Local Knowledge Base Research Articles

Local Knowledge Base and Entrepreneurial Opportunities in Regions: A Configurational Perspective

According to the central hypothesis of the knowledge spillover theory of entrepreneurship – the more knowledge is produced in a region, the more entrepreneurial opportunities are available in it. Recent research extended the theory further, showing that the level of entrepreneurial activities, depends on the configuration of the local knowledge base not only on its size. This paper is aimed at discovering which knowledge profiles are more (and which less) supportive of creating entrepreneurial opportunities. It is motivated by disagreements and conflicting evidence on fundamental matters across all three frequently used dimensions - the type of knowledge source that contributes to new knowledge within a region (i.e., universities or private incumbent firms); the presence/absence of a dominant source of new knowledge within a region (i.e., an anchor tenant); and the specialization/diversity of technological knowledge that is created within a region. We analyze data from 33 regions using statistics on patent filings between 2011 and 2015 and a number of created startups in these regions. Using fuzzy-set Qualitative Comparative Analysis (fsQCA) we confirm that a region’s potential for knowledge spillovers, and, thus, entrepreneurial activities, depends on the configuration of the local knowledge base, specifying what configurations are antecedent to creating more entrepreneurial opportunities. Our analysis also shows low synergy from the simultaneous presence of knowledge created by universities and industry in a given area, as these two are, under some conditions, substituting each other in stimulating knowledge-intensive entrepreneurship at the local level. Third, we show that the specialization/diversity of technological knowledge that is created within a region does not play an important role in creating a high number of entrepreneurial opportunities. These findings bring a new perspective and contribute to settling down some of the lasting debates, opening opportunities for further research and rethinking policy measures.

The role of universities in the development of the local knowledge base: supporting innovation ecosystems through skills development and entrepreneurship

The role of universities in the development of the local knowledge base: supporting innovation ecosystems through skills development and entrepreneurship

The role of universities in the development of the local knowledge base: supporting innovation ecosystems through skills development and entrepreneurship

The role of universities in the development of the local knowledge base: supporting innovation ecosystems through skills development and entrepreneurship

Companies as local skill-providers? The ‘skills ecosystem’ in Mexico

A focus of economic geography is on how actors promote the local knowledge base. Studies usually show that companies act as recipients of locally generated knowledge, and not as producers of knowlege. Recruiting graduates of local vocational schools and universities enables the companies to play this role. However, companies sometimes also act as providers of knowledge, in the sense that they offer apprenticeships or comprehensive internships for pupils and students. This article focuses on this interface of ‘dual’ actvities, which connect the companies with vocational schools and universities. Recently, the concept of the skills ecosystem, which addresses this interface within the local actor network, has become popular. Starting with this concept and embedding it into the debate of economic geography, this contribution focusses on Mexico and investigates how the local actors integrate companies in dual activities. Based on qualitative methods, the study illustrates that there is a two-part skills ecosystem in Mexico’s Central Highland. The smaller part consists of dual vocational education and training and is found in the technological ‘high-road’ part of the economy. The other part mainly comprises low-tech and mostly smaller companies that do not engage in dual practices. The conclusion discusses the contribution of a skills ecosystems-approach to future research.

Does a local knowledge base in Industry 3.0 foster diversification in Industry 4.0 technologies? Evidence from European regions

AbstractThe aim of the paper is to shed light on the role played by regional knowledge bases in Industry 3.0 in fostering new technologies in Industry 4.0 in European regions (NUTS 3) over the period 1991–2015. We find that 4.0 technologies appear to be quite related to 3.0 technologies, with some heterogeneity among different technology fields. The paper investigates the geographical implications. We find that the probability of developing Industry 4.0 technologies is higher in regions that are specialized in Industry 3.0 technologies. However, other types of knowledge bases also sustain regional diversification in Industry 4.0 technologies.

Identifying Citizens' Place Values for Integrated Planning of Road Infrastructure Projects

AbstractProjects for road infrastructure and spatial development easily meet public resistance because of a lack of local knowledge of place values by (often non‐local) planners. The aim of this study is to explore how insights in place values might improve the local knowledge base for planners of integrated road infrastructure projects and spatial development. We developed, tested and analysed the results from a novel online value‐mapping tool called the ‘Place Value Identifier’. The developed method allows us to (i) relate to ‘soft’ valuable places identified by Public Participation GIS as a complement to ‘hard’ land use data, (ii) define Valued yet Unprotected places based on combining ‘hard’ and ‘soft’ values and (iii) illustrate how these insights can be used for integrated planning of road infrastructure projects. The findings of this study show the increased potential of value mapping techniques and illustrate possible resistance areas around road infrastructure planning projects. This knowledge may assist planners in creating and selecting acceptable project alternatives that may invoke high public acceptance.

Climatic and socio-economic factors supporting the co-circulation of dengue, Zika and chikungunya in three different ecosystems in Colombia.

Dengue, Zika and chikungunya are diseases of global health significance caused by arboviruses and transmitted by the mosquito Aedes aegypti, which is of worldwide circulation. The arrival of the Zika and chikungunya viruses to South America increased the complexity of transmission and morbidity caused by these viruses co-circulating in the same vector mosquito species. Here we present an integrated analysis of the reported arbovirus cases between 2007 and 2017 and local climate and socio-economic profiles of three distinct Colombian municipalities (Bello, Cúcuta and Moniquirá). These locations were confirmed as three different ecosystems given their contrasted geographic, climatic and socio-economic profiles. Correlational analyses were conducted with both generalised linear models and generalised additive models for the geographical data. Average temperature, minimum temperature and wind speed were strongly correlated with disease incidence. The transmission of Zika during the 2016 epidemic appeared to decrease circulation of dengue in Cúcuta, an area of sustained high incidence of dengue. Socio-economic factors such as barriers to health and childhood services, inadequate sanitation and poor water supply suggested an unfavourable impact on the transmission of dengue, Zika and chikungunya in all three ecosystems. Socio-demographic influencers were also discussed including the influx of people to Cúcuta, fleeing political and economic instability from neighbouring Venezuela. Aedes aegypti is expanding its range and increasing the global threat of these diseases. It is therefore vital that we learn from the epidemiology of these arboviruses and translate it into an actionable local knowledge base. This is even more acute given the recent historical high of dengue cases in the Americas in 2019, preceding the COVID-19 pandemic, which is itself hampering mosquito control efforts.

Building Agroforestry Policy Bottom-Up: Knowledge of Czech Farmers on Trees in Farmland

Czech agriculture is dealing with the consequences of climate change. Agroforestry cultures are being discursively reintroduced for better adaptability and resilience, with the first practical explorations seen in the field. Scholars have been working with farmers and regional stakeholders to establish a baseline for making agroforestry policy viable and sustainable. In a research effort that lasted three years, a large group of Czech farmers was interviewed via questionnaire surveys, standardized focus groups and in-depth personal interviews regarding their knowledge of agroforestry systems, their willingness to participate in these systems, and their concerns and expectations therewith. The information obtained helped the researchers gain better understanding of issues related to implementation of these systems. It was found that although trees are present on Czech farms and farmers appreciate their aesthetic and ecological landscape functions, knowledge about possible local synergies with crops and animals is lacking. This local knowledge gap, together with lack of market opportunities for the output of agroforestry systems and undeveloped administrative processes, have been identified as the greatest obstacles to the establishment of agroforestry systems. The researchers argue that the discovered cognitive and technological “lock-in” of the farmers may represent a risk to climate change adaptability and resilience. For the development of complex and localised land use (e.g., agroforestry) in such a context, the researchers suggest participative on-farm research, which would broaden the local knowledge base related to ecology and entrepreneurship.

Drug Repurposing for Parkinson’s Disease by Integrating Knowledge Graph Completion Model and Knowledge Fusion of Medical Literature

The prevalence of Parkinson’s disease increases a tremendous medical and economic burden to society. Therefore, the effective drugs are urgently required. However, the traditional development of effective drugs is costly and risky. Drug repurposing, which identifies new applications for existing drugs, is a feasible strategy for discovering new drugs for Parkinson’s disease. Drug repurposing is based on sufficient medical knowledge. The local medical knowledge base with manually labeled data contains a large number of accurate, but not novel, medical knowledge, while the medical literature containing the latest knowledge is difficult to utilize, because of unstructured data. This paper proposes a framework, named Drug Repurposing for Parkinson’s disease by integrating Knowledge Graph Completion method and Knowledge Fusion of medical literature data (DRKF) in order to make full use of a local medical knowledge base containing accurate knowledge and medical literature with novel knowledge. DRKF first extracts the relations that are related to Parkinson’s disease from medical literature and builds a medical literature knowledge graph. After that, the literature knowledge graph is fused with a local medical knowledge base that integrates several specific medical knowledge sources in order to construct a fused medical knowledge graph. Subsequently, knowledge graph completion methods are leveraged to predict the drug candidates for Parkinson’s disease by using the fused knowledge graph. Finally, we employ classic machine learning methods to repurpose the drug for Parkinson’s disease and compare the results with the method only using the literature-based knowledge graph in order to confirm the effectiveness of knowledge fusion. The experiment results demonstrate that our framework can achieve competitive performance, which confirms the effectiveness of our proposed DRKF for drug repurposing against Parkinson’s disease. It could be a supplement to traditional drug discovery methods.

Maritime knowledge clusters: A conceptual model and empirical evidence

Maritime Knowledge Clusters (MKCs) are the advanced stage in the evolution path of maritime clusters. Earlier literature however mostly focused on either the characteristics i.e. critical success factors or competitiveness of maritime clusters in general and not on the components of MKCs, their interrelationships, and impact on the performance of constituent firms. This study proposes and tests a conceptual model of Maritime Knowledge Clusters (MKC) and the relations between their components, making use of survey data from 203 shipping professionals located in Singapore. Three theoretical perspectives, namely, relational view, knowledge-based view and resource-based view, have been employed. The results demonstrate that MKCs are substantiated by three critical components, in which the local knowledge cluster can develop global systemic mechanisms, thus further strengthening global linkages. Policy makers must endeavor to create a conducive local cluster environment with maritime firms being located in geographical proximity, and having local networks, knowledge base, and knowledge flows. They form the fundamental foundation for the growth of MKCs. Besides, maritime firms shall optimize the MKC’s local knowledge cluster to facilitate their global expansion, which will in turn boost the internationalization of the MKC.

A framework for pre-processing of social media feeds based on integrated local knowledge base

Most of the previous studies on the semantic analysis of social media feeds have not considered the issue of ambiguity that is associated with slangs, abbreviations, and acronyms that are embedded in social media posts. These noisy terms have implicit meanings and form part of the rich semantic context that must be analysed to gain complete insights from social media feeds. This paper proposes an improved framework for pre-processing of social media feeds for better performance. To do this, the use of an integrated knowledge base (ikb) which comprises a local knowledge source (Naijalingo), urban dictionary and internet slang was combined with the adapted Lesk algorithm to facilitate semantic analysis of social media feeds. Experimental results showed that the proposed approach performed better than existing methods when it was tested on three machine learning models, which are support vector machines, multilayer perceptron, and convolutional neural networks. The framework had an accuracy of 94.07% on a standardized dataset, and 99.78% on localised dataset when used to extract sentiments from tweets. The improved performance on the localised dataset reveals the advantage of integrating the use of local knowledge sources into the process of analysing social media feeds particularly in interpreting slangs/acronyms/abbreviations that have contextually rooted meanings.

From logs to Stories: Human-Centred Data Mining for Cyber Threat Intelligence

An average medium-sized organisation logs approx. 10 to 500 mln events per day on the system. Only less than 5% of threat alerts are being investigated by the specialised staff, leaving the security hole open for potential attacks. Insufficient information in alert message produced in machine-friendly rather than human-friendly format causes cognitive overload on currently limited cybersecurity resources. In this paper, the model that generates the report in natural language by means of applying novel storytelling techniques from security logs is proposed. The solution caters for different levels of reader expertise and preference by providing adjustable templates, filled from both local and global knowledge base. The validation is performed on case study from Security Operations Centre (SOC) at educational institution. The report generated proves superior to existing approach in terms of comprehension (increased cognition) and completeness (enriched context). The evaluation demonstrates power of storytelling in potential threats interpretation in cybersecurity context.

Local perception and factors affecting conservation status of gallery forests, habitat of orchid species

In a context of fragmentation of forest landscape and sustainable use of orchid species, an analysis of the local knowledge base is necessary for developing conservation strategies of orchid habitat. This study aims at i) assessing factors affecting local perception of conservation status of orchid habitats, and ii) determining degradation factors of their habitat in Sudanian woodlands and the consequences of the degradation. We used a questionnaire-based survey, to cross knowledges on orchids and habitat degradation, from the four main socio-cultural groups, including the age, the sex and the level of education, translating into 390 respondents. We used the binomial and multinomial logistic regression to assess the local perception on conservation status of orchid habitats with reference to different ethno-demographic groups. We also used the negative binomial generalized linear model (GLM) to test for an existing dependence relation between the local perceptions of the “consequences” of the degradation of gallery forest (as response variable), and the ethno-demographic variables. Ethnicity and level of education significantly influence knowledge on orchids, as well as the perception on their habitat conservation status (p<0.05). The socio-cultural groups Fulani, Gourmantché and Waaba perceived more the degradation of their gallery forests, habitat of orchid species. The three main factors of degradation from local view are tree cutting (65.64%), shifting agriculture (44.10%) and wildfires (39.49%). Whatever the considered GLMs, there is not a significant influence of the ethnodemographic variables on local perception of the consequences of the gallery forests degradation (p< 5%). However, education and ethnicity significantly influence perception on the conservation status of orchid habitat. Assessing how this local perception translates into quantitative data in the field is crucial to develop adaptive strategies for the long term conservation of orchid species and sustainable use of their habitats.

Building Resilience to Chronic Landslide Hazard Through Citizen Science

Landslides disrupt livelihoods, cause loss of human lives and damages to property and infrastructure. In the case of Nepal, the destructive impact of landslides has been steadily increasing as a result of the rising occupation of marginal land and extreme weather events caused by climate change. In particular, the impacts of seasonal, shallow landslides have been underestimated due to underreporting, and lack appropriate policy response. Within this paper, we argue that citizen science – the practice of incorporating the general public in the process of knowledge co-production – may help address this issue by increasing the knowledge base of stakeholders at different levels. We present the preliminary results from an interdisciplinary scoping study of two landslide sites in Western Nepal, in Bajhang and Bajura, where the Landslide-EVO research project, including a citizen science component, is currently being implemented. The aim of the project is to innovate participatory environmental monitoring and to generate evidence to support resilience. Our exploratory qualitative investigation outlines the strategies currently employed by the local communities that continue living in the landslide affected areas. These include demographic shifts and patterns, land use changes and occupational diversification. We argue that these existing local adaptation and mitigation practices compound a wealth of experiential knowledge. Based on evidence from literature, as well as our first-hand experience of starting citizen science activities in the both landslide sites, we argue that citizen science has the potential to build on local knowledge base and strengthen the adaptive capacities of different level stakeholders. Our theoretical contribution is the proposed typology of citizen-science interventions. We distinguish between community science, participatory environmental monitoring and virtual citizen science, providing examples of how they can benefit stakeholders at different levels and/or different types of research. Finally, we examine the ways in which different types of citizen science could be applied in our case study sites, specifying the conditions under which they can attain maximum usefulness.

Graph-ranking collective Chinese entity linking algorithm

Entity linking (EL) systems aim to link entity mentions in the document to their corresponding entity records in a reference knowledge base. Existing EL approaches usually ignore the semantic correlation between the mentions in the text, and are limited to the scale of the local knowledge base. In this paper, we propose a novel graphranking collective Chinese entity linking (GRCCEL) algorithm, which can take advantage of both the structured relationship between entities in the local knowledge base and the additional background information offered by external knowledge sources. By improved weighted word2vec textual similarity and improved PageRank algorithm, more semantic information and structural information can be captured in the document. With an incremental evidence mining process, more powerful discrimination capability for similar entities can be obtained. We evaluate the performance of our algorithm on some open domain corpus. Experimental results show the effectiveness of our method in Chinese entity linking task and demonstrate the superiority of our method over state-of-the-art methods.

Scientific knowledge production in European regions: patterns of growth, diversity and complexity

ABSTRACTWe study regional patterns of scientific knowledge production in Europe using all scientific publications in the period 2000–2014 attributed to 813 scientific subfields. We show that the existing scientific portfolio of regions offers opportunities for related diversification and discourages the creation of knowledge on topics unrelated to the local knowledge base. Many lagging regions show clear growth, but complex knowledge production remains highly concentrated in regions in the North and West of Europe. For lagging regions there are advantages in not specializing too soon and to first diversify before moving into developing more complex knowledge.

Co‐inventor networks and knowledge production in specialized and diversified cities

AbstractResearch on the US urban system has shown that metropolitan regions with more local and non‐local network ties outperform cities where economic agents are isolated. Yet, little attention is given to the character of the local knowledge base and how that influences network structure. We show that co‐inventor networks differ between cities that produce specialized and diversified knowledge. Models of tie‐formation show inventors in specialized cities value spatial proximity less and cognitive proximity more than inventors in diversified cities as they partner with non‐local inventors. These findings suggest that the influence of social networks on knowledge production is conditioned by the architecture of the local knowledge base.

PAPG – Personalized Anti-Phishing Guard

Security and privacy have been considered a corner stone in all electronic transactions nowadays. People are becoming very cautious when conducting electronic transactions over internet. One of the major issues that frightens them is identity theft. Identity theft might be conducted using phishing techniques that aims to trick the user to provide his credentials in a well-organized tactic. Efforts have been done towards fighting against phishing attacks and hence identify theft. However, most of these efforts are either computationally exhaustive to the electronic device or depend on a third party to perform the task. In this paper, we propose a plugin called Personalized Anti-Phishing Guard - PAPG that is managed personally on the device and is used to guard the user against phishing attacks. The plugin maintains data locally and may not need to synchronize with a third party. Besides, PAPG depends on the user's feedback to build the local knowledge base that is used to support the decision. The user might also store his profile and reuse it with other devices and from different locations without having to configure it again

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

AbstractProductivity 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.

A three-phase decision making approach for self-adaptive systems using web services

A self-adaptive system adapts itself to changes in a dynamic environment. The core in self-adaptive systems is making an adaptation decision. The current practice focuses on a single layer of decision making using either a local knowledge base or a shared knowledge base shared by multiple units through a network. While the use of a local knowledge base is efficient, it suffers from its limited maturity. A shared knowledge base can address the maturity problem, but it is inefficient in adaptation due to communication overheads. In this work, we present a three-phase decision making approach for self-adaptive systems to improve precision while being competitive for efficiency. The approach consists of three phases for making a decision. The first phase uses the local knowledge base of the self-adaptive unit to identify an object. If the object cannot be identified locally, the unit sends a request to shared knowledge bases through web services in the second and third phases. The approach makes use of B-kNN for object identification and web services for accessing shared knowledge bases. We conducted quantitative validation in terms of accuracy, precision, recall, and F-measure using a set of scenarios. The results show that 99.58% of accuracy, 94.01% of recall, 94.04% of precision, and 94.01% of F-measure can be achieved. We also conducted comparative analysis by comparing the presented approach with the traditional approach and the cloud-based approach. The results show that the presented approach improves 45% in object identification with an increase of 0.66 s over the traditional approach and the same performance in object identification with a decrease of 0.95 s over the cloud-based approach.