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Subject matter: means of information support for the negotiation process on the organization of cooperative production of aircraft. Goal: an outline of the process of developing effective means of information support for negotiations on cooperative production, which will provide financial savings, firstly, by reducing the negotiation time, and secondly, is reducing the likelihood of making irrational decisions that can lead to additional costs during project implementation. Tasks: Increase the efficiency of the negotiation process on the organization of cooperative production by creating a problem-oriented methodological basis, and on this basis - software tools for the development, deployment and operation of the appropriate decision support system. Methods: system analysis and set theory, for the formal presentation of relevant information about the subject area "Negotiations on cooperative aircraft production"; ontological engineering, in particular the IDEF5 standard, as well as elements of expert systems technology, for building an ontological decision support system. Results: an aggregate of methodological tools for the synthesis of ontological decision support systems was developed during negotiations on the organization of cooperative production of aircraft, a scenario example of organizing an ontological dialogue in the environment of an ontological decision support system for defining an aircraft model as an object of cooperative production was given. Conclusions: the need to supplement the mathematical support of traditional negotiation support systems with special methodological means is shown to increase the efficiency of information support for the negotiation process on organizing cooperative production in aircraft construction; it is substantiated that for the program implementation of the system of information support for negotiations on the organization, it is advisable to use the ontological approach; the architecture of the intellectual core of the ontological decision support system for organizing the negotiation process for organizing the cooperative production of aircraft in the form of a hierarchy of frames, with the organization of knowledge-based inference by means of attached procedures is proposed; a scenario example of the formation of a decision on the choice of an aircraft model for the organization of cooperative production in the environment of an ontological decision support system is given., Предмет: средства информационной поддержки процесса проведения переговоров по организации кооперационного производства (ПОКП) воздушных судов (ВС). Цель: изложение процесса разработки эффективных средств информационной поддержки переговоров о кооперационном производстве, которые обеспечат экономию финансов, во-первых, за счет сокращения сроков переговоров, а во-вторых - снижения вероятности принятия нерациональных решений, которые могут привести к дополнительным затратам при реализации проекта. Задача: Повышение эффективности процесса проведения переговоров по организации кооперационного производства, за счет создания проблемно-ориентированного методического базиса, а на этой основе – программных средств разработки, развертывания и эксплуатации соответствующей системы поддержки принятия решений. Методы: системный анализ и теория множеств, для формального представления релевантной информации о предметной области "Переговоры о кооперационном производстве воздушных судов"; онтологический инжиниринг, в частности стандарт IDEF5, а также элементы технологии экспертных систем, для построения онтологической системы поддержки принятия решений (ОСППР). Результаты: разработан комплекс методических средств синтеза онтологических систем поддержки принятия решений в ходе проведения ПОКП ВС, приведен сценарный пример организации онтологического диалога в среде ОСППР по определению модели ВС как объекта кооперационного производства. Выводы: показана необходимость дополнения математического обеспечения традиционных систем поддержки переговоров специальными методическими средствами, для повышения эффективности информационной поддержки процесса переговоров по организации кооперативного производства в самолетостроении; обосновано, что для программной реализации системы информационной поддержки ПОКП целесообразно использование онтологического подхода; предложена архитектура интеллектуального ядра ОСППР по организации процесса ПОКП ВС в форме иерархии фреймов, с организацией вывода на знаниях с помощью присоединенных процедур; приведен сценарный пример формирования решения по выбору модели ВС для организации кооперационного производства в среде ОСППР., Предмет: засоби інформаційної підтримки процесу проведення переговорів по організації коопераційного виробництва (ПОКВ) повітряних суден (ПС). Мета: викладення процесу розробки ефективних засобів інформаційної підтримки переговорів про коопераційне виробництво, які забезпечать економію фінансів, по-перше, за рахунок скорочення термінів переговорів, а по-друге – зниження ймовірності прийняття нераціональних рішень, що призведуть до додаткових витрат при реалізації проекту. Завдання: Підвищення ефективності процесу проведення переговорів щодо організації коопераційного виробництва, за рахунок створення проблемно-орієнтованого методичного базису, а на цій основі - програмних засобів розробки, розгортання і експлуатації відповідної системи підтримки прийняття рішень. Методи: системний аналіз та теорія множин, для формального представлення релевантної інформації щодо предметної галузі "Переговори про коопераційне виробництво повітряних суден"; онтологічний інжиніринг, зокрема стандарт IDEF5, а також елементи технології експертних систем для побудови онтологічної системи підтримки прийняття рішень (ОСППР). Результати: розроблено комплекс методичних засобів розроблення онтологічних систем підтримки прийняття рішень в ході проведення переговорів про організацію кооперативного виробництва (КВ) повітряних суден (ПС); наведено сценарний приклад організації онтологічного діалогу у середовищі ОСППР щодо визначення моделі ПС як об’єкту КВ. Висновки: показана необхідність доповнення математичного забезпечення традиційних систем підтримки переговорів спеціальними методичними засобами для підвищення ефективності інформаційної підтримки процесу переговорів щодо організації кооперативного виробництва в літакобудуванні; обґрунтовано, що для програмної реалізації системи інформаційної підтримки ПОКВ) доцільне використання онтологічного підходу; запропоновано інтелектуальне ядро онтологічної системи підтримки прийняття рішень щодо процесу ПОКВ) ПС побудувати у формі ієрархії фреймів з організацією виведення на знаннях за допомогою приєднаних процедур; наведено сценарний приклад формування рішення щодо вибору моделі ПС для організації КВ у середовищі ОСППР.

The object of research is the sustainable development of IT enterprises. One of the most problematic places is the specification of the subject area, taking into account the specific activity of the IT enterprise. Today, there is a certain amount of research in the field of sustainable development, however, given the specific nature of the IT enterprise, namely that its main asset is software development, there are no researches related to the analysis of the sustainable development of IT enterprises. That is why, above all, the question arises in the representation of knowledge about the subject area. As a result, it is important to develop a model that displays the domain objects and the links between them. Such a tool is ontologies, which are a formalized representation of domain knowledge. The paper discusses the problem of building an ontology for a subject area of sustainable development of an IT enterprise, which includes the following steps: –justification of the use of ontologies for domain modelling; –a brief description of the basic steps of building an ontology in accordance with the IDEF5 standard. The collection and source analysis for the formation of verbal subject domain specifications. It should be noted that the developed ontology includes three types of representation: verbal, graphic and analytical. Within this paper, only two representations (graphical and analytical) are given. The IDEF5 Schematic Language (SL) was used to graphically represent the ontology, which includes several types of ontology diagrams. In the framework of our study, we used the Composition Schematics, which is a mechanism for graphically representing the composition of ontology classes. For analytic representation, predicate calculations were used, because with increasing and refining the ontology of the subject area, this mathematical apparatus ensures the reliability of the results. The developed ontology involves refinement and expansion in accordance with the requirements of projects where this ontology will be used, for example, to develop an expert system for solving problems of the subject area.

The work of this p aper is done within the context of European Project E! 8949 PrEmISES 1. One of the central pieces of PrEmISES[16] is the use of automatic tool generation algorithms for the creation of ontologies. The objective of this work is to extend the work from PrEmISES for raising the medical literacy of empowered patients by creating a digital library that contains relevant articles from different medical domain. It should be noted that a large number of persons are interested in finding relevant information about their condition. We have chosen to discuss in this research the automated generation of ontologies for the medical domain. By the use of ontologies that gives key terms from a domain, the creation of the digital library can be done by crawling from the Internet articles which are related to the profile of the user and that are based on the key terms of the ontologies. The articles from the digital library can be used by a patient to raise his / her knowledge and literacy related to his/her diseases.

The ongoing rapid growth of diversity of data and their wide use to solve different complex tasks requires more sophisticated techniques of knowledge management and automated reasoning. Recent research efforts resulted in a significant number of semantic reference systems enriched with vocabularies, thesauri, terminologies, and ontologies. The extensive use of ontologies in the mainstream computer science has spread to many other branches of knowledge. These branches are included in a new approach to building modern intelligent systems, reusing and sharing pieces of declarative knowledge. In the meanwhile, a lot of effort has been made to produce standard ontologies for medicine and biology. This paper brings an overview and presentation of the state of the art in terminologies, ontologies and important resources/systems and tools for industry and academia in medicine and biology. It could be useful for researchers involved in multidisciplinary and interdisciplinary research areas and projects that include medicine, biology, and information technology.

International audience; The increasing importance of ontologies has resulted in the development of a large number of ontologies in both coordinated and non-coordinated efforts. The number and complexity of such ontologies make hard to ontology and tool developers to select which ontologies to use and reuse. So far, there are no mechanism for making such decisions in an informed manner. Consequently, methods for evaluating ontology quality are required. OQuaRE is a method for ontology quality evaluation which adapts the SQuaRE standard for software product quality to ontologies. OQuaRE has been applied to identify the strengths and weaknesses of different ontologies but, so far, this framework has not been evaluated itself. Therefore, in this paper we present the evaluation of OQuaRE, performed by an international panel of experts in ontology engineering. The results include the positive and negative aspects of the current version of OQuaRE, the completeness and utility of the quality metrics included in OQuaRE and the comparison between the results of the manual evaluations done by the experts and the ones obtained by a software implementation of OQuaRE.

International audience; Ontologies have been increasingly used over the past few decades in a wide range of application domains spanning both academic and industrial communities. As ontologies are the cornerstone of the Semantic Web, the technologies developed in this context, including ontology languages, specialized databases and query languages, have become widely used. However, the expressiveness of the proposed ontology languages does not always cover the needs of specific domains. For instance, engineering is a domain for which the LIAS laboratory has proposed dedicated solutions with a worldwide recognition. The underlying assumptions made in the context of the Semantic Web, an open and distributed environment, do not apply to the controlled environments of our projects where the correctness and completeness of modeling can be guaranteed to a certain degree. As a consequence, we have developed over the last decades a specialized standard ontology language named PLIB associated with the OntoDB/OntoQL platform to manage ontological engineering data within a database. The goal of this paper is threefold: (1) to share our experience in manipulating ontologies in the engineering domain by describing their specificities and constraints; (2) to define a comprehensive classification of ontologies with respect to three main research communities: Artificial Intelligence, Databases and Natural Language Processing and (3) to present a persistent solution, called OntoDB, for managing extremely large semantic data sets associated with an ontological query language, called OntoQL. These objectives are illustrated by several examples that show the effectiveness and interest of our propositions in several industrial projects in different domains including vehicle manufacturing and CO2 storage.

Ontologies are an essential component of semantic knowledge bases and applications, and nowadays they are used in a plethora of domains. Despite the maturity of ontology languages, support tools and engineering techniques, the testing and validation of ontologies is a field which still lacks consolidated approaches and tools. This paper attempts at partly bridging that gap, taking a first step towards the extension of some traditional software testing techniques to ontologies expressed in a widely-used format. Mutation testing and coverage testing, revisited in the light of the peculiar features of the ontology language and structure, can can assist in designing better test suites to validate them, and overall help in the engineering and refinement of ontologies and software based on them.

This is the author's final version of the work. Copyright (C) 2013 The Society of Chemical Engineers. Journal of Chemical Engineering of Japan, Vol.46,No.6, pp.396-406, 2013.

International audience; Complex engineering systems execute within different contexts and domains. The heterogeneity induced by these contexts is usually implicitly handled in the development cycle of such systems. We claim that reducing this heterogeneity can be achieved by handling explicitly the knowledge mined from these domains and contexts. Verification and validation activities are improved due to the expression and verification of new constraints and properties directly extracted from the context and domains associated to the models. In this paper, we advocate the use of domain ontologies to express both domain and context knowledge. We propose to enrich design models that describe complex information systems, with domain knowledge, expressed by ontologies, provided by their context of use. This enrichment is achieved by annotation of the design models by references to ontologies. Three annotation mechanisms are proposed. The resulting annotated models are checked to validate the new minded domain properties. We have experimented this approach in a model driven engineering (MDE) development setting.

International audience; Requirement Analysis is a crucial and arduous taskin data warehouse design process. We provide a solution tosupport data warehouse design, which combines need driven anddata driven design approaches. Our solution is based on ontologyrepresentation language and work results on ontology alignment.Our design method occurs in two steps. The first one, whichis interactive and semi-automatic, transforms user requirementsinto ontologies, the second step that is fully automatic, alignsthe resulting ontologies from the first step and produces anontology associated to the Multidimensional Data model. Thewaste management field will be our guideline to illustrate ourapproach.

International audience; Modeling languages are concerned with providing techniques and tool support for the design, synthesis and analysis of the models resulting from a given modeling activity, this activity being usually part of a system development model or process. These languages quite successfully focused on the analysis of the designed system exploiting the expressed semantic power of the underlying modeling language. The semantics of these modeling languages are well understood by the system designers and/or the modeling language users i.e. implicit semantics.In general, modeling languages are not equipped with resources, concepts or entities handling explicitly domain engineering features and characteristics (domain knowledge) in which the modeled systems evolve.Indeed, the designer has to explicitly handle the knowledge issued and/or mined from an analysis of this application domain i.e. explicit semantics. Nowadays, making explicit the domain knowledge inside system design models does not obey to any methodological rule validated by the practice. The modeling languages users introduce through types, constraints, profiles, etc. these domain knowledge features.Our claim is that ontologies are good candidates for handling explicit domain knowledge. They define domain theories and provide resources for uniquely identifying domain knowledge concepts. Therefore, allowing models to make references to ontologies is a modular solution for models to explicitly handle domain knowledge.Overcoming the absence of explicit semantics expression in the modeling languages used to specify systems models will increase the robustness of the designed system models. Indeed, the axioms and theorems resulting from the ontologies, thanks to references, can be used to strengthen the properties of the designed models.The objective of this paper is to offer rigorous mechanisms for handling domain knowledge in design models. This paper also shows how these mechanisms are set up in the cases of static, dynamic and formal models.

Ontologies are instruments of knowledge organization that have been developed through several methodologies. These methodologies are well established, but their steps often are not well explained. Thus, only knowledge engineers are able to perform all steps required in the development of ontologies. Here, we describe a methodology that details each step of the ontology development cycle. The goal of this methodology - called OntoForInfoScience - is to overcome issues of technical jargon and logical-philosophical principles faced by experts in Knowledge Organization from the field of Information Science. These are the usual issues when one deals with and constructs ontologies. In order to identify those issues, our methodology was produced by information scientists during the development of ontology in the blood domain. This ontology, called Hemonto, is domain ontology about blood components under development within the scope of a scientific project. In this paper, we present a brief description of OntoForInfoScience, as well as the practical results of the development of the blood ontology. We conclude that the new methodology is useful for information scientists when creating formal ontological representations. In addition to the methodology per se, our research also provides partial results of the HEMONTO development.

Motivation: Describing biological sample variables with ontologies is complex due to the cross-domain nature of experiments. Ontologies provide annotation solutions; however, for cross-domain investigations, multiple ontologies are needed to represent the data. These are subject to rapid change, are often not interoperable and present complexities that are a barrier to biological resource users. Results: We present the Experimental Factor Ontology, designed to meet cross-domain, application focused use cases for gene expression data. We describe our methodology and open source tools used to create the ontology. These include tools for creating ontology mappings, ontology views, detecting ontology changes and using ontologies in interfaces to enhance querying. The application of reference ontologies to data is a key problem, and this work presents guidelines on how community ontologies can be presented in an application ontology in a data-driven way. Availability: http://www.ebi.ac.uk/efo Contact: malone@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Domain ontology as an instrument for knowledge representation, sharing, reuse and interoperability takes an increasingly important role in the approaches for personalised intelligent e-learning architectures and systems. However, wider practical acceptance of domain ontology as an engineering product and as a part of web-based learning systems is still needed. We believe that one of the barriers for wider spreading of domain ontologies in different fields, including e-learning, is the problem of the design and maintenance of high quality ontologies. As the importance of the quality of learning resources is obvious, the quality of domain ontology for e-learning is even more important, because ontology is intended to be re-used in design and implementation of various learning resources. In this paper, we analyse the quality-related characteristics of domain ontology. We propose a framework for evaluation of the quality of domain ontology for web-based learning. Further, we propose a model for ontology evaluation, based on its technical and complexity-related characteristics. We identify main conceptual (semantic) quality characteristics, and analyse the relationship between both types of ontology quality characteristics. Also we present an application of proposed framework to the evaluation of ontologies for web based learning.

There is an upsurge in applying fuzzy ontologies to represent vague information in the knowledge representation field. Current research in the fuzzy ontologies paradigm mainly focuses on developing formalism languages to represent fuzzy ontologies, designing fuzzy ontology editors, and building fuzzy ontology applications in different domains. Less focus falls on establishing a formal methodological approach for building fuzzy ontologies. Existing fuzzy ontology development methodologies, such as the IKARUS-Onto methodology and fuzzy ontomethodology, provide formalized schedules for the conversion from crisp ontologies into fuzzy ones. However, a formal guidance on how to build fuzzy ontologies from scratch still lacks in this paper. Therefore, this paper presents the first methodology, named fuzzy ontology development methodology (FODM), for developing fuzzy ontologies from scratch. The proposed FODM can provide a very good guideline for formally constructing fuzzy ontologies in terms of completeness, comprehensiveness, generality, efficiency, and accuracy. To explain how the FODM works and demonstrate its usefulness, a fuzzy seabed characterization ontology is built based on the FODM and described step by step.

peer-reviewed In work that is ongoing, the authors are examining the extent of software development process terminology drift. Initial findings suggest there is a degree of term confusion, with the mapping of concepts to terms lacking precision in some instances. Ontologies are concerned with identifying the concepts of relevance to a field of endeavour and mapping those concepts to terms such that term confusion is reduced. In this paper, we discuss how ontologies are developed. We also identify various sources of software process terminology. Our work to date indicates that the systematic development of a software development process ontology would be of benefit to the entire software development community. The development of such an ontology would in effect represent a systematic refactoring of the terminology and concepts produced over four decades of software process innovation.

One of the main goals of the Software Engineering (SE) discipline is to find higher abstraction levels and ways to reuse software in order to increase its productivity and quality. Ontologies, which are typically considered as a technique or an artifact used in one or more software lifecycle phases, may be used to help achieve that goal. This paper provides a literature review, discussion and analysis of the existing solutions for implementing ontologies in SE. We selected several software development paradigms (including Software Product Lines, Component-Based Development, Generative Programming and Model-Driven Engineering) for our classification and discussion of different approaches proposed in the literature. It was established that ontologies are suitable for providing a common vocabulary to avoid misunderstanding between different parties in SE, requirements specification, features specification, variability management, components specification, components matching, model transformations and code generation. Based on the conducted review, guidelines for further research are given.

Bodies of Knowledge (BOK), contain the relevant knowledge for a discipline, and it is necessary for the development of the science, and application in the professional, and occupational profiles, and the possible incidence in the industry of Ecuador. In this paper, it is shown an evaluation of professional and occupational profiles based on standard Software Engineering Body of Knowledge SWEBOK 2004 (Spanish Version), and the development of ontological model, in order to obtain the necessary information to establish the relationship, and the criteria to evaluate the profiles based on the guide of knowledge.

Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications.

International audience; Waste management is a major concern of the various policy makers, economic and social. It is present each time he comes to launching development projects and / or development of urban infrastructure. The implementation of Data Warehousing assisting in decision making in this context, proves very useful, but at the same time a very complex task. We provide a solution to support Data Warehouse design. Our solution combines the two classical approaches to design, it is both oriented and need-oriented data. It is based on ontology representation languages an the results of work on the alignment. Our Data Warehouse design process has two stages. The first one which is interactive and semi-automatic, transforms user requirements into ontologies, the second step that is fully automatic, aligns the resulting ontologies from the first step and produces an ontology associated to the multidimensional data model. The waste management field will be our guideline to illustrate our approach.

Background With the increasing presence of biomedical data sources on the Internet more and more research effort is put into finding possible ways for integrating and searching such often heterogeneous sources. Ontologies are a key technology in this effort. However, developing ontologies is not an easy task and often the resulting ontologies are not complete. In addition to being problematic for the correct modelling of a domain, such incomplete ontologies, when used in semantically-enabled applications, can lead to valid conclusions being missed. Results We consider the problem of repairing missing is-a relations in ontologies. We formalize the problem as a generalized TBox abduction problem. Based on this abduction framework, we present complexity results for the existence, relevance and necessity decision problems for the generalized TBox abduction problem with and without some specific preference relations for ontologies that can be represented using a member of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\mathcal {EL}}$ \end{document}Eℒ family of description logics. Further, we present algorithms for finding solutions, a system as well as experiments. Conclusions Semantically-enabled applications need high quality ontologies and one key aspect is their completeness. We have introduced a framework and system that provides an environment for supporting domain experts to complete the is-a structure of ontologies. We have shown the usefulness of the approach in different experiments. For the two Anatomy ontologies from the Ontology Alignment Evaluation Initiative, we repaired 94 and 58 initial given missing is-a relations, respectively, and detected and repaired additionally, 47 and 10 missing is-a relations. In an experiment with BioTop without given missing is-a relations, we detected and repaired 40 new missing is-a relations.

In this paper, we propose a novel structure-based partitioning algorithm able to break a large ontology into different modules related to specific topics for the domain of interest. In particular, we leverage the topological properties of the ontology graph and exploit several techniques derived from Network Analysis to produce an effective partitioning without considering any information about semantics of ontology relationships. An automated partitioning tool has been developed and several preliminary experiments have been conducted to validate the effectiveness of our approach with respect to other techniques.

The Enterprise Modeling (EM) community has recognized formal ontologies as a promising instrument to address some of the challenges in the EM field. The studies have shown that the main benefits of applying formal ontologies in the realm of EM are the machine-reasoning, flexibility of the logic-based schema and ability to integrate easily a number of data sources. However, in order to realize the potential benefits, among others, formal ontologies fulfilling specific requirements need to be available. In this paper, we discuss a set of generic requirements towards formal ontologies in the realm of EM and check their fulfillment with the aim to identify directions of future work.

This paper presents a framework for developing engineering design ontologies within the aerospace industry. The aim of this approach is to strengthen the modularity and reuse of engineering design ontologies to support knowledge management initiatives within the aerospace industry. Successful development and effective utilisation of engineering ontologies strongly depends on the method/framework used to develop them. Ensuring modularity in ontology design is essential for engineering design activities due to the complexity of knowledge that is required to be brought together to support the product design decision-making process. The proposed approach adopts best practices from previous ontology development methods, but focuses on encouraging modular architectural ontology design. The framework is comprised of three phases namely: (1) Ontology design and development; (2) Ontology validation and (3) Implementation of ontology structure. A qualitative research methodology is employed which is composed of four phases. The first phase defines the capture of knowledge required for the framework development, followed by the ontology framework development, iterative refinement of engineering ontologies and ontology validation through case studies and experts’ opinion. The ontology-based framework is applied in the combustor and casing aerospace engineering domain. The modular ontologies developed as a result of applying the framework and are used in a case study to restructure and improve the accessibility of information on a product design information-sharing platform. Additionally, domain experts within the aerospace industry validated the strengths, benefits and limitations of the framework. Due to the modular nature of the developed ontologies, they were also employed to support other project initiatives within the case study company such as role-based computing (RBC), IT modernisation activity and knowledge management implementation across the sponsoring organisation. The major benefit of this approach is in the reduction of man-hours required for maintaining engineering design ontologies. Furthermore, this approach strengthens reuse of ontology knowledge and encourages modularity in the design and development of engineering ontologies.

International audience; Encyclopedic knowledge bases can be powerful tools for the acquisition of fundamental knowledge for learners. However, the structure and the very nature of these documents can impede learning processes. By extracting domain ontologies from reference books and using this same material to populate an intelligent learning system, we propose a methodology for lifelong learners.

International audience; Evaluation is a crucial phase in ontological lifecycle, especially for ontologies that are produced using automated or semi-automated methods. In this paper, we focus on the evaluation phase in the frame of the ontology building process. We begin by a review of different ontology construction lifecycles. Next, we try to review state-of-the-art methods for ontology construction with a special interest on evaluation for assessing the quality of ontologies. Afterwards we highlight the main limits and difficulties of these methods with respect to evaluation. We conclude with a first sketch on how evaluation should be undertaken for guiding the development of high quality ontologies that are more relevant to the requirements of a particular domain.

Using an ontology to automatically generate questions for ordinary people requires a structure and concepts compliant with human thought. Here we present methods to develop a pragmatic, expert-based and a basic-level ontology and a framework to evaluate these ontologies. Comparing these ontologies shows that expert-based ontologies are most easy to construct but lack required cognitive semantic characteristics. Basic-level ontologies have structure and concepts which are better in terms of cognitive semantics but are most expensive to construct. © 2014 - IOS Press and the authors.

The search for possible applications in the field of Education, by means of domain specific ontologies on the issues of assessment of learning (assessment) and system (evaluation), has been implemented through the construction, testing and evolution of an integrated learning environment called EduOntoWiki. It is supported by ontological structures related to science education based on active consent of specific communities of practice. It allows selecting and incorporating the ontologies in order to organize learning objects to improve learning process. It is created as a setting in which they had the relevant formal descriptions (coding ontology) and informal (and narrative contextualization of concepts), where the possible intra-and inter-community relations were made explicit and recognized by all participants through specific social software. The last point in particular, has led to our hypothesis: only the transition from a technology-driven model (formal, static) to a community-driven model (dynamic and integrated in an open learning environment) could make really effective and relevant the ontologies that we built in several communities of practice. Therefore, the final goal was the realization of an integrated learning environment attempting the difficult transition from an "ontological-formal" system to an "ontological-relational" one.

Ontologies in the biomedical domain are becoming a key element for data integration and search. The usefulness of the applications which use ontologies is often directly influenced by the quality of ontologies, as incorrect or incomplete ontologies might lead to wrong or incomplete results for the applications. Therefore, there is an increasing need for repairing defects in ontologies. In this paper we focus on completing ontologies. We provide an algorithm for completing the is-a structure in \({\cal{EL}}\) ontologies which covers many biomedical ontologies. Further, we present an implemented system based on the algorithm as well as an evaluation using three biomedical ontologies.

In high-level-architecture (HLA)-based distributed heterogeneous collaborative engineering environments (CEEs), the construction of federation object model files is time consuming. This paper presents an ontology fusion approach aiming at establishing a common understanding in such collaborative environments. The proposed approach has three steps: ontology mapping, ontology alignment, and ontology merging. Ontology mapping employs a top-down approach to explore all bridge relations between two terms from different ontologies based on bridge axioms and deduction rules. Ontology alignment adopts a bottom-up approach to discover implicit bridge relations between two terms from different domain ontologies based on equivalent inference. Ontology merging generates a new collaboration ontology from discovered equivalent bridge relations. It adopts an axiom-based ontology fusion strategy and takes heavy-weighted ontologies into consideration. It can find all the explicit and derived interontology relations. In a typical CEE, the proposed approach has a great potential to improve the efficiency of preparation for HLA-based collaborative engineering processes, reduce the work load for adaptive adjustment of existing platforms, and enhance the reusability and flexibility of CEEs. A case study has been conducted to validate the feasibility of the proposed approach.

International audience; Recent research has argued about the importance of considering security during Requirements Engineering (RE) stage. Literature also emphasizes the importance of using ontologies to facilitate requirements elicitation. Ontologies are known to be rich sources of knowledge, and, being structured and equipped with reasoning features, they form a powerful tool to handle requirements. We believe that security being a multi-faceted problem, a single security ontology is not enough to guide SR Engineering (SRE) efficiently. Indeed, security ontologies only focus on technical and domain independent aspects of security. Therefore, one can hypothesize that domain knowledge is needed too. Our question is "how to combine the use of security ontologies and domain ontologies to guide requirements elicitation efficiently and effectively?" We propose a method that exploits both types of ontologies dynamically through a collection of heuristic production rules. We demonstrate that the combined use of security ontologies with domain ontologies to guide SR elicitation is more effective than just relying on security ontologies. This paper presents our method and reports a preliminary evaluation conducted through critical analysis by experts. The evaluation shows that the method provides a good balance between the genericity with respect to the ontologies (which do not need to be selected in advance), and the specificity of the elicited requirements with respect to the domain at hand.

This paper defines a language for modeling ontologies in business applications and application services. This language consists of the modeling constructs from Natural Language Modeling (NLM). It will be shown in this article how the application of this modeling language will enable us to model a hierarchy for a domain ontology.

With the increased use of ontologies in semantically-enabled applications, the issues of debugging and aligning ontologies have become increasingly important. The quality of the results of such applications is directly dependent on the quality of the ontologies and mappings between the ontologies they employ. A key step towards achieving high quality ontologies and mappings is discovering and resolving modeling defects, e.g., wrong or missing relations and mappings. In this paper we present a unified framework for aligning taxonomies, the most used kind of ontologies, and debugging taxonomies and their alignments, where ontology alignment is treated as a special kind of debugging. Our framework supports the detection and repairing of missing and wrong is-a structure in taxonomies, as well as the detection and repairing of missing (alignment) and wrong mappings between ontologies. Further, we implemented a system based on this framework and demonstrate its benefits through experiments with ontologies from the Ontology Alignment Evaluation Initiative.

Ontologies provide a conceptualization of a domain of interest which can be used for different objectives, such as for providing a formal description of the domain of interest for documentation purposes, or for providing a mechanism for reasoning upon the domain. For instance, they are the core element of the Ontology-Based Data Access (OBDA) [3,8] paradigm, in which the ontology is utilized as a conceptual view, allowing user access to the underlying data sources. With the aim to use an ontology as a formal description of the domain of interest, the use of expressive languages proves to be useful. If instead the goal is to use the ontology for reasoning tasks which require low computational complexity, the high expressivity of the language used to model the ontology may be a hindrance. In this scenario, the approximation of ontologies expressed in very expressive languages through ontologies expressed in languages which keep the computational complexity of the reasoning tasks low is pivotal.

Ontology creation and management related processes are very important to define and develop semantic services. Ontology Engineering is the research field that provides the mechanisms to manage the life cycle of the ontologies. However, the process of building ontologies can be tedious and sometimes exhaustive. OWL VisMod is a tool designed for developing ontological engineering based on visual analytics concep tual modeling for OWL ontologies life cycle management, supporting both creation and understanding tasks. This paper is devoted to evaluate OWL VisMod through a set of defined tasks. The same tasks also will be done with the most known tool in Ontology Engineering, Protégé, in order to compare the obtained results and be able to know how is OWL VisMod perceived for the expert users. The comparison shows that both tools have similar acceptation scores, but OWL VisMod presents better feelings regard ing user’s perception tasks due to the visual analytics influence. Publicado

In this paper we present GOSPL, which stands for Grounding Ontologies with Social Processes and Natural Language. GOSPL is a method and tool that supports stakeholders in iteratively interpreting and modeling their common hybrid ontologies using their own terminology for semantic interoperability between autonomously developed and maintained information systems. Hybrid ontologies are ontologies in which concepts are both formally and informally described with the help of a special linguistic resource called glossary. Social interactions between the community members drive the ontology evolution process and result in more stable and agreed upon ontologies.

International audience; BACKGROUND: Ontologies are being developed for the life sciences to standardise the way we describe and interpret the wealth of data currently being generated. As more ontology based applications begin to emerge, tools are required that enable domain experts to contribute their knowledge to the growing pool of ontologies. There are many barriers that prevent domain experts engaging in the ontology development process and novel tools are needed to break down these barriers to engage a wider community of scientists. RESULTS: We present Populous, a tool for gathering content with which to construct an ontology. Domain experts need to add content, that is often repetitive in its form, but without having to tackle the underlying ontological representation. Populous presents users with a table based form in which columns are constrained to take values from particular ontologies. Populated tables are mapped to patterns that can then be used to automatically generate the ontology's content. These forms can be exported as spreadsheets, providing an interface that is much more familiar to many biologists. CONCLUSIONS: Populous's contribution is in the knowledge gathering stage of ontology development; it separates knowledge gathering from the conceptualisation and axiomatisation, as well as separating the user from the standard ontology authoring environments. Populous is by no means a replacement for standard ontology editing tools, but instead provides a useful platform for engaging a wider community of scientists in the mass production of ontology content.

The Open Information Extraction Project is one of the most ambitious attempts in the area of automatically constructing ontologies by harvesting information from the web. What we will call their Know-It-All Ontology contains about 6 billion items, consisting of triples and rules. The downside of such automatically constructed ontologies is that they contain a vast number of errors: some arising from errors in the original web data and some from errors in extracting the data. In this project we explore whether techniques we have developed in the domain of ontology repair can be used to detect and correct some of these errors. In particular, we explore whether the errors in their ontology can be automatically detected by using a theorem prover. We also present a manual classification of the errors as a preliminary feasibility exploration, and discuss our future work towards automatically correcting the ontology based on the error classification.

euzenat2012a; International audience; Finding alignments between ontologies is a very important operation for ontology engineering. It allows for establishing links between ontologies, either to integrate them in an application or to relate developed ontologies to context. It is even more critical for networked ontologies. Incorrect alignments may lead to unwanted consequences throughout the whole network and incomplete alignments may fail to provide the expected consequences. Yet, there is no well established methodology available for matching ontologies. We propose methodological guidelines that build on previously disconnected results and experiences.

The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far. In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning. We present Seon, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time. For validation, we discuss three different approaches, which are backed by Seon and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization.

International audience; Despite existing methodologies in the field, most requirements engineers are poorly trained to define security requirements. This is due to a considerable lack of security knowledge. Some security ontologies have been proposed, but a gap still exists between the two fields of security requirement engineering and ontologies. This paper is a survey, it proposes an analysis and a typology of existing security ontologies and their use for requirements definition.

Background There are significant challenges associated with the building of ontologies for cell biology experiments including the large numbers of terms and their synonyms. These challenges make it difficult to simultaneously query data from multiple experiments or ontologies. If vocabulary terms were consistently used and reused across and within ontologies, queries would be possible through shared terms. One approach to achieving this is to strictly control the terms used in ontologies in the form of a pre-defined schema, but this approach limits the individual researcher's ability to create new terms when needed to describe new experiments. Results Here, we propose the use of a limited number of highly reusable common root terms, and rules for an experimentalist to locally expand terms by adding more specific terms under more general root terms to form specific new vocabulary hierarchies that can be used to build ontologies. We illustrate the application of the method to build vocabularies and a prototype database for cell images that uses a visual data-tree of terms to facilitate sophisticated queries based on a experimental parameters. We demonstrate how the terminology might be extended by adding new vocabulary terms into the hierarchy of terms in an evolving process. In this approach, image data and metadata are handled separately, so we also describe a robust file-naming scheme to unambiguously identify image and other files associated with each metadata value. The prototype database http://sbd.nist.gov/ consists of more than 2000 images of cells and benchmark materials, and 163 metadata terms that describe experimental details, including many details about cell culture and handling. Image files of interest can be retrieved, and their data can be compared, by choosing one or more relevant metadata values as search terms. Metadata values for any dataset can be compared with corresponding values of another dataset through logical operations. Conclusions Organizing metadata for cell imaging experiments under a framework of rules that include highly reused root terms will facilitate the addition of new terms into a vocabulary hierarchy and encourage the reuse of terms. These vocabulary hierarchies can be converted into XML schema or RDF graphs for displaying and querying, but this is not necessary for using it to annotate cell images. Vocabulary data trees from multiple experiments or laboratories can be aligned at the root terms to facilitate query development. This approach of developing vocabularies is compatible with the major advances in database technology and could be used for building the Semantic Web.

Part 4: Semantics and Environment; International audience; The provision of accurate, comprehensive and condensed information contained in distributed environmental information systems via public search interfaces raises several technological challenges. Our approach to tackle these challenges is based on a consequent use of ontologies. Starting with an analysis of requirements resulting from semantic search scenarios, we explain the advantages of using ontologies based on standards and aim to reuse and transform terminological systems available in the environmental domain into ontologies. We develop an architecture guided by the premise of exerting a minimum of influence on existing search infrastructures. As a consequence of using a (possibly large) number of ontologies, tools for ontology management are needed. A key argument for using ontologies is that nowadays – as an outcome of the Semantic Web initiative - very powerful processing tools are available. We elaborate ontology mapping as an example and outline how a comprehensive ontology management can be achieved.

This position paper presents an introduction to the special issue of Applied Ontology on “Beautiful Ontologies”. It discusses the relevance of considering the notion of beauty when building and evaluating ontologies, making a parallel with other disciplines such as physics and software engineering. It also discusses the difference between the beauty of an ontology and the notion of quality generally considered in ontology evaluation. Finally, it discusses examples included in the articles of the special issue, to show that a beautiful ontology can be seen as one that integrates contradictory requirements.

Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms an important cornerstone for data integration, automatic inference and knowledge discovery based on formal representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/ReasonableOntologies.

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them., Author Summary An ontology represents the concepts and their interrelation within a knowledge domain. Several ontologies have been developed in biomedicine, which provide standardized vocabularies to describe diseases, genes and gene products, physiological phenotypes, anatomical structures, and many other phenomena. Scientists use them to encode the results of complex experiments and observations and to perform integrative analysis to discover new knowledge. A remaining challenge in ontology development is how to evaluate an ontology's representation of knowledge within its scientific domain. Building on classic measures from information retrieval, we introduce a family of metrics including breadth and depth that capture the conceptual coverage and parsimony of an ontology. We test these measures using (1) four commonly used medical ontologies in relation to a corpus of medical documents and (2) seven popular English thesauri (ontologies of synonyms) with respect to text from medicine, news, and novels. Results demonstrate that both medical ontologies and English thesauri have a small overlap in concepts and relations. Our methods suggest efforts to tighten the fit between ontologies and biomedical knowledge.

Semantic alignment between ontologies is a crucial task for information integration. There are many ongoing efforts to develop matching systems implementing various alignment techniques but it is impossible to predict what strategy is most successful for an application domain or a given pair of ontologies. Very often the quality of the results could be improved by considering the specificities of the ontologies to be aligned. In this paper, we propose a pattern-based approach implemented in the TaxoMap Framework helping an engineer to refine mappings to take into account specific conventions used in ontologies. Experiments in the topographic field within the ANR (The French National Research Agency) project GeOnto show the usefulness of such an environment both for a domain expert and an engineer, especially when the number of mappings is very large.

BackgroundMany areas of biology are open to mathematical and computational modelling. The application of discrete, logical formalisms defines the field of biomedical ontologies. Ontologies have been put to many uses in bioinformatics. The most widespread is for description of entities about which data have been collected, allowing integration and analysis across multiple resources. There are now over 60 ontologies in active use, increasingly developed as large, international collaborations. There are, however, many opinions on how ontologies should be authored; that is, what is appropriate for representation. Recently, a common opinion has been the "realist" approach that places restrictions upon the style of modelling considered to be appropriate.Methodology/principal findingsHere, we use a number of case studies for describing the results of biological experiments. We investigate the ways in which these could be represented using both realist and non-realist approaches; we consider the limitations and advantages of each of these models.Conclusions/significanceFrom our analysis, we conclude that while realist principles may enable straight-forward modelling for some topics, there are crucial aspects of science and the phenomena it studies that do not fit into this approach; realism appears to be over-simplistic which, perversely, results in overly complex ontological models. We suggest that it is impossible to avoid compromise in modelling ontology; a clearer understanding of these compromises will better enable appropriate modelling, fulfilling the many needs for discrete mathematical models within computational biology.

As biology is becoming a data-driven discipline, ontologies become increasingly important for systematically capturing the existing knowledge. This essay discusses current trends and how ontologies can also be used for discovery.