Search

Probabilistic model checking (PMC) is a well-established method for the quantitative analysis of state based operational models such as Markov decision processes. Description logics (DLs) provide a well-suited formalism to describe and reason about knowledge and are used as basis for the web ontology language (OWL). We investigate how such knowledge described by DLs can be integrated into the PMC process, introducingontology-mediatedPMC. Specifically, we proposeontologized programsas a formalism that links ontologies to behaviors specified by probabilistic guarded commands, the de-facto standard input formalism for PMC tools such as Prism. Through DL reasoning, inconsistent states in the modeled system can be detected. We present three ways to resolve these inconsistencies, leading to different Markov decision process semantics. We analyze the computational complexity of checking whether an ontologized program is consistent under these semantics. Further, we present and implement a technique for the quantitative analysis of ontologized programs relying on standard DL reasoning and PMC tools. This way, we enable the application of PMC techniques to analyze knowledge-intensive systems.We evaluate our approach and implementation on amulti-server systemcase study,where different DL ontologies are used to provide specifications of different server platforms and situations the system is executed in.

A traceability system can quickly and accurately query product supply chain circulation information. It is difficult to obtain processing information and achieve sharing due to the big data, long duration, large amount of equipment and scattered and easily lost processing information. In tracing the application of the case-based reasoning (CBR) to product processing, we can solve the problem of establishing an accurate and complete mathematical model between the processing information and product quality defects by finding a target record in a large amount of historical data. The traceability model of ontology and CBR uses the web ontology language (OWL) for case representation to simplify the CBR framework for big data, classifies process data and standardizes information storage. A joint optimization algorithm based on the genetic algorithm (GA) and CBR is proposed to establish an optimization model of process traceability, which is applied to case retrieval and reuse. This algorithm optimizes the genetic operator by combining the optimal preservation strategy and roulette selection method and uses an exponential-scale transformation method to stretch the fitness function. The experiments show that the optimized traceability model can infer information from garbled codes, wrong codes and missing messages to quickly determine the problematic products, thus effectively improving the traceability accuracy of product processing.

Ontology-mediated querying (OMQ) provides a paradigm for query answering according to which users not only query records at the database but also query implicit information inferred from ontology. A key challenge in OMQ is that the implicit information may be infinite, which cannot be stored at the database and queried by off -the -shelf query engine. The commonly adopted technique to deal with infinite entailments is query rewriting, which, however, comes at the cost of query rewriting at runtime. In this work, the partial materialization method is proposed to ensure that the extension is always finite. The partial materialization technology does not rewrite query but instead computes partial consequences entailed by ontology before the online query. Besides, a query analysis algorithm is designed to ensure the completeness of querying rooted and Boolean conjunctive queries over partial materialization. We also soundly and incompletely expand our method to support highly expressive ontology language, OWL 2 DL. Finally, we further optimize the materialization efficiency by role rewriting algorithm and implement our approach as a prototype system SUMA by integrating off-the-shelf efficient SPARQL query engine. The experiments show that SUMA is complete on each test ontology and each test query, which is the same as Pellet and outperforms PAGOdA. Besides, SUMA is highly scalable on large datasets.

User-friendly formal specification and verification of concurrent and distributed systems for various domains, such as automatic control systems, telecommunications, and business processes, are active research topics due to their practical significance. In this paper, we present methods of developing verification-oriented domain-specific process ontologies used to describe concurrent systems of subject domains. One of the advantages of such ontologies is their formal semantics, which provides formal verification of the described systems. Our method is based on an abstract verification-oriented process ontology. We use two methods of domain specialization of the abstract process ontology. The declarative method relies on specializing classes of the original ontology, introducing new declarative classes, and using a new set of axioms to set restrictions on classes and relations of the abstract ontology. The constructive method uses semantic markup and pattern matching techniques to link domain concepts to classes of the abstract process ontology. We provide detailed ontological specifications for these techniques. Our methods preserve the formal semantics of the original process ontology; therefore, formal verification methods can be applied to the resulting domain-specific process ontologies. We demonstrate that the constructive method is a refined version of the declarative method. We illustrate our methods on the example of constructing an ontology for standard elements of automatic control systems: we develop declarative descriptions of the classes and restrictions of the domain-specific ontology in the Protege system in the Web Ontology Language (OWL) using inference rules written in the Semantic Web Rule Language (SWRL) and construct a system of semantic markup patterns that implements standard elements of automatic control systems.

The project “Semantic Technologies for Situation Awareness” was concerned with detecting certain critical situations from data obtained by observing a complex hard- and software system, in order to trigger actions that allow this system to save energy. The general idea was to formalize situations as ontology-mediated queries, but in order to express the relevant situations, both the employed ontology language and the query language had to be extended. In this paper we sketch the general approach and then concentrate on reporting the formal results obtained for reasoning in these extensions, but do not describe the application that triggered these extensions in detail.

International audience; Two main families of ontology languages are considered in the context of data access, namely Horn description logics and existential rules. In this paper, we review the semantic relationships between these families in the light of the ontology-mediated query answering problem. To this end, we rely on the standard translation of description logics in first-order logic and on the notion of semantic emulation. We focus on description logics and classes of existential rules for which the con-junctive query answering problem has polynomial data complexity.

In the context of the Description Logic DL-Liteℛ≠, i.e., DL-Liteℛ without UNA and with inequality axioms, we address the problem of adding to unions of conjunctive queries (UCQs) one of the simplest forms of negation, namely, inequality. It is well known that answering conjunctive queries with unrestricted inequalities over DL-Liteℛ ontologies is in general undecidable. Therefore, we explore two strategies for recovering decidability, and, hopefully, tractability. Firstly, we weaken the ontology language, and consider the variant of DL-Liteℛ≠ corresponding to rdfs enriched with both inequality and disjointness axioms. Secondly, we weaken the query language, by preventing inequalities to be applied to existentially quantified variables, thus obtaining the class of queries named UCQ≠,bs. We prove that in the two cases, query answering is decidable, and we provide tight complexity bounds for the problem, both for data and combined complexity. Notably, the results show that answering UCQ≠,bs over DL-Liteℛ≠ ontologies is still in AC0 in data complexity.

An ontology language for ontology mediated query answering (OMQA-language) is universal for a family of OMQA-languages if it is the most expressive one among this family. In this paper, we focus on three families of tractable OMQA-languages, including first-order rewritable languages and languages whose data complexity of the query answering is in AC0 or PTIME. On the negative side, we prove that there is, in general, no universal language for each of these families of languages. On the positive side, we propose a novel property, the locality, to approximate the first-order rewritability, and show that there exists a language of disjunctive embedded dependencies that is universal for the family of OMQA-languages with locality. All of these results apply to OMQA with query languages such as conjunctive queries, unions of conjunctive queries and acyclic conjunctive queries., Comment: 10 pages, 1 figure, the full version of a paper accepted for AAAI 2020. Some typos have been corrected

We present a machine-readable movement writing for sleight-of-hand moves with cards -- a "Labanotation of card magic." This scheme of movement writing contains 440 categories of motion, and appears to taxonomize all card sleights that have appeared in over 1500 publications. The movement writing is axiomatized in $\mathcal{SROIQ}$(D) Description Logic, and collected formally as an Ontology of Card Sleights, a computational ontology that extends the Basic Formal Ontology and the Information Artifact Ontology. The Ontology of Card Sleights is implemented in OWL DL, a Description Logic fragment of the Web Ontology Language. While ontologies have historically been used to classify at a less granular level, the algorithmic nature of card tricks allows us to transcribe a performer's actions step by step. We conclude by discussing design criteria we have used to ensure the ontology can be accessed and modified with a simple click-and-drag interface. This may allow database searches and performance transcriptions by users with card magic knowledge, but no ontology background., Comment: 8 pages. Preprint. Final version appeared in ICSC 2019. Copyright of final version is held by IEEE

Interoperable ontologies already exist in the biomedical field, enabling scientists to communicate with minimum ambiguity. Unfortunately, ontology languages, in the semantic web, such as OW...

Information integration is great for military operations because the range of pertinent information sources is significantly distinct and dynamic. This article develops an intelligent knowledge treasure comprised of military resource ontology and procedures, as a learning model for better interoperability of heterogeneous resources of the Indian military. This model can interpret and learn the context of military information automatically, thereby facilitating the military commanders with decision making in several operations, such as command and control, teaching and training, military coalition, situation awareness and many more. To design the military resource ontology, this article specifies the core concepts of the ontology based on terms derived from heterogeneous resources. WWW standard ontology language, OWL has been used to codify the ontology. This article develops an intelligent tool—“QueryOnto”—as an interface to the military resource ontology that provides a commander decision support service and demonstrates how to apply the military ontology in practice. The developed ontology has been verified and validated with the best known approaches and metrics available. The presented model is helpful for military commanders to train their juniors in a systematic way and will provide an efficient web-based learning of different military tasks in future.

One of the most important goals in an organization is to have risks under an acceptance level along the time. All organizations are exposed to real-time security threats that could have an impact on their risk exposure levels harming the entire organization, their customers and their reputation. New emerging techniques, tactics and procedures (TTP) which remain undetected, the complexity and decentralization of organization assets, the great number of vulnerabilities proportional to the number of new type of devices (IoT) or still the high number of false positives, are only some examples of real risks for any organization. Risk management frameworks are not integrated and automated with near real-time (NRT) risk-related cybersecurity threat intelligence (CTI) information. The contribution of this paper is an integrated architecture based on the Web Ontology Language (OWL), a semantic reasoner and the use of Semantic Web Rule Language (SWRL) to approach a Dynamic Risk Assessment and Management (DRA/DRM) framework at all levels (operational, tactic and strategic). To enable such a dynamic, NRT and more realistic risk assessment and management processes, we created a new semantic version of STIX™v2.0 for cyber threat intelligence as it is becoming a de facto standard for structured threat information exchange. We selected an international leading organization in cybersecurity to demonstrate new dynamic ways to support decision making at all levels while being under attack. Semantic reasoners could be our ideal partners to fight against threats having risks under control along the time, for that, they need to understand the data. Our proposal uses an unprecedented mix of standards to cover all levels of a DRM and ensure easier adoption by users.

The Web Ontology Language (OWL) is a widely used knowledge representation language for describing knowledge in application domains by using classes, properties, and individuals. Ontology classification is an important and widely used service that computes a taxonomy of all classes occurring in an ontology. It can require significant amounts of runtime, but most OWL reasoners do not support any kind of parallel processing. We present a novel thread-level parallel architecture for ontology classification, which is ideally suited for shared-memory SMP servers, but does not rely on locking techniques and thus avoids possible race conditions. We evaluated our prototype implementation with a set of real-world ontologies. Our experiments demonstrate a very good scalability resulting in a speedup that is linear to the number of available cores.

Computing technologies are increasingly dynamic and ubiquitous in everyday life nowadays. Context information plays a crucial role in such dynamically changing environments and the different types of contextual conditions bring new challenges to context-sensitive access control. This information mostly can be derived from the crisp sets. For example, we can utilize a crisp set to derive a patient and nurse are co-located in the general ward of the hospital or not. Some of the context information characterizations cannot be made using crisp sets, however, they are equally important in order to make access control decisions. Towards this end, this article proposes an approach to Context-Aware Access Control using Fuzzy logic (FCAAC) for data and information resources. We introduce a formal context model to represent the fuzzy and other contextual conditions. We also introduce a formal policy model to specify the policies by utilizing these conditions. Using our formal approach, we combine the fuzzy model with an ontology-based approach that captures such contextual conditions and incorporates them into the policies, utilizing the ontology languages and the fuzzy logic-based reasoning. We introduce a unified data ontology and its associated mapping ontology in terms of facilitating access control to cloud-based data resources. We justify the feasibility of our approach by demonstrating the practicality through a prototype implementation, several healthcare case studies and a usability study. Finally, we demonstrate an experimental evaluation in terms of query response time. The experiment results demonstrate the satisfactory performance of our proposed FCAAC approach.

Graph-based data formats are flexible in representing data. In particular semantic data models, where the schema is part of the data, gained traction and commercial success in recent years. Semantic data models are also the basis for the Semantic Web - a Web of data governed by open standards in which computer programs can freely access the provided data. This thesis is concerned with the correctness of programs that access semantic data. While the flexibility of semantic data models is one of their biggest strengths, it can easily lead to programmers accidentally not accounting for unintuitive edge cases. Often, such exceptions surface during program execution as run-time errors or unintended side-effects. Depending on the exact condition, a program may run for a long time before the error occurs and the program crashes. This thesis defines type systems that can detect and avoid such run-time errors based on schema languages available for the Semantic Web. In particular, this thesis uses the Web Ontology Language (OWL) and its theoretic underpinnings, i.e., description logics, as well as the Shapes Constraint Language (SHACL) to define type systems that provide type-safe data access to semantic data graphs. Providing a safe type system is an established methodology for proving the absence of run-time errors in programs without requiring execution. Both schema languages are based on possible world semantics but differ in the treatment of incomplete knowledge. While OWL allows for modelling incomplete knowledge through an open-world semantics, SHACL relies on a fixed domain and closed-world semantics. We provide the formal underpinnings for type systems based on each of the two schema languages. In particular, we base our notion of types on sets of values which allows us to specify a subtype relation based on subset semantics. In case of description logics, subsumption is a routine problem. For the type system based on SHACL, we are able to translate it into a description logic subsumption problem.

In this paper, we propose a stroke medical ontology that provides medical knowledge to accompany AI-based stroke disease prediction system's results that were arrived at based on EMG information. This system was developed as a result of the limitations mentioned above being encountered in previous studies. We approached the problem from a viewpoint of knowledge engineering with the aim of modeling medical knowledge related to strokes. Using web ontology language (OWL), a standard ontology language, we developed schema-level stroke ontologies with concepts and properties based on the brain's anatomical structures, lesions, and disease related to strokes. Also, we developed an instance-level medical terms ontology that can span standard medical terms such as those in the international classification diseases (ICD), systematized nomenclature of medicine - clinical terms (SNOMED-CT), and foundational model of anatomy (FMA). The above schema ontology and instance ontology are meaningfully mapped to each other to apply layered ontology modeling techniques that separate schemas from instances. Through semantic web rule language (SWRL)-based inference, we predict lesions, diseases, and anatomical brain structural ripple effects based on the patient's current lesions and diseases. The inferred knowledge information is provided via the SPARQL protocol and RDF query language (SPARQL), a standard ontology query language. To verify the stroke medical ontology proposed in this paper, we developed an ontology-based stroke disease prediction system. This system achieved knowledge augmentation performance of 67.82% by comparing the patients' current lesions and diseases with the lesions, diseases, and areas of disability found by SWRL-based inference using actual stroke emergency data from 37 patients.

Diagrams are an integral part of our communication and thus automated processing of diagrams becomes increasingly relevant. One way to realise automated processing is by using declarative specifications of diagrams. These declarative specifications have similarities to domain knowledge modelled in ontologies. The Web Ontology Language (OWL), an open standard of W3C, is a prominent ontology language to model domain knowledge. In this paper, OWL is evaluated for its suitability to specify visual syntax of diagrams. Specifically, reviews of literature are conducted to establish relevant evaluation criteria and to obtain evidence about OWL for the established criteria. The evaluation indicates that OWL is used widely, which could be attributed to its standardisation and expressiveness, and consequently, it has extensive tool support, including reasoners and editors, with noticeable presence of open source tools. The evaluation also indicates that although OWL has been used successfully for diagram specifications, further research is required to understand entirely its strengths and limitations as a visual syntax specification technique.

Semantics interoperability is important for cyber-physical systems to enable complex data processing and facilitating interworking. OPC Unified Architecture (OPC UA) provides an extensible information model but lacks formal semantics. Ontologies based on the Web Ontology Language (OWL) can provide such formal semantics. Thus, we present a transformation approach that converts OPC UA information models (or only parts of them) into domain-specific ontologies. The transformation process consists of two steps. The first step is a mapping from OPC UA to OWL Full. The second step performs a graph transformation, based on SPARQL rules, into the domain-specified target ontology. The adaption of this transformation to the source information model and the target ontology can be accomplished by only adapting these transformation rules. The presented approach is evaluated for a use case of an industrial heating process to show its flexibility.

Augmented Reality is a technology that offers enormous potential in the industry. Due to a lack of expertise, however, companies are facing various challenges in exploiting this potential. This includes the demand-oriented configuration of the AR system. Depending on the planned use case and company-specific influencing factors and general conditions, suitable AR devices are to be identified and functionalities need to be purposefully selected. The necessary knowledge is implicitly available in numerous sources and difficult to access for companies. In the scope of this work, therefore, a machine-readable knowledge base is developed, in which the knowledge relevant for the AR system configuration is consolidated and formalized. Four aspects of knowledge are considered: The AR system structure including technical and functional components; Potential influences through ambient factors; Experience and application knowledge; The specification of specific AR devices. The knowledge base is realized based on the Web Ontology Language (OWL) and thus enables a digital and partially automated processing of the knowledge using software tools and algorithms. In combination with Artificial Intelligence solutions, the developed knowledge base can be transferred to other systems in the future and provide a powerful tool for system configuration.

In this chapter we introduce the first-order languages. They obey simple, clear formation rules. In later chapters we shall discuss whether, and to what extent, all mathematical propositions can be formalized in such languages.

The high expressivity of the Web Ontology Language (OWL) makes it possible to describe complex relationships between classes, roles, and individuals in an ontology. However, this high expressivity can be an obstacle to correct usage and wide adoption. Past attempts to ameliorate this have included the development of specific, presumably human-friendly syntaxes, such as the Manchester syntax or graphical interfaces for OWL axioms, albeit with limited success. If modelers want to develop suitable OWL axioms it is important to make this as easy as possible.

Summary Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset’s allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers’ discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide., Graphical abstract, Highlights Biomedical advances depend on the efficient and compliant re-use of sensitive human data The Data Use Ontology standardizes terms and definitions for consented data uses The Data Use Ontology facilitates discovery of, request for, and access to datasets Over 200,000 datasets worldwide have been annotated using the Data Use Ontology, The GA4GH Data Use Ontology (DUO) provides unambiguous, machine-readable standard language for consent forms and the data sharing policies they represent. Lawson et al. describe the DUO standard and implementations throughout the data access workflow to expedite data access while maintaining or improving compliant processes.

Healthcare industry is digitising its healthcare operations and generating huge amounts of sensitive medical data to make prompt and informed decisions in patients’ health diagnosis and care. The healthcare industry is subjected to a variety of security threats (e.g., data tampering, theft, and counterfeit drugs). Blockchain is gaining traction to address such security threats and improve data integrity by turning healthcare operations into decentralised, transparent, and immutable manners. However, there is conceptual ambiguity and semantic gaps about blockchain as a countermeasure solution for healthcare security threats. In this work, we use the web ontology language to create blockchain-based security ontology (HealthOnt) to remove conceptual ambiguity and semantic gaps. The HealthOnt offers coherent and formal information models that present blockchain as a countermeasure solution for security threats of traditional healthcare applications.

The collection and sharing of accurate data is paramount to the fight against COVID-19. However, the health system in many countries is fragmented. Furthermore, because no one was prepared for COVID-19, manual information systems have been put in place in many health facilities to collect and record COVID-19 data. This reality brings many challenges such as delay, inaccuracy and inconsistency in the COVID-19 data collected for the control and monitoring of the pandemic. Recent studies have developed ontologies for COVID-19 data modeling and acquisition. However, the scopes of these ontologies have been the modeling of patients, available medical infrastructures, and biology and biomedical aspects of COVID-19. This study extends these existing ontologies to develop the COVID-19 ontology (COVIDonto) to model the origin, symptoms, spread and treatment of COVID-19. The NeOn methodology was followed to gather data from secondary sources to formalize the COVIDonto ontology in Description Logics (DLs). The COVIDonto ontology was implemented in a machine-executable form with the Web Ontology Language (OWL) in Protege ontology editor. The COVIDonto ontology is a formal executable model of COVID-19 that can be leveraged in web-based applications to integrate health facilities in a country for the automatic acquisition and sharing of COVID-19 data. Moreover, the COVIDonto could serve as a medium for cross-border interoperability of government systems of various countries and facilitate data sharing in the fight against the COVID-19 pandemic.

The domain of Subject-oriented Business Process Management (S-BPM) is somewhat outstanding due to its embracing of Subject Orientation. However, at the same time, it is also a classic BPM domain concerned with typical aspects like creation and exchange of diagrammatic process models, elicitation of domain knowledge, and implementing process (models) into organisations and IT systems. Nevertheless, the Abstract State Machine (ASM) concept, a formal and abstract specification means for algorithms, has been and is fundamental and an important cornerstone for the S-BPM community. The first formal specifications for S-BPM has been developed by Egon Borger using ASM means—namely a specification for an interpreter engine for the subject-oriented modeling language PASS, the Parallel Activity Specification Schema. However, for the sake of intuitive and comprehensive use, ASM can be enriched with defining the passive aspects of PASS, namely the (data) structure of process models and data object appearing in the processes. Here it is useful to complement ASM description means with concepts that are better suited for that tasks. This work analyzes how the S-BPM research community has combined ASM with the Web Ontology Language (OWL) to generate a precise, while comprehensible, system specification for the execution of formal, subject-oriented process models. Furthermore, it will be argued why this combination is worthwhile overcoming the weaknesses of both generic and technology independent specification approaches.

Quality data is of decisive importance for controlling critical cyber-physical systems. Most common real-life systems are driven by unstructured, decentralized, and growing amounts of data, while validation requires coherent data, that is well structured, consistent, and without ambiguities. Often, ontologies are well-suited for capturing domain knowledge data, deriving requirements, providing analysis, and developing applications. Still, ontological representations fall short when it comes to formal verification and validation, especially for large complex systems. In this research, we suggest a fully automated approach to transform ontology axioms, expressed in the Web Ontology Language (OWL), to Event-B predicates. We show, in a practical example, how bridging OWL to Event-B can scale the validation of ontology-driven AI systems.

This chapter presents the ontology for Multi-Paradigm Modelling (MPM) specfied using the Web Ontology Language (OWL) as introduced in Chapter 2 . A thorough state of the art on MPM's core notions including multi-formalism and model management approaches, languages and tools is presented. In particular, model management approaches have been characterized according to their modularity and incremental execution properties as required to scale for the large complex CPSs we face today. Based on this state of the art, an outline of the MPM ontology is developed by introducing its main classes and properties. The validation of the ontology is presented by showing how it can adequatly model the two case studies briefly introduced in Chapter 2 .

In this chapter, we provide an overview of the current trends in using semantic technologies in the IoT domain, presenting practical applications and use cases in different domains, such as in the healthcare domain (home care and occupational health), disaster management, public events, precision agriculture, intelligent transportation, building and infrastructure management. More specifically, we elaborate on semantic web-enabled middleware, frameworks and architectures (e.g. semantic descriptors for M2M) proposed to overcome the limitations of device and data heterogeneity. We present recent advances in structuring, modelling (e.g. RDFa, JSON-LD) and semantically enriching data and information derived from sensor environments, focusing on the advanced conceptual modelling capabilities offered by semantic web ontology languages (e.g. RDF/OWL2). Querying and validation solutions on top of RDF graphs and Linked Data (e.g. SPARQL, SPIN and SHACL) are also presented. Furthermore, insights are provided on reasoning, aggregation, fusion and interpretation solutions that aim to intelligently process and ingest sensor information, infusing also human awareness for advanced situational awareness.

The growing complexity of information systems requires offloading knowledge-based decision making to software. The common approach is to encode formalized domain knowledge in form of ontology. However, this approach has many inherent flaws, such as subjectivity of ontology design, difficulties to formalize different perspectives and points of view for the same domain, complexity and large size of ontologies. This problem can be resolved by using multiple networked ontologies. The ultimate criterion for successful ontology design is provided by the effectiveness and efficiency of resolving real life problems using ontology. On the other hand, the efficiency of ontology usage depends also on language used to formulate ontology. Each language is geared towards the specific application area and has limitations which make it a poor choice for problems in another area. Therefore, the versatile knowledge based system should be able change knowledge representation form on task by task basis. The structure and functions of such a system are described. For the central repository of knowledge it is proposed to use a language, based on multisort algebras. In order to represent language transformations this algebraic language is enhanced by adding mappings domain. As an example, the mapping between RDF schema and algebraic language is provided.

Multiple ontology languages have been developed over the years, which brings afore two key components: how to select the appropriate language for the task at hand and language design itself. This engineering step entails examining the ontological ‘commitments’ embedded into the language, which, in turn, demands for an insight into what the effects of philosophical viewpoints may be on the design of a representation language. But what are the sort of commitments one should be able to choose from that have an underlying philosophical point of view, and which philosophical stances have a knock-on effect on the specification or selection of an ontology language? In this paper, we provide a first step towards answering these questions. We identify and analyse ontological commitments embedded in logics, or that could be, and show that they have been taken in well-known ontology languages. This contributes to reflecting on the language as enabler or inhibitor to formally characterising an ontology or an ontological investigation, as well as the design of new ontology languages following the proposed design process.

According to the characteristics of uncertainty, coordination and distribution in tactical intention recognition, this paper proposes a process of tactical intention recognition oriented to intelligent command and control decision support, which combines the perceptual ability of deep learning, the decision ability of reinforcement learning and the reasoning ability of multi-entity Bayesian network, with complementary advantages. At the same time, in order to realize the exchange, sharing and reuse of information, improve the representation and reasoning ability of traditional Bayesian network for the uncertainty in the field of tactical intention recognition, a method of tactical intention recognition based on probabilistic network ontology language is proposed. Simulation results verify the effectiveness and feasibility of the method.

Vehicular networks have emerged in recent years offering novel medical services to vehicular users. In-vehicle equipment such as On-Board Units (OBUs), Internet of Things (IoT) devices and sensors are used to supervise the health of on-board users and create an increasing amount of medical information. This information should be processed and then transmitted to medical units (e.g. hospitals) with the lowest possible processing and communication delays. Furthermore, it should be organized considering well-defined standards, in order to be easily reusable from third-party medical systems. Thus, the medical staff will be able to remotely provide immediate medical support to vehicular patients. In this paper, an interoperable Storage as a Service (STaaS) scheme which delivers medical data through a 5G wireless network architecture is described. The Health Level 7 (HL7) standard is applied for the manipulation of the collected medical data, ensuring the interoperability of the proposed scheme with third-party systems that use the same standard. The Web Ontology Language (OWL) is used to produce and manipulate the relative ontological descriptions about the collected data.

Finding a logical formula that separates positive and negative examples given in the form of labeled data items is fundamental in applications such as concept learning, reverse engineering of database queries, and generating referring expressions. In this paper, we investigate the existence of a separating formula for incomplete data in the presence of an ontology. Both for the ontology language and the separation language, we concentrate on first-order logic and three important fragments thereof: the description logic ALCI, the guarded fragment, and the two-variable fragment. We consider several forms of separability that differ in the treatment of negative examples and in whether or not they admit the use of additional helper symbols to achieve separation. We characterize separability in a model-theoretic way, compare the separating power of the different languages, and determine the computational complexity of separability as a decision problem.

Building ontologies in The Web Ontology Language (OWL) as a knowledge representation about a particular domain gained a lot of interest over the recent years. Thanks to its large community and many options concerning tooling and methods for representation and transformations, OWL is being used not only in Linked Open Data and Artificial Intelligence but also in conceptual modelling. OWL allows capturing concepts and their properties, including relationships which can also be done using traditional conceptual models, for example, in Unified Modelling Language (UML). Both UML and OWL have their own specifics when compared to each other, and one may be more suitable than the other in concrete cases. There are several methods for transformation between knowledge representation in OWL and UML. In this paper, we review key methods for transforming UML to OWL and vice versa. To compare the methods, we use a non-trivial conceptual model that contains all commonly used constructs, e.g., generalization sets, composition, or relationships with constraints. The methods are evaluated in terms of information loss during transformation, the need for human intervention, and versatility.

We describe the development and envisioned use case applications of a comprehensive insider threat ontology—“Sociotechnical and Organizational Factors for Insider Threat” (SOFIT)—that comprises more than 300 indicators of technical, behavioral, and organizational factors. Requirements, design, and engineering development for the Web Ontology Language (OWL) implementation of the SOFIT knowledge base are reviewed; additional relationships among constructs are defined that extend the representation beyond a simple taxonomic hierarchy and that enable inferences for qualitative and quantitative threat assessment. We show how queries may be constructed to support these inferences and threat assessments. Several major application concepts are reviewed to show how the ontology may be used by the insider threat research and operational communities. To this end, the SOFIT knowledge base may be shared with stakeholders to advance research and practice for proactive insider threat mitigation.

Efforts have been made in the Semantic Web to combine rules with ontologies. One result of these efforts is the development of the Semantic Web Rule Language (SWRL) which is designed to integrate closely with the Web Ontology Language (OWL). Both SWRL and OWL adhere to the open-world semantics of first-order logic, and thus are not suitable for modelling integrity constraints in applications where complete knowledge about some parts of the domain can be assumed. In this paper, we investigate this problem and present a practical approach to modelling and validating constraints in the Semantic Web. Building on existing work, we show that by employing a constraint semantics for both OWL and SWRL, we can model common constraints as OWL axioms or SWRL rules. We also show that by using a query reduction technique, we can validate constraints using existing OWL/SWRL reasoners. Finally, we demonstrate the usefulness of our approach via a real-world case study.

This paper introduces a novel, semantics-informed geologic mapping process, whose application domain is the production of a synthetic geologic map of a large administrative region. A number of approaches concerning the expression of geologic knowledge through UML schemata and ontologies have been around for more than a decade. These approaches have yielded resources that concern specific domains, such as, e.g., lithology. We develop a conceptual model that aims at building a digital encoding of several domains of geologic knowledge, in order to support the interoperability of the sources. We apply the devised terminological base to the classification of the elements of a geologic map of the Italian Western Alps and northern Apennines (Piemonte region). The digitally encoded knowledge base is a merged set of ontologies, called OntoGeonous. The encoding process identifies the objects of the semantic encoding, the geologic units, gathers the relevant information about such objects from authoritative resources, such as GeoSciML (giving priority to the application schemata reported in the INSPIRE Encoding Cookbook), and expresses the statements by means of axioms encoded in the Web Ontology Language (OWL). To support interoperability, OntoGeonous interlinks the general concepts by referring to the upper part level of ontology SWEET (developed by NASA), and imports knowledge that is already encoded in ontological format (e.g., ontology Simple Lithology). Machine-readable knowledge allows for consistency checking and for classification of the geological map data through algorithms of automatic reasoning.

To achieve efficient development of high-quality product, manufacturing constraints must be fully taken into account at the early design stage. However, designers lack in-depth knowledge of manufacturing and production. Many time-consuming iterations of design changes are required between designers and manufacturing engineers. In order to minimize this knowledge gap, this paper presents an ontology-based product design framework for manufacturability verification and knowledge reuse to support the sharing and reuse of design and manufacturing knowledge. It aims at providing advices and feedback of restraints of manufacturing processes to the designers during the design process. The proposed framework consists of three major layers which include a foundation layer, a domain layer, and an instance layer. We use the Web Ontology Language (OWL), a standard of ontology representation language, to formalize the foundation layer. It contains the core product model and the standard ISO 10303 AP224 application protocol. The domain layer comprises extensional concepts and relationships for design and manufacturing integration and a rule base for manufacturability verification, which is represented in Semantic Web Rule Language (SWRL). In the instance layer, an inference engine is developed based on ontology and rule inference. It provides recommendations of manufacturability. Two case studies are provided as application examples to demonstrate the effectiveness of the framework.

In this paper we introduce Eddy, a new open-source tool for the graphical editing of OWL~2 ontologies. Eddy is specifically designed for creating ontologies in Graphol, a completely visual ontology language that is equivalent to OWL~2. Thus, in Eddy ontologies are easily drawn as diagrams, rather than written as sets of formulas, as commonly happens in popular ontology design and engineering environments. This makes Eddy particularly suited for usage by people who are more familiar with diagramatic languages for conceptual modeling rather than with typical ontology formalisms, as is often required in non-academic and industrial contexts. Eddy provides intuitive functionalities for specifying Graphol diagrams, guarantees their syntactic correctness, and allows for exporting them in standard OWL 2 syntax. A user evaluation study we conducted shows that Eddy is perceived as an easy and intuitive tool for ontology specification.

Autonomous systems consist of agents that have access to interpretable information allowing them to make intelligent decisions. This requires the systems to have a good semantic knowledge base. A good semantic knowledge repository has to be backed by a well-defined domain ontology. For the sake of decidability, ontology languages do not provide the required expressiveness. Rules present an efficient support thereby enabling even better decision making. Their complexity and their exponentially growing number imply the need for an automatic management. In this paper, we introduce a new method for the automatic dependency extraction among rule bases associated to ontologies. This is founded on a novel technique of dependency extraction. A prototype of our proposal was implemented and applied on two different rule bases associated to ontologies from different domains. Our results succeeded in increasing the number of the extracted dependency relationships.

Semantic technologies have emerged as a prominent research area within Big Earth Data. These technologies have provided significant benefits for data discovery and integration. Yet, the formality of the Semantic Web, in languages such as the Web Ontology Language (OWL), does not always integrate well with the numerical, statistical, and geometric methods of the geosciences. Two prominent challenges in this area are how to semantically model individual measurements and what to do when geoscience needs are not addressed by languages such as OWL. This has led to a fragmented Big Earth Data community with either no solution or incompatible semantic solutions. We use an oceanographic example to highlight the limitations and challenges surrounding the semantic encoding of observations and the use of semantics during analysis. We then present potential solutions to each challenge showing that a full end-to-end application of semantic technologies is not only feasible, but beneficial to Big Earth Data.

The article describes the process of creating a Finnish language FrameNet or FinnFN, based on the original English language FrameNet hosted at the International Computer Science Institute in Berkeley, California. We outline the goals and results relating to the FinnFN project and especially to the creation of the FinnFrame corpus. The main aim of the project was to test the universal applicability of frame semantics by annotating real Finnish using the same frames and annotation conventions as in the original Berkeley FrameNet project. From Finnish newspaper corpora, 40,721 sentences were automatically retrieved and manually annotated as example sentences evoking certain frames. This became the FinnFrame corpus. Applying the Berkeley FrameNet annotation conventions to the Finnish language required some modifications due to Finnish morphology, and a convention for annotating individual morphemes within words was introduced for phenomena such as compounding, comparatives and case endings. Various questions about cultural salience across the two languages arose during the project, but problematic situations occurred only in a few examples, which we also discuss in the article. The article shows that, barring a few minor instances, the universality hypothesis of frames is largely confirmed for languages as different as Finnish and English.

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.

Cloud computing is increasingly used so that the number of providers offering services is rapidly increasing. Thus, a need to organize these services and to express relations between them arises. To answer this need, ontologies are used. To query these services, the authors use query languages, such as SPARQL, that return two types of results: either a list of required services, or an empty list. However, the second result is not desired. In fact, if the required service is not available, users want to be offered by a list of similar ones instead of the empty list. It is in this sense that the similarity, which provides more results ranked according to their utilities, is used. This paper first presents the Cloud ontology on which the authors' work is based. It then defines and compares between two Cloud service discovery methods which are: the discovery based on query languages and the discovery based on similarity. To show the efficiency of the search based on similarity, the authors propose a search engine that allows the users to query services using a simple to use interface.

In the context of the Semantic Web, fuzzy extensions to OWL (the W3C standard ontology language) and Description Logics (DLs, the logical foundation of OWL) have been extensively investigated, and there are many real fuzzy DL ontology knowledge bases. Therefore, how to store fuzzy DL ontology knowledge bases has become an important issue. In this paper, we propose an approach and implement a tool for storing fuzzy DL ontology knowledge bases in fuzzy relational databases. Our chosen formalism is a fuzzy extension of the very expressive DL SHOIN(D), which is the main logical foundation of the standard ontology language OWL, so that our storage approach can store not only fuzzy DL-knowledge bases but also fuzzy ontology knowledge bases. Firstly, we give a formal definition of fuzzy DL-knowledge bases. In the definition, we consider the constructors of both fuzzy SHOIN(D) DL and fuzzy OWL ontology and add some common fuzzy datatypes (e.g., trapezoidal values, interval values, approximate values, and labels) into the knowledge bases. On this basis, we propose an approach which can store fuzzy DL-knowledge bases in fuzzy relational databases, and provide an example to well explain the approach. The correctness and quality of the storage approach are proved and analyzed. Furthermore, following the proposed approach, we implemented a prototype tool, which can automatically store fuzzy DL-knowledge bases. Finally, we make a discussion about the query problem and make a comparison with the existing works.

Definitions of ‘mixed’ or ‘intertwined’ languages derive almost entirely from studies of languages that combine elements from genetically unrelated sources. The Mayan language Tojol-ab’al displays a mixture of linguistic features from two related Mayan languages, Chuj and Tseltal. The systematic similarities found in related languages not only make it methodologically difficult to identify the source of specific linguistic features but also mean that inherited similarity can alter the processes and outcomes of language mixing in ways that parallel observed patterns of code-switching between related languages. Tojol-ab’al, therefore, arguably represents a distinct type of mixed language, one that may only result from mixture involving related languages.