Hybrid ontology for semantic information retrieval model using keyword matching indexing system.

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.

Using Semantic Web Technologies for Integrating Domain Specific Modeling and Analytical Tools

This paper discusses the potential advantages and pitfalls of using semantic web technologies for representing and integrating modeling and analysis tools. Analytical tools are often not designed to be integrated with information sources and general-purpose modeling tools, and often do not support detection of problems across domains. Additionally, these modeling tools may not capture and represent explicitly the information needed to leverage the capabilities of analysis tools. The method described uses semantic web technology as the integrating mechanism between domain specific modeling (DSM) tools and analytical tools. We describe a method and tool set for representing the analytical knowledge through semantic web ontologies that map between the metamodels of both the DSM and analytical tools. We compare an earlier tool-chain prototype with a significantly revised prototype to reflect on the benefits from using semantic web technologies as an integrating mechanism. A potential advantage is the ability to represent the relationships between modeling and analytical tools explicitly and transparently.

Construction of XBRL Semantic Metamodel and Knowledge Base Based on Ontology

In order to improve the interoperability of XBRL format financial reporting on the semantic level, a novel XBRL financial reporting metamodel and a fact data semantic metamodel are proposed, which uses the Semantic Web technologies and Ontology theory. Then, a XBRL knowledge base is constructed based on this metamodel .Using the metamodel-based translation mechanism from XBRL to OWL / RDF, all the semantic information in XBRL taxonomy and instance documents is translated into OWL ontology and RDF instance. Finally, a knowledge base covering the semantic information of financial reporting domain is constructed.

How semantic technologies transform e-government domain

Purpose – In e-government domain, an efficient semantic interoperability of services is a big challenge. In this context, semantic technologies play a fundamental role in numerous e-government programs. Indeed, these technologies enable enriching the public service description provided by different public administrations with additional semantic information, which allows automatic services processing and information exchange between involved members in comprehensive and interpretable manner, thus facilitating service integration and cooperation. The most frequently used technologies in this area are semantic web services (SWS) technology and ontology. Thus, for modelling efficiently public services, the authors have exploring, studying and analyzing some of e-government researches and projects that applied semantic technologies. Design/methodology/approach – In this paper, the authors address how semantic technologies transform the e-government domain through exploring and studying the use of SWS and ontology in numerous researches and projects applying these technologies in this area. Then, the authors have compared them through a set of criteria defined according to e-government requirements such as the SWS modelling approach for describing semantically public services, ontologies for representing governmental specific features (e.g. regulation and organizational structures) and developed portal for supporting user ' s perspectives. Based on this study, the authors have identified the appropriate standards, frameworks, and models for developing the framework. Findings – The authors have developed a framework for public services which enhances existing approaches with additional aspects. Thus, the approach comparing to other works improves the service ontology with a cognitive semantic approach to support the multiple usage contexts and situations of services. This semantic is developed in form of a Sit/Ctx ontology developed based on CSs model represented through conceptual graphs theory. The authors have used this model besides OWL-S, improving thus the level of automation of discovery and composition process of public services, which become more efficient with taken into consideration their interrelations and situations. Originality/value – The approach comparing to other works improves the service ontology with a cognitive semantic approach to support the multiple usage contexts and situations of public services, providing thus the end-users (people and enterprises) a better orientation in discovery process.

Making species checklists understandable to machines - a shift from relational databases to ontologies.

BackgroundThe scientific names of plants and animals play a major role in Life Sciences as information is indexed, integrated, and searched using scientific names. The main problem with names is their ambiguous nature, because more than one name may point to the same taxon and multiple taxa may share the same name. In addition, scientific names change over time, which makes them open to various interpretations. Applying machine-understandable semantics to these names enables efficient processing of biological content in information systems. The first step is to use unique persistent identifiers instead of name strings when referring to taxa. The most commonly used identifiers are Life Science Identifiers (LSID), which are traditionally used in relational databases, and more recently HTTP URIs, which are applied on the Semantic Web by Linked Data applications.ResultsWe introduce two models for expressing taxonomic information in the form of species checklists. First, we show how species checklists are presented in a relational database system using LSIDs. Then, in order to gain a more detailed representation of taxonomic information, we introduce meta-ontology TaxMeOn to model the same content as Semantic Web ontologies where taxa are identified using HTTP URIs. We also explore how changes in scientific names can be managed over time.ConclusionsThe use of HTTP URIs is preferable for presenting the taxonomic information of species checklists. An HTTP URI identifies a taxon and operates as a web address from which additional information about the taxon can be located, unlike LSID. This enables the integration of biological data from different sources on the web using Linked Data principles and prevents the formation of information silos. The Linked Data approach allows a user to assemble information and evaluate the complexity of taxonomical data based on conflicting views of taxonomic classifications. Using HTTP URIs and Semantic Web technologies also facilitate the representation of the semantics of biological data, and in this way, the creation of more “intelligent” biological applications and services.Electronic supplementary materialThe online version of this article (doi:10.1186/2041-1480-5-40) contains supplementary material, which is available to authorized users.

Ontology-based support for peer assessment in inquiry-based learning

Ontologies have emerged as one of the most popular and widely accepted tools in Technology-Enhanced Learning TEL supporting knowledge representation and learning content creation. In this paper, we are interested in semantic web technologies and ontologies to deliver peer assessment as part of Inquiry Based Learning IBL environment. We focus in particular on Problem Based Learning PBL scenarios, which are often based on the use of social media, to enable collaboration and communication between learners. We aim to deliver formative peer assessment activities as part of PBL activities adapted to learner specific context, profile and progress in the learning process. Detailed scenarios descriptions are proposed and semantic models are specified to produce peer assessment activities. A heavyweight peer assessment ontology is described as part of an inquiry learning ontology and linked to other semantic models. A semantic web framework is finally proposed to integrate the defined semantic models.

Datatype evolution on RDF ontologies

Semantic web ontologies are not static but evolve as the understanding of the domain or the domain itself grows or evolves. This evolution is usually independent of the ontological instance descriptions which are stored in knowledge bases KBs. Therefore, the effects of an ontology update on instance descriptions which are migrated to a new ontology version should be studied. In this paper, we are interested in the effects of the migration of literal-valued instance triples of an RDF KB to a new ontology version. Note that the datatype of the property of such an instance description may have evolved in the new ontology version, e.g., from integer to decimal or from float to double. We manage the quality of these migrated instance triples and design flexible interactive methods through which the curator of the KB can intervene to correct the 'inaccuracies' of the literal values that are incurred by the migration process. In addition, specialised algorithms are presented, which are highly efficient, since their time and space complexity is linear with respect to the size of the literal-valued instance triples of the KB. Our work is particularly important to e-science applications that are commonly curated and have many datatype properties.

Sociological ontology of the digital public sphere: the case of Web 2.0/3.0

Digital public sphere is immersed in the present conjuncture of accelerated transformation and probable rupture, which certainly will affect the way we exercise our citizenship in contemporary times. This social and political tsunami is partly based on the change of paradigm of Web 2.0 or Social Web to Web 3.0 or Semantic Web. To clarify such a process, this paper discusses some of the key issues and theoretical positions on public space, from seminal Habermas’s perspective to new problematics raised by the networked society. The author suggests the construction of a Sociological Ontology of Social and Semantic Web, based on a Semantic-Logical Sociology and Methodology. These procedures are applied through the analysis and hermeneutics of a Wikipedia page entitled ‘Web 2.0’, where sociological experimental tools are used, as Semantic-Logical fields, trees and networks, central and peripheral concepts, and trichotomies.