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International audience; Agroforestry consists of combining trees with agriculture, both on farms and in the agricultural landscape. Within a context of sustainable development, agroforestry can improve soil conservation and reduce the use of toxic chemicals on crops, as well as improving biodiversity. Interdisciplinary by nature, the field of agroforestry mobilizes a large body of knowledge from environmental and life sciences using systemic approaches. In this framework, field observation data are acquired in partnership with several categories of stakeholders such as scientists, foresters, farmers, breeders, politicians and land managers. For data management effciency, we propose the software application AOBRA (a softwareApplication with Ontology-Based Reasoning for Agroforestry). The core of AOBRA is a domain ontology called "Agroforestry" which serves as a basis for capitalizing and sharing knowledge in agroforestry. By exploiting the capabilities of inference and linkages to other areas of expertise on the Web offered by the use of an ontology model, it aims to provide a broad view of agroforestry designs, and to allow the comparison between different spatial layouts of trees and crops

Background A key challenge for improving the quality of health care is to be able to use a common framework to work with patient information acquired in any of the health and life science disciplines. Patient information collected during dental care exposes many of the challenges that confront a wider scale approach. For example, to improve the quality of dental care, we must be able to collect and analyze data about dental procedures from multiple practices. However, a number of challenges make doing so difficult. First, dental electronic health record (EHR) information is often stored in complex relational databases that are poorly documented. Second, there is not a commonly accepted and implemented database schema for dental EHR systems. Third, integrative work that attempts to bridge dentistry and other settings in healthcare is made difficult by the disconnect between representations of medical information within dental and other disciplines’ EHR systems. As dentistry increasingly concerns itself with the general health of a patient, for example in increased efforts to monitor heart health and systemic disease, the impact of this disconnect becomes more and more severe. To demonstrate how to address these problems, we have developed the open-source Oral Health and Disease Ontology (OHD) and our instance-based representation as a framework for dental and medical health care information. We envision a time when medical record systems use a common data back end that would make interoperating trivial and obviate the need for a dedicated messaging framework to move data between systems. The OHD is not yet complete. It includes enough to be useful and to demonstrate how it is constructed. We demonstrate its utility in an analysis of longevity of dental restorations. Our first narrow use case provides a prototype, and is intended demonstrate a prospective design for a principled data backend that can be used consistently and encompass both dental and medical information in a single framework. Results The OHD contains over 1900 classes and 59 relationships. Most of the classes and relationships were imported from existing OBO Foundry ontologies. Using the LSW2 (LISP Semantic Web) software library, we translated data from a dental practice’s EHR system into a corresponding Web Ontology Language (OWL) representation based on the OHD framework. The OWL representation was then loaded into a triple store, and as a proof of concept, we addressed a question of clinical relevance – a survival analysis of the longevity of resin filling restorations. We provide queries using SPARQL and statistical analysis code in R to demonstrate how to perform clinical research using a framework such as the OHD, and we compare our results with previous studies. Conclusions This proof-of-concept project translated data from a single practice. By using dental practice data, we demonstrate that the OHD and the instance-based approach are sufficient to represent data generated in real-world, routine clinical settings. While the OHD is applicable to integration of data from multiple practices with different dental EHR systems, we intend our work to be understood as a prospective design for EHR data storage that would simplify medical informatics. The system has well-understood semantics because of our use of BFO-based realist ontology and its representation in OWL. The data model is a well-defined web standard.

[EN] Knowledge engineering relies on ontologies, since they provide formal descriptions of real¿world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi¿automatically or automatically from scratch. It not only improves the efficiency of the ontology development pro¿ cess but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great poten¿ tial of ontology learning, we present an automatic ontology¿based model evolution approach to ac¿ count for highly dynamic environments at runtime. This approach can extend initial models ex¿ pressed as ontologies to cope with rapid changes encountered in surrounding dynamic environ¿ ments at runtime. The main contribution of our presented approach is that it analyzes heterogene¿ ous semi¿structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology¿based model. Within this approach, we aim to automatically evolve an initial ontology¿based model through the ontology learning approach. Therefore, this approach is illustrated using a proof¿of¿concept implementation that demonstrates the ontology¿based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology¿based models. First, we consider a feature¿based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria¿based eval¿ uation to assess the content of the evolved models. Finally, we perform an expert¿based evaluation to assess an initial and evolved models¿ coverage from an expert¿s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.

Crowdsourcing plays an important role in modern IT landscape, enabling the use of human information processing abilities to solve problems that are still hard for machines. One of the specific (and most demanded) applications of crowdsourcing is collecting item annotations, i.e., describing the contents of complex items with a help of labels (tags). Input received from crowdsourcing participants is typically unreliable, therefore, to increase the quality of annotations, each item is typically processed by several participants and the obtained annotations have to be aggregated. The paper considers a special case of annotating, where a set of possible labels, as well as the set of relationships between the labeled items and the labels are defined by an OWL 2 ontology (OWL QL). Such semantic item annotations turn out to be very useful in organizing large collections of items and enabling semantic search in them. In order to increase annotations quality, the paper proposes two aggregation methods - Onto Voting and OntoSB, differing in that the first one is agnostic with respect to participants' reliability and the second one accounts for variations in reliability. Simulation experiments with ontology-based annotations of varying quality show that the proposed aggregation methods increase the quality of collected ontology-based item annotations.

In this paper, the relation between the FAIR (Findable, Accessible, Interoperable, Reusable) ontologies and accountability and transparency of ontology-based AI systems is analysed. Also, governance-related gaps in ontology quality evaluation metrics were identified by examining their relation with FAIR principles and FAcct (Fairness, Accountability, Transparency) governance aspects. A simple SKOS vocabulary, titled "Hospital Adverse Incidents Classification Scheme" (HAICS) has been used as a use case for this study. Theoretically, we found that there is a straight relation between FAIR principles and FAccT AI, which means that FAIR ontologies enhance transparency and accountability in ontology-based AI systems. We suggest that "FAIRness" should be assessed as one of the ontology quality evaluation aspects.

1.AbstractTraditional cultural beliefs influence perceptions of animals, and in some cases can result in persecution of wildlife. Stigmas against species associated with witchcraft, for example, can act as a barrier to the uptake of more cost effective, sustainable, and environmentally sound practices such as reducing crop damage by controlling rodent agricultural pests by relying on indigenous predators rather than pesticides. One way of enhancing perceptions of wildlife to increase participation in such ecologically-based rodent management (EBRM) schemes, is the development of environmental education initiatives. Low intensity programmes are cost-effective and can produce positive attitudinal shifts, but their impact has not been assessed for species strongly associated with witchcraft. We set out to test whether a single presentation on the natural history of owls (order Strigiformes) could improve perceptions of these species, and increase willingness to participate in an EBRM scheme that involved the installation of owl boxes to increase owl populations and reduce rodent populations and crop damage in agricultural fields. We used a questionnaire survey to assess perceptions of owls at four schools in two villages in South Africa. Our initial survey sampled perceptions of respondents before listening to the presentation. A follow-up survey conducted three months later sampled the perceptions of respondents that had listened to the presentation as well as perceptions of a control group that did not listen to the presentation. We found that associations between owls and witchcraft was a common theme driving negative perceptions of owls. Respondents that watched the presentation had more positive perceptions of owls than respondents that had not watched the presentation, and they were more likely to be willing to put up owl boxes near their home. Despite this shift, negative perceptions of owls still dominated responses due to cultural associations with the occult. These findings indicate that even low-intensity programmes can be effective at enhancing perceptions of wildlife associated with witchcraft. We suggest that environmental education programmes featuring culturally taboo species should adopt a culturally sensitive and locally tailored approach, focus on the benefits these species provide, and may be more effective when delivered with greater intensity.

The key task of the current stage of the education system development is the task of transition to new methods of organization and support of the educational process, allowing providing high-quality training of graduates with practical knowledge and skills in demand in the labor market. To solve this task is possible only with the use of innovative methods and tools for the creation of information-educational environment in the formation and processing of learning objects and software services distributed in the educational network and allowing the flexible implementation of the processes of educational and methodological support of educational activities. The aim of the research is to determine the possibilities of using the ontological approach to the construction of innovative automated education management systems, including the construction of individual learning trajectories. In this regard, the problem of modeling and algorithmization of the formation of educational programs as a chain of interrelated training modules and objects, taking into account the implementation of a competence-based model of learning in a distributed educational environment of the University, seems to be interesting. The modern education system is to be aimed primarily at adaptive generation of educational content, taking into account the needs of students and in accordance with constantly changing conditions and needs of the labor market. At present, research in this area mainly concerns issues such as the use of multimedia technologies and simulation programs, knowledge testing technologies based on the competency model, methods for measuring the degree of satisfaction with the quality of training, and management methods based on the portfolio of students. These issues and problems are considered with varying degrees of detail, for example, in [1, 2, 3, 4] and other works. The developed methodology for the formation of educational programs is based on the research results of these authors and is developing towards the use of ontologies that can integrate various concepts of professional and educational standards and create on their basis fundamentally new educational modules and objects. In our opinion, it corresponds to world trends in the development of science in this field of knowledge. It is propose d to use the methods and methodologies of knowledge engineering as engineering methods: ontological engineering methods used for semantic modeling of the information and educational environment, allowing to represent the subject area in the form of a set of interconnected ontologies; methods for constructing and visualizing ontologies; methodologies for building databases and knowledge bases and forming complex queries to databases and knowledge bases; methods for semantic analysis of metadata; graph theory. As a result of the analysis of the objects of the information and educational environment, the requirements for the meta-description of educational objects and services are identified, and a description of the concepts of basic ontologies of the informational and educational environment is presented. The article also discusses the possibilities of using modern software for the formation of an intellectual repository of ontologies. Thus, the proposed toolkit for generating educational programs, in our opinion, will allow us to formalize, simplify, and significantly accelerate the process offlexible development of materials necessary for organizing and supporting the educational process. Application of the described approach makes it possible to improve the quality of training of specialists due to the prompt creation of organizational, managerial and educational materials adapted to the requirements of the labor market, taking into account the peculiarities of forms and methods of training.

In recent years, there has been a growing interest in the application of foundational ontologies, i.e., formal ontological theories in the philosophical sense, to provide a theoretically sound foundation for improving the theory and practice of conceptual modeling and knowledge representation. This paper addresses one particular foundational theory of events termed UFO-B, which has been successfully employed as a reference model for addressing problems from complex media management, enterprise architecture, software engineering, and modeling of events in petroleum exploration. Despite its success, there is still no formalization of UFO-B in a decidable knowledge representation language that could support reasoning about complex events and event relations. We address this gap by proposing a number of alternative translations from UFO-B's original axiomatization (in first-order logic and in the Alloy formal language) to the description logic SROIQ, which is the formal underpinning of OWL 2 DL. Additionally, to support practical applications, we translated these SROIQ theories to OWL 2 DL TBoxes, which were validated by showing that all the intended models of UFO-B (the logical models of the UFO-B specification in Alloy) that we generated are consistent with these UFO-B TBoxes. In a sense, the specification in Alloy implements the specification in first-order logic, while the OWL 2 TBoxes implement the SROIQ specifications. Incidentally, the methodology that we designed for the translation from UFO-B's original axiomatization in FOL and Alloy to SROIQ came to be a key contribution of this work by providing us evidence of the inadequacy of DLs for the specification of comprehensive foundational ontologies.

Advances in computers and communications have significantly changed almost every aspect of our daily activity. In this maze of change, governments around the world cannot remain indifferent. Public administration is evolving and taking on a new form through e-government. A large number of organizations have set up websites, establishing an online interface with the citizens and businesses with which it interacts. However, most organizations, especially the decentralized agencies of the ministries and local authorities, do not offer their information electronically despite the fact that they provide many information services that are not integrated with other e-government services. Besides, these services are mainly focused on serving citizens and businesses and less on providing services to employees. In this paper, we describe the process of developing an ontology to support the administrative procedures of decentralized government organizations. Finally, we describe the development of an e-government portal that provides employees services that are processed online, using the above ontology for modeling and data management.

This chapter introduces the different background material needed to understand the next three Chapters respectively presenting the Cyber-Physical Systems (CPS), Multi-Paradigm Modelling (MPM) and Multi-Paradigm Modelling for Cyber-Physical Systems (MPM4CPS) ontologies. It first introduces the approach and tools that have been used to define the ontologies. It then provides an overview of the ontological framework and a description of the shared ontology, which provides concepts defined in order to frame in a more general context some of the more specific notions of the other ontologies. Then, the chapter presents a brief introduction of the two CPS engineering environments employing MPM that were used to explore the domains covered by the ontologies and to guide readers through our exploration path.

UIDB/LIN/03213/2020 UIDB/03213/2020 UIDP/03213/2020 In this chapter, the relationship between digital humanities, information science and lexicography is analysed, exploring new possibilities and challenges and revisiting lexicographic theories and practices resulting from the need to digitise dictionaries. Methodology applied to Vocabulário Ortográfico da Língua Portuguesa (Orthographic Vocabulary of the Portuguese Language; VOLP-1940) is described, aiming at promoting accessibility to cultural heritage content while fostering reusability. VOLP-1940 is the first orthographic vocabulary published by the Academia das Ciências de Lisboa (ACL), in 1940. The goal is to analyse the vocabularies with computational methods to better assess the importance of this work for the evolution of science and mentalities in the 20th century and to contribute to the current movement of creating innovative, data-driven computational methods for text digitisation, encoding and analysis. VOLP-1940's digitisation aims to create a lexicographical resource encoded in TEI (Text Encoding Initiative), with structured information in SKOS (Simple Knowledge Organisation System), to guarantee its future connection to other systems and resources. This trans-disciplinary approach combines theories and methods of linguistics, lexicography and information science, placing the TEI and SKOS standards at the core of this research. This research also aims to fill in a gap in Portuguese lexicography, given that legacy dictionaries are still rare online. publishersversion published

The rapid proliferation of smart devices, surveillance cameras, infrastructures and buildings enhanced with the Internet of Things (IoT) technologies has led to a huge explosion of contents, especially in the video domain, determining an ever increasing interest towards the development of methods and tools for automatic analysis and interpretation of video sequences. Through the years, the availability of contextual knowledge has proven to improve video analysis performances in several ways, although the formal representation of semantic content in a shareable and fusion oriented manner is still an open problem, also considering the wide diffusion of Fog and Edge computing architectures for video analytics lately. In this context, an interesting answer has come from Semantic Web (SW) technologies, that opened a new perspective for the so-called Knowledge Based Computer Vision (KBCV), adding novel analytics opportunities, improving accuracy, and facilitating data exchange between video analysis systems in an open extensible manner. In this work, we propose a survey of the papers from the last eighteen years, back when first applications of semantic technologies to video analytics have appeared. The papers, analyzed under different perspectives to give a comprehensive overview of the technologies involved, reveal an interesting trend towards the adoption of SW technologies for video analytics scopes. As a result of our work, some insights about future challenges are also provided.

International audience; This chapter presents the Multi-Paradigm Modelling for Cyber-Physical Systems (MPM4CPS) ontology. This ontology integrates the Shared, MPM and CPS ontologies respectively introduced in Chapters 2, 3 and 4. It includes cross-cutting notions such as viewpoints, model-based development processes and modelling paradigms that together relate the formalisms and workflows (and their paradigms) to the part of CPSs developed with these formalisms. A brief state of the art on these notions is first presented, on which the MPM4CPS ontology builds. An overview of the ontology is then developed by introducing its main classes and properties. The validation of the ontology is finally presented by showing how it can adequately model the two case studies briefly introduced in Chapter 2. The chapter also discusses perspectives and future work on this integrated ontological framework, which can serve as a basis to develop model management solutions to relate and combine modelling languages and tools, in order to better develop cyber-physical systems with appropriate formalismes and workflows.

Nowadays, companies and official bodies are using the data as a principal asset to take strategic decisions. The advances in big data processing, storage and analysis techniques have allowed to manage the continuous increase in the volume of data. This increase in the volume of data together with its high variability and the large number of sources lead to a constant growing of the complexity of the data management environment. Data governance is the key for simplifying that complexity: it is the element that controls the decision making and responsibilities for all the processes related to data management. This paper discusses an approach to data governance based on ontological reasoning to reduce data management complexity. The proposed data governance system is built over an autonomous system based on distributed components. It implements semantic techniques and automatic ontology-based reasoning. The different components use a Shared Knowledge Plane to interact. Its fundamental piece is an ontology that represents all the data management processes included in data governance. A prototype of such a system has been implemented and tested for Telefonica’s global video service. The results obtained show the feasibility of using this type of technology to reduce the complexity of managing big data environments.

Many different digital representations of a building are produced over the course of its lifecycle. While these representations individually contain metadata required to support different stages of the building's lifecycle, they are largely not interoperable due to differences in structure, syntax and semantics. This impedes the development and deployment of data-driven applications providing fault detection and diagnosis, virtual metering or optimal control. [4] introduces a new platform for the continuous curation of a unified, Brick [1]-based metadata model that can be maintained throughout the building lifecycle. In this demonstration, we present a live proof-of-concept implementation of the platform with support for several metadata representations in the context of a simulated building lifecycle.

Many different digital representations of a building are produced over the course of its lifecycle. These representations contain the metadata required to support different stages of the building, from initial planning and design, to construction and commissioning, through operations, audits, retrofits and maintenance. However, because of differences in the semantics, structure and syntax of these representations, the metadata they contain is not interoperable. We present a novel method for leveraging these representations to create a unified, authoritative Brick metadata model for a building that can be continually maintained over the course of the building lifecycle. A simple synchronization protocol relays inferred Brick metadata from existing metadata sources such as gbXML, BuildingSync, Project Haystack and Modelica to a central integration server, which merges the metadata into a valid Brick model.

Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis, research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user&rsquo, s intention in written criminal posts based on ML.

A huge volume of information available today is in the form of images and searching the wanted images is very difficult and highly time-consuming. The search may take longer periods as the search volume on the internet is very huge and also the relevance of extracted images is still not up to the mark. The technologies like ontology and languages like OWL help us to tag the images that describe the semantic of the images. Hence, it helps in faster searching of the wanted images. Also, another challenge with OWL and Semantic web is the speed in which one can derive the relationships between various objects extracted from the images. The challenge is to extract the semantic from the images more efficiently using a parallel approach. In this paper, we explore the different techniques for generating semantic knowledge using parallel approaches like the T-box approach, merge classification, extract concept for matching ontology. We propose an enhanced method to speed-up the computation by combining T-box and merge classification techniques.

SoftwareX 12, 100571 (2020). doi:10.1016/j.softx.2020.100571, Published by Elsevier, Amsterdam [u.a.]

The last few years have seen substantial progress in the field of smart objects (SOs): their number, diversity, performance and pervasiveness have all been quickly increasing and this evolution is expected to continue. To the best of our knowledge, little work has been made to leverage this abundance of resources to develop assistive devices for Visually Impaired People (VIP). However, we believe that SOs can both enhance traditional assistive functions (i.e. obstacle detection, navigation) and offer new ways of interacting with the environment. After describing spatial and non-spatial perceptive functions enabled by SOs, this article presents the SO2SEES, a system designed to be an interface between its user and neighboring SOs. The SO2SEES allows VIP to query surrounding SOs in an intuitive manner, relying on knowledge bases distributed on Internet of Things (IoT) cloud platforms and the SO2SEES's own back-end. To evaluate and validate the exposed concepts, we have developed a simple working implementation of the SO2SEES system using semantic web standards. A controlled-environment test scenario has been built around this early SO2SEES system to demonstrate its feasibility. As future works, we plan to conduct field experiments of this first prototype with VIP end users.

The IoT ecosystem is complex and includes hardware devices, services, software, connectivity, standards and protocols. IoT is continuously and persistently contributing to the ubiquity of technology in all aspects of human endeavours, and trends seem to indicate that myriad of IoT solutions are developed and/or bought for various purposes, from the personal home to industrial usages, with the alarming lack of security concerns, giving priority mostly to the fast and cost-effective development. Given the complex nature of such solutions, it is rarely seen that the security aspect is considered through all of the IoT ecosystem elements ; also, one of the significant reasons being that the IoT solutions are not always developed in a holistic nature, within the same company, but only provide parts of the complete IoT solution. In order to enable easier and more secure development of IoT solutions, this paper presents first steps towards developing the open ontology for IoT ecosystem elements security, relating them with existing security concepts, primitives, threats, vulnerabilities and practices.

Advanced service robots are not, as of yet, widely adopted, partly due to the effectiveness of robots’ object recognition capabilities, the issue of object heterogeneity, a lack of knowledge sharing, and the difficulty of knowledge management. To encourage more widespread adoption of service robots, we propose an ontology-based framework for cooperative robot learning that takes steps toward solving these problems. We present a use case of the framework in which multiple service robots offload compute-intensive machine vision tasks to cloud infrastructure. The framework enables heterogeneous 3D object recognition with the use of ontologies. The main contribution of our proposal is that we use the Unified Robot Description Format (URDF) to represent robots, and we propose the use of a new Robotic Object Description (ROD) ontology to represent the world of objects known by the collective. We use the WordNet database to provide a common understanding of objects across various robotic applications. With this framework, we aim to give a widely distributed group of robots the ability to cooperatively learn to recognize a variety of 3D objects. Different robots and different robotic applications could share knowledge and benefit from the experience of others via our framework. The framework was validated and then evaluated using a proof-of-concept, including a Web application integrated with the ROD ontology and the WordNet API for semantic analysis. The evaluation demonstrates the feasibility of using an ontology-based framework and using the Ontology Web Language (OWL) to provide improved knowledge management while enabling cooperative learning between multiple robots.

Purpose With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels. Design/methodology/approach This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation. Findings Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper. Originality/value The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.

Within an enterprise, various stakeholders create different conceptual models, such as process, data, and requirements models. These models are fundamentally based on similar underlying enterprise (domain) concepts, but they differ in focus, use different modelling languages, take different viewpoints, utilize different terminology, and are used to develop different enterprise artefacts; as such, they typically lack consistency and interoperability. This issue can be solved by enterprise-specific ontologies, which serve as a reference during the conceptual model creation. Using such a shared semantic repository makes conceptual models interoperable and facilitates model integration. The challenge to accomplish this is twofold: on the one hand, an up-to-date enterprise-specific ontology needs to be created and maintained, and on the other hand, different modellers also need to be supported in their use of the enterprise-specific ontology. In this article, we propose to tackle these challenges by means of a recommendation-based conceptual modelling and an ontology evolution framework, and we focus in particular on ontology-based modelling support. To this end, we present our framework for Business Process Modelling Notation (BPMN) as a conceptual modelling language, and focus on how modellers can be assisted during the modelling process and how this impacts the semantic quality of the resulting models. Subsequently, we present a first, large-scale explorative experiment involving 140 business students to evaluate the BPMN instantiation of our framework. The experiments show promising results with regard to incurred overhead, intention of use and model interoperability.

Product configuration deals with design of a new product from existing components. Recently, research on product configuration has shifted to the stage of conceptual modelling. Conceptual product models do not depend on the modelling purpose and therefore can be tailored to the current customer needs. The paper proposes an ontology-based scenario for configuration of immaterial products. The scenario suggests three product configuration operations: removal, supplement, and change. Product customization is supported by involvement of the customer in the process of configuration and by using information from the customer profile. The scenario execution is demonstrated by a particular case of configuration of a mobile operator product in the form of supplement this product with a service. An ontology for mobile product operator is proposed. OWL and SPARQL are used for ontology specification and querying.

The amount of social information published in the Web has dramatically increased in the past years. FOAF, Dublin Core, and vCard are examples of popular vocabularies that are used to present computer understandable profiles of persons in social media. These profiles describe facts about a person such as his or her interests, and thereby we can easily find persons having the same interests. However, in reality we are rather interested in about the closeness of persons' interests than whether they have precisely the same interests. Such aspects are easily understandable for humans but they are not machine understandable without additional semantics. Therefore we have extended the vocabularies of social media by domain specific taxonomies. In addition, to increase the expression power of persons' profiles we have also attached weights to persons' interests. We have also consider a variety of ways for expressing and computing the closeness of weighted profiles. (c) 2017 The Authors. Published by Elsevier B.V.

Numerous images embedded in the painted decorations in early modern Central and Eastern European synagogues conveyed allegorical messages to the congregation. The symbolism was derived from biblical verses, stories, legends, and prayers, and sometimes different allegories were combined to develop coherent stories. In the present case study, which concerns a bird, seemingly a nocturnal raptor, depicted on the ceiling of the Unterlimpurg Synagogue, I explore the symbolism of this image in the contexts of liturgy, eschatology, and folklore. I undertake a comparative analysis of paintings in medieval and early modern illuminated manuscripts&mdash, both Christian and Jewish&mdash, and in synagogues in both Eastern and Central Europe. I argue that in some Hebrew illuminated manuscripts and synagogue paintings, nocturnal birds of prey may have been positive representations of the Jewish people, rather than simply a response to their negative image in Christian literature and art, but also a symbol of redemption. In the Unterlimpurg Synagogue, the night bird of prey, combined with other symbolic elements, represented a complex allegoric picture of redemption, possibly implying the image of King David and the kabbalistic nighttime prayer Tikkun Ḥaẓot. This case study demonstrates the way in which early modern synagogue painters created allegoric paintings that captured contemporary religious and mystical ideas and liturgical developments.

The difficulty of representing and organizing knowledge in reasonably complete ways raises at least two research questions: “how to check that particular relations are systematically used not just whenever possible but whenever relevant for knowledge providers?” and “how to extend best practices, ontology patterns or methodologies advocating the systematic use of particular relations and, at the same time, automatize the checking of compliance with these methods?”. As an answer, this article proposes a generic “ontology design rule” (ODR). A general formulation of this generic ODR is: in a given KB, for each pair of knowledge base objects (types or individuals) of a given set chosen by the user of this ODR, there should be either statements connecting these objects by relations of particular given types or statements negating such relations. This article further specifies this ODR and shows its interests for subtype relations and other transitive relations, e.g. part relations and specialization relations with genus & differentia. This article shows how this ODR can be implemented via OWL and SPARQL, at least for common simple cases (and, generically, via an higher-order logic based language).

Wikidata is an outstanding data source with potential application in many scenarios. Wikidata provides its data openly in RDF. Our study aims to evaluate the usability of Wikidata as a data source for robots operating on the web of data, according to specifications and practices of linked data, the Semantic Web and ontology reasoning. We evaluated from the perspective of two use cases of data crawling robots, which are guided by our general motivation to acquire richer data for Europeana, a data aggregator from the Cultural Heritage domain. The first use case regards general data consumption applications based on RDF, RDF-Schema, OWL, SKOS and linked data. The second case regards applications that explore semantics relying on Schema.org and SKOS. We conclude that a human operator must assist linked data applications to interpret Wikidata’s RDF because of the choices that were taken at Wikidata in the definition of its expression in RDF. The semantics of the RDF output from Wikidata is “locked-in” by the usage of Wikidata’s own ontology, resulting in the need for human intervention. Wikidata is only a few steps away from high quality machine interpretation, however. It contains extensive alignment data to RDF, RDFS, OWL, SKOS and Schema.org, but a machine interpretation of those alignments can only be done if some essential Wikidata alignment properties are known.

Mobile ad-hoc network is collection of mobile nodes like laptops; cell phones which have limited processing power and limited battery power also have limited bandwidth. MANET is self-adjustable with dynamic topology network, because of its mobile environment, routing face challenges. Main challenges in routing are to maximize delivery ratio and minimize End-to-End delay with minimum use of nodes resources, because mobile nodes have limited batter power and bandwidth. DSDV is proactive type routing protocol which trying to minimize route discovery time by manage route information in routing table and broadcast to other neighbors. OWL is a reactive routing protocol which uses DFS instead of broadcasting of RREQ to all its neighbors. In this paper we are going to compare these two routing protocol on the basis of their power consumption in different phases of routing.

In this paper, a novel approach using Description Logic (DL) based inference rules, for ontology matching is presented. Alignment concerns ontology concepts, with the application of similarity measures to perform concepts and instances relationship alignments. Moreover, external knowledge, in the form of WordNet dictionary is then used to solve usual matching problems encountered with synonyms, polesemy, homonyms, etc. Illustrative examples are then presented to support the developed approach.

Computational ontologies are machine-processable structures which represent particular domains of interest. They integrate knowledge which can be used by humans or machines for decision making and problem solving. The main aim of this systematic review is to investigate the role of formal ontologies in information systems development, i.e., how these graphs-based structures can be beneficial during the analysis and design of the information systems. Specific online databases were used to identify studies focused on the interconnections between ontologies and systems engineering. One-hundred eighty-seven studies were found during the first phase of the investigation. Twenty-seven studies were examined after the elimination of duplicate and irrelevant documents. Mind mapping was substantially helpful in organising the basic ideas and in identifying five thematic groups that show the main roles of formal ontologies in information systems development. Formal ontologies are mainly used in the interoperability of information systems, human resource management, domain knowledge representation, the involvement of semantics in unified modelling language (UML)-based modelling, and the management of programming code and documentation. We explain the main ideas in the reviewed studies and suggest possible extensions to this research.

International audience; This article first distinguishes constraints from rules, and descriptive constraints from prescriptive ones. Both kinds can be used to calculate a constraint-based completenesses (as opposed to a real-world-based completeness), i.e. evaluating how much of a knowledge base is complete with respect to some constraints, e.g. for evaluating how well this base follows given ontology design patterns or best practices. Such evaluations may also guide knowledge elicitation and modelisation. This article explores the ways constraints can be represented via relations between classes, hence via any knowledge representation language (KRL) that has an expressiveness at least equal to RDF or RDFS. Compared to the popular practice of both representing and checking constraints via queries, this approach is as simple, offers more possibilities for exploiting both knowledge and constraints, and permits the selection and use of inference engines adapted to the expressiveness of the exploited knowledge instead of the use of restricted or ad hoc constraint-validation tools. This approach is also modular in the sense it separates content from usage: the represented "content focused constraints" can then be exploited via few "content independent" queries, one for each usage and kind of constraint. This approach provides more possibilities.

Background The Gene Ontology (GO) consists of over 40,000 terms for biological processes, cell components and gene product activities linked into a graph structure by over 90,000 relationships. It has been used to annotate the functions and cellular locations of several million gene products. The graph structure is used by a variety of tools to group annotated genes into sets whose products share function or location. These gene sets are widely used to interpret the results of genomics experiments by assessing which sets are significantly over- or under-represented in results lists. F Hoffmann-La Roche Ltd. has developed a bespoke, manually maintained controlled vocabulary (RCV) for use in over-representation analysis. Many terms in this vocabulary group GO terms in novel ways that cannot easily be derived using the graph structure of the GO. For example, some RCV terms group GO terms by the cell, chemical or tissue type they refer to. Recent improvements in the content and formal structure of the GO make it possible to use logical queries in Web Ontology Language (OWL) to automatically map these cross-cutting classifications to sets of GO terms. We used this approach to automate mapping between RCV and GO, largely replacing the increasingly unsustainable manual mapping process. We then tested the utility of the resulting groupings for over-representation analysis. Results We successfully mapped 85% of RCV terms to logical OWL definitions and showed that these could be used to recapitulate and extend manual mappings between RCV terms and the sets of GO terms subsumed by them. We also show that gene sets derived from the resulting GO terms sets can be used to detect the signatures of cell and tissue types in whole genome expression data. Conclusions The rich formal structure of the GO makes it possible to use reasoning to dynamically generate novel, biologically relevant groupings of GO terms. GO term groupings generated with this approach can be used in. over-representation analysis to detect cell and tissue type signatures in whole genome expression data. Electronic supplementary material The online version of this article (10.1186/s13326-018-0175-z) contains supplementary material, which is available to authorized users.

International audience; The contextual information in the built environment is highly heterogeneous, it goes from static information (e.g., information about the building structure) to dynamic information (e.g., user's space-time information, sensors detections and events that occurred). This paper proposes to semantically fuse the building's contextual information with extracted data from a smart camera network by using ontologies and semantic web technologies. The developed ontology allows interoperability between the different contextual data and enables, without human interaction, real-time event detections and system reconfiguration to be performed. The use of semantic knowledge in multi-camera monitoring systems guarantees the protection of the user's privacy by not sending nor saving any image, just extracting the knowledge from them. This paper presents a new approach to develop an "all-seeing" smart building, where the global system is the first step to attempt to provide Artificial Intelligence (AI) to a building.

Part 1: Social Media - Games - Ontologies; International audience; Diagrams are widely used in our day to day communication. A knowledge of the spatial patterns used in diagrams is essential to read and understand them. In the context of diagrams, spatial patterns mean accepted spatial arrangements of graphical and textual elements used to represent diagram-specific concepts. In order to assist with the automated understanding of diagrams by computer applications, this paper presents an ontology-based approach to recognise diagram-specific concepts from the spatial patterns in diagrams. Specifically, relevant spatial patterns of diagrams are encoded in an ontology, and the automated instance classification feature of the ontology reasoners is utilised to map spatial patterns to diagram-specific concepts depicted in a diagram. A prototype of this approach to support automated recognition of UML and domain concepts from class diagrams and its performance are also discussed in this paper. This paper concludes with a reflection of the strengths and limitations of the proposed approach.

The current Digital Libraries (DLs) usually return as answer of a user's query a ranked list of the resources included in the DLs but no semantic relation among the resources are reported. Using the Semantic Web technologies it is possible to improve these search functionalities introducing narratives as new search method. As narratives we intend semantic networks of events that are linked to the objects of the DLs and are endowed with a set of semantic relations that connect an event to another. These semantic networks may help the users to obtain a more complete knowledge on the subject of their searches. In this paper, we present a software architecture for building narratives in order to introduce them in DLs. Our architecture is composed of several tools (automatic and semi-automatic tools) for creating, storing and visualizing narratives. When possible, we reused open source components already available on-line, and for the software we developed, we freely distribute it for research aims.

Ontologies are complex systems of axioms in which unanticipated consequences of changes are both frequent, and diffcult for ontology authors to apprehend. The effects of modelling actions range from unintended inferences to outright defects such as incoherency or even inconsistency. One of the central ontology authoring activities is verifying that a particular modelling step has had the intended consequences, often with the help of reasoners. For users ofProtégé, this involves, for example, exploring the inferred class hierarchy.This paper provides evidence that making entailment set changes explicit to authors significantly improves the understanding of authoring actions regarding both correctness and speed. This is tested by means of the InferenceInspector, a Protégé plugin we created that provides authors with specific details about the effects of an authoring action. We empirically validate the effectiveness of the Inference Inspector in two studies. In a first, exploratory study we determine the feasibility of the Inference Inspector for supporting verification and isolating authoring actions. In a second, controlled study we formally evaluate the Inference Inspector and determine that making changes to key entailment sets explicit significantly improves author verification compared to the standard static hierarchy/framebased approach. We discuss the advantages of the Inference Inspector for different types of verification questions and find that our approach is best suited for verifying added restrictions where no new signature, such as class names, is introduced, with a 42% improvement in verification correctness.