You are Missing a Concept! Enhancing Ontology-Based Data Access with Evolving Ontologies

Pomp, André; Lipp, Johannes; Meisen, Tobias

doi:10.1109/icosc.2019.8665620

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…As far as future developments are concerned, we intend to extend SeMi to compute semantic models, in a context of a constant evolution of the ontology or of the KG [33]. This feature can be particularly useful in real-world situations, in which new data sources and the related semantic models are included in a continuous data integration process.…”

Section: Discussionmentioning

confidence: 99%

SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks

2020

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Section: Discussionmentioning

confidence: 99%

SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks

2020

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“…4. Evaluation results: The average Jaccard index of each attendee's semantic model with those of others for data set one, comparing unassisted and assisted modeling [38]. Figure 4 shows for data set one the average Jaccard indexes for each participant when comparing the no tool setup with the assisted run, while Figure 5 shows the results for data set two, respectively.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…To assess the quality of the semantic Table 1. Overview of the dierent data sets used for our evaluation, based on [38] and extended by three more data sets. models, we choose a qualitative evaluation.…”

Section: Autonomous Evolution From External Knowledge Basesmentioning

confidence: 99%

“…This paper is an extension of our work [38]. In comparison, to our original work, this paper includes the following improvements: more details about our architecture and implementation an additional algorithm for the continuous evolution of the knowledge graph's ontology three extra data sets included in the evaluation more evaluation details and results…”

Section: Introductionmentioning

confidence: 99%

“…Evaluation results: The average Jaccard index of each attendee's semantic model with those of others for data set two, comparing unassisted and assisted modeling[38].…”

mentioning

confidence: 99%

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Enabling the Continuous Evolution of Ontologies for Ontology-Based Data Management

Pomp

Lipp

Meisen

2019

IJRC

Self Cite

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Companies suer from heterogeneous data sources that are distributed across all business units and locations. Accessing, discovering, and understanding these data sources is challenging because data scientists have to deal with dierent protocols, data formats, and even company-specic organizational issues such as rewalls and privacy policies. Ontology-Based Data Management (OBDM) provides dierent aspects for reducing the barriers of integrating, accessing and managing heterogeneous data sources by using ontologies. For that, we establish a mapping between data sources and one or multiple target ontologies. However, the biggest challenges in ODBM are creation and administration of required ontologies. Usually, these ontologies are created in advance, for instance, by ontology engineers and domain experts that closely work together for manually designing and even maintaining the ontology. In order to enhance the process of designing and maintaining an ontology, we propose an approach consisting of an evolving knowledge graph that includes an internal ontology, which continuously evolves on demand as domain experts add new data sources and dene the mapping between the ontology and that data source. For this purpose, we develop an intuitive, user-oriented wizard and combine it with a semi-supervised evolution strategy that supports the user with the help of external knowledge databases. Moreover, we equip the system with additional logic that allows to automatically link related concepts of the ontology. We evaluate the accuracy and usability of our approach by conducting a user 20 André Pomp et al. study. The results show that mappings become more objective and consistent with our provided user wizard, resulting in a knowledge graph with higher connectivity and stability.

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Engineering Digital Twins and Digital Shadows as Key Enablers for Industry 4.0

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You are Missing a Concept! Enhancing Ontology-Based Data Access with Evolving Ontologies

Cited by 8 publications

References 14 publications

SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks

SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks

Enabling the Continuous Evolution of Ontologies for Ontology-Based Data Management

Engineering Digital Twins and Digital Shadows as Key Enablers for Industry 4.0

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