The Foundation of Our Knowledge—Sources and Records

2021 ◽  
pp. 25-47
Keyword(s):  
Knowledge Sources

Knowledge creation in small building and construction firms

2013 ◽  
Vol 1 (1) ◽  
pp. 125-142 ◽  
Author(s):  
Susanne Durst ◽  
Ingi Runar Edvardsson ◽  
Guido Bruns
Keyword(s):  
Construction Industry ◽  
Knowledge Creation ◽  
Vital Role ◽  
Knowledge Sources ◽  
Structured Interviews ◽  
Construction Firms ◽  
External Knowledge ◽  
Body Of Knowledge ◽  
Small Building

Studies on knowledge creation are limited in general, and there is a particular shortage of research on the topic in small and medium-sized enterprises (SMEs). Given the importance of SMEs for the economy and the vital role of knowledge creation in innovation, this situation is unsatisfactory. Accordingly, the purpose of our study is to increase our understanding of how SMEs create new knowledge. Data are obtained through semi-structured interviews with ten managing directors of German SMEs operating in the building and construction industry. The findings demonstrate the influence of external knowledge sources on knowledge creation activities. Even though the managing directors take advantage of different external knowledge sources, they seem to put an emphasis on informed knowledge sources. The study´s findings advance the limited body of knowledge regarding knowledge creation in SMEs.

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 The first source is limited and possibly outdated, while the second is uncertain.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.

Knowledge Value, Task Complexity, and Enterprise Technology Implementation

2019 ◽  
Vol 10 (1) ◽  
pp. 19-38
Author(s):  
Sharath Sasidharan
Keyword(s):  
Social Networks ◽  
Technology Implementation ◽  
Task Complexity ◽  
Performance Outcomes ◽  
Planning System ◽  
Knowledge Sources ◽  
Enterprise Technology ◽  
Applied Knowledge ◽  
The Impact ◽  
Knowledge Value

Employees utilize their informal social networks for acquiring system-related knowledge during enterprise technology implementation. Prior research on knowledge acquisition through social networks has not considered the domain proficiency of knowledge sources or the quality of knowledge flows. This study assigns domain-proficiency levels to knowledge sources and introduces the concept of knowledge value: the net impact of acquired knowledge on performance outcomes. Conceptualized as the differential in the domain proficiency of the knowledge source and the knowledge recipient, knowledge value is examined in the context of both factual and applied knowledge, in relation to task complexity and its influence on performance outcomes. Data collected during the implementation of an enterprise resource planning system indicate that knowledge value has a significant impact on performance outcomes, but the impact of applied knowledge is moderated by task complexity. The results stress the importance of considering domain proficiency of knowledge sources during knowledge-network modelling.

scholarly journals Knowledge Sources for Constituent Parsing of German, a Morphologically Rich and Less-Configurational Language

2013 ◽  
Vol 39 (1) ◽  
pp. 57-85 ◽  
Author(s):  
Alexander Fraser ◽  
Helmut Schmid ◽  
Richárd Farkas ◽  
Renjing Wang ◽  
Hinrich Schütze
Keyword(s):  
State Of The Art ◽  
Lessons Learned ◽  
Knowledge Sources ◽  
Lexical Knowledge ◽  
Context Free Grammar ◽  
The Impact ◽  
Context Free ◽  
Probabilistic Context

We study constituent parsing of German, a morphologically rich and less-configurational language. We use a probabilistic context-free grammar treebank grammar that has been adapted to the morphologically rich properties of German by markovization and special features added to its productions. We evaluate the impact of adding lexical knowledge. Then we examine both monolingual and bilingual approaches to parse reranking. Our reranking parser is the new state of the art in constituency parsing of the TIGER Treebank. We perform an analysis, concluding with lessons learned, which apply to parsing other morphologically rich and less-configurational languages.

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