ProGOMap: Automatic Generation of Mappings From Property Graphs to Ontologies

Fathy, Naglaa; Gad, Walaa; Badr, Nagwa L.; Hashem, Mohamed

doi:10.1109/access.2021.3104293

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…These applications assist in reducing processing delays, thereby improving e ciency of data processing. An advanced model for ontology creation is proposed in [7], wherein researchers are able to generate ontologies from unstructured property graphs via deep learning approach. This model has high accuracy of representation, but has limited security, which limits its usability for real-time deployments.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Purbey

Khandelwal

Choudhary³

2023

SIViP

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Ontology generation is a process of relationship analysis, and representation for multiple data categories using automatic or semi-automatic approaches. This process requires a domain knowledgebase that describes given input data using entity-to-entity relations. A wide variety of approaches are proposed for this purpose, and each of them processes & converts input data using multiple relationship evaluation stages. These stages include data-preprocessing, correlation analysis, entity mapping, and ontology generation. A very few of these approaches are dataset independent, and most of them do not implement security measures during ontology generation, which limits their security, scalability & deployment capabilities during real-time implementation. Thus, in this text a blockchain based secure & e cient ontology generation model for multiple data genres using augmented strati cation (BOGMAS) is described. The BOGMAS model uses a semi-supervised approach for ontology generation from almost any structured or unstructured dataset. It uses a variance-based method (VBM) for reduction of redundant numerical features from the dataset, while textual features are converted to numerical values via standard word2vec model, and then processed using VBM. This model uses a combination of linear support vector machine (LSVM), and extra trees (ET) strati ers for variance estimation, which makes the model highly e cient, and reduces redundant features from the output ontology. These feature sets & their variances are given to a correlation engine for relationship estimation, and ontology generation.Each ontology record is secured using a mutable proof-of-work (PoW) based blockchain model, which assists in imbibing transparency, traceability, and distributed peer-to-peer processing capabilities. The generated ontology is represented using an incremental OWL (W3C Web Ontology Language) format, which assists in dynamically sizing the ontology depending upon incoming data. Performance of the proposed BOGMAS model is evaluated in terms of precision & recall of representation, memory usage, computational complexity, and accuracy of attack detection. It is observed that the proposed model is highly e cient in terms of precision, recall & accuracy performance, but has incrementally higher computational complexity & delay of ontology formation when compared with existing approaches. Due to this incremental increase in delay, the proposed model is observed to be applicable for a wide variety of real-time scenarios, which include but are not limited to, medical ontology generation, sports ontology generation, and internet of things (IoT) ontology generation with high security levels.

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Section: Literature Reviewmentioning

confidence: 99%

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Purbey

Khandelwal

Choudhary³

2023

SIViP

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“…The author proposed an ontology learning algorithm that develops the ontology from the property graph, aligns the developed ontology corresponding to domain ontology, and automated mapping performed on relational to RDF model and non-relational to resource description framework (RDF) model. The drawback of this model is the way of addressing terminological heterogeneity issues because the string similarity is computed using two models only [15]. The knowledge extraction methodology is proposed to retrieve the domain knowledge from the freebase RDF dumps, this methodology addressed the challenges while retrieving the triples from freebase triples.…”

Section: Literature Reviewmentioning

confidence: 99%

Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability

Shanmurthy

Poongodi

Balusamy

et al. 2022

IJEECS

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Semantic web technology is adapted to the internet of things (IoT) for web - based applications to globally connect the services. Web ontology language (OWL) domain ontology is a powerful machine - readable language for domain knowledge representation. The developer stored the IoT application relevant ontology in a repository or catalogue. Hence, IoT application - related ontology files are available for reus e, but many of the IoT application - relevant ontology files are publicly not available or inaccessible. The proposed idea is to extract the contextual knowledge of IoT applications that contain inaccessible ontology files. The context - wise specific domain I oT applications are not obtainable, hence respective ontology - based research papers are identified and their frequent terms are computed. The selected contextual dominant frequent terms from the transport domain are passed into the skip - gram flavour of wor d2vector modelled n atural language processing ( NLP ) corpus which produces most similar terms. The domain experts select the appropriate terms to annotate in OWL ontology for contextual knowledge augmentation. Finally, 1422 contextual terms were generated b ased on dominant terms of selected IoT applications.

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“…The MIRROR system [18] yields an R2RML mapping file containing the mappings for a given relational database. Naglaa et al proposed a ProGOMap (Property Graph to Ontology Mapper) system for the automatic generation of mappings from property graphs to a domain ontology [19]. The PG-to-Ontology mappings can be automatically generated by using the aligned axioms.…”

Section: Related Workmentioning

confidence: 99%

“…In their prototype, ontology-based data access is implemented based on a mapping between an ontology and the ingested data. All of the approaches in [15][16][17][18][19][20] focus on constructing mappings between relational scheme and domain ontology through a search-based algorithm. Different from these search-based approaches, our method attempts to learn mapping rules by leveraging the amount of knowledge bases, including not only historical relational schemes and domain ontology, but also knowledge graphs.…”

Section: Related Workmentioning

confidence: 99%

Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base

Mayer

Zhang

et al. 2022

Mathematics

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A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known.

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ProGOMap: Automatic Generation of Mappings From Property Graphs to Ontologies

Cited by 8 publications

References 28 publications

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability

Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base

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