A New Fuzzy Ontology Development Methodology (FODM) Proposal

Li, Xin; Martínez-Ortega, José-Fernán; Rubio, Gregorio

doi:10.1109/access.2016.2621756

Cited by 10 publications

(2 citation statements)

References 30 publications

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“…With the extensibility, it could be stretched in width in order to suit different applications or scenarios. In addition, the SWARMs ontology should be formalized following two more non-functional requirements [34]. (1) Understandability : The nomenclature of the SWARMs ontology should be easily understandable to all stakeholders, e.g., ontology engineers, marine experts, end users and operators.…”

Section: Modeling Considerations For the Swarms Ontologymentioning

confidence: 99%

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

Bilbao

Martín-Wanton

et al. 2017

Sensors

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In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.

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Section: Modeling Considerations For the Swarms Ontologymentioning

confidence: 99%

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

Bilbao

Martín-Wanton

et al. 2017

Sensors

Self Cite

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“…It is worth noting that although the methodological aspects of the fuzzy ontologies development process has been so far neglected except in FODM [12] and IKARUS-Onto [13], however, they don't introduce the rough set theory in which linguistic hedges (also called modi ers) can be considered as special linguistic expressions to describe the vague properties of fuzzy concepts. Generally, linguistic hedges (modi ers) are special linguistic expressions by which linguistic terms are modi ed.…”

Section: Introductionmentioning

confidence: 99%

FUZRUF-onto: a Methodology to Develop Fuzzy Rough Ontologies

sanyour

2024

Preprint

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Nowadays, semantic web technologies play a crucial role in knowledge representation paradigm. With the raise of imprecise and vague knowledge, there is an upsurge demand in applying a concrete well-established procedure to represent such knowledge. Ontologies, particularly fuzzy ontologies are increasingly applied in application scenarios in which handling of vague knowledge is significant. However, such fuzzy ontologies utilize fuzzy set theory to provide quantitative methods to manage vagueness. In various cases of real-life scenarios, people need to express their everyday requirements using linguistic adverbs such as very, exactly, mostly, possibly, etc. The aim is to show how fuzzy properties can be complemented by Rough Set methods to capture another type of imprecision caused by approximation spaces. Rough sets theory offers a qualitative approach to model such vagueness via describing fuzzy properties at multiple levels of granularity using approximation sets. Using rough-set theory, each fuzzy concept is represented by two approximations. The lower approximation PL(C) consists of a set of fuzzy properties that are definitely observable in the concept. The upper approximation PU(C) on the other hand contains fuzzy properties that are possibly associated with the concept but may not be observed. This paper introduces a methodology named FUZRUF-onto methodology, which is a formal guidance on how to build fuzzy rough ontologies from scratch using extensive research in the area of fuzzy rough combination. Fuzzy set and rough set theories are applied to capture the inherently fuzzy relationships among concepts expressed by natural languages. The methodology provides a very good guideline for formally constructing fuzzy rough ontologies in terms of completeness, correctness, consistency, understandability, and conciseness. To explain how the FUZRUF-onto works, and demonstrate its usefulness, a practical step by step example is provided.

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Multilingual Semantic Representation of Smart Connected World Data

Sharma

Jain

2021

Smart Connected World

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A New Fuzzy Ontology Development Methodology (FODM) Proposal

Cited by 10 publications

References 30 publications

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

FUZRUF-onto: a Methodology to Develop Fuzzy Rough Ontologies

Multilingual Semantic Representation of Smart Connected World Data

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