Ontology Language Research Articles

Semantically triggered qualitative simulation of a geological process

The field of geology has been the subject of a range of research efforts aiming to formalize geological domain knowledge, notably through geological domain ontologies. The main focus of existing geological ontologies primarily lies in describing static geological objects and their properties, paying less attention to the knowledge concerning geological processes and events. Meanwhile, the geological process modeling and simulation predominantly rely on quantitative numerical approaches that necessitate comprehensive and abundant data as input. However, many geological processes took place on a million-year time scale with insufficient data and non-direct observations. Given the inherent incompleteness of geological data, geologists still rely on qualitative reasoning to validate their interpretations. There is currently a dearth of applicable methods to facilitate qualitative reasoning and simulate geological processes based on domain knowledge.We propose to model the effects of a geological process through an object-oriented program, while keeping an ontological representation of the situation at each instant. To combine the two models, we propose using semantically defined ‘process triggers.’ These process triggers are defined as part of the ontology, in accordance with the Basic Formal Ontology. They enable geologists to describe the precise moment when a geological process is triggered and initiated. On the computational program side, we employ the ‘Semantic Micro Object Language’ to embody the knowledge and rules provided by geologists, facilitating the simulation of geological processes. Through an evaluation experiment, our proposed approach demonstrates promising results within a reasonable timeframe. As proof of concept, we have applied our method to a real-world scenario of petroleum thermal maturation in Ekofisk Field and got a promising result. Our approach provides a formalism that allows a powerful code to interact with domain ontologies, which paves the path for future knowledge reasoning.

Artificial Intelligence Techniques in Semantic Web Services

It relates to the conventional machine interaction services, is the server end of a Client-Server system relates to the data on web that is understandable by the machines, its primary objective is to permit the humans and machines together. The World Wide Web is progressing quickly in the direction of the concept of semantic language for database. The ontology tool Protégé consists of the SPARQL Query Tab to execute the queries, whereas the alternative is the Apache Jena Fuseki. The semantic web framework is comprised of different Application interfaces that interacting collectively to execute RDF data. The Fuseki supports the SPARQL queries over Hyper Text Transfer Protocol. Apache Jena Fuseki is based framework for developing Artificial Intelligence Techniques in Semantic Web Services. The proposed methodology can handle queries of various convolutions and complexities. Semantic web services on the server end provide structured and standardized information about the services they offer, typically using technologies related to ontology ensure that the data is both readable and process by machines based on ontology. The Experimental Analysis is given along with the results based on different ontology based technologies. In the disciplines of design, Thinking and implementation, along with representation ontologies are used for semantic web services.

On the Human Condition in the Zhuangzi

This article argues that xing 性 in the Zhuangzi 莊子 should not be understood as “human nature” but as “human condition.” It introduces the problem of interpreting xing as “human nature” by surveying relevant English-language literature before detailing the interpretive paradigm of Chinese accounts showing how the latter’s appropriation of the language of substance ontology hinders an accurate portrayal of Daoist xing. It argues that xing should be interpreted in connection to the concept of ming 命understood as contingent, natural, and temporal “external conditions”. It argues that xing are “internal conditions” that function in conjunction with ming as “external conditions” where “habit” (xi 習) mediates their interaction.

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives.

Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap. This report had two objectives. First, it aimed to synthesize implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems through a semantic and ontological approach. Second, it introduced an original framework of this use-case-based taxonomy on implementation barriers and facilitators within a QI process. We synthesized terms from combined datasets of all-site implementation barriers and facilitators through QI cause-and-effect analysis and qualitative thematic analysis. We developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme to categorize synthesized terms and structure. This framework employed a semantic and ontological approach. It was built upon existing terms and models from the QI Plan, Do, Study, Act phases, the Consolidated Framework for Implementation Research domains, and the fishbone cause-and-effect categories. The QIIT followed a hierarchical and relational classification scheme. Its taxonomy was linked to four QI Phases, five Implementing Domains, and six Conceptual Determinants modified by customizable Descriptors and Binary or Likert Attribute Scales. This case report introduces a novel approach to standardize the process and taxonomy to describe evidence translation to QI implementation barriers and facilitators. This classification scheme reduces redundancy and allows semantic agreements on concepts and ontological knowledge representation. Integrating existing taxonomies and models enhances the efficiency of reusing well-developed taxonomies and relationship modeling among constructs. Ultimately, employing STs helps generate comparable and sharable QI evaluations for forecast, leading to sustainable implementation with clinically informed innovative solutions.

Using advanced web ontology language properties for deriving novel and consistent association rules

Using advanced web ontology language properties for deriving novel and consistent association rules

MBORS: Mosquito vector Biocontrol Ontology and Recommendation System

Background & objectives: Mosquito vectors are disease-causing insects, responsible for various life-threatening vector-borne diseases such as dengue, Zika, malaria, chikungunya, and lymphatic filariasis. In practice, synthetic insecticides are used to control the mosquito vector, but, the continuous usage of synthetic insecticides is toxic to human health resulting in communicable diseases. Non-toxic biocontrol agents such as bacteria, fungus, plants, and mosquito densoviruses play a vital role in controlling mosquitoes. Community awareness of mosquito biocontrol agents is required to control vector-borne diseases. Mosquito vector-based ontology facilitates mosquito biocontrol by providing information such as species names, pathogen-associated diseases, and biological controlling agents. It helps to explore the associations among the mosquitoes and their biocontrol agents in the form of rules. The Mosquito vector-based Biocontrol Ontology Recommendation System (MBORS) provides the knowledge on mosquito-associated biocontrol agents to control the vector at the early stage of the mosquitoes such as eggs, larvae, pupae, and adults. This paper proposes MBORS for the prevention and effective control of vector-borne diseases. The Mosquito Vector Association ontology (MVAont) suggests the appropriate mosquito vector biocontrol agents (MosqVecRS) for related diseases. Methods: Natural Language Processing and Data mining are employed to develop the MBORS. While Tokenization, Part-of-speech Tagging (POS), Named Entity Recognition (NER), and rule-based text mining techniques are used to identify the mosquito ontology concepts, the data mining apriori algorithm is used to predict the associations among them. Results: The outcome of the MBORS results in MVAont as Web Ontology Language (OWL) representation and MosqVecRS as an Android application. The developed ontology and recommendation system are freely available on the web portal. Interpretation & conclusion: The MVAont predicts harmless biocontrol agents which help to diminish the rate of vector-borne diseases. On the other hand, the MosqVecRS system raises awareness of vectors and vector-borne diseases by recommending suitable biocontrol agents to the vector control community and researchers.

Q-Learning Based Cognitive Domain Ontology Representation and Solving on Low Power Computing Platforms

Q-Learning Based Cognitive Domain Ontology Representation and Solving on Low Power Computing Platforms

Research of Parts Geometric Tolerance Measurement and Certification Based on Ontology

Geometric tolerance measurement certification of parts is a key factor in determining product quality, production cost and competitiveness. The existing certification schemes for tolerance measurement rely heavily on the experience and proficiency of surveyors, which increases the uncertainty of the certification results for tolerance measurement. In this paper, the ontology is introduced into the geometric tolerance measurement certification, which provides a feasible method for the automation and intelligence of the geometric tolerance measurement certification of parts. Firstly, the engineering semantics of the tolerance measurement and certification process is extracted from the part drawings, and the OWL ontology representation model of the product is established. Secondly, according to the expert knowledge in the field of tolerance measurement certification, the semantic Web rule language SWRL is used to extract, induce and summarize the terminology relations involved in tolerance measurement certification, and these relations are converted into inference rules. Then, according to the semantic model and relation rules established above, the geometric tolerance measurement certification scheme of parts is automatically generated, so as to construct the ontology knowledge base of geometric tolerance measurement certification of parts. Finally, taking reducer box hole as an example, the automatic generation process of box hole coaxiality measurement authentication scheme is demonstrated, and its effectiveness is verified.

The Phenomenon and the Notion of Discourse in the Modern Ontology of Language

The paper presents a theoretical substantiation of the concept of «discourse» as the most important ontological category in contemporary linguistics. It further explains the semantic evolution of the concept of «discourse» from philosophical scholasticism to modern humanitarian knowledge. The essence of the notion of «discourse» is unveiled through a number of fundamental oppositions in the ontology of the object of linguistics (the language), with the opposition of system and phenomenon, considered as the two sides of the ontology of language, being the most important of them. In each of its sides, the object strives to achieve ontological integrity. Systemic integrity is opposed to the phenomenological totality of language, which is what the notion of «discourse» is about. Systemic integrity in its material aspect relies on a certain sum of elements that are in structural relation to each other and create the structure of language. The discursive integrity of language in its material aspect is based on a speech fact (a word in its speech status, an utterance, a text), which stands in an interpretive relation to (an)other speech fact(s). An intertextual connection arises between speech facts; one fact follows another in a linear-historical sequence. The sum of speech facts constitutes the wholeness of expressive extent of discourse. In its ideal aspect, integrity is subject to the effects of certain semantic constants. As far as the systemic integrity of language is concerned, such a constant is a linguistic function (cognitive, communicative). In its phenomenological integrity, language is governed by the concept, which is the highest semantic setting of discourse, being the intuition of discourse. The two dimensions of integrity are inextricably linked, and complement each other in the general ontology of language. Combining a set of activity-based and sociocultural approaches to language studies, the contemporary paradigm in the development of linguistics can be characterized as phenomenological. The paper is intended for specialists in the field of general theory of language, and history of linguistics.

Ontological analysis in the problems of container applications threat modelling

Objectives. The main purpose of the work is the experimental verification of the method of automatic threat modelling based on the ontological approach using the example of multicomponent container applications presented in the form of data flow diagrams.Methods. Methods of ontological modelling and knowledge management are used in the work. The Web Ontology Language is used to represent knowledge; automatic reasoning based on description logics is used for threat modelling.Results. A machine-readable set (dataset) of 200 data flow diagrams is developed; each diagram is obtained from the configuration of a real container application and is presented as an ontology and a knowledge graph. An ontological two-level domain-specific threat model of container applications is formed. An experiment is conducted to compare the coverage by threats using the common approach and using domain-specific threats for created dataset. For 95 % of the diagrams, the domain-specific threat model showed the coverage similar or greater than the common approach.Conclusion. The results of the experiment prove the suitability and effectiveness of the ontological approach for automatic threat modelling. The created dataset can be used for various research in the field of automation of threat modelling.

Development of Semantic-Web Based Forest Resources Database

There are some forest resources (such as Trees, Shrubs, and Water bodies) in the university main campus. These resources are used for various purposes ranging from teaching, researches medicinal and domestic purposes and among many others. However, there is no record of official documentation on them. Thus, this study aims to provide a semantic-web based database system for efficient management of forest resources. We adopted the W3C semantic web framework in the design and development of this work. Specifically, we focused on the layers 1 to 5 of the framework. The layers include; Resource Description Framework (RDF), Web Ontology Language (OWL), Extensible Markup Language (XML), User Interface). The Uschold’s and Kings’ method was applied in creating the ontology. The implementation process was carried out by populating the database with the Taxonomies of the Trees on University of Energy and Natural Resources (U.E.N.R) campus by means of creating an Ontology File using protégé. Some of the basic information that we provided by the proposed semantic web database include trees, their scientific name, geographical coordinate, physical location, medicinal value and other important data. We performed alpha test by involving six senior forest science students who used the application, evaluated it and came out with a good result which is shown in the chapter four of this documentation.

Using semantic story maps to describe a territory beyond its map

The paper presents the Story Map Building and Visualizing Tool (SMBVT) that allows users to create story maps within a collaborative environment and a usable Web interface. It is entirely open-source and published as a free-to-use solution. It uses Semantic Web technologies in the back-end system to represent stories through a reference ontology for representing narratives. It builds up a user-shared semantic knowledge base that automatically interconnects all stories and seamlessly enables collaborative story building. Finally, it operates within an Open-Science oriented e-Infrastructure, which enables data and information sharing within communities of narrators, and adds multi-tenancy, multi-user, security, and access-control facilities. SMBVT represents narratives as a network of spatiotemporal events related by semantic relations and standardizes the event descriptions by assigning internationalized resource identifiers (IRIs) to the event components, i.e., the entities that take part in the event (e.g., persons, objects, places, concepts). The tool automatically saves the collected knowledge as a Web Ontology Language (OWL) graph and openly publishes it as Linked Open Data. This feature allows connecting the story events to other knowledge bases. To evaluate and demonstrate our tool, we used it to describe the Apuan Alps territory in Tuscany (Italy). Based on a user-test evaluation, we assessed the tool’s effectiveness at building story maps and the ability of the produced story to describe the territory beyond the map.

Offensive language in user-generated comments in Lithuanian

Abstract The aim of the current research is to investigate the feasibility of identifying offensive language in Lithuanian by utilising the Simplified Offensive Language Taxonomy (SOLT). The key principle behind this taxonomy is its ability to complement existing offensive language ontologies and tagset systems, with the ultimate goal of integrating it into publicly accessible Linguistic Linked Open Data (LLOD) resources. The dataset used in the current study is a publicly available corpus of user-generated comments collected from a Lithuanian portal (Amilevičius et al. 2016). The study identified that offensive language predominantly focuses on collective derogatory language rather than individuals. The most common category of offensive language is related to physical and mental disabilities, followed by ideological offenses, xenophobic and sexist remarks, and less frequent categories like ageism, classism, homophobia, and religious discrimination. These results highlight the diverse range of offensive language online and underscore the need to combat discrimination and promote respectful discourse, particularly concerning marginalised groups.

A Yolŋu ontology of language announces itself in academia

In this paper I position myself in a modest mediator role, supporting the entity of ‘Yolŋu language’ in bringing to life a Yolŋu ontology of language in academia. As a matter of urgency, in reconceptualising ‘language’ in curriculum development in academia, my work is to theorise the Yolŋu ontology of language under the category of philology: philo- (love) and -logy (word). Modern philology is a rather vague category for studying language, but I claim philology can respect and take Yolŋu language ontology seriously. I am arguing for supporting a revival of this way knowing language in academia. It can certainly help me and many academics to stop committing ourselves to having the world of things ‘out there’, and the words to name them ‘over here’. It can help me to study words uttered and written and their meanings in situ, which I argue is one aspect of care for Yolŋu concepts in the academy. This task is not mine alone, yet pursued in a collective epistemic space within academia, where politicoepistemic diplomacy is conducted.

Development of a Novel Production Model for Labour Productivity: Modular Construction Toolkit Design

The building industry faces a number of prominent challenges in the coming period. In this article, we focus on productivity in construction, which lags behind other industries despite technological developments. There is an urgent need for more efficient production methods. In other words, the potential for increasing productivity in construction is enormous. As in other industries, the key to this lies in process orientation, process standardization, and digitization. Lean construction approaches offer innovative solutions here by aiming to maximize customer value while minimizing waste, applying the principles of lean production to construction processes. However, building products are distinct in nature. Efforts to standardize them have achieved partial success, but only within specific product categories and for certain customer needs. Most construction activities remain highly unique. An alternative solution lies in standardizing work processes and not the final product. By adopting this method, one can considerably decrease individuality in production without compromising the essential uniqueness of the building product. Consequently, it is crucial to gain a deeper understanding of the standardization of production processes and the dynamics within the construction sector. This article introduces a modular construction toolkit designed to standardize production processes at construction sites. This toolkit consists of a series of consistent process steps, each linked to a standard time metric. Using this classification, a production model is constructed from a select number of recurring processes, leading to an ontological representation of production. This modular approach allows diverse production processes to be compared based on productivity, as they are composed of consistent and comparable sub-processes. Such a comparison is crucial for continuous production optimization. This method also enables the pinpointing of the most wasteful processes across various construction sites. While the primary data generation use case is centred on special civil engineering (special foundation engineering), the core concepts can be applied to general building construction.

Semi-Automatic Building and Learning of a Multilingual Ontology

Most online platforms, applications, and Websites use a massive amount of heterogeneous evolving data. These data must be structured and normalized before integration to improve the search and increase the relevance of results. An ontology can address this critical task by efficiently managing data and providing structured formats through techniques such as the Web Ontology Language (OWL). However, building an ontology can be costly, primarily if conducted manually. In this context, we propose a new methodology for automatically building and learning a multilingual ontology using Arabic as the base language via a corpus collected from Wikipedia. Our proposed methodology relies on Finite-state transducers (FSTs). FSTs are regrouped into a cascade to reduce errors and minimize ambiguity. The produced ontology is extended to English and French and independent language images via a translator we developed using APIs. The rationale for starting with the Arabic corpus to extract terms is that entity linking is more convenient from Arabic to other languages. In addition, many Wikipedia articles in English and French (for instance) do not have associated Arabic articles, but the opposite is true. In addition, dealing with Arabic terms permits us to enrich the Arabic module of the free linguistic platform we use in dictionaries and graphs. To assess the efficiency of our proposed methodology, we conducted performance metrics. The reported results are encouraging and promising.

A knowledge-based decision support system for inferring supportive treatment recommendations for diabetes mellitus.

Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, heart attack, stroke, and lower limb amputation. A Clinical Decision Support System (CDSS) can assist healthcare practitioners in their daily effort and can improve the quality of healthcare provided to DM patients and save time. In this study, a CDSS that can predict DM risk at an early stage has been developed for use by health professionals, general practitioners, hospital clinicians, health educators, and other primary care clinicians. The CDSS infers a set of personalized and suitable supportive treatment suggestions for patients. Demographic data (e.g., age, gender, habits), body measurements (e.g., weight, height, waist circumference), comorbid conditions (e.g., autoimmune disease, heart failure), and laboratory data (e.g., IFG, IGT, OGTT, HbA1c) were collected from patients during clinical examinations and used to deduce a DM risk score and a set of personalized and suitable suggestions for the patients with the ontology reasoning ability of the tool. In this study, OWL ontology language, SWRL rule language, Java programming, Protégé ontology editor, SWRL API and OWL API tools, which are well known Semantic Web and ontology engineering tools, are used to develop the ontology reasoning module that provides to deduce a set of appropriate suggestions for a patient evaluated. After our first-round of tests, the consistency of the tool was obtained as 96.5%. At the end of our second-round of tests, the performance was obtained as 100.0% after some necessary rule changes and ontology revisions were done. While the developed semantic medical rules can predict only Type 1 and Type 2 DM in adults, the rules do not yet make DM risk assessments and deduce suggestions for pediatric patients. The results obtained are promising in demonstrating the applicability, effectiveness, and efficiency of the tool. It can ensure that necessary precautions are taken in advance by raising awareness of society against the DM risk.

How to Tell Easy from Hard: Complexities of Conjunctive Query Entailment in Extensions of ALC

It is commonly known that the conjunctive query entailment problem for certain extensions of (the well-known ontology language) ALC is computationally harder than their knowledge base satisfiability problem while for others the complexities coincide, both under the standard and the finite-model semantics. We expose a uniform principle behind this divide by identifying a wide class of (finitely) locally-forward description logics, for which we prove that (finite) query entailment problem can be solved by a reduction to exponentially many calls of the (finite) knowledge base satisfiability problem. Consequently, our algorithm yields tight ExpTime upper bounds for locally-forward logics with ExpTime-complete knowledge base satisfiability problem, including logics between ALC and µALCHbregQ (and more), as well as ALCSCC with global cardinality constraints, for which the complexity of querying remained open. Moreover, to make our technique applicable in future research, we provide easy-to-check sufficient conditions for a logic to be locally-forward based several versions of the on model-theoretic notion of unravellings. Together with existing results, this provides a nearly complete classification of the “benign” vs. “malign” primitive modelling features extending ALC, missing out only the Self operator. We then show a rather counter-intuitive result, namely that the conjunctive entailment problem for ALCSelf is exponentially harder than for ALC. This places the seemingly innocuous Self operator among the “malign” modelling features, like inverses, transitivity or nominals.

Ontology of language, with applications to demographic data

Here we present what we believe is a novel account of what languages are, along with an axiomatically rich representation of languages and language-related data that is based on this account. We propose an account of languages as aggregates of dispositions distributed across aggregates of persons, and in doing so we address linguistic competences and the processes that realize them. This paves the way for representing additional types of language-related entities. Like demographic data of other sorts, data about languages may be of use to researchers in a number of areas, including biomedical research. Data on the languages used in clinical encounters are typically included in medical records, and capture an important factor in patient-provider interactions. Like many types of patient and demographic data, data on a person’s preferred and primary languages are organized in different ways by different systems. This can be a barrier to data integration. We believe that a robust framework for representing language in general and preferred and primary language in particular – which has been lacking in ontologies thus far – can promote more successful integration of language-related data from disparate data sources.

Systematic Literature Review On The Ontology Representation Related The Hadith Corpus

Studies on hadith have received a lot of attention since, for Muslims, they serve as a secondary source to the Quran. Although the sanad is a primary area of interest in hadith study, understanding the hadith's content requires knowledge of its matan as well. Ontology, one of the philosophical sciences of knowing, is a study that refers to the reality being investigated. According to this paradigm's ontology, the hadith text is centred and in a normative state. Researchers have attempted to construct ontology based on Islamic knowledge including the Quran, hadith, and others after realising that ontology can reflect a clear form of knowledge or repository that is standard and straightforward. The purpose of this article is to conduct an organised review of the literature on the conception of ontology and hadith corpus. The PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) paradigm was followed during the systematic literature review (SLR) authoring process. By making use of earlier studies, this work adds knowledge and a clear understanding to the conception of ontology and the hadith corpus.