scholarly journals Cloud-Based Probabilistic Knowledge Services for Instruction Interpretation

2017 ◽  
pp. 649-664 ◽  
Author(s):  
Daniel Nyga ◽  
Michael Beetz
Keyword(s):  
Probabilistic Knowledge ◽  
Knowledge Services

scholarly journals A Compositional Approach to Probabilistic Knowledge Compilation

2021 ◽  
Author(s):  
Giso H. Dal ◽  
Alfons W. Laarman ◽  
Arjen Hommersom ◽  
Peter J.F. Lucas
Keyword(s):  
Knowledge Compilation ◽  
Compositional Approach ◽  
Probabilistic Knowledge

scholarly journals ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 146876-146886
Author(s):  
Claudio Bettini ◽  
Gabriele Civitarese ◽  
Davide Giancane ◽  
Riccardo Presotto
Keyword(s):  
Activity Recognition ◽  
Data Driven ◽  
Knowledge Based ◽  
Probabilistic Knowledge ◽  
Hybrid Data

scholarly journals A Study on the Impact of R&D Intensity on Business Performance: Evidence from South Korea

2021 ◽  
Vol 7 (2) ◽  
pp. 135
Author(s):  
Hun Park ◽  
Jun-Hwan Park ◽  
Sujin Lee ◽  
Hyuk Hahn
Keyword(s):  
South Korea ◽  
Research And Development ◽  
Business Performance ◽  
Decision Makers ◽  
The Other ◽  
Research Groups ◽  
Innovative Firms ◽  
The Impact ◽  
Knowledge Services

The role of R&D (research and development) intensity on the effect of knowledge services on the business performance of firms has been discussed by using PLS-SEM and PLS-MGA methods. Research groups were divided into two groups, innovative and non-innovative. Respondents were classified into innovative firms if their R&D intensity was over 3% and vice versa. PLS-SEM and PLS-MGA results were compared for two groups and valuable insights were extracted. For innovative firms, knowledge services seemed to be verified and processed by the decision makers and utilized to achieve their business performance. On the other hand, a large number of non-innovative firms seemed to have a stronger tendency to utilize knowledge services directly for their business without sufficient verification by the decision makers.

Automatic Ontology Learning from Multiple Knowledge Sources of Text

2018 ◽  
Vol 14 (2) ◽  
pp. 1-21 ◽  
Author(s):  
B Sathiya ◽  
T.V. Geetha
Keyword(s):  
Knowledge Base ◽  
Web Pages ◽  
Knowledge Sources ◽  
Statistical Measure ◽  
Ontology Learning ◽  
Web Page ◽  
Semantic Query ◽  
Probabilistic Knowledge ◽  
Discovery Algorithms ◽  
Different Sources

The prime textual sources used for ontology learning are a domain corpus and dynamic large text from web pages. The first source is limited and possibly outdated, while the second is uncertain. To overcome these shortcomings, a novel ontology learning methodology is proposed to utilize the different sources of text such as a corpus, web pages and the massive probabilistic knowledge base, Probase, for an effective automated construction of ontology. Specifically, to discover taxonomical relations among the concept of the ontology, a new web page based two-level semantic query formation methodology using the lexical syntactic patterns (LSP) and a novel scoring measure: Fitness built on Probase are proposed. Also, a syntactic and statistical measure called COS (Co-occurrence Strength) scoring, and Domain and Range-NTRD (Non-Taxonomical Relation Discovery) algorithms are proposed to accurately identify non-taxonomical relations(NTR) among concepts, using evidence from the corpus and web pages.

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