Ontology Matching Research Articles

"Honoring the storyteller": the potential of Playback Theater in health policy and systems research.

Abstract Stories and storytelling stimulate inquiries in health policy, initiate and become an integral part of policy dialogues. They can also be used as a health policy advocacy tool. Storytelling is a compelling way to engage with various actors in the health policy realm, co-creating knowledge and action in the social world of health systems. Playback Theatre (PT) is an improvisational form of theatre in which audience members share their life stories, which are then enacted on the spot by a group of citizen actors. Citizen actors are everyday people who are not necessarily professional performers but are trained in PT. PT's emphasis on emotional expression and representation allows individuals to deeply engage with the stories of others, leading to greater empathy and understanding across diverse social groups. If applied with a critical consciousness, we argue that PT methodology can illuminate health policy and systems research storytelling processes, given its ontological and epistemological alignment with social constructivism and its orientation towards values such as human dignity and social justice. In this article, we explore the possibilities and the limits of PT for storytelling in the field of HPSR, as it emphasises stories as much as the storyteller.

Integrating and retrieving learning analytics data from heterogeneous platforms using ontology alignment: Graph-based approach.

This study explores the possibility of integrating and retrieving heterogenous data across platforms by using ontology graph databases to enhance educational insights and enabling advanced data-driven decision-making. Motivated by some of the well-known universities and other Higher Education Institutions ontology, this study improvises the existing entities and introduces new entities in order to tackle a new topic identified from the preliminary interview conducted in the study to cover the study objective. The paper also proposes an innovative ontology, referred to as Student Performance and Course, to enhance resource management and evaluation mechanisms on course, students, and MOOC performance by the faculty. The model solves the issues of data accumulation and their heterogeneity, including the problem of having data in different formats and various semantic similarities, and is suitable for processing large amounts of data in terms of scalability. Thus, it also offers a way to confirm the process of data retrieval that is based on performance assessment with the help of an evaluation matrix.

Enhancing SPARQL Query Rewriting for Complex Ontology Alignments

SPARQL query rewriting is a fundamental mechanism for uniformly querying heterogeneous ontologies in the Linked Data Web. However, the complexity of ontology alignments, particularly rich correspondences (c : c), makes this process challenging. Existing approaches primarily focus on simple (s : s) and partially complex (s : c) alignments, thereby overlooking the challenges posed by more expressive alignments. Moreover, the intricate syntax of SPARQL presents a barrier for non-expert users seeking to fully exploit the knowledge encapsulated in ontologies.This article proposes an innovative approach for the automatic rewriting of SPARQL queries from a source ontology to a target ontology, based on a user's need expressed in natural language. It leverages the principles of equivalence transitivity as well as the advanced capabilities of large language models such as GPT-4. By integrating these elements, this approach stands out for its ability to efficiently handle complex alignments, particularly (c : c) correspondences, by fully exploiting their expressiveness. Additionally, it facilitates access to aligned ontologies for users unfamiliar with SPARQL, providing a flexible solution for querying heterogeneous data.

Anchor-based ontology partitioning and Genetic Programming with Relevance Reasoning for large-scale biomedical ontology matching

Anchor-based ontology partitioning and Genetic Programming with Relevance Reasoning for large-scale biomedical ontology matching

OntoCS: Advancing Computer Science Education through Ontology-Driven Knowledge Management for Curriculum Standardization

This research focuses on advancing the knowledge management process within the computer science curriculum through the application of ontology techniques, aligning with established standards such as those outlined by the IEEE and ACM Computer Associations, and the Thailand Qualifications Framework for Higher Education (TQF: HEd). The research introduces three distinct ontologies: (1) a curriculum ontology (OCC) designed to represent course descriptions provided by lecturers, (2) an ontology (OTQF) aligning with the TQF: HEd in Computer Science, serving as a standardized framework for universities in Thailand to guide the preparation and enhancement of teaching and learning curriculum, ensuring uniform quality and standards across educational institutions. Additionally, (3) a knowledge-based ontology for Computer Science standard curriculum (KOSCC) is introduced, utilized as a benchmark for evaluating individual courses and overall curriculum levels. Hybrid matching algorithms, incorporating instance-based, constraint-based, and property relation-based techniques, are employed in the ontology matching process between course descriptions in OCC and KOSCC. The proposed OntoCS, contributes to the enhancement of computer science education by seamlessly integrating ontology-driven knowledge management, thereby fostering a cohesive and standardized educational context. The outcomes of this research provide valuable support for instructors in designing subjects and courses that align consistently with curriculum standards.

Enhancing IoT Scalability and Interoperability Through Ontology Alignment and FedProx

The rapid expansion of IoT devices has introduced major challenges in ensuring data interoperability, enabling real-time processing, and achieving scalability, especially in decentralized edge computing environments. In this paper, an advanced framework of FedProx with ontology-driven data standardization is proposed, which can meet such challenges comprehensively. On the one hand, it can guarantee semantic consistency across different kinds of IoT devices using unified ontology, so that data from multiple sources could be seamlessly integrated; on the other hand, it solves the non-IID issues of data and limited resources in edge servers by FedProx. Experimental findings indicate that FedProx outperforms FedAvg, with a remarkable accuracy level of 89.4%, having higher convergence rates, and attaining a 30% saving on communication overhead through gradient compression. In addition, the ontology alignment procedure yielded a 95% success rate, thereby ensuring uniform data preprocessing across domains, including traffic monitoring and parking management. The model demonstrates outstanding scalability and flexibility to new devices, while maintaining high performance during ontology evolution. These findings highlight its great potential for deployment in smart cities, environmental monitoring, and other IoT-based ecosystems, thereby enabling the creation of more efficient and integrated solutions in these areas.

Scalable Instance Matching Using Lucene and Mongodb

The advancement of the semantic web and Linked Open Data (LOD) cloud has led to the creation and integration of various knowledge bases defined by ontologies. A significant challenge within the LOD paradigm is identifying resources that refer to the same real-world object to enable large-scale data integration and sharing. In this context, instance matching has emerged as a key solution, linking co-referent instances from heterogeneous data sources using owl:sameAs links. Traditional approaches focus on schema-level matching but often fail to address property-level heterogeneity. Moreover, given the large scale of instances, examining all possible instance pairs is impractical. This paper proposes a scalable and efficient instance-matching approach using MongoDb (Humongous database) and Lucene. MongoDb stores instances at any scale and Lucene uses inverted indexes to identify matching candidates. Experiments on the instance matching track from the Ontology Alignment Evaluation Initiative (OAEI’2022) show that our approach matches the F-measure score of RE-Miner, the top performer in OAEI’2020, while surpassing all other participants in OAEI’2020, 2021 and 2022. Additionally, it operates 17 times faster than RE-Miner, four times faster than Lily and 15 times faster than LogMap, the fastest in OAEI’2020, 2021 and 2022, respectively. Moreover, we evaluate our approach on other knowledge bases from OAEI’2010. Once again, our approach gets highly competitive resuts compared to state-of-the-art approaches.

A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology

The Provenance Ontology (PROV-O) is a World Wide Web Consortium (W3C) recommended ontology used to structure data about provenance across a wide variety of domains. Basic Formal Ontology (BFO) is a top-level ontology ISO/IEC standard used to structure a wide variety of ontologies, such as the OBO Foundry ontologies and the Common Core Ontologies (CCO). To enhance interoperability between these two ontologies, their extensions, and data organized by them, a mapping methodology and set of alignments are presented according to specific criteria which prioritize semantic and logical principles. The ontology alignments are evaluated by checking their logical consistency with canonical examples of PROV-O instances and querying terms that do not satisfy the alignment criteria as formalized in SPARQL. A variety of semantic web technologies are used in support of FAIR (Findable, Accessible, Interoperable, Reusable) principles.

OI-LLM: A Scalable Framework to Integrate Large Scale Ontologies Using Large Language Model

Ontology integration plays a vital role in forming a unified knowledge base through existing knowledge. The integration process is triggered through ontology matching and ontology merging processes. The scale of ontologies dominates the ontology matching process. Earlier researchers have used the Ontology (Meta) Matching (OMM) technique to generate an efficient set of ontology alignments. The set of ontology alignments is used to match two ontologies. This process involves limitations such as the help of domain experts in choosing applicable similarity measures, opting for appropriate and applicable resources (such as WordNet, UMLS, etc.) to generate efficient semantic similarity, and requiring more computations to generate alignments. This raises the issue of increased computational complexity. To resolve this problem, researchers have employed a divide-and-conquer algorithmic strategy for executing multiple matching tasks concurrently with parallel processing. There is still a need to improve computational complexity while handling the integration of large-scale ontologies. With such needs, this article proposes a general and scalable framework to integrate large-scale ontologies. The proposed algorithm uses nature-inspired computing algorithms to apply the natural behavior of the ant-colony optimization algorithm to balance and optimize the working behavior of the machines during parallel processing. The LLM is used to generate highly efficient similarity results during the matching process, which results in a cohesive integrated ontology. The OAEI Anatomy track is used to test the performance of the framework to assess the computational time. The experimental results show that there is an improvement in computational time. It is possible to achieve the principles of ontology coherence and entity coverage.

An Ontology Alignment based on Machine learning for Integration of Patient Health Data

An Ontology Alignment based on Machine learning for Integration of Patient Health Data

A semantic approach for cultural heritage ontology matching and integration based on textual and multimedia information

A semantic approach for cultural heritage ontology matching and integration based on textual and multimedia information

CroMatcher 2.0: A Comprehensive Analysis of the Improved Ontology Matching System

CroMatcher 2.0: A Comprehensive Analysis of the Improved Ontology Matching System

Compact Multitasking Multichromosome Genetic Algorithm for Heuristic Selection in Ontology Matching

Compact Multitasking Multichromosome Genetic Algorithm for Heuristic Selection in Ontology Matching

Multilingual entity alignment by abductive knowledge reasoning on multiple knowledge graphs

Multilingual entity alignment by abductive knowledge reasoning on multiple knowledge graphs

Toward Ontological Alignment: Coordinating Student Ideas with the Representational System of a Computational Modeling Unit for Science Learning

Computational modeling tools present unique opportunities and challenges for student learning. Each tool has a representational system that impacts the kinds of explorations students engage in. Inquiry aligned with a tool’s representational system can support more productive engagement toward target learning goals. However, little research has examined how teachers can make visible the ways students’ ideas about a phenomenon can be expressed and explored within a tool’s representational system. In this paper, we elaborate on the construct of ontological alignment—that is, identifying and leveraging points of resonance between students’ existing ideas and the representational system of a tool. Using interaction analysis, we identify alignment practices adopted by a science teacher and her students in a computational agent-based modeling unit. Specifically, we describe three practices: (1) Elevating student ideas relevant to the tool’s representational system; (2) Exploring and testing links between students’ conceptual and computational models; and (3) Drawing on evidence resonant with the tool’s representational system to differentiate between theories. Finally, we discuss the pedagogical value of ontological alignment as a way to leverage students’ ideas in alignment with a tool’s representational system and suggest the presented practices as exemplary ways to support students’ computational modeling for science learning.

Agent-OM: Leveraging LLM Agents for Ontology Matching

Ontology matching (OM) enables semantic interoperability between different ontologies and resolves their conceptual heterogeneity by aligning related entities. OM systems currently have two prevailing design paradigms: conventional knowledge-based expert systems and newer machine learning-based predictive systems. While large language models (LLMs) and LLM agents have revolutionised data engineering and have been applied creatively in many domains, their potential for OM remains underexplored. This study introduces a novel agent-powered LLM-based design paradigm for OM systems. With consideration of several specific challenges in leveraging LLM agents for OM, we propose a generic framework, namely Agent-OM (Agent for Ontology Matching), consisting of two Siamese agents for retrieval and matching, with a set of OM tools. Our framework is implemented in a proof-of-concept system. Evaluations of three Ontology Alignment Evaluation Initiative (OAEI) tracks over state-of-the-art OM systems show that our system can achieve results very close to the long-standing best performance on simple OM tasks and can significantly improve the performance on complex and few-shot OM tasks.

Ontology Matching and Repair Based on Semantic Association and Probabilistic Logic

Ontology Matching and Repair Based on Semantic Association and Probabilistic Logic

How to Classify Domain Entities Into Top-Level Ontology Concepts Using Large Language Models

Classifying domain entities into their respective top-level ontology concepts is a complex problem that typically demands manual analysis and deep expertise in the domain of interest and ontology engineering. Using an efficient approach to classify domain entities enhances data integration, interoperability, and the semantic clarity of ontologies, which are crucial for structured knowledge representation and modeling. Based on this, our main motivation is to help an ontology engineer with an automated approach to classify domain entities into top-level ontology concepts using informal definitions of these domain entities during the ontology development process. In this context, we hypothesize that the informal definitions encapsulate semantic information crucial for associating domain entities with specific top-level ontology concepts. Our approach leverages state-of-the-art language models to explore our hypothesis across multiple languages and informal definitions from different knowledge resources. In order to evaluate our proposal, we extracted multi-label datasets from the alignment of the OntoWordNet ontology and the BabelNet semantic network, covering the entire structure of the Dolce-Lite-Plus top-level ontology from most generic to most specific concepts. These datasets contain several different textual representation approaches of domain entities, including terms, example sentences, and informal definitions. Our experiments conducted 3 study cases, investigating the effectiveness of our proposal across different textual representation approaches, languages, and knowledge resources. We demonstrate that the best results are achieved using a classification pipeline with a K-Nearest Neighbor (KNN) method to classify the embedding representation of informal definitions from the Mistral large language model. The findings underscore the potential of informal definitions in reflecting top-level ontology concepts and point towards developing automated tools that could significantly aid ontology engineers during the ontology development process.

Efficient ontology matching through compact linear genetic programming with surrogate-assisted local search

Efficient ontology matching through compact linear genetic programming with surrogate-assisted local search

ONTOLOGY MATCHING AND MANAGEMENT USING MACHINE LEARNING ASPECTS

Industry 4. zero is a brand-new generation of data era that ambitions to expand expertise bases for tracking developments in enterprise 4. zero. This paper proposes a framework to cope with the improvement of Knowledge Bases for Monitoring Trends in Industry4.zero.In this framework, we suggest an ontological model (COInd4 ontology) for the manufacturing area that describes the assets and strategies within side the manufacturing unit, and sensor observations are analysed via way of means of remark affects the use of contextual data past classical reasoning mechanism for stopping the forecasted undesired sensor effects that effect on from concept. The framework is primarily based totally on an information-pushed technique for Knowledge Graphs (CSV, JSON, and diverse styles of information) technique for growing Machine studying interoperability combining principles from diverse current ontologies for discovered predictive fashions and execution of the fashions. Moreover, LOTHBROK is designed for estimating cardinalities and paying attention to information locality. The assessment confirmed that TAO can gain appreciably quicker question processing overall performance as compared to the nation of the artwork while processing difficult queries in addition to while besides, TAO gives better transparency, flexibility, and cognitive ergonomics than its options Hontology and Accommodation Ontology. It affords a custom-designed approach to abide via way of means of the necessities of the Greek Programme Diavgeia and proposes the identical time approach to encode authorities and administrative decisions/acts that might be universally followed to combine public files produced via way of means of different EU Member States, with positive changes content-wise.