MODL: A Modular Ontology Design Library

Shimizu, Cogan; Hirt, Quinn; Hitzler, Pascal

doi:10.48550/arxiv.1904.05405

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…In the third step, they partitioned these ontologies through PATO, OAPT and AD algorithms before analysing these results in the last step. Recently, Shimizu et al widely contributed to ontology modularization by developing MODL in the study of Shimizu et al [21], a modular ontology design library. It is a collection of documented ODP (ontology design patterns).…”

Section: Related Workmentioning

confidence: 99%

A Hidden Markov Model-Based Approach for Lightweight Ontology Modularization Using K-Means Clustering

Warda,

Ahmed,

Hayatou

et al. 2024

ISI

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Nowadays, ontologies are backbone of Semantic Web. Several domains use ontologies as knowledge models. As their number is constantly increasing, designers are opting to reuse some of those that exist to build new ones. When it is impossible to reuse a part depending on its organization, they import the whole ontology and this makes the manipulation cumbersome especially if the ontology has large concepts. Therefore, segmenting ontologies into partitions, if they are not yet, becomes a constant challenge for designers. This paper presents an approach to modularize ontology using hidden Markov model. Ontology triples are extracted within ontology through SPARQL queries and labelled with integers. The labelled triples constituted a Markov chain where ontology concepts are states and ontology relationships are symbols. This set is used to initialize HMM parameters such as states transition probabilities and symbols observation probabilities matrix and initial states probabilities vector. The transition probabilities matrix of HMM is then used as input of K-Means algorithm to generated modules of ontology concepts. This approach does not handled ontology axioms, which characterize heavy ontologies, and only lightweight ontologies are considered. Experiment on eighteen ontologies, obtained modules satisfied ontology modularization criteria such as independence, non-redundancy, correctness and completeness.

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Section: Related Workmentioning

confidence: 99%

A Hidden Markov Model-Based Approach for Lightweight Ontology Modularization Using K-Means Clustering

Warda,

Ahmed,

Hayatou

et al. 2024

ISI

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“…Taking the data graph of Figure 1 and the axioms of Figure 11, we can check to see if the expected result EID15 is entailed by the ontology and the data, and since it is not, we may consider expanding the axioms to assert that location Transitive type . Ontology Design Patterns (ODPs) are another common feature of modern methodologies [49,168], specifying generalisable ontology modelling patterns that can be used as inspiration for modelling similar patterns, as modelling templates [135,468], or as directly reusable components [158,462]. Several pattern libraries have been made available online, ranging from carefully curated ones [18,462] to open and community moderated ones [158].…”

Section: Ontology Engineeringmentioning

confidence: 99%

“…Ontology Design Patterns (ODPs) are another common feature of modern methodologies [49,168], specifying generalisable ontology modelling patterns that can be used as inspiration for modelling similar patterns, as modelling templates [135,468], or as directly reusable components [158,462]. Several pattern libraries have been made available online, ranging from carefully curated ones [18,462] to open and community moderated ones [158]. As an example, in modelling an ontology for our scenario, we may decide to follow the Core Event ontology pattern proposed by Krisnadhi and Hitzler [288], which specifies a spatio-temporal extent, sub-events, and participants of an event, further suggesting competency questions, formal definitions, etc., to support this pattern.…”

Section: Ontology Engineeringmentioning

confidence: 99%

Knowledge Graphs

Hogan,

Blomqvist,

Cochez

et al. 2020

Preprint

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In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

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“…There exist various techniques for ontology modularization. Some of these techniques are described in [4][5][6][9][10][11][12][13][14]. The most popular language used in ontologies design is OWL (Web Ontology Language) and its extensions.…”

Section: Introductionmentioning

confidence: 99%

Modular Ontology Design: A State-of-Art of Diseases Ontology Modeling and Possible Issue

Lazarre¹,

Kaladzavi²,

Amaria³

et al. 2022

RIA

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The evolution of ontological engineering leaded authors to use some techniques of software engineering to design ontologies. Are obtained from these techniques the monolithic or modularized Ontologies. When is difficult to reuse some concepts of monolithic ontologies, modularized Ontologies facilitate ontology management, understandability and reuse. This paper aims to survey on ontology modularization techniques and their contribution in biomedical ontologies design. Modularization reposed on appropriated techniques and some challenges related to ontology reused, scalable querying, collaborative authoring, and distributed reasoning. For most of disease ontologies, more especially ontologies which reused IDO, these challenges are not considered, and most of them are implemented with OWL language and the novel mode to construct ontology’s purpose is to facilitate reuse and interoperability of ontologies ensured by modularization.

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MODL: A Modular Ontology Design Library

Cited by 3 publications

References 9 publications

A Hidden Markov Model-Based Approach for Lightweight Ontology Modularization Using K-Means Clustering

A Hidden Markov Model-Based Approach for Lightweight Ontology Modularization Using K-Means Clustering

Knowledge Graphs

Modular Ontology Design: A State-of-Art of Diseases Ontology Modeling and Possible Issue

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