Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms

Li, Longmei; Yevseyeva, Iryna; Basto‐Fernandes, Vitor; Trautmann, Heike; Jing, Ning; Emmerich, Michael

doi:10.1007/978-3-319-54157-0_28

Cited by 19 publications

(10 citation statements)

References 43 publications

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“…Despite the relevance of the above-mentioned sites where we can find many ontologies, we found an interesting work authored by Li et al [55] that introduces a full-featured hand-made ontology to represent the knowledge of Preference-Based MultiObjective Evolutionary Algorithms (PMOEA) present in 62 original scientific papers that have been represented manually in the ontology. The main interest of this ontology is the availability of the knowledge domain experts and ontology designers to share the rationale and experience behind their design decisions for this specific case.…”

Section: Publicly Available Ontologiesmentioning

confidence: 99%

An ontology knowledge inspection methodology for quality assessment and continuous improvement

Roldan-Molina

Ruano-Ordás

Basto‐Fernandes

et al. 2021

Data & Knowledge Engineering

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Section: Publicly Available Ontologiesmentioning

confidence: 99%

An ontology knowledge inspection methodology for quality assessment and continuous improvement

Roldan-Molina

Ruano-Ordás

Basto‐Fernandes

et al. 2021

Data & Knowledge Engineering

Self Cite

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“…This ontology can be used for suggesting strategies for solving optimization problems. At the same time, an OWL ontology has been proposed in Li, Yevseyeva, Basto-Fernandes, Trautmann, Jing, and Emmerich (2017) to model and systematize the knowledge of preference-based multi-objective evolutionary algorithms. These ontologies are validated in use cases focused on algorithmic and parameter selection in academic problems.…”

Section: Related Workmentioning

confidence: 99%

BIGOWL: Knowledge centered Big Data analytics

Barba-González

García-Nieto

Roldán‐García

et al. 2019

Expert Systems with Applications

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Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data analytics. Knowledge can take part in workflow design, constraint definition, parameter selection and configuration, human interactive and decision-making strategies. This paper proposes BIGOWL, an ontology to support knowledge management in Big Data analytics. BIGOWL is designed to cover a wide vocabulary of terms concerning Big Data analytics workflows, including their components and how they are connected, from data sources to the analytics visualization. It also takes into consideration aspects such as parameters, restrictions and formats. This ontology defines not only the taxonomic relationships between the different concepts, but also instances representing specific individuals to guide the users in the design of Big Data analytics workflows. For testing purposes, two case studies are developed, which consists in: first, real-world streaming processing with Spark of traffic Open Data, for route optimization in urban environment of New York city; and second, data mining classification of an academic dataset on local/cloud platforms. The analytics workflows resulting from the BIGOWL semantic model are validated and successfully evaluated.

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“…DL Query is another important feature of Protégé, which can help to access, analyze and explore the knowledge domain described in the ontology. Examples of reasoning and query are available in [122]. Based on these functions, we can use the PMOMH ontology for different purposes, which are given next.…”

Section: Using the Pmomh Ontologymentioning

confidence: 99%

“…As far as we know, Evolutionary Computation (EC) ontology [19] and diversity-oriented optimization ontology [18] have been proposed recently, and their contributions were considered in the design of the PMOMH ontology. An ontology of preference-based multi-objective evolutionary algorithms (PMOEAs) was built and basic query examples were given [122]. In this paper we extend and improve the ontology, and provide more use cases to demonstrate the benefits of the provided ontology.…”

Section: Introduction To Ontologiesmentioning

confidence: 99%

An Ontology of Preference-Based Multiobjective Metaheuristics

Li,

Yevseyeva,

Basto-Fernandes

et al. 2016

Preprint

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User preference integration is of great importance in multi-objective optimization, in particular in many objective optimization. Preferences have long been considered in traditional multicriteria decision making (MCDM) which is based on mathematical programming. Recently, it is integrated in multi-objective metaheuristics (MOMH), resulting in focus on preferred parts of the Pareto front instead of the whole Pareto front. The number of publications on preference-based multiobjective metaheuristics has increased rapidly over the past decades. There already exist various preference handling methods and MOMH methods, which have been combined in diverse ways. This article proposes to use the Web Ontology Language (OWL) to model and systematize the results developed in this field. A review of the existing work is provided, based on which an ontology is built and instantiated with state-of-the-art results. The OWL ontology is made public and open to future extension. Moreover, the usage of the ontology is exemplified for different usecases, including querying for methods that match an engineering application, bibliometric analysis, checking existence of combinations of preference models and MOMH techniques, and discovering opportunities for new research and open research questions.

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Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms

Cited by 19 publications

References 43 publications

An ontology knowledge inspection methodology for quality assessment and continuous improvement

An ontology knowledge inspection methodology for quality assessment and continuous improvement

BIGOWL: Knowledge centered Big Data analytics

An Ontology of Preference-Based Multiobjective Metaheuristics

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