The Data Mining OPtimization Ontology

Keet, C. Maria; Ławrynowicz, Agnieszka; d’Amato, Claudia; Kalousis, Alexandros; Nguyen, Phong; Palma, Raúl; Stevens, Robert; Hilario, Mélanie

doi:10.2139/ssrn.3199185

Cited by 13 publications

(21 citation statements)

References 26 publications

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“…'number of instances', have been imported from the Data Mining Optimization (DMOP) Ontology 9 (Keeta et al 2015). We also added QSAR-specific dataset descriptors 'aggregated fingerprint'.…”

Section: Dataset Meta-featuresmentioning

confidence: 99%

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

et al. 2017

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We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance, as it is a key step in the development of new medicines. The standard QSAR learning problem Learn (2018) 107:285-311 is: given a target (usually a protein) and a set of chemical compounds (small molecules) with associated bioactivities (e.g. inhibition of the target), learn a predictive mapping from molecular representation to activity. Although almost every type of machine learning method has been applied to QSAR learning there is no agreed single best way of learning QSARs, and therefore the problem area is well-suited to meta-learning. We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 3 molecular representations, applied to more than 2700 QSAR problems. (These results have been made publicly available on OpenML and represent a valuable resource for testing novel meta-learning methods.) We then investigated the utility of algorithm selection for QSAR problems. We found that this meta-learning approach outperformed the best individual QSAR learning method (random forests using a molecular fingerprint representation) by up to 13%, on average. We conclude that meta-learning outperforms base-learning methods for QSAR learning, and as this investigation is one of the most extensive ever comparisons of base and meta-learning methods ever made, it provides evidence for the general effectiveness of meta-learning over base-learning.

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“…'number of instances', have been imported from the Data Mining Optimization (DMOP) Ontology 9 (Keeta et al 2015). We also added QSAR-specific dataset descriptors 'aggregated fingerprint'.…”

Section: Dataset Meta-featuresmentioning

confidence: 99%

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

et al. 2017

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“…For DOLCE we select the following ontologies: DMOP.owl, which is an ontology about data mining optimisation [7], the Naive animal ontology2.owl ontology of animals 3 , OntoDerm 5.3.owl about dermatology [4], the SceneOntology.owl ontology of spatial scenes and objects specifically for visual recognition 4 , and SEGOv3.owl for describing relations between geographic occurrences and properties observed by sensors 5 . For BFO, we select the following ontologies: bco.owl about biological collections 6 , the epidemiology ontology.owl for the epidemiology and pub-lic health domain 7 , ero.owl intended for representing biomedical research resources 8 , IDO.9.19.07.owl representing infectious diseases [3], the proper name on -tology v1.8.owl that contains proper names for re-use in biomedical ontologies 9 , and the SAO.owl ontology about subcellular anatomy of the nervous system [12].…”

Section: Methodsmentioning

confidence: 99%

“…SUGOI maps a domain entity's superentity in the s O f to its corresponding superentity in the t O f using the mapping ontology. This is illustrated in Fig 1 for the entity dmop:DataType from the DMOP ontology [7], changing the link from DOLCE to one in GFO, and this resulting axiom is called a GT, good target linking axiom. If the domain entity's superentity does not have a corresponding mapping entity, SUGOI then treats that superentity as a domain entity and looks for a corresponding mapping entity at a higher level up in the taxonomy.…”

Section: Foundational Ontology Interchangeability Algorithmmentioning

confidence: 99%

“…Thus, a larger number of links between s O d and s O f entities for DOLCE and GFO ontologies can cause a lower raw interchangeability values, whereas for the set of BFO s O f , the correlation is much weaker. Second, the raw interchangeability is slightly higher when there are more mappings between source and target foundational ontologies among the interchanged ones: there are more DOLCE to GFO mappings (15) than DOLCE to BFO mappings (7), and the average interchangeability for the test ontologies are 33.29% and 26.99%, respectively. The same pattern exists for BFO to DOLCE vs BFO to GFO (58.62% vs 67.23%) and for GFO to BFO vs GFO to DOLCE (13.10% vs 18.19%).…”

Section: Entity-level Analysismentioning

confidence: 99%

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Feasibility of Automated Foundational Ontology Interchangeability

Khan

Keet

2014

Lecture Notes in Computer Science

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Abstract. While a foundational ontology can solve interoperability issues among the domain ontologies aligned to it, multiple foundational ontologies have been developed. Thus, there are still interoperability issues among domain ontologies aligned to different foundational ontologies. Questions arise about the feasibility of linking one's ontology to multiple foundational ontologies to increase its potential for uptake. To answer this, we have developed the tool SUGOI, Software Used to Gain Ontology Interchangeability, which allows a user to interchange automatically a domain ontology among the DOLCE, BFO and GFO foundational ontologies. The success of swapping based on equivalence varies by source ontology, ranging from 2 to 82% and averaging at 36% for the ontologies included in the evaluation. This is due to differences in coverage, notably DOLCE's qualities and BFO and GFO's roles, and amount of mappings. SUGOI therefore also uses subsumption mappings so that every domain ontology can be interchanged, preserves the structure of the ontology, and increases its potential for usability.

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“…In parallel to this, ontologies can play vital role in solving domain specific problems by making use of knowledge representation and reasoning [1][2][3][4]. Due to the nature of existing hardware clusters [5], it is now common to have a cluster of hardware architectures [6] equipped with an NVIVIA GPGPUs and Xeon-Phi coprocessor in addition to traditional Intel CPUs.…”

Section: Introductionmentioning

confidence: 99%

Ontology Design for Solving Computationally-Intensive Problems on Heterogeneous Architectures

et al. 2018

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Viewing a computationally-intensive problem as a self-contained challenge with its own hardware, software and scheduling strategies is an approach that should be investigated. We might suggest assigning heterogeneous hardware architectures to solve a problem, while parallel computing paradigms may play an important role in writing efficient code to solve the problem; moreover, the scheduling strategies may be examined as a possible solution. Depending on the problem complexity, finding the best possible solution using an integrated infrastructure of hardware, software and scheduling strategy can be a complex job. Developing and using ontologies and reasoning techniques play a significant role in reducing the complexity of identifying the components of such integrated infrastructures. Undertaking reasoning and inferencing regarding the domain concepts can help to find the best possible solution through a combination of hardware, software and scheduling strategies. In this paper, we present an ontology and show how we can use it to solve computationally-intensive problems from various domains. As a potential use for the idea, we present examples from the bioinformatics domain. Validation by using problems from the Elastic Optical Network domain has demonstrated the flexibility of the suggested ontology and its suitability for use with any other computationally-intensive problem domain.

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The Data Mining OPtimization Ontology

Cited by 13 publications

References 26 publications

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Feasibility of Automated Foundational Ontology Interchangeability

Ontology Design for Solving Computationally-Intensive Problems on Heterogeneous Architectures

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