Large-scale incremental OWL/RDFS reasoning over fuzzy RDF data

Jagvaral, Batselem; Wangon, Lee; Park, Hyunkyu; Jeon, MyungJoong; Lee, Nam-Gee; Park, Young-Tack

doi:10.1109/bigcomp.2017.7881709

Cited by 3 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, there are two diffuse triples sharing the same triple part (s, p, o) [n] and (s, p, o) [m] m < n , where the one with the lower degree of distortion m is removed. By using RDFS and pD * , Jagvaral et al [17] proposed a fuzzy RDF framework for incremental reasoning, which receives new data and performs reasoning without re-inferring previous data.…”

Section: Related Work Analysismentioning

confidence: 99%

A Metamodel for Bridging Heterogeneous Ontologies

et al. 2020

View full text Add to dashboard Cite

Soft ontologies aim to provide more flexible knowledge representations as compared with hierarchy-based ontology models. Soft ontologies can restructure itself according to the necessities of an interaction context. This is especially desirable in systems that need to deal with dynamic domains and situations with a high degree of uncertainty. However, the definition of soft ontologies is a challenging task due to the lack of generic and well defined components. In addition, such definition may suffer from problems with interoperability and integration with other models. We argue that soft ontologies should coexist with other models in an interoperable framework. In this paper, we propose and formalize a metamodel to provide adequate conciliation among various ontology representation approaches. We develop soft ontologies based on matrices of probabilities and apply a triplification process in which concepts are represented as fuzzy RDF statements. Our proposal was evaluated in a case study in an educational context, which uses a soft ontology to express a repertoire of actions in an mBot robot. The results indicate the feasibility of our fuzzy RDF approach to represent soft ontologies and the use of the metamodel to support interoperability in heterogeneous ontology networks.

show abstract

Section: Related Work Analysismentioning

confidence: 99%

A Metamodel for Bridging Heterogeneous Ontologies

et al. 2020

View full text Add to dashboard Cite

show abstract

Supporting Relevance Feedback with Concept Learning for Semantic Information Retrieval in Large OWL Knowledge Base

Liu

2018

Knowledge Management and Acquisition for Intelligent Systems

View full text Add to dashboard Cite

Construction of Knowledge Graphs: Current State and Challenges

Hofer,

Obraczka,

Saeedi

et al. 2024

Information

View full text Add to dashboard Cite

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender systems and question-answering, the need for generalized pipelines to construct and continuously update such KGs is increasing. While the individual steps that are necessary to create KGs from unstructured sources (e.g., text) and structured data sources (e.g., databases) are mostly well researched for their one-shot execution, their adoption for incremental KG updates and the interplay of the individual steps have hardly been investigated in a systematic manner so far. In this work, we first discuss the main graph models for KGs and introduce the major requirements for future KG construction pipelines. Next, we provide an overview of the necessary steps to build high-quality KGs, including cross-cutting topics such as metadata management, ontology development, and quality assurance. We then evaluate the state of the art of KG construction with respect to the introduced requirements for specific popular KGs, as well as some recent tools and strategies for KG construction. Finally, we identify areas in need of further research and improvement.

show abstract

Large-scale incremental OWL/RDFS reasoning over fuzzy RDF data

Cited by 3 publications

References 9 publications

A Metamodel for Bridging Heterogeneous Ontologies

A Metamodel for Bridging Heterogeneous Ontologies

Supporting Relevance Feedback with Concept Learning for Semantic Information Retrieval in Large OWL Knowledge Base

Construction of Knowledge Graphs: Current State and Challenges

Contact Info

Product

Resources

About