Innovation and the knowledge-base for entrepreneurship: investigating SME innovation across European regions using fsQCA

2021 ◽  
pp. 1-22
Author(s):  
Malcolm Beynon ◽  
Paul Jones ◽  
David Pickernell
Keyword(s):  
Knowledge Base ◽  
European Regions

scholarly journals Between spilling over and boiling down: network-mediated spillovers, local knowledge base and productivity in European regions

2018 ◽  
Vol 19 (6) ◽  
pp. 1233-1260 ◽  
Author(s):  
Nicola Cortinovis ◽  
Frank van Oort
Keyword(s):  
Knowledge Base ◽  
Local Knowledge ◽  
Instrumental Variable ◽  
Knowledge Spillovers ◽  
Geographical Proximity ◽  
European Policy ◽  
European Regions ◽  
Policy Perspective ◽  
Policy Initiatives ◽  
Smart Specialization

Abstract Productivity across European regions is related to three types of networks that mediate R&D-related knowledge spillovers: trade, co-patenting and geographical proximity. Both our panel and instrumental variable estimations for European regions suggest that network relations are crucial sources of R&D spillovers, but with potentially different features. Both import and co-patenting relations affect local productivity directly, but spillovers from innovation-leading regions are effective only when they are import-mediated and when recipient regions have a solid knowledge base. From a policy perspective, this may frustrate recent European policy initiatives, such as Smart Specialization, which are designed to benefit all regions in Europe.

WinFriends: Building your English Knowledge-Base (Version 5.02)

2017 ◽  
Vol 20 (1) ◽  
pp. 208-220
Author(s):  
J. F. Coll
Keyword(s):  
Knowledge Base

Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference

2018 ◽  
Author(s):  
Prachi Jain ◽  
Shikhar Murty ◽  
Mausam . ◽  
Soumen Chakrabarti
Keyword(s):  
Knowledge Base ◽  
Hybrid Model ◽  
Matrix Factorization ◽  
Relation Extraction ◽  
Tensor Factorization ◽  
Evaluation Protocol

This paper analyzes the varied performance of Matrix Factorization (MF) on the related tasks of relation extraction and knowledge-base completion, which have been unified recently into a single framework of knowledge-base inference (KBI) [Toutanova et al., 2015]. We first propose a new evaluation protocol that makes comparisons between MF and Tensor Factorization (TF) models fair. We find that this results in a steep drop in MF performance. Our analysis attributes this to the high out-of-vocabulary (OOV) rate of entity pairs in test folds of commonly-used datasets. To alleviate this issue, we propose three extensions to MF. Our best model is a TF-augmented MF model. This hybrid model is robust and obtains strong results across various KBI datasets.

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