Formal Knowledge Models Research Articles

Blockchain-based ontology driven reference framework for security risk management

Security risk management (SRM) is crucial for protecting valuable assets from malicious harm. While blockchain technology has been proposed to mitigate security threats in traditional applications, it is not a perfect solution, and its security threats must be managed. This paper addresses the research problem of having no unified and formal knowledge models to support the SRM of traditional applications using blockchain and the SRM of blockchain-based applications. In accordance with this, we present a blockchain-based reference model (BbRM) and an ontology driven reference framework (OntReF) for the SRM of traditional and blockchain-based applications. The BbRM consolidates security threats of traditional and blockchain-based applications, structured following the SRM domain model and offers guidance for creating the OntReF using the domain model. OntReF is grounded on unified foundational ontology (UFO) and provides semantic interoperability and supporting the dynamic knowledge representation and instantiation of information security knowledge for the SRM. Our evaluation approaches demonstrate that OntReF is practical to use.

The Prescription of Drug Ontology 2.0 (PDRO): More Than the Sum of Its Parts.

While drugs and related products have profoundly changed the lives of people around the world, ongoing challenges remain, including inappropriate use of a drug product. Inappropriate uses can be explained in part by ambiguous or incomplete information, for example, missing reasons for treatments, ambiguous information on how to take a medication, or lack of information on medication-related events outside the health care system. In order to fully assess the situation, data from multiple systems (electronic medical records, pharmacy and radiology information systems, laboratory management systems, etc.) from multiple organizations (outpatient clinics, hospitals, long-term care facilities, laboratories, pharmacies, registries, governments) on a large geographical scale is needed. Formal knowledge models like ontologies can help address such an information integration challenge. Existing approaches like the Observational Medical Outcomes Partnership are discussed and contrasted with the use of ontologies and systems using them for data integration. The PRescription Drug Ontology 2.0 (PDRO 2.0) is then presented and entities that are paramount in addressing this problematic are described. Finally, the benefits of using PDRO are discussed through a series of exemplar situation.

Tackling the DM Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning

AbstractKnowledge-based AI typically depends on a knowledge engineer to construct a formal model of domain knowledge – but what if domain experts could do this themselves? This paper describes an extension to the Decision Model and Notation (DMN) standard, called Constraint Decision Model and Notation (cDMN). DMN is a user-friendly, table-based notation for decision logic, which allows domain experts to model simple decision procedures without the help of IT staff. cDMN aims to enlarge the expressiveness of DMN in order to model more complex domain knowledge, while retaining DMNs goal of being understandable by domain experts. We test cDMN by solving the most complex challenges posted on the DM Community website. We compare our own cDMN solutions to the solutions that have been submitted to the website and find that our approach is competitive. Moreover, cDMN is able to solve more challenges than any other approach.

A Systematic Model for Process Development Activities to Support Process Intelligence

Process, manufacturing, and service industries currently face a large number of non-trivial challenges ranging from product conception, going through design, development, commercialization, and delivering in a customized market’s environment. Thus, industries can benefit by integrating new technologies in their day-by-day tasks gaining profitability. This work presents a model for enterprise process development activities called the wide intelligent management architecture model to integrate new technologies for services, processes, and manufacturing companies who strive to find the most efficient way towards enterprise and process intelligence. The model comprises and structures three critical systems: process system, knowledge system, and transactional system. As a result, analytical tools belonging to process activities and transactional data system are guided by a systematic development framework consolidated with formal knowledge models. Thus, the model improves the interaction among processes lifecycle, analytical models, transactional system, and knowledge. Finally, a case study is presented where an acrylic fiber production plant applies the proposed model, demonstrating how the three models described in the methodology work together to reach the desired technology application life cycle assessment systematically. Results allow us to conclude that the interaction between the semantics of formal knowledge models and the processes-transactional system development framework facilitates and simplifies new technology implementation along with enterprise development activities.

Formal knowledge model for online social network forensics

Currently, examining social media networks is an integral part of most investigations. However, getting a clear view of the events relevant to the incident from a large set of data, such as social media, is a challenging task. Automation of the forensic and analysis process is the only solution to manage large data sets and get useful information. However, automation in digital forensics is a technical issue with legal implications. The legal system accepts only those automated processes that are reproducible, explainable, and rigorously testable. Therefore, automated forensic processes must be based on formal theories, which are rare in digital forensics. This article explains a theoretical and formal knowledge model for forensic automation on online social networks. This model consists of an event-based knowledge model, which provides theoretical concepts that can assist in the construction and interpretation of the events related to the incident under investigation. The proposed model is implemented through an ontology to provide semantically rich and formal representation to the concepts. This article also describes the feasibility of legally acceptable automated analysis operators, based on a formal theory, for online social network forensics.

Methodological aspects of construction of methods of modeling and analytical processing of databases and knowledge management system of armament and military equipment

It is suggested to manage the development of weapons and military equipment in a single network environment of the relevant information and analytical system by coordinating the mechanisms of interaction of information resources, which reflect different descriptions of the armament and military equipment. Such interaction should be built on the basis of transdisciplinary integration procedures, which will ensure synchronization of activities of all participants of the process and semantic and temporal synchronization of all categories and concepts of information resources and digital assets. Formation of many concepts, as the structure of semantic units-concepts and formal model of subject knowledge is ensured by the use of ontological methods. The management of information processing processes is built on the use of taxonomies that reflect the properties of information processes. As a corporate high-performance information-analytical search platform, the core of which is a two-tier system of indexing information resources, the use of semantic control and ontological interface IT-TODOS is proposed. The main linguistic functions, tasks, word processing, data preparation to preserve the database structure, function monitoring and administration implemented in the linguistic subsystem. 
 The formation of the model of the problem of choice and its solution on the basis of ontology is carried out in the information-analytical system, which includes: ontological interface, modules of basic program integration, module of creation of digital document collection, indexing tools, polymic synonymic zone, module of interactive interaction multicriteria benchmarking and forecasting module.
 The approach presented in the article will allow for a powerful monitoring of the processes of development, operation and modernization of armament and military equipment by analyzing the content, aggregating and rating huge amounts of information contained in file electronic collections of text documents.

Fuzzy Logic Based Modelling of Cast Component Properties

Digitalization of manufacturing requires building models to represent accumulated data and knowledge on the products and processes. The use of formal knowledge models allows for increase of the automation level leading to more sustainable manufacturing. Casting is important for different industries because it offers a great freedom of designing for weight reduction. This paper presents an approach to modelling of cast component properties that is based on fuzzy logic. The approach includes learning of the fuzzy inference rules from the data. The constructed fuzzy logic models can be used to tune the manufacturing process to produces cast components with desired properties. The evaluation of the results demonstrates that the accuracy of the two created models are 3.58% and 3.15% respectively with the learned fuzzy inference rules being identical to the manually created ones. The presented approach can help to automate the management of cast component manufacturing.

Human resource optimisation through semantically enriched data

The industrial domain is experiencing a so-called fourth industrial revolution in which the evergrowing complexity of manufacturing information, the increasing amount of knowledge and the use of web-oriented techniques, represent three crucial factors that are accelerating the growth of complexity of industrial systems. On the other hand, continuous-evolving requirements in industrial environments, due to technology outbreaks and a new global marketplace, have led to an on-going evolution of human resource management through the creation and adoption of alternative business models. In the past decade, semantic models such as ontologies have been proven to be effective for many knowledge-intensive applications, since they provide formal models of domain knowledge that can be exploited in different ways. For all these reasons, an innovative human resource optimisation (HRO) engine is introduced, which employs semantically enhanced information and conditional random field (CRFs) probabilistic models with knowledge derived from industrial shop floor level, and proposes the right person for the right job in real-time shop floor operations towards optimising decisions on how to implement and schedule either repeatedly or non-occurring tasks. Industrial information data flow and semantic enrichment were ensured through the combined use of a common interface data exchange model (CIDEM) and ontologies, after which a feasibility study at a chemical plant presented interesting preliminary results.

Novel Research on a Fibration Knowledge-Based Solution for Engineered Surface Tolerances within the Modern GPS Context

Geometrical product specification and verification (GPS) is the only worldwide tolerancing specification language available for unambiguously communicating functional requirements and precisely expressing geometrical tolerances of workpieces on technical drawings. Although the understanding and application of GPS are of vital importance for modern manufacturing industry, the latest GPS standards contain huge amounts of data, information and knowledge that are obscurely linked together, so they are very difficult for designers, engineers and metrologists to understand, remember and comprehensively use them. This is especially true for surface texture that is one of the most complicated geometrical characteristics in GPS domain. To overcome the implementation barrier of GPS surface texture, a novel knowledge-based solution supported formally by a unified knowledge modeling of the complete GPS surface texture standard chain has been researched. This knowledge model can serve as a formal basis for developing more intelligent and reliable tolerance design and measurement systems. This paper starts with an introduction on how to build up a formal knowledge model based on category theory, called categorical fibration model, to unified acquisition and representation of all surface texture knowledge. It then moves on to define a recursive knowledge refinement process for the devised model to enable the recursive multi-level GPS knowledge transformations and refinements. This knowledge model is validated naturally by checking rules identified from category theoretic notions. Finally, this paper presents a general framework for applying the modeled GPS knowledge in practice, from the aspects of the integrated and complete modeling of all surface texture knowledge, detailed knowledge acquisition and modeling examples for four sub-knowledge bases of the proposed software solution, core functional features of this software, and a comprehensive case study for testing its validity and usability. This knowledge-based solution facilitates life cycle collaboration and integration of the complete GPS surface texture chain to reduce product development cost and lead time, and to ensure functional requirements of geometrical workpieces.

9.1.1 Semantic Platforms for Cyber‐Physical Systems

AbstractThis paper describes the development of semantic platforms to support the modeling, architectural design, realization, and operation of cyber‐physical systems. Platform architectures work directly with formal models of domain knowledge (not just raw data) that are determinate, provable (ambiguity free), executable, and with semantic parameters and rule engines that reason with physical quantities and across domains. These concepts are exercised in a case study application: the “decision‐dilemma problem” for traffic systems.

The knowledge multiplier

The paper develops a formal model of external knowledge and identifies the role of knowledge multipliers. Social interactions and knowledge multipliers play a crucial role in determining the rate of technological change. The analytical identification of the knowledge multiplier expression constitutes a key step in the appreciation of the crucial role of knowledge interactions. First, social considerations endogenously change the knowledge production function of each firm. The knowledge multiplier is the specific mechanism by means of which external knowledge contributes to enhance the innovative capacity of each firm. The production function of knowledge shows that the larger the knowledge multiplier is the stronger are the cumulative positive effects of external knowledge on the generation of new knowledge. Second, social considerations explain the long-run dynamics of innovation. Social reinforcement and the knowledge multiplier determine the rise or fall in the rate of accumulation of technological knowledge.

Informatics in Radiology: Radiology Gamuts Ontology: Differential Diagnosis for the Semantic Web

The Semantic Web is an effort to add semantics, or "meaning," to empower automated searching and processing of Web-based information. The overarching goal of the Semantic Web is to enable users to more easily find, share, and combine information. Critical to this vision are knowledge models called ontologies, which define a set of concepts and formalize the relations between them. Ontologies have been developed to manage and exploit the large and rapidly growing volume of information in biomedical domains. In diagnostic radiology, lists of differential diagnoses of imaging observations, called gamuts, provide an important source of knowledge. The Radiology Gamuts Ontology (RGO) is a formal knowledge model of differential diagnoses in radiology that includes 1674 differential diagnoses, 19,017 terms, and 52,976 links between terms. Its knowledge is used to provide an interactive, freely available online reference of radiology gamuts ( www.gamuts.net ). A Web service allows its content to be discovered and consumed by other information systems. The RGO integrates radiologic knowledge with other biomedical ontologies as part of the Semantic Web.

Epilepsy informatics and an ontology-driven infrastructure for large database research and patient care in epilepsy.

The epilepsy community increasingly recognizes the need for a modern classification system that can also be easily integrated with effective informatics tools. The 2010 reports by the United States President's Council of Advisors on Science and Technology (PCAST) identified informatics as a critical resource to improve quality of patient care, drive clinical research, and reduce the cost of health services. An effective informatics infrastructure for epilepsy, which is underpinned by a formal knowledge model or ontology, can leverage an ever increasing amount of multimodal data to improve (1) clinical decision support, (2) access to information for patients and their families, (3) easier data sharing, and (4) accelerate secondary use of clinical data. Modeling the recommendations of the International League Against Epilepsy (ILAE) classification system in the form of an epilepsy domain ontology is essential for consistent use of terminology in a variety of applications, including electronic health records systems and clinical applications. In this review, we discuss the data management issues in epilepsy and explore the benefits of an ontology-driven informatics infrastructure and its role in adoption of a "data-driven" paradigm in epilepsy research.

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring).

Monitoring of multicomponent systems: Object-independent models and methods

The models and methods of monitoring of multicomponent systems, which generalize a practical experience gained in the construction of monitoring information systems, are discussed. An application domain is defined by the mathematical model of an asynchronous multicomponent system, and the time-dependent state of the model is described by a set of hypotheses. The states of individual components are recorded by sensors, the signals of which are used to verify hypotheses. The hypothesis selection reliability depends on the validity of information contained in signals coming from sensors. The proposed methods enable the identification of repetitive and contradictory signals and clusterization of signals belonging to independent and simultaneous informational processes. The features of the proposed approach are the for-mal model of application-domain knowledge and methods based on abstract mathematical objects, which can be employed to solve monitoring problems in different application domains.

Computational Neuroscience Ontology: a new tool to provide semantic meaning to your models

The diversity of modeling approaches in computational neuroscience makes model sharing, retrieval, reuse and reproducibility difficult and even sometimes impossible. To address this problem, standardized languages have been developed by and for the community, such as NeuroML[1], PyNN [2] and NineML (http://software.incf.org/software/nineml). Although these languages enable software interoperability and therefore model reuse and reproducibility, they lack semantic information that would facilitate efficient model sharing and retrieval. In the context of the INCF Multi-Scale Modeling (MSM) program, we have developed an ontology to annotate spiking network models described with NineML and other structured model description languages. Ontologies are formal models of knowledge in a particular domain and composed of classes that represent concepts defining the field as well as the logical relations that link these concepts together [3]. These classes and relations have unique identifiers and definitions that allow unambiguous annotation of digital resources such as web pages or model source code. Implemented in a machine-readable format, these knowledge models can be used to design more efficient and intuitive information retrieval systems for experts in the field. We are proposing the first version of the Computational Neuroscience Ontology or CNO. This ontology is composed of 207 classes representing general concepts related to computational neuroscience organized in a hierarchy of concepts. CNO is currently available on Bioportal (http://bioportal.bioontology.org/ontologies/3003). The design of CNO follows some of the recommendations of the Open Biological and Biomedical Ontologies (OBO) community and is compatible with the ontologies developed and maintained within the Neuroscience Information Framework (NIF, [4]http://www.neuinfo.org). Integration with this large federation of neuroscience ontologies has two main advantages: (1) it allows the linking of models with biological information, creating a bridge between computational and experimental knowledge bases; (2) as ontology development is an iterative process that relies on inputs from the community, NIF has developed NeuroLex (http://neurolex.org), an effective collaborative platform, available for community inputs on the content in CNO. With the further development of CNO based on inputs from the community, we hope CNO will provide a useful framework to federate digital resources in the field of computational neuroscience.

Core linguistic computations: How are they expressed in the mind/brain?

Abstract Over the last half century, linguists have introduced very detailed formal models of language knowledge, acquisition and use, and have proposed refined typologies of the kinds of mental computations involved in language. These models, in turn, capture and systematize observations and ideas developed in centuries of rational reflections on language. It is in the mutual interest of both linguists and cognitive neuroscientists to bridge the gap between mind and brain in this domain, and make substantial progress in connecting abstract models of linguistic competence and performance, and the study of the neural implementation of the computing mechanisms. In this paper I would like to offer a concise tutorial on some core ideas and analytic devices introduced in formal linguistics to deal with linguistic computations and language variation, with special reference to parametric and minimalist models. Then, I would like to conclude by phrasing some questions that current ideas on mental linguistic computations raise for the brain sciences.

Distant control of student skills by formal model of domain knowledge

We propose to use the domain ontology for student skills examination. Students have to build the personal domain ontologies and thesauri of the examined discipline (based on natural language texts relevant to subject domain – lectures, textbooks, manuals, etc.). These thesauri are then compared with the reference ones built by the tutor. The analysis of student mistakes allows the proposal of personalised recommendations and to improve the course materials in general. Semantic Web technologies are used for knowledge representation and processing for the purpose of interoperability.

An autonomic problem determination system using adaptive multi-levels dictionaries and singular value decomposition techniques

An autonomic Problem Determination (PD) system can adapt to changing environments, react to existing or new error conditions and predict possible problems from a collection of seemingly unrelated events and take actions proactively. In this paper, we propose such a system with the use of the Singular Value Decomposition (SVD) technique and dynamic and adaptive multi-levels dictionaries. Our proposed PD system applies an iterative method that enables a dynamic interaction between a set of sparsely related events and the current dictionaries, with its entries being continuously updated to reflect the relative importance of each event. Updating the dictionaries triggers an update of the SVD matrix, thereby accelerating the convergence of the SVD matrix. Our PD system can capture knowledge in a hierarchical form for complex knowledge representation. It does not require a formal knowledge model or intensive training by examples. It is an efficient system with sufficient accuracy for autonomic and adaptive problem determination.

The management of medical knowledge: between non-structured documents and ontologies

In this paper, we study the ways of representing medical knowledge in an information system. We argue that the data-processing support is not neutral and conditions the representations that are built. In addition, the requirements of medical activity — work with the computerized record, needs for indexing, constraints of coding, etc. — have consequences for modelling and for applications which each correspond to different assumptions about the handling of information and knowledge. In particular, the diversity of the activity is such that the question of the documents and their supports has to be considered, as they generally provide poorly structured information; formal models of knowledge as ontologies also have to be addressed.