Using Compact Coevolutionary Algorithm for Matching Biomedical Ontologies

Xue, Xingsi; Chen, Jie; Chen, Junfeng; Chen, Dongxu

doi:10.1155/2018/2309587

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…To improve the matching efficiency, Araújo et al [15] presented the matching system through parallel computing (PC) technique and Amin et al [16] matching ontology based on cloud computing (CC). At the same time, SIA-based technique has achieved great performance in the ontology matching [1,2,[17][18][19][20] domain [21][22][23][24][25][26].…”

Section: Related Workmentioning

confidence: 99%

Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences

et al. 2021

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Data heterogeneity is the obstacle for the resource sharing on Semantic Web (SW), and ontology is regarded as a solution to this problem. However, since different ontologies are constructed and maintained independently, there also exists the heterogeneity problem between ontologies. Ontology matching is able to identify the semantic correspondences of entities in different ontologies, which is an effective method to address the ontology heterogeneity problem. Due to huge memory consumption and long runtime, the performance of the existing ontology matching techniques requires further improvement. In this work, an extended compact genetic algorithm-based ontology entity matching technique (ECGA-OEM) is proposed, which uses both the compact encoding mechanism and linkage learning approach to match the ontologies efficiently. Compact encoding mechanism does not need to store and maintain the whole population in the memory during the evolving process, and the utilization of linkage learning protects the chromosome’s building blocks, which is able to reduce the algorithm’s running time and ensure the alignment’s quality. In the experiment, ECGA-OEM is compared with the participants of ontology alignment evaluation initiative (OAEI) and the state-of-the-art ontology matching techniques, and the experimental results show that ECGA-OEM is both effective and efficient.

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Section: Related Workmentioning

confidence: 99%

Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences

et al. 2021

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“…Such techniques are unable to deal with a large number of data properties (i.e., large-scale data) and are less efficient in accuracy but have the advantage of less time consumption. Advanced techniques such as those described in [ 38 , 39 ] employ advanced algorithms, for example, hybrid evolutionary algorithms. Such techniques are complex and more time consuming but have greater accuracy and work better on large scale data.…”

Section: Related Workmentioning

confidence: 99%

Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities

Ghazal

Malik

Raza

et al. 2021

Sensors

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Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing information of users, tasks, access policies and resources in such applications. It also does not incorporate semantically meaningful business roles, which could have a diverse impact upon access decisions in such multi-domain collaborative business environments. We propose an Intelligent Role-based Access Control (I-RBAC) model that uses intelligent software agents for achieving intelligent access control in such highly dynamic multi-domain environments. The novelty of this model lies in using a core I-RBAC ontology that is developed using real-world semantic business roles as occupational roles provided by Standard Occupational Classification (SOC), USA. It contains around 1400 business roles, from nearly all domains, along with their detailed task descriptions as well as hierarchical relationships among them. The semantic role mining process is performed through intelligent agents that use word embedding and a bidirectional LSTM deep neural network for automated population of organizational ontology from its unstructured text policy and, subsequently, matching this ontology with core I-RBAC ontology to extract unified business roles. The experimentation was performed on a large number of collaboration case scenarios of five multi-domain organizations and promising results were obtained regarding the accuracy of automatically derived RDF triples (Subject, Predicate, Object) from organizational text policies as well as the accuracy of extracted semantically meaningful roles.

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“…Effort has been put into cross-referencing biomedical ontology terms through the BioPortal [17]. This process of creating correspondences between biomedical related domain ontologies is known as biomedical ontology mapping [18]. It is, however, tedious to make these alignments manually and to keep them updated across the huge number of evolving bioontologies and terms therein.…”

Section: Introductionmentioning

confidence: 99%

Intelligent ontology alignment to improve semantic data integration across research domains in biomedicine

Malik,

Inau

2023

Preprint

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Ontology alignment is a key component of semantic web interoperability, with a long history of applications in traditional data integration tasks that address the problem of semantic heterogeneity. Ontology alignment tools take two ontologiesas input and determine alignments as output i.e. a set of correspondences between semantically related units of these ontologies. These correspondences can then be used to merge ontologies, link data across knowledge domains, answer semantic queries, navigate through knowledge graphs, and many more. The process of determining alignments, called matching, therefore is a basic requirement for linking knowledge across scientific domains covered by one or more ontologies. A matching exists if entities from different ontologies are semantically equivalent. The goal of this study is to find all semantically equivalent entity pairs given a source ontology Os and a target ontology Ot, each consisting of a set of entities. We propose to use Inverse Document Frequency (IDF) and Jaccard distance to find candidate entities with high precision and less computational effort. Our goal to cross-link different research fields in the biomedical and clinical domains.

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Using Compact Coevolutionary Algorithm for Matching Biomedical Ontologies

Cited by 13 publications

References 27 publications

Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences

Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences

Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities

Intelligent ontology alignment to improve semantic data integration across research domains in biomedicine

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