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Endowing ubiquitous robots with cognitive capabilities for recognizing emotions, sentiments, affects, and moods of humans in their context is an important challenge, which requires sophisticated and novel approaches of emotion recognition. Most studies explore data-driven pattern recognition techniques that are generally highly dependent on learning data and insufficiently effective for emotion contextual recognition. In this article, a hybrid model-based emotion contextual recognition approach for cognitive assistance services in ubiquitous environments is proposed. This model is based on: 1) a hybrid-level fusion exploiting a multilayer perceptron (MLP) neural-network model and the possibilistic logic and 2) an expressive emotional knowledge representation and reasoning model to recognize nondirectly observable emotions; this model exploits jointly the emotion upper ontology (EmUO) and the n-ary ontology of events HTemp supported by the NKRL language. For validation purposes of the proposed approach, experiments were carried out using a YouTube dataset, and in a real-world scenario dedicated to the cognitive assistance of visitors in a smart devices showroom. Results demonstrated that the proposed multimodal emotion recognition model outperforms all baseline models. The real-world scenario corroborates the effectiveness of the proposed approach in terms of emotion contextual recognition and management and in the creation of emotion-based assistance services.

A domain-independent conceptual model that aims to be highly reusable across specific domain applications is provided by upper-level ontologies which usually describe abstract concepts. In this paper, the authors proposed Sup_Ont, a fundamental upper ontology. In this ontology, the structure of the universe shows the concept of reality that is defined to have an existence which is known as truth. The devised super ontology and hence the domain ontologies can be reused across applications because of the generalized representation scheme used that is an EHCPR. An extended hierarchical censored production rules (EHCPRs) system is a knowledge representation system for reasoning with real-life problems and a step towards a generalized representation system. An EHCPR is a unit of knowledge resulting in a knowledge base that shows modularity and hierarchy. Extended hierarchical censored production rules (EHCPRs) have been used to represent the knowledge in intelligent systems.

The importance of standardized operation of medical equipment is self-evident. An effective method is to construct the medical equipment domain ontology as the background knowledge base, and make judgments on the legality of the user’s operation sequence based on the ontology, and guide the user to legally operate the equipment. After analysis, medical device data mainly comes from internal sensors. If we want to build an ontology background knowledge base, we can combine data with user operations, map the ontology to events, and build a directed graph model through events to form the final ontology. In this paper, we take the atomized particle preparation device in the field of medical equipment as an example, and selects and constructs the ontology by analyzing the internal structure and use process of the device.The application-level ontology selects the SSN ontology in the sensor field, and the upper ontology selects the general event ontology SEM. Combining the above two ontology and the characteristics of the medical device field, the ontology SSN_SEM is constructed, and the actual data of the device is semantically annotated through the SSN_SEM ontology, which verifies the validity of the ontology.

Building an ontology is a difficult and time- consuming task. In order to make this task easier and faster, some automatic methods can be employed. This paper examines the feasibility of using rules and concepts discovered during the classification tree building process in the C4.5 algorithm, in a completely automated way, for the purposes of building an ontology from data. By building the ontology directly from continuous data, concepts and relations can be discovered without specific knowledge about the domain. This paper also examines how this method reproduces the classification capabilities of the classification three within an ontology using concepts and class expression axioms.

The flexible finite element analysis (FEA) modeling process is addressed within the framework of scripting programming language such as ANSYS Parametric Design Language(APDL). Resorting to recently proposed upper ontology and specific ontology, the FEA modeling processes are expressed as the entities and relations among entities in an ontology tree. An algorithm to obtain a script fragment grammar (SFG) that describe the combination rules of script fragments by instantiating FEA ontology tree is provided. In addition, a method of automatically generating FEA parametric scripts based on SFG derivations is proposed. Then, the whole procedure is applied to develop an automatically generating FEA script software for an air separator product family. The proposed method can effectively reduce repetitive operations in the parametric FEA modeling process, which thereby improves the overall efficiency of the FEA modeling process.

In recent years, there has been a growing interest in modeling value in the context of Enterprise Architecture, which has been driven by a need to align the vision and strategic goals of an enterprise with its business architecture. Nevertheless, the current literature shows that the concept of value is conceptually complex and still causes a lot of confusion. For example, we can find in the literature the concept of value being taken as equivalent to notions as disparate as goals, events, objects and capabilities. As a result, there is still a lack of proper support for modeling all aspects of value as well as its relations to these aforementioned notions. To address this issue, we propose in this paper a pattern language for value modeling in ArchiMate, which is based on the Common Ontology of Value and Risk, a well-founded reference ontology developed following the principles of the Unified Foundation Ontology. This enables us to delineate a clear ontological foundation, which addresses the ambiguous use of the value concept. The design of the Value Pattern Language will be guided by the Design Science Research Methodology. More specifically, a first iteration of the build-and-evaluate loop is presented, which includes the development of the pattern language and its demonstration by means of a case study of a low-cost airline.

The relationship between cultural heritage, digital technologies and visual models involves an increasingly wide area of research, oriented towards the renewal of archives and museums for the preservation and promotion of culture. Recent research activities are the result of the progressive strengthening of digital technologies and the needs of a new generation of “digital” users, which requires museums to update their means of communication using Semantic Web languages and technologies shaped by a social conceptualization of a graph-based representation of information.The growth of several digitized heritage collections increases the necessity of proper methodologies to develop a structured system able to access to these collections and the large amount of data, metadata and paradata related to the digitized objects in a structured and organized way, defining a set of collection information models (CIM), that considers not only the digitizing process but also the data collection process, layered by an Upper Ontology level structure, based on CIDOC-CRM.

© 2013 IEEE. The Centre on Persuasive Systems for Wise Adaptive Living (PERSWADE) aims at developing and applying persuasive technologies and system science for social innovation that can help humanity to move toward sustainable, wise, adaptive living. The PERSWADE collaborative knowledge base needs to be designed with the intent to bring together, enrich and logically relate heterogeneous content, such as datasets, scientific literature and any kind of multimedia and social content, to support a participatory approach and help to translate science into action. PERSWADE-CORE, the foundation ontology described in this paper, plays a critical role in this by providing the backbone semantic infrastructure to enable collaboration through efficient data and knowledge integration, sharing and reuse. It also serves the purpose of clarifying and explaining the goals, functions and operations of the Centre. Because of its purpose, PERSWADE-CORE has been designed to be easy-to-use and easy-to-adapt by allowing generic, as well as more specific, relationships among concepts. The PERSWADE approach prioritizes interoperability and relies on the Semantic Web infrastructure. Furthermore, its design is intrinsically aimed at collaborative environments in which ontologies are expected to evolve as a response to users' activity.

The Allotrope Foundation (AF) is a group of pharmaceutical, device vendor, and software companies that develops and releases technologies [the Allotrope Data Format (ADF), the Allotrope Foundation Ontology (AFO), and the Allotrope Data Models (ADM)] to simplify the exchange of electronic data. We present here the first comprehensive history of the AF, its structure, a list of members and partners, and an introduction to the technologies. Finally, we provide current insights into the adoption and development of the technologies by summarizing the Fall 2020 Allotrope Connect virtual conference. This overview provides an easy access to the AF and highlights opportunities for collaboration.

Fuzzy ontology is a generalization of crisp ontology for modeling uncertain information and has been applied in recent years for supporting different activities of semantic web. However, there are great collections of crisp ontologies developed so far in various domains which are not appropriate for decision making in fuzzy environment. Accordingly, this paper aims at presenting an approach to automatically convert a crisp ontology to fuzzy ontology in the context of social networks. Furthermore, this paper demonstrates that the combination of a learning process of crisp ontology with proposed approach, decreases computational complexity of fuzzy ontology learning due to breaking the task to two optimal steps. Accordingly, the approach allows for an advantageous application of various crisp clustering techniques in fuzzy ontology context.

The dramatic increase in the use of knowledge discovery applications requires end users to write complex database search requests to retrieve information. Such users are not only expected to grasp the structural complexity of complex databases but also the semantic relationships between data stored in databases. In order to overcome such difficulties, researchers have been focusing on knowledge representation and interactive query generation through ontologies, with particular emphasis on improving the interface between data and search requests in order to bring the result sets closer to users research requirements. This paper discusses ontology-based information retrieval approaches and techniques by taking into consideration the aspects of ontology modelling, processing and the translation of ontological knowledge into database search requests. It also extensively compares the existing ontology-to-database transformation and mapping approaches in terms of loss of data and semantics, structural mapping and domain knowledge applicability. The research outcomes, recommendations and future challenges presented in this paper can bridge the gap between ontology and relational models to generate precise search requests using ontologies. Moreover, the comparison presented between various ontology-based information retrieval, database-to-ontology transformations and ontology-to-database mappings approaches provides a reference for enhancing the searching capabilities of massively loaded information management systems. Keywords: Information systems, Ontology, Domain knowledge, Database, Information retrieval, Knowledge management

Current urban and district energy management systems lack a common semantic referential for e�ectively interrelating intelligent sensing, data models and energy models with visualization, analysis and decision support tools. This paper describes the structure, as well as the rationale that led to this structure, of an ontology that captures the real-world concepts of a district energy system, such as a district heating and cooling system. This ontology (called eedistrict ontology) is intended to support knowledge provision that can play the role of an intermediate layer between high-level energy management software applications and local monitoring and control software components. In order to achieve that goal, the authors propose to encapsulate queries to the ontology in a scalable web service, which will facilitate the development of interfaces for third-party applications. Considering the size of the ee-district ontology once populated with data from a speci�c district case study, this could prove to be a repetitive and time-consuming task for the software developer. This paper therefore assesses the feasibility of ontology-driven automation of web service development that is to be a core element in the deployment of heterogeneous district-wide energy management software.

With the explosive growth of various social media applications, individuals and organizations are increasingly using their contents (e.g. reviews, forum discussions, blogs, micro-blogs, comments, and postings in social network sites) for decision-making. These contents are typical big data. Opinion mining or sentiment analysis focuses on how to extract emotional semantics from these big data to help users to get a better decision. That is not an easy task, because it faces many problems, such as different context may make the meaning of the same word change variously, at the same time multilingual environment restricts the full use of the analysis results. Ontology provides knowledge about specific domains that are understandable by both the computers and developers. Building ontology is mainly a useful first step in providing and formalizing the semantics of information representation. We proposed an ontology DEMLOnto based on six basic emotions to help users to share existed information. The ont...

PaaS is a Cloud computing service that provides a computing platform to develop, run, and manage applications without the complexity of infrastructure maintenance. SMEs are reluctant to enter the growing PaaS market due to the possibility of being locked in to a certain platform, mostly provided by the market's giants. The PaaSport Marketplace aims to avoid the provider lock-in problem by allowing Platform provider SMEs to roll out semantically interoperable PaaS offerings and Software SMEs to deploy or migrate their applications on the best-matching offering, through a thin, non-intrusive Cloud broker. In this paper, we present the PaaSport semantic model, namely an OWL ontology, extension of the DUL ontology. The ontology is used for semantically representing (a) PaaS offering capabilities and (b) requirements of applications to be deployed. The ontology has been designed to optimally support a semantic matchmaking and ranking algorithm that recommends the best-matching PaaS offering to the application developer. The DUL ontology offers seamless extensibility, since both PaaS Characteristics and parameters are defined as classes; therefore, extending the ontology with new characteristics and parameters requires the addition of new specialized subclasses of the already existing classes, which is less complicated than adding ontology properties. The PaaSport ontology is evaluated through verification tools, competency questions, human experts, application tasks and query performance tests.

Aristotle was the founder not only logics but also of ontology which he describes in Metaphysics and Categories as a theory of general properties of all entities and categorical aspects they should be analyzed. Meanwhile it is commonly accepted that we inherited from him not one but two different logics: early dialectical logoi of Topics and later formal syllogistic of Prior Analytics. The last considers logics the same way as the modern symbolic logic do. According to J. Bochenski the symbolic logic is “a theory of general objects” (by apt turn in phrase, a "physics of the object in general”) hence logics, as it is interpreted now, has the same subject as ontology. But does Aristotle himself counts that ontology (as it is accepted to speak now) is just a kind of “prolegomenon” to logic? In the paper some aspects of this issue are studied at length.

The correlated exploitation of disparate and heterogeneous data sources is important to the efficacy of many analytics tasks. Currently in application domains of major interest, such as in the maritime and aviation domains, available technology provides real time surveillance data from moving entities, which together with archival static data, can be processed in an integrated way to detect complex events and support decision making. The variety of data in disparate sources, the heterogeneity of data formats, as well as the volume of data, make data retrieval, integration, and especially reasoning with these data, challenging tasks. This paper presents an ontology-based distributed framework that addresses conjunctively these challenges: Data retrieval, integration and reasoning with data from heterogeneous static or regularly updated data sources. The proposed OBDAIR framework provides the means to support building scalable data-driven domain-specific applications that support decision-making and problem-solving. This is achieved by processing large volumes of heterogeneous data close to the sources, supporting knowledge generation in a distributed/decentralized but still unified manner. OBDAIR integrates modular ontology representation frameworks and ontology-based data access frameworks: This article presents an instantiation of OBDAIR using the modular ontology representation framework E − SHIQ , and the Ontop ontology-based access system. This OBDAIR instance has been evaluated at recognising important complex events in the maritime domain using real-world data. Experiments show the potential of OBDAIR to detect complex events in large geographic areas with computational efficiency.

The application of emerging technologies of Internet of Things (IoT) and cloud computing have increasing the popularity of smart homes, along with which, large volumes of heterogeneous data have been generating by home entities. The representation, management and application of the continuously increasing amounts of heterogeneous data in the smart home data space have been critical challenges to the further development of smart home industry. To this end, a scheme for ontology-based data semantic management and application is proposed in this paper. Based on a smart home system model abstracted from the perspective of implementing users’ household operations, a general domain ontology model is designed by defining the correlative concepts, and a logical data semantic fusion model is designed accordingly. Subsequently, to achieve high-efficiency ontology data query and update in the implementation of the data semantic fusion model, a relational-database-based ontology data decomposition storage method is developed by thoroughly investigating existing storage modes, and the performance is demonstrated using a group of elaborated ontology data query and update operations. Comprehensively utilizing the stated achievements, ontology-based semantic reasoning with a specially designed semantic matching rule is studied as well in this work in an attempt to provide accurate and personalized home services, and the efficiency is demonstrated through experiments conducted on the developed testing system for user behavior reasoning.

A domain-specific ontology for IT benchmarking has been developed to bridge the gap between a systematic characterization of IT services and their data-based valuation. Since information is generally collected during a benchmark exercise using questionnaires on a broad range of topics, such as employee costs, software licensing costs, and quantities of hardware, it is commonly stored as natural language text; thus, this information is stored in an intrinsically unstructured form. Although these data form the basis for identifying potentials for IT cost reductions, neither a uniform description of any measured parameters nor the relationship between such parameters exists. Hence, this work proposes an ontology for the domain of IT benchmarking, available at https://w3id.org/bmontology. The design of this ontology is based on requirements mainly elicited from a domain analysis, which considers analyzing documents and interviews with representatives from Small- and Medium-Sized Enterprises and Information and Communications Technology companies over the last eight years. The development of the ontology and its main concepts is described in detail (i.e., the conceptualization of benchmarking events, questionnaires, IT services, indicators and their values) together with its alignment with the DOLCE-UltraLite foundational ontology.

This paper aims to develop an ontology (AFPS-Onto) which formalizes the knowledge of active fall protection system (AFPS) design, with attempt to facilitate knowledge sharing and reuse. METHONTOLOGY was adopted as a method to build the AFPS-Onto. The AFPS-Onto consists of nine core concepts: hazard, actor, task, ifc building element, construction method, constraint, safety resource, hazard control measure, and residual risk. The concepts, relations, attributes, and axioms were coded using Protege. The ontology was evaluated through automated consistency checking, criteria-based and task-based evaluation. The AFPS-Onto fills the knowledge gap by providing a formal and shared vocabulary for the domain of AFPS design. This can promote knowledge reuse and sharing among professional engineers. In addition, the ontology can be used to develop knowledge-based systems to help design effective AFPS. Future effort can be made to develop ontologies of other control measures against fall from heights and combine them into a fall from heights ontology (FFH-Onto).

Ontologies have become a popular means of knowledge sharing and reuse. This has motivated development of large independent ontologies within the same or different domains with some overlapping information among them. In order to match such large ontologies, automatic matchers become an inevitable solution. This work explores the use of a predictive statistical model to establish an alignment between two input ontologies. We demonstrate how to integrate ontology partitioning and parallelism in the ontology matching process in order to make the statistical predictive model scalable to large ontology matching tasks. Unlike most ontology matching tools which establish 1:1 cardinality mappings, our statistical model generates one-to-many cardinality mappings.

While several studies in the last decade explore the potential benefits of virtual worlds in education settings, less attention has been given to the research of solutions to help overcome implementation barriers. One of the existing areas of concern is related to the difficulties on the exploitation of data obtained from educational virtual worlds. This paper proposes an OWL-based ontology to address a solution to the problem of inconsistency of databases that record information about student interactions with learning objects within these environments. The steps that have been followed for the development of the ontology are described, guided by Stanford’s 101 model. To discuss the feasibility and exemplify the ontology, an instance of an existing virtual world interaction is presented. The conclusion is that the proposed ontology can be helpful to researchers and development groups as it delivers a reusable model to gather data in a uniform way.

The number of available legal documents has presented an enormous growth in recent years, and the digital processing of such materials is prompting the necessity of systems that support the automatic relevant information extraction. This work presents a system for ontology-based information extraction from natural language texts, able to identify a set of legal events. The system is based on an innovative methodology based on domain ontology of legal events and a set of linguistic rules, integrated through inference mechanism, resulting in a flexible approach and scalable approach. A case study with the use of documents from the Superior Court in Brazil is related, with satisfactory results in precision and recall.

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.

International audience; Objective: Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as SPARQL) and APIs (such as OWLAPI) are not as easy-to-use as object programming languages are. Moreover, they provide few solutions to difficulties encountered with biomedical ontologies. Our objective was to design a tool for accessing easily the entities of an OWL ontology, with high-level constructs helping with biomedical ontologies. Methods: From our experience on medical ontologies, we identified two difficulties: (1) many entities are represented by classes (rather than individuals), but the existing tools do not permit manipulating classes as easily as individuals, (2) ontologies rely on the open-world assumption, whereas the medical reasoning must consider only evidence-based medical knowledge as true. We designed a Python module for ontology-oriented programming. It allows access to the entities of an OWL ontology as if they were objects in the programming language. We propose a simple high-level syntax for managing classes and the associated " role-filler " constraints. We also propose an algorithm for performing local closed world reasoning in simple situations. Results: We developed Owlready, a Python module for a high-level access to OWL ontologies. The paper describes the architecture and the syntax of the module version 2. It details how we integrated the OWL ontology model with the Python object model. The paper provides examples based on Gene Ontology (GO). We also demonstrate the interest of Owlready in a use case focused on the automatic comparison of the contraindications of several drugs. This use case illustrates the use of the specific syntax proposed for manipulating classes and for performing local closed world reasoning. Conclusion: Owlready has been successfully used in a medical research project. It has been published as Open-Source software and then used by many other researchers. Future developments will focus on the support of vagueness and additional non-monotonic reasoning feature, and automatic dialog box generation.

An ontology is a conceptualisation of domain knowledge. It is employed in semantic web services technologies to describe the meanings of services so that they can be dynamically searched for and composed according to their meanings. It is essential for dynamic service discovery, composition, and invocation. Whether an ontology is well constructed has a tremendous impact on the accuracy of the semantic description of a web service, the complexity of the semantic definitions, the efficiency of processing messages passed between services, and the precision and recall rates of service retrieval from service registrations. However, measuring the quality of an ontology remains an open problem. Works on the evaluation of ontologies do exist, but they are not in the context of semantic web services. This paper addresses this problem by proposing a quality model of ontology and defining a set of metrics that enables the quality of an ontology to be measured objectively and quantitatively in the context of semantic descriptions of web services. These metrics cover the contents, presentation, and usage aspects of ontologies. The paper also presents a tool that implements these metrics and reports a case study on five real-life examples of web services.

Knowledge-Intensive Case-Based Reasoning Systems (KI-CBR) mainly depend on ontologies. Ontology can play the role of case-base knowledge. The combination of ontology and fuzzy logic reasoning is critical in the medical domain. Case-base representation based on fuzzy ontology is expected to enhance the semantic and storage of CBR knowledge-base. This paper provides an advancement to the research of diabetes diagnosis CBR by proposing a novel case-base fuzzy OWL2 ontology (CBRDiabOnto). This ontology can be considered as the first fuzzy case-base ontology in the medical domain. It is based on a case-base fuzzy Extended Entity Relation (EER) data model. It contains 63 (fuzzy) classes, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, and 105 fuzzy datatypes. We populated the ontology with 60 cases and used SPARQL-DL for its query. The evaluation of CBRDiabOnto shows that it is accurate, consistent, and cover terminologies and logic of diabetes mellitus diagnosis.

An ontology is defined as an explicit specification of a conceptualization, which is an important tool for modeling, sharing and reuse of domain knowledge. However, ontology construction by hand is a complex and a time consuming task. This research presents a fully automatic method to build bilingual domain ontology from text corpora and ontology design patterns (ODPs) in Alzheimer’s disease. This method combines two approaches: ontology learning from texts and matching with ODPs. It consists of six steps: (i) Term & relation extraction (ii) Matching with Alzheimer glossary (iii) Matching with ontology design patterns (iv) Score computation similarity term & relation with ODPs (v) Ontology building (vi) Ontology evaluation. The result of ontology composed of 381 terms and 184 relations with 200 new terms and 42 new relations were added. Fully automatic ontology construction has higher complexity, shorter time and reduces role of the expert knowledge to evaluate ontology than manual ontology construction. This proposed method is sufficiently flexible to be applied to other domains.

In the domain of IT benchmarking (ITBM), a variety of data and information are collected. Although these data serve as the basis for business analyses, no unified semantic representation of such data yet exists. Consequently, data analysis across different distributed data sets and different benchmarks is almost impossible. This paper presents a system architecture and prototypical implementation for an integrated data management of distributed databases based on a domain-specific ontology. To preserve the semantic meaning of the data, the ITBM ontology is linked to data sources and functions as the central concept for database access. Thus, additional databases can be integrated by linking them to this domain-specific ontology and are directly available for further business analyses. Moreover, the web-based system supports the process of mapping ontology concepts to external databases by introducing a semi-automatic mapping recommender and by visualizing possible mapping candidates. The system al...

Building proper reference ontologies is a hard task. There are a number of methods and tools that traditionally have been used to support this task. These include the use of foundational theories, the reuse of domain and core ontologies, the adoption of development methods, as well as the support of proper software tools. In this context, an approach that has gained increasing attention in recent years is the systematic application of ontology patterns . However, a pattern-based approach to ontology engineering requires: the existence of a set of suitable patterns that can be reused in the construction of new ontologies; a proper methodological support for eliciting these patterns, as well as for applying them in the construction of these new models. The goal of this paper is twofold: (i) firstly, we present an approach for deriving conceptual ontology patterns from ontologies. These patterns are derived from ontologies of different generality levels, ranging from foundational to domain ontologies; (ii) secondly, we present guidelines that describe how these patterns can be applied in combination for building reference domain ontologies in a reuse-oriented process. In summary, this paper is about the construction of ontology patterns from ontologies, as well as the construction of ontologies from ontology patterns.

An ontology-based approach for thermal energy management of eco-industrial park is proposed in this paper. The ontology captures core concepts of this domain as well as the relationships between them; together with instances, the ontology can serve as knowledge base for related energy management tools. The development of this application ontology is based on existing knowledge base, e.g. OntoCAPE in this paper. The advantages of ontology-based approach are shown by an application case, the case shows how ontology can overcome data heterogeneity both structurally and semantically through its own reasoning ability, then allow intelligent decision making by using disparate data from remote databases, which implies the possibility of self-optimization without human intervention in the future scenario. With properly designed cyber-infrastructure systems, the prospective application of the ontology-based approach can unleash the potential of artificial intelligence for eco-industrial park thermal energy management in the future. NRF (Natl Research Foundation, S’pore) Published version

Background Research for ontology evaluation is scarce. If biomedical ontological datasets and knowledgebases are to be widely used, there needs to be quality control and evaluation for the content and structure of the ontology. This paper introduces how to effectively utilize a semiotic-inspired approach to ontology evaluation, specifically towards drug-related ontologies hosted on the National Center for Biomedical Ontology BioPortal. Results Using the semiotic-based evaluation framework for drug-based ontologies, we adjusted the quality metrics based on the semiotic features of drug ontologies. Then, we compared the quality scores before and after tailoring. The scores revealed a more precise measurement and a closer distribution compared to the before-tailoring. Conclusion The results of this study reveal that a tailored semiotic evaluation produced a more meaningful and accurate assessment of drug-based ontologies, lending to the possible usefulness of semiotics in ontology evaluation.

Several satellite data receiving and distributing centers across the world support data storage, processing, and retrieval based on satellite, sensor, product, latitude, longitude, date and time, etc. These systems address queries on satellite products that are mostly high-level concepts. A more sophisticated retrieval system that supports ontological concepts, subconcepts, and concept hierarchical queries delivers refined results that broaden the scientific horizon of the application domain. To achieve this, the current research designed and implemented an ontology concept-based satellite data management and retrieval methodology. This enhances the performance of the satellite data retrieval system and supports semantic queries. The performance of the retrieval system depends upon the strategy followed to maintain domain ontologies and satellite data instances. Three ontology-based satellite data management strategies are discussed, and their performance was evaluated by taking real and benchmark metrics. A semantic query set of 25 queries was chosen covering various concepts, subconcepts, and concept hierarchical-related queries that involve various SPARQL query constructs. The test bed is taken from real-time satellite data received from Kalpana-1 of various sizes of triple stores.

During the past years, ontologies are widely used for representing knowledge of complex domains. Despite that the ontologies (classical ontologies) have become standard for representing knowledge; however, they are not able to represent and reason with uncertainty which is one of the characteristics of the world that must be handled. Probabilistic Ontologies have come to remedy this defect. This paper is part of this framework in which the authors have proposed a new method of probabilistic ontology construction, named Prob-Ont, by integrating uncertainty to elements of OWL ontology (especially to instances and/or relations). As a case study, the authors have constructed a probabilistic ontology for the domain of scientific documentation system (dblp).

For over twenty-five years Ford Motor Company has been utilizing an AI-based system to manage process planning for vehicle assembly at its assembly plants around the world. The scope of the AI system, known originally as the Direct Labor Management System and now as the Global Study Process Allocation System (GSPAS), has increased over the years to include additional functionality on Ergonomics and Powertrain Assembly (Engines and Transmission plants). The knowledge about Ford’s manufacturing processes is contained in an ontology originally developed using the KL-ONE representation language and methodology. To preserve the viability of the GSPAS ontology and to make it easily usable for other applications within Ford, we needed to re-engineer and convert the KL-ONE ontology into a semantic web OWL/RDF format. In this article, we will discuss the process by which we re-engineered the existing GSPAS KL-ONE ontology and deployed semantic web technology in our application.

The task of building an ontology from a textual corpus starts with the conceptualization phase, which extracts ontology concepts. These concepts are linked by semantic relationships. In this paper, we describe an approach to the construction of an ontology from an Arabic textual corpus, starting first with the collection and preparation of the corpus through normalization, removing stop words and stemming; then, to extract terms of our ontology, a statistical method for extracting simple and complex terms, called "the repeated segments method" are applied. To select segments with sufficient weight we apply the weighting method term frequency---inverse document frequency (TF---IDF), and to link these terms by semantic relationships we apply an automatic method of learning linguistic markers from text. This method requires a dataset of relationship pairs, which are extracted from two external resources: an Arabic dictionary of synonyms and antonyms and the lexical database Arabic WordNet. Finally, we present the results of our experimentation using our textual corpus. The evaluation of our approach shows encouraging results in terms of recall and precision.

International audience; Context: Recent research discusses the use of ontologies, dictionaries and thesaurus as a means to improve activity labels of process models. However, the trade-off between quality improvement and extra effort is still an open question. It is suspected that ontology-based support could require additional effort for the modeler. Objective: In this paper, we investigate to which degree ontology-based support potentially increases the effort of modeling. We develop a theoretical perspective grounded in cognitive psychology, which leads us to the definition of three design principles for appropriate ontology-based support. The objective is to evaluate the design principles through empirical experimentation. Method: We tested the effect of presenting relevant content from the ontology to the modeler by means of a quantitative analysis. We performed controlled experiments using a prototype, which generates a simplified and context-aware visual representation of the ontology. It logs every action of the process modeler for analysis. The experiment refers to novice modelers and was performed as between-subject design with vs. without ontology-based support. It was carried out with two different samples. Results: Part of the effort-related variables we measured showed significant statistical difference between the group with and without ontology-based support. Overall, for the collected data, the ontology support achieved good results. Conclusion: We conclude that it is feasible to provide ontology-based support to the modeler in order to improve process modeling without strongly compromising time consumption and cognitive effort

Ontology structure, a core of semantic web is an excellent tool for knowledge representation and semantic visualization. Moreover, knowledge reuse is made possible through similarity measure estimation between two ontologies, threshold estimation and use of simple if-then rules for checking relevancy and irrelevancy measures. Reduced semantic representations of the ontology provide reduced knowledge visualization which is critical especially for e-health data processing and analysis. This usually occurs due to the presence of implicit knowledge and polymorphic objects and can be made semantically rich through the construction by resolving this implicit knowledge occurring in the form of non-dominant words and conditional dependence actions. This paper presents the working of the automated framework for the construction of semantic rich ontology structures and store in the repository. This construction uses dyadic deontic logic based Graph Derivation Representation in order to construct semantically rich ontologies. Moreover, in order to retrieve a set of relevant documents in response to the cloud user document, the degree of similarity between two ontologies is estimated using the traditional cosine similarity measure and simple if-then rules are used to determine the number of relevant documents and obtain such document's metadata for further processing. These working modules will be extremely beneficial to the authenticated cloud users for document retrieval, information extraction and domain dictionary construction which are especially used for e-health applications. The proposed framework is implemented using diabetes dataset and the effectiveness of the experimental results is high when compared to other Graph Derivation Representation methods. The graphical results shown in the paper is an added visualization for viewing the performance of the proposed framework. Keywords: Ontology, Implicit knowledge, Conditional dependence, Graph derivation representation, Dyadic deontic logic, Cosine similarity

Provenance is becoming increasingly important as more and more people are using data that they themselves did not generate. In the last decade, significant efforts have been directed toward developing generic, shared data provenance ontologies that support the interoperability of provenance across systems. An issue that is impeding the use of such provenance ontologies is that a generic provenance ontology, no matter how complete it is, is insufficient for capturing the diverse, complex provenance requirements in different domains. In this paper, the authors propose a novel approach to adapting and extending the W7 model, a well-known generic ontology of data provenance. Relying on various knowledge expansion mechanisms provided by the Conceptual Graph formalism, the authors' approach enables us to develop domain ontologies of provenance in a disciplined yet flexible way.

The ongoing research in the field of computer ontologies, ontological engineering, decision-making systems as well as an emerging mutual interest for philosophic, physiological and linguistic aspects of ontology of designing among humanitarian and technical scientific communities have stipulated the need for further distinction of the boundaries of the researched scientific discipline. This paper continues and develops the ideas, presented by the author in the article titled "Scientific basis of ontology of designing", published in the first issue of the journal in 2013. This work attempts to further outline the research area of the journal by elaborating the scientific problematic of the journal, making focuses and placing accents, relevant at the current stage of the discipline’s development, to distinguish the place of the ontology of designing amongst the current well-established but ever-changing differentiation of sciences. The article presents the results of analysis performed on the key concepts of ontology of designing based on the scientometric resources of network origin. It is shown, that the conventional segregation of scientific disciplines was largely determined by their development, accumulated experience, traditions, cultural features of their genesis, linguistic and conceptual content. Ontology of designing is still considered to be an integrative scientific discipline based on methods of system and ontological analysis, information technologies, computer modeling, artificial intelligence, descriptive logic, CAD systems and decision-making support systems, data and knowledge bases.