Ontology-driven Information Systems Research Articles

ОСНОВИ МЕТОДУ КОМПЛЕКСНОГО ВИКОРИСТАННЯ НЕЙРОМЕРЕЖЕВОЇ ТА ОНТОЛІНГВІСТИЧНОЇ ПАРАДИГМ: КОМПЛЕКСНИЙ ПІДХІД

This article presents an integrated approach that combines neural network and ontolinguistic paradigms. The method encompasses methodological underpinnings, information technology, and the MedRehabBot system. Collectively, they embody the core principles of meta-learning and structured prompts, ultimately enhancing the efficiency of information system interaction with Chatbots and information retrieval rooted in ontologies. The method also offers the flexibility to adapt the MedRehabBot system for utilization within different Large Language Model (LLM) systems. Keywords: transdisciplinary scientific research, ontology, ontological engineering, ontology-driven information system, LLM-system, ChatGPT, MedRehabBot, prompt engineering, Chatbot.

Ontology-Driven Computer Systems

This article delves into the evolving frontier of ontology-driven natural language information processing. Through an in-depth examination, we put forth a novel linguistic processor architecture, uniquely integrating linguistic and ontological paradigms during semantic analysis. Distancing from conventional methodologies, our approach showcases a profound merger of knowledge extraction and representation techniques. A central highlight of our research is the development of an ontology-driven information system, architected with an innate emphasis on self-enhancement and adaptability. The system’s salient capability lies in its adept handling of elementary knowledge, combined with its dynamic aptitude to foster innovative concepts and relationships. A particular focus is accorded to the system’s application in scientific information processing, signifying its potential in revolutionising knowledge-based applications within scientific domains. Through our endeavours, we aim to pave theway for more intuitive, precise, and expansive ontology-driven tools in the realm of knowledge extraction and representation.

Towards an Ontology-Driven Information System for Archaeological Pottery Studies: The Greyware Experience

The archaeological analysis of medieval and modern pottery has benefited from the consolidation of archaeometry in the domain of Medieval Archaeology in the past few decades. As part of an ongoing research project devoted to the characterization of pottery production, distribution processes and technological transfer, we deal with a considerable amount of data that are very diverse in origin and nature and must be exploited within an integrated information system in order to provide information for historical knowledge. The Greyware system has been designed to fulfil this goal and provides the main categories for pottery analysis within a shareable and reusable scenario. Its development and application prove that a little semantics goes a long way and that the creation of domain ontologies for archaeological research is an iterative process under development, as long as several projects sharing data, resources and time can develop a collaborative framework to maximize the assets of individual expertise and collaborative work. In this paper, we discuss the requirements of the system, the challenge of developing strategies for normalized data management and their potential for exploiting historical vestiges from an integrated perspective.

OntoJob Query Processor: An Ontology Driven Query Processing Method in Jobology Information System

There are a large numbers of jobsites/job portals that provide information about employment on the internet. These websites facilitate employers to post job lists. Job seekers go through those job posts and apply for the same. But, due to the availability of dozens of job portals, job seekers are unable to concentrate on the efforts to see the best outcomes. The overall objective of the paper is to develop a prototype system that provides a platform for the job seeker to access all the job lists from various job sites on a single click, at the same place with respect to the fired query. For this purpose, an ontology driven information system named as Jobology is discussed that integrates various Jobboards using the approach of ontology alignment. The system takes user’s query in keyword format and in response generates a set of SPARQL queries. These SPARQL queries are then fired on their respective ontologies and in turn they yield the results. These results are merged and finally presented to user. As a contribution of this paper, we have proposed an “OntoJob” Query processor that takes job seeker query in keyword format and in turn generates a set of SPARQL queries with respect to every jobboard. The proposed approach is implemented in JAVA using OWLAPI on window platform. To evaluate the proposed work, comparison analysis between Jobboards, proposed ontologies and integrated system was performed. The results came out to be very satisfactory.

Phenoscape: Identifying Candidate Genes for Evolutionary Phenotypes.

Phenotypes resulting from mutations in genetic model organisms can help reveal candidate genes for evolutionarily important phenotypic changes in related taxa. Although testing candidate gene hypotheses experimentally in nonmodel organisms is typically difficult, ontology-driven information systems can help generate testable hypotheses about developmental processes in experimentally tractable organisms. Here, we tested candidate gene hypotheses suggested by expert use of the Phenoscape Knowledgebase, specifically looking for genes that are candidates responsible for evolutionarily interesting phenotypes in the ostariophysan fishes that bear resemblance to mutant phenotypes in zebrafish. For this, we searched ZFIN for genetic perturbations that result in either loss of basihyal element or loss of scales phenotypes, because these are the ancestral phenotypes observed in catfishes (Siluriformes). We tested the identified candidate genes by examining their endogenous expression patterns in the channel catfish, Ictalurus punctatus. The experimental results were consistent with the hypotheses that these features evolved through disruption in developmental pathways at, or upstream of, brpf1 and eda/edar for the ancestral losses of basihyal element and scales, respectively. These results demonstrate that ontological annotations of the phenotypic effects of genetic alterations in model organisms, when aggregated within a knowledgebase, can be used effectively to generate testable, and useful, hypotheses about evolutionary changes in morphology.

The review of modern ontology driven information systems and services, prospect of their application in e-learning

It is done a comparative overview of current ontology driven information systems that are used or could be used for e-learning systems, and discussed current approaches to e-learning in higher education. The main purpose of the study is to analyze the functionality of the systems «Cyc», «Ontolingua», «The World FactBook» and their possible interactions for intercomputer knowledge sharing. Ontology driven information systems are closely related to the conceptualization of the ontological categories and improvement of hierarchic entities at all levels. Ontological principles serve as a unifying mechanism between scientific knowledge of a particular domain and general knowledge focused on solving one of the major problems of artificial intelligence – analysis, synthesis and understanding natural language by computer. The use of ontologies in education is a promising area of research. With knowledge engineering can perform the following tasks: automatic generation of educational-methodical documentation based on ontology of the disciplines; building multi-disciplinary ontology with automatic generation of educational content; determining the validity of the information found in the Internet by students; improving the quality of assessment by using the tools of construction of tests and automatic analysis of the responses. An important direction in the development of e-learning is the formalization and automation of the process of building ontology by creating algebra of the ontologies.

An interactive ontology-driven information system for simulating background radiation and generating scenarios for testing special nuclear materials detection algorithms

This paper describes an original approach to generate scenarios for the purpose of testing the algorithms used to detect special nuclear materials (SNM) that incorporates the use of ontologies. Separating the signal of SNM from the background requires sophisticated algorithms. To assist in developing such algorithms, there is a need for scenarios that capture a very wide range of variables affecting the detection process, depending on the type of detector being used. To provide such a capability, we developed an ontology-driven information system (ODIS) for generating scenarios that can be used for testing of algorithms for SNM detection. The Ontology-Driven Scenario Generator (ODSG) is an ODIS based on information supplied by subject matter experts and other documentation. The details of the creation of the ontology, the development of the ontology-driven information system, and the design of the web user interface (UI) are presented along with specific examples of scenarios generated using the ODSG. We demonstrate that the paradigm behind the ODSG is capable of addressing the problem of semantic complexity at both the user and developer levels. Compared to traditional approaches, an ODIS provides benefits such as faithful representation of the users׳ domain conceptualization, simplified management of very large and semantically diverse datasets, and the ability to handle frequent changes to the application and the UI. The approach makes possible the generation of a much larger number of specific scenarios based on limited user-supplied information.

The Data Mining OPtimization Ontology

The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner.

The Data Mining OPtimization Ontology

The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner.

BeAware!—Situation awareness, the ontology-driven way

Information overload is a severe problem for human operators of large-scale control systems as, for example, encountered in the domain of road traffic management. Operators of such systems are at risk to lack situation awareness, because existing systems focus on the mere presentation of the available information on graphical user interfaces—thus endangering the timely and correct identification, resolution, and prevention of critical situations. In recent years, ontology-based approaches to situation awareness featuring a semantically richer knowledge model have emerged. However, current approaches are either highly domain-specific or have, in case they are domain-independent, shortcomings regarding their reusability. In this paper, we present our experience gained from the development of BeAware!, a framework for ontology-driven information systems aiming at increasing an operator's situation awareness. In contrast to existing domain-independent approaches, BeAware!'s ontology introduces the concept of spatio-temporal primitive relations between observed real-world objects thereby improving the reusability of the framework. To show its applicability, a prototype of BeAware! has been implemented in the domain of road traffic management. An overview of this prototype and lessons learned for the development of ontology-driven information systems complete our contribution.

AN ONTOLOGY FOR ASSET MANAGEMENT

AbstractOntology plays important role in knowledge sharing, reuse, and communication. Repository ontology is usually the first step in the development of an intelligent decision support system. This paper first briefly reviews the literature in the area and then presents the ontologies for condition monitoring and maintenance. A short description of the software application and user interface ontologies is presented. The focus of this work is the repository ontology for the stated purpose. It shows the data structure and information needed for the maintenance management of the industrial assets with its objects, attributes and relationships. The methodology that has been used consists of different steps of defining the scope of the ontology followed by the consideration of using existing ontologies. It is based on the ontology-driven Information System theories and developed by Unified Modelling Language tool.

Ontologies and knowledge sharing in urban GIS

Data and knowledge exchange among users of urban information systems presents many challenges. This paper discusses issues related to the use of ontologies in the development of urban geographic information systems and proposes the creation of software components from diverse ontologies as a way to share knowledge and data. These software components are derived from ontologies using an object-oriented mapping. The translation of an ontology into an active information system component leads to ontology-driven information systems and, in the specific case of geographic applications, to ontology-driven geographic information systems. We analyze the urban environment from the ontologist's point of view and make some inferences about the relationship between knowledge and data sharing, and the theory of bona fide and fiat objects. We also discuss implementation issues, such as the use of Ontolingua as an ontology editor, and CORBA IDL generator, CORBA, and Java as object platforms.