Matching Large Scale Ontologies Based on Filter and Verification

Li, Yingxin; Jianhui, Zhou; Liu, Jihong; Hou, Yanze

doi:10.1155/2020/8107968

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…But the process of building RTO is accomplished automatically. Refer to our previous work [34], and an adaptive filter is used in this paper to eliminate the entities that irrelevant to user requirements. Based on adaptive filter, building RTO automatically can be realized.…”

Section: B Sub-ontology Extractionmentioning

confidence: 99%

Building Real-Time Ontology Based on Adaptive Filter for Multi-Domain Knowledge Organization

Jianhui

Song

et al. 2021

IEEE Access

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Multi-domain knowledge organization is an effective way of correlating cross-domain knowledge or intercommunicating between cross-domain knowledge systems. As a knowledge organization model, ontology is widely used in information and management systems. To organize multi-domain knowledge, ontologies in different domains correlate to each other directly or indirectly. Generally, matching and integrating ontologies of different domain into a large scale ontology is the common way of directly correlating, while building a top level ontology is the main method for indirectly correlating. As the scale of domain ontologies get larger and larger, both direct and indirect methods become more difficult and time-consuming. In order to improve the organization of multi-domain knowledge, this paper presents a novel ontology organization method to build real-time ontology by adaptive filter while user presenting requirements. Only the entities related to user requirements are integrated, while building a real-time ontology. Firstly, the method searches domain ontologies that are related to user requirements. Then subontologies are extracted from search results by filter, and they are integrated into a new ontology under direction of filter, i.e. real-time ontology. Finally, four criteria are introduced to evaluate real-time ontology. The experiment results illuminate that real-time ontology perform excellently in accuracy, recall, correctness and especially time-consuming.INDEX TERMS Real-time ontology, multi-domain knowledge organization, ontology matching, ontology integration, knowledge engineering

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Section: B Sub-ontology Extractionmentioning

confidence: 99%

Building Real-Time Ontology Based on Adaptive Filter for Multi-Domain Knowledge Organization

Jianhui

Song

et al. 2021

IEEE Access

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“…The formal graph was proven needed in the retrieval process [19] [20] and valuable in storage management. Ontology as graph uses a graph model for ontology matching [21] [22], an essential step in data integration. Although it only works for simple graphs, the formal graph as a bipartite graph can represent the layer of the network [23].…”

Section: Introductionmentioning

confidence: 99%

The Formal Graph of APRDF

Wardani,

Siregar,

Wijayanto

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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A new alternative model for expressing more complex knowledge has been proposed as an attributed predicate RDF (APRDF). By handling attributes that represent any additional triples of the main triple, APRDF serves as a predicate. Therefore, the formal graph model of APRDF must be defined. Lastly, this work recommends that the APRDF's conventional diagram is a digraph-hypergraph mix. The previous formal graph of RDF is a hypergraph even though, visually intuitively, it is a digraph. It also contains inconsistency. The other new serialization needs to describe its formal model. Eventually, this work can provide the formal graph model of APRDF and maintain consistency. There have been a few definitions proposed. The direct impact of this formal model is that APRDF outperformed the other model significantly when retrieving complex queries within its formal graph. In querying, the initial implementation of the proposed formal graph takes an average of 62 milliseconds. Compared to the other graph models, the proposed formal graph can reduce query time by an average of 90,7 milliseconds on the BF-arch graph and 121,05 milliseconds on the naive/default graph. As the formal graph model is preserved, the attributed predicate of APRDF assumed will drive a new model in the retrieving process that more in using a predicate formed as a link in a graph. It will also be impacted in the mining process by more elaborate links/edges (link mining).

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A Novel Accurate and Time Efficient Map Reduce Approach for Biomedical Ontology Alignment

Sangeetha,

Vidhyapriya

2023

J. Electr. Eng. Technol.

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Matching Large Scale Ontologies Based on Filter and Verification

Cited by 6 publications

References 17 publications

Building Real-Time Ontology Based on Adaptive Filter for Multi-Domain Knowledge Organization

Building Real-Time Ontology Based on Adaptive Filter for Multi-Domain Knowledge Organization

The Formal Graph of APRDF

A Novel Accurate and Time Efficient Map Reduce Approach for Biomedical Ontology Alignment

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