An Approach to Comparing Different Ontologies in the Context of Hydrographical Information

Vilches‐Blázquez, Luis M.; Ramos, Ja.; López-Pellicer, Francisco J.; Corcho, Óscar; Nogueras-Iso, Javier

doi:10.1007/978-3-642-00304-2_13

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…The study carefully investigated the literature where ontologies were reviewed or compared based on parameters, like Mascardi et al (2007), Vilches-Blázquez et al (2009), Zhang et al (2017), Liu et al (2013), Prantner et al (2007), Dong et al (2008) and Sinha and Dutta (2020). Based on these investigations, a set of parameters was extracted (detailed in the next section) and used to compare the DMO.…”

Section: Methodsmentioning

confidence: 99%

“…Efforts have been made where ontologies have been compared in a single domain. Thus, Vilches-Blázquez et al (2009) compared three hydrographical ontologies based on features sources used: reliability of building approaches, ontology richness, formalization language, granularity and the design criteria followed. Similarly, Zhang et al (2017) presented the architecture of four mainstream unit's ontologies and then compared them based on structural organization, pattern design and application areas.…”

Section: State-of-the-art Workmentioning

confidence: 99%

“…OD is a costly process; hence, funding for the creation of ontologies is an essential factor. Operation used (OU) (Sinha and Dutta, 2020) Since not all ontologies can be built from scratch, operations such as integration, extension and pruning can be used to build the ontology (Sinha and Dutta, 2020) 5 Design methodology (DM) (Sinha and Dutta, 2020;Prantner et al, 2007;Vilches-Bl azquez et al, 2009) It explains how the ontology was created using a specific methodology. Following current methodologies that have been proven and accepted guarantees a systematic approach to OD (Sinha and Dutta, 2020;Prantner et al, 2007;Vilches-Bl azquez et al, 2009) 6 Hierarchy development (HD) (Sinha and Dutta, 2020) It explains the top-down, bottom-up and middle-out approaches to developing a class hierarchy for ontology in general (Sinha and Dutta, 2020;Uschold and Gruninger, 1996) 7…”

Section: Core Parametersmentioning

confidence: 99%

“…Representation language (RL) (Sinha and Dutta, 2020;Mascardi et al, 2007;Vilches-Bl azquez et al, 2009) It explains how the ontology was represented in language like ontology web language (OWL) (Sinha and Dutta, 2020;Mascardi et al, 2007;Vilches-Bl azquez et al, 2009) 10 Knowledge representation formalism (KRF)…”

Section: Core Parametersmentioning

confidence: 99%

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A review of data mining ontologies

Sinha

Gajbe

Debnath

et al. 2021

DTA

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PurposeThis work provides a generic review of the existing data mining ontologies (DMOs) and also provides a base platform for ontology developers and researchers for gauging the ontologies for satisfactory coverage and usage.Design/methodology/approachThe study uses a systematic literature review approach to identify 35 DMOs in the domain between the years 2003 and 2021. Various parameters, like purpose, design methodology, operations used, language representation, etc. are available in the literature to review ontologies. Accompanying the existing parameters, a few parameters, like semantic reasoner used, knowledge representation formalism was added and a list of 20 parameters was prepared. It was then segregated into two groups as generic parameters and core parameters to review DMOs.FindingsIt was observed that among the 35 papers under the study, 26 papers were published between the years 2006 and 2016. Larisa Soldatova, Saso Dzeroski and Pance Panov were the most productive authors of these DMO-related publications. The ontological review indicated that most of the DMOs were domain and task ontologies. Majority of ontologies were formal, modular and represented using web ontology language (OWL). The data revealed that Ontology development 101, METHONTOLOGY was the preferred design methodology, and application-based approaches were preferred for evaluation. It was also observed that around eight ontologies were accessible, and among them, three were available in ontology libraries as well. The most reused ontologies were OntoDM, BFO, OBO-RO, OBI, IAO, OntoDT, SWO and DMOP. The most preferred ontology editor was Protégé, whereas the most used semantic reasoner was Pellet. Even ontology metrics for 16 DMOs were also available.Originality/valueThis paper carries out a basic level review of DMOs employing a parametric approach, which makes this study the first of a kind for the review of DMOs.

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Section: Methodsmentioning

confidence: 99%

Section: State-of-the-art Workmentioning

confidence: 99%

Section: Core Parametersmentioning

confidence: 99%

Section: Core Parametersmentioning

confidence: 99%

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A review of data mining ontologies

Sinha

Gajbe

Debnath

et al. 2021

DTA

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“…To solve water features’s heterogeneity problem, many hydrography ontologies, such as the United States Geological Survey’s Surface Water Ontology (SWO) [ 9 ], Spanish National Geographic Institute (IGN)’s hydrOntology [ 14 ], the Hydro3 module from the University of Maine’s HydroGazetteer [ 13 ] and the Cree surface water ontology [ 15 ], have been developed and maintained to describe hydrographic features and the relationships between them. However, these ontologies are independently developed with varying project perspectives and objectives, and therefore, they might use the same terminology for different concepts, they may be at different levels of abstraction, they may not include all of the same concepts, and they may not even be in the same language [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Hydrography Ontology Alignment Through Compact Particle Swarm Optimization Algorithm

Wang

Yao

Wan

et al. 2020

Lecture Notes in Computer Science

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With the explosive growth in generating data in the hydrographical domain, many hydrography ontologies have been developed and maintained to describe hydrographical features and the relationships between them. However, the existing hydrography ontologies are developed with varying project perspectives and objectives, which inevitably results in the differences in terms of knowledge representation. Determining various relationships between two entities in different ontologies offers the opportunity to link hydrographical data for multiple purposes, though the research on this topic is in its infancy. Different from the traditional ontology alignment whose cardinality is 1:1, i.e. one source ontology entity is mapping with one target ontology entity and vice versa, and the relationship is the equivalence, matching hydrography ontologies is a more complex task, whose cardinality could be 1:1, 1:n or m:n and the relationships could be equivalence or subsumption. To efficiently optimize the ontology alignment, in this paper, a discrete optimal model is first constructed for the ontology matching problem, and then a Compact Particle Swarm Optimization algorithm (CPSO) based matching technique is proposed to efficiently solve it. CPSO utilizes the compact real-value encoding and decoding mechanism and the objective-decomposing strategy to approximate the PSO’s evolving process, which can dramatically reduce PSO’s memory consumption and runtime while at the same time ensure the solution’s quality. The experiment exploits the Hydrography dataset in Complex track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance. The experimental results show that CPSO-based approach can effectively reduce PSO’s runtime and memory consumption, and determine high-quality hydrography ontology alignments.

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