Quality Of Ontology Research Articles

Examining HPO by organ and system to facilitate practical useby clinicians.

The Human Phenotype Ontology (HPO) is widely used for annotating clinical text data, and sufficient annotation is crucial for the effective utilization of clinical texts. It was known that the use of LLMs can successfully extract symptoms and findings, but cannot annotate them with the HPO. We hypothesized that one of thepotential issue for this is the lack of appropriate terms in the HPO. Therefore, during the Biomedical Linked Annotation Hackathon 8 (BLAH8), we attempted the following two tasksin order to grasp the overall picture of HPO. (1) Extract allHPO terms for each of the 23 HPO subclasses (defined as categories) directly under the HPO "Phenotypic abnormality" and then(2) search for major attributes in each of 23 categories. We employed LLM for these two tasks related to examining HPO and, at the same time, found that LLM didn't work well without ingenuity for tasks that lacked sentences and context. A manual search for terms within each category revealed that the HPO contains a mix of terms with four major attributes: (1) Disease Name, (2) Condition, (3) Test Data, and (4) Symptoms and Findings. Manual curation showed that the ratio of symptoms and findings varied from 0 to 93.1% across categories. For clinicians, who are end-users of medical terminologyincluding HPO, it is difficult to understand ontologies. However, for good quality ontology is also important for good-quality data, and a clinician's help is essential. It is also important to make the overall picture and limitations of ontologies easy to understand in order to bring out the explanatory power of LLMs and artificial intelligence.

Ontology in Dance Domain - A Survey

This article presents a literature review on the domain of dance research that exploits ontology artifacts to manage their domain knowledge. Any dance form around the world is rich in knowledge because of its historical and geographical diversity, movement rules, and interpretive aspects. Researchers found various approaches to manage this knowledge base, among which the ontology development process is preferred by most people owing to its superficial and easy-to-manage characteristics. However, the heterogenic use of ontology in different dance research aspects demands an organized study to understand its contributions to the domain. Our survey approach towards this objective starts with a systematic literature selection and further grouping them into four categories based on ontology involvement. Second, we discuss each group of articles by their contributions and the level of ontology involvement. Third, a novel evaluation framework is proposed, which assesses each selected article based on nineteen attributes from ontology quality, development, and applications perspectives. We rank each article into three qualitative measures, i.e., High(H), Medium(M), and Low(L), for our attribute set based on our understanding. Finally, We comprehensively analyze the outcomes of our qualitative assessment to present the current research status and their limitations in the candidate domain. This review aspires to be a cornerstone resource, enlightening researchers about the current landscape and future prospects of ontological involvement in dance research.

Minimal Macro-Based Rewritings of Formal Languages: Theory and Applications in Ontology Engineering (and Beyond)

In this paper, we introduce the problem of rewriting finite formal languages using syntactic macros such that the rewriting is minimal in size. We present polynomial-time algorithms to solve variants of this problem and show their correctness. To demonstrate the practical relevance of the proposed problems and the feasibility and effectiveness of our algorithms in practice, we apply these to biomedical ontologies authored in OWL. We find that such rewritings can significantly reduce the size of ontologies by capturing repeated expressions with macros. This approach not only offers valuable assistance in enhancing ontology quality and comprehension but can also be seen as a general methodology for evaluating features of rewriting systems (including syntactic macros, templates, or other forms of rewriting rules), which can be analyzed in terms of their influence on computational problems.

Enhancing Human-in-the-Loop Ontology Curation Results through Task Design

The success of artificial intelligence (AI) applications is heavily dependent on the quality of data they rely on. Thus, data curation, dealing with cleaning, organising, and managing data, has become a significant research area to be addressed. Increasingly, semantic data structures such as ontologies and knowledge graphs empower the new generation of AI systems. In this article, we focus on ontologies as a special type of data. Ontologies are conceptual data structures representing a domain of interest and are often used as a backbone to knowledge-based intelligent systems or as an additional input for machine learning algorithms. Low-quality ontologies, containing incorrectly represented information or controversial concepts modelled from a single viewpoint, can lead to invalid application outputs and biased systems. Thus, we focus on the curation of ontologies as a crucial factor for ensuring trust in the enabled AI systems. While some ontology quality aspects can be automatically evaluated, others require a human-in-the-loop evaluation. Yet, despite the importance of the field, several ontology quality aspects have not yet been addressed and there is a lack of guidelines for optimal design of human computation tasks to perform such evaluations. In this article, we advance the state-of-the-art by making two novel contributions. First, we propose a human computation (HC)–based approach for the verification of ontology restrictions —an ontology evaluation aspect that has not yet been addressed with HC techniques. Second, by performing two controlled experiments with a junior expert crowd, we empirically derive task design guidelines for achieving high-quality evaluation results related to (i) the formalism for representing ontology axioms and (ii) crowd qualification testing . We find that the representation format of the ontology does not significantly influence the campaign results. Nevertheless, contributors expressed a preference in working with a graphical ontology representation. Additionally, we show that an objective qualification test is better fitted at assessing contributors’ prior knowledge rather than a subjective self-assessment and that prior modelling knowledge of the contributors had a positive effect on their judgements. We make all artefacts designed and used in the experimental campaign publicly available.

Toward an ontology for EA modeling and EA model quality

Models have long since been used, in different shapes and forms, to understand, communicate about, and (re)shape, the world around us; including many different social, economic, biological, chemical, physical, and digital aspects. This is also the case in the context of enterprise architecture (EA), where we see a wide range of models in many different shapes and forms being used as well. Researchers in EA modeling usually introduce their own lexicon, and perspective of what a model actually is, while accepting (often implicitly) the accompanying ontological commitments. Similarly, practitioners of EA modeling implicitly also commit to (different) ontologies, resulting in models that have an uncertain ontological standing. This is because, for the subject domain of enterprise architecture models (as opposed to the content of such models), no single ontology has gained major traction. As a result, studies into aspects of enterprise architecture models, such as “model quality” and “return on modeling effort”, are fragmented, and cannot readily be compared or combined. This paper proposes a comprehensive applied ontology, specifically geared to enterprise architecture modeling. Ontologies represent structured knowledge about a particular subject domain. It allows for study into, and reasoning about, that subject domain. Our ontology is derived from a theory of modeling, while clarifying concepts such as “enterprise architecture model”, and introduces novel concepts such as “model audience” and “model objective”. Furthermore, the relevant interrelations between these different concepts are identified and defined. The resulting ontology for enterprise architecture models is represented in OntoUML, and shown to be consistent with the foundational ontology for modeling, Unified Foundational Ontology.

Ontologies of Herbal Medicine: A thematic review analysis from 2011-2022

This study examines various approaches for ontology construction and evaluation in medicinal plant ontology, focusing on the challenges associated with this domain. Thirty review articles on three databases revealed five alternative approaches for ontology generation in herbal medicine: well-structured methodology, customized methodology, semiautomatic approach, socio-technical approach, domain expert techniques, and top-level approach. However, 68% of the research did not include ontology evaluation. Ontology quality was often assessed using competency questions, expert opinion, and automated error detection. The study provides ontology developers with a better understanding of established methodologies for developing and evaluating standard ontologies in this area and identifies gaps in existing research for future studies.

A conceptual model for ontology quality assessment

With the continuous advancement of methods, tools, and techniques in ontology development, ontologies have emerged in various fields such as machine learning, robotics, biomedical informatics, agricultural informatics, crowdsourcing, database management, and the Internet of Things. Nevertheless, the nonexistence of a universally agreed methodology for specifying and evaluating the quality of an ontology hinders the success of ontology-based systems in such fields as the quality of each component is required for the overall quality of a system and in turn impacts the usability in use. Moreover, a number of anomalies in definitions of ontology quality concepts are visible, and in addition to that, the ontology quality assessment is limited only to a certain set of characteristics in practice even though some other significant characteristics have to be considered for the specified use-case. Thus, in this research, a comprehensive analysis was performed to uncover the existing contributions specifically on ontology quality models, characteristics, and the associated measures of these characteristics. Consequently, the characteristics identified through this review were classified with the associated aspects of the ontology evaluation space. Furthermore, the formalized definitions for each quality characteristic are provided through this study from the ontological perspective based on the accepted theories and standards. Additionally, a thorough analysis of the extent to which the existing works have covered the quality evaluation aspects is presented and the areas further to be investigated are outlined.

Knowledge Representation and Management 2022: Findings in Ontology Development and Applications.

To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook. We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022. We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model. In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus.

Нечіткі дані у семантичних wiki-ресурсах: моделі, джерела та методи обробки

We analyze main types of dirty data processed by intelligente information systems, criteria of data classification and means of detection non-classical properties of data. Results of this analysis are represented by ontological model that contains taxonomy of classical and nonclassical data and knowledge-oriented methods of their transformation. Special attention is paid to semantically incorrect data that corresponds to vague knowledge. This ontological model intended to provide more effectively methods for transforming raw data into smart data suitable for automatic analysis, knowledge acquisition and reuse in other information systems. The ontological approach provides integration of the proposed model with other external ontologies that formalize characteristics of various methods and software tools that can be used fo data analysis (data mining, inductive inference, semantic queries, and instrimental tools for testing various aspects of the ontology quality, etc.). The work uses the experience of knowledge base developing of the portal version of the Great Ukrainian Encyclopedia e-VUE. This information resource is based on the semantic Wiki technology, it has a large volume, a complex structure and contains a large number of various heterogeneous information objects. Wiki resources are interesting from the point of view of collaborative processing the fuzzy data that describe heterogeneous information objects and knowledge structures. Due to the fact that the creation of this information resource involves a large number of specialists of various scientific fields, who have different areas of expertise and qualifications in use of knowledge-oriented technologies, there are many differences in the understanding of the rules for presenting and structuring data, and therefore a significant part of the Encyclopedia content needs additional verification of its correctness. Therefore, we need in formalized and scalable solutions for detection and processing various types of inconsistence, incompleteness and semantic incorrectness of data. The proposed approach can be useful for the creation of other large-scale resources based on both the semantic Wiki technology and other technological platforms for collaborative processing of distributed data and knowledge.

Data Quality Assessment and Recommendation of Feature Selection Algorithms: An Ontological Approach

Feature selection plays an important role in machine learning and data mining problems. Identifying the best feature selection algorithm that helps to remove irrelevant and redundant features is a complex task. This research tries to address it by recommending a feature selection algorithm based on dataset meta-features. The main contribution of the work is the use of Semantic Web principles to develop a recommendation model for the feature selection algorithm. As a result, dataset meta-features are modeled in a domain ontology, and a set of Semantic Web rule language (SWRL) predictive rules have been proposed to recommend a feature selection algorithm. The result of this research is a feature selection algorithm recommendation based on the data characteristics and quality (FSDCQ) ontology, which not only helps with recommendations but also finds the data points with data quality violations. An experiment is conducted on the classification datasets from the UCI repository to evaluate the proposed ontology. The usefulness and effectiveness of the proposed method is evaluated by comparing it with the widely used method in the literature for the recommendation. Results show that the ontology-based recommendations are equally good as the widely used recommendation model, which is k-NN, with added benefits.

A systematic comparison and evaluation of building ontologies for deploying data-driven analytics in smart buildings

Ontologies play a critical role in data exchange, information integration, and knowledge sharing across diverse smart building applications. Yet, semantic differences between the prevailing building ontologies hamper their purpose of bringing data interoperability and restrict the ability to reuse building ontologies in real-world applications. In this paper, we propose and adopt a framework to conduct a systematic comparison and evaluation of four popular building ontologies (Brick Schema, RealEstateCore, Project Haystack, and Digital Buildings) from both axiomatic design and assertions in a use case, namely the Terminological Box (TBox) evaluation and the Assertion Box (ABox) evaluation. In the TBox evaluation, we use the SQuaRE-based Ontology Quality Evaluation (OQuaRE) framework and concede that Project Haystack and Brick Schema are more compact with respect to the ontology axiomatic design. In the ABox evaluation, we apply an empirical study with sample building data that suggests Brick Schema and RealEstateCore have greater completeness and expressiveness in capturing the main concepts and relations within the building domain. The results indicate that there is no universal building ontology for integrating Linked Building Data (LBD). We also discuss ontology compatibility and investigate building ontology design patterns (ODPs) to support ontology matching, alignment, and harmonisation.

K-RET: Knowledgeable Biomedical Relation Extraction System.

Relation Extraction (RE) is a crucial process to deal with the amount of text published daily, for example, to find missing associations in a database. RE is a text mining task for which the state-of-the-art approaches use bidirectional encoders, namely, BERT. However, state-of-the-art performance may be limited by the lack of efficient external knowledge injection approaches, with a larger impact in the biomedical area given the widespread usage and high quality of biomedical ontologies. This knowledge can propel these systems forward by aiding them in predicting more explainable biomedical associations. With this in mind, we developed K-RET, a novel, knowledgeable biomedical relation extraction system that, for the first time, injects knowledge by handling different types of associations, multiple sources and where to apply it, and multi-token entities. We tested K-RET on three independent and open-access corpora (DDI, BC5CDR, and PGR) using four biomedical ontologies handling different entities. K-RET improved state-of-the-art results by 2.68% on average, with the DDI Corpus yielding the most significant boost in performance, from 79.30% to 87.19% in F-measure, representing a p-value of 2.91 × 10-12. https://github.com/lasigeBioTM/K-RET. Supplementary data are available at Bioinformatics online.

System Cognition and Analytic Technology of Cultivated Land Quality from a Data Perspective

As cultivated land quality has been paid more and more scientific attention, its connotation generalization and cognitive bias are widespread, bringing many challenges to the investigation and evaluation of regional cultivated land quality and its data analysis and mining. Establishing a systematic and interdisciplinary cognitive approach to cultivated land quality is urgent and necessary. Therefore, we explored and developed a conceptual framework of the model for the cultivated land quality analysis from the data perspective, including cultivated land quality ontology, mapping, correlation, and decision models. We identified the primary content of cultivated land quality perceptions and four cognitive mechanisms. We built vital technologies, such as the collaborative perception of the quality of cultivated land, intelligent treatment, diagnostic evaluation, and simulation prediction. Applying this analysis framework, we sorted out the frequency of indicators that characterize the function of cultivated land according to the literature in recent years and have built the cognitive system of cultivated land quality in the black soil region of Northeast China. The system’s central component was production capacity and it had three components: a foundation, a guarantee, and an effect. The black soil region cultivated land quality evaluation system has seven purposes involving 20–31 key indicators: production supply, threat control, farmland infrastructure regulation, cultivated land ecological maintenance, economics, social culture, and environmental protection. In various application contexts, the system had many critical supporting technologies. The results demonstrate that the framework has strong adaptability, efficiency, and scalability, which might offer a theoretical direction for further studies on the evaluation of the quality of cultivated land in the area. The analysis framework established in this study is helpful to deepen the understanding of cultivated land quality systems from the perspective of big data. Taking the big data of cultivated land quality as the driving force, combined with the technical methods of cultivated land quality analysis, the evaluation results of cultivated land quality under different scenarios and different objectives are optimized. In addition, the framework can serve the practice of farmland management and engineering improvement, adapt to the management needs of different objects and different scales, and achieve the combination of theory and practice.

Demand-driven NEV supplier selection: An integrated method based on ontology–QFD–CBR

With the rapid development of new energy vehicles (NEVs), the market competition in the NEV industry is becoming increasingly fierce. Selecting the right supplier has become a critical aspect for NEV manufacturers. Therefore, based on the user’s demand information, selecting a suitable NEV supplier to support the NEV manufacturer’s management decision is a noteworthy research problem. The purpose of this study is to develop an integrated method for demand-driven NEV supplier selection based on ontology–quality function deployment (QFD)–case-based reasoning (CBR). The method is composed of three parts: 1) construction of domain ontology of NEV component supplier selection criteria based on text information mining; 2) extraction of demand attributes and determination of their weight based on latent Dirichlet allocation (LDA) and Kano model, as well as determination of expected attributes and their weights based on QFD; and 3) selection of an NEV component supplier based on CBR. To illustrate the use of the proposed method, an empirical study on the supplier selection of the XP NEV manufacturer is given. This method is helpful in selecting the most suitable component supplier for NEV manufacturers and relevant decision-makers.

Phrase2Onto: A Tool to Support Ontology Extension

Due to importance of data FAIRness (Findable, Accessible, Interoperable, Reusable), ontologies as a means to make data FAIR have attracted more and more attention in different communities and are being used in semantically-enabled applications. However, to obtain good results while using ontologies in these applications, high quality ontologies are needed of which completeness is one of the important aspects. An ontology lacking information can lead to missing results. In this paper we present a tool, Phrase2Onto, that supports users in extending ontologies to make the ontologies more complete. It is particularly suited for ontology extension using a phrase-based topic model approach, but the tool can support any extension approach where a user needs to make decisions regarding the appropriateness of using phrases to define new concepts. We describe the functionality of the tool and a user study using Pizza Ontology. The user study showed a good usability of the system and high task completion. Further, we report on a real application where we extend the Materials Design Ontology.

Automatic knowledge exchange between ontologies and semantic graphs

This article presents an innovative knowledge management approach for the validation and transfer of knowledge between semantic networks or ontologies and a target ontology represented in web ontology language (OWL) format. This process has been designed for addressing the quality improvement of ontologies automatically created by learning techniques. The knowledge transfer process is a semi-automatic computer aided method to assist the domain expert to improve the target ontology. To validate our proposal, we have used an automatically generated target ontology. We used knowledge transfer from the well-known Babelnet semantic graph and a manually generated ontology to improve the quality of the target ontology. Finally, to show the suitability of our proposal in the ontology-fixing process, we compare the improved target ontology resulting from the application of the proposed validation and knowledge transfer techniques with its original version. We developed an example of our proposal in our OntologyFixer tool, which is available on a GitHub repository ( https://github.com/gabyluna/OntologyFixer/tree/ontofixer_v2 ).

Cultural ecology cognition and heritage value of huizhou traditional villages

Heritage value is a prerequisite for protecting and utilising traditional villages. In response to the unclear definition of heritage value, this study develops a conceptual model and an indicator system of traditional village heritage value based on cultural ecology. Additionally, the study analyses heritage value using a three-dimensional matrix model and cluster analysis. The results show significant differences in the heritage value of Huizhou traditional villages. High heritage values are mainly distributed in Yi County and She County, while low heritage values are mainly distributed at the edges of the region. Among the diversity, continuity and integrity differences, the integrity difference is the highest, and is reflected in the quality of heritage ontology and the relationship between heritage ontology and the surrounding environment. In addition, four types of Huizhou traditional villages are identified: resource single type, inheritance dilemma type, quality dilemma type, and heritage vitality type. Utilisation strategies are proposed for the four types to guide the classified management of traditional villages.

Extending ontology pitfalls for better ontology evaluation

This article presents a framework that detects potential ontology building errors to improve the ontology quality. These potential errors are called ontology pitfalls in the literature. This work extends the existing ontology pitfall set in the literature and suggests new solutions for ontology repair. We have also developed a Java implementation for detection of the proposed pitfalls. These pitfalls were evaluated with well-known ontologies using this implementation.

An interval type‐2 fuzzy ontological model: Predicting water quality from sensory data

SUMMARYWith the advent of break‐through sensing technology, performing data capturing and analysis for knowledge engineering has become more opportunistic. The task of efficiently analyzing sensor based data for effective decision making poses a significant challenge. Conventional prediction and recommender systems lack comprehensive analysis of all parameters and aspects, thus compromising prediction results. At the decision‐making level, traditional knowledge driven prediction systems deploy classical ontology for knowledge representation and analysis. However, classical ontologies are not considered as powerful tools due to their inability to handle vagueness in data for real‐world applications. On the contrary, fuzzy ontology deals with the issue of hazy and uncertain data for effective analysis to give promising results. This work presents interval type 2 fuzzy ontological knowledge model that predicts water quality of sensor based water samples and providing solutions with respect to the corresponding quality state. The proposed knowledge model constitutes of two newly developed ontologies: water sensor observations ontology (crisp ontology to model sensor observational data) and water quality ontology (interval type 2 fuzzy ontology for modeling the water quality prediction process). The inference mechanism is based on interval type‐2 fuzzy partitioning and computation. Besides water quality prediction and providing solutions, the proposed model handles the issue of interoperability and exchange of consensual knowledge among multiple disciplines. The proposed knowledge model is validated with real‐life water sensor based parameterized data captured from various geographically dispersed monitoring stations with approximately 50,000 samples at each station.

Evaluation and decision-making framework for concrete surface quality based on computer vision and ontology

PurposeDefects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.Design/methodology/approachCombining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.FindingsComputer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.Originality/valueThis paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.