Local Knowledge Base Research Articles

Adversarial contrastive representation training with external knowledge injection for zero-shot stance detection

Zero-shot stance detection (ZSSD) is a task that involves identifying the author’s perspective on specific issues in text, particularly when the target topic has not been encountered during the model training process, to address rapidly evolving topics on social media. This paper introduces a ZSSD framework named KEL-CA. To enable the model to more effectively utilize transferable stance features for representing unseen targets, the framework incorporates a multi-layer contrastive learning and adversarial domain transfer module. Unlike traditional contrastive or adversarial learning, our framework captures both correlations and distinctions between invariant and specific features, as well as between different stance labels, and enhances the generalisation ability and robustness of the features. Subsequently, to address the problem of insufficient information about the target context, we designed a dual external knowledge injection module that uses a large language model (LLM) to extract external knowledge from a Wikipedia-based local knowledge base and a Chain-of-Thought (COT) process to ensure the timeliness and relevance of the knowledge to infer the stances of unseen targets. Experimental results demonstrate that our approach outperforms existing models on two benchmark datasets, thereby validating its efficacy in ZSSD tasks.

Principle and Application for Rumination Computing Algorithms

To fully analyze, mine, and utilize the information and knowledge implied in problem resolving use cases, this paper proposed the autonomous learning method based on machine inducting, hypothesis formulating, and result verifying, which was similar to the biological process of cows ruminating, called rumination computing. Firstly, after inducting and summarizing over 1080 mathematic application problem, the system architecture and general algorithm for humanoid automatic resolving mathematic application problems were represented, which typically included functional modules such as commonsense knowledge base, domain knowledge base, and local knowledge base, preprocessing, word segmentation and part of speech tagging, semantic framework matching, global semantic analyzing, thinking mechanism implementing, etc. Secondly, after the use case solutions were approved, three typical rumination computing modes, including vocabulary sequence, semantic relationship, and computing action, were introduced based on the correct results, resolving steps, and basic rumination actions. The rumination computing step plan was formulated, new knowledge was obtained from the commonsense and results verification, so the continuous autonomous learning loop for machine thinking was formed. Detailed explanations were provided for the three core algorithms implemented (rumination framework algorithm, rumination semantic algorithm, rumination action algorithm). Then, by specific mathematic application problem humanoid resolving user cases, the above three types of rumination computing modes were illumined.

MED-ChatGPT CoPilot: a ChatGPT medical assistant for case mining and adjunctive therapy.

The large-scale language model, GPT-4-1106-preview, supports text of up to 128 k characters, which has enhanced the capability of processing vast quantities of text. This model can perform efficient and accurate text data mining without the need for retraining, aided by prompt engineering. The research approach includes prompt engineering and text vectorization processing. In this study, prompt engineering is applied to assist ChatGPT in text mining. Subsequently, the mined results are vectorized and incorporated into a local knowledge base. After cleansing 306 medical papers, data extraction was performed using ChatGPT. Following a validation and filtering process, 241 medical case data entries were obtained, leading to the construction of a local medical knowledge base. Additionally, drawing upon the Langchain framework and utilizing the local knowledge base in conjunction with ChatGPT, we successfully developed a fast and reliable chatbot. This chatbot is capable of providing recommended diagnostic and treatment information for various diseases. The performance of the designed ChatGPT model, which was enhanced by data from the local knowledge base, exceeded that of the original model by 7.90% on a set of medical questions. ChatGPT, assisted by prompt engineering, demonstrates effective data mining capabilities for large-scale medical texts. In the future, we plan to incorporate a richer array of medical case data, expand the scale of the knowledge base, and enhance ChatGPT's performance in the medical field.

Breakthrough inventions in China: the role of intra- and inter-regional collaboration and local knowledge base

ABSTRACT Recently, the prevalent recommendation for industrialised regions is to diversify their local knowledge base through intra- and inter-regional collaborations, to maintain a competitive advantage in dynamic regional economies. However, this has resulted in a series of policies that lean towards a ‘one-size-fits-all’ approach. Theoretically, regions with varying local knowledge bases should follow unique technological trajectories. In this context, this article intends to examine the impact of collaboration, as well as its interaction effects with the local knowledge base, on the likelihood of breakthrough inventions. Our key findings, derived from a 15-year panel dataset (2002–2016) covering 200 prefecture-level cities in China, include: (1) Intra-regional collaboration and a balanced mix of intra- and inter-regional collaboration both positively influence the likelihood of breakthrough inventions; (2) The potential for breakthrough inventions depends on the interplay between collaboration and the local knowledge base. Specifically, internal collaboration is more beneficial for diversified regions, external collaboration aids more specialised regions, and both inter-regional collaboration or balanced mix of intra- and inter-regional collaboration can enhance the probability of breakthrough inventions for cities with a complex knowledge base. Our study holds significant implications for regional development, proposing a viable technological trajectory to mitigate path dependency.

Research on the Construction Method of Knowledge Base of Automatic Question Answering System for Labor Law

At present, with the rapid development of China's economy, labor relations in society are becoming more and more tense. Therefore, it is urgent and necessary to develop an automatic question and answer system for labor law to solve problems in legislation, law enforcement, justice, rights protection, etc., and the construction of labor law knowledge base is the core system of the automatic question and answer system of labor law. With the development and training of large AI models, the automatic question answering system is becoming more and more mature, and this paper will use FastGPT to build a local knowledge base and build a knowledge base of the automatic question answering system for labor law.

Leveraging on circular economy technologies for recombinant dynamics: do localised knowledge and digital complementarities matter?

ABSTRACT This paper investigates the relationship between local knowledge bases and recombinant dynamics in circular economy (CE) technologies. We focus on the role of accumulated green and digital complementary capabilities and posit that they are positively associated to the regional ability to absorb and integrate new technological opportunities in CE-based recombinations. The empirical analysis, conducted on a dataset of European NUTS2 regions over the period 1985–2015, suggests that both green and digital complementary localised capabilities represent crucial leverage for regional recombinant activities around CE technologies.

Diagnosis of package installation incompatibility via knowledge base

Python package installation is far from trivial. Users encounter a variety of errors when installing Python libraries, including dependency conflicts and incompatibilities. Existing solutions focus on parsing third-party dependencies but ignore the impact of local settings and user requirements. In this paper, we propose a novel approach, HELP, to help Python users tackle installation errors. We first establish a local knowledge base by extracting the PyPI database and dependency analysis. When the user provides the installation requirements, HELP extracts multiple constraints including user requirements, Python version constraints, and dependency constraints, and models them into SMT expressions. Then HELP solves the installation problem by using the SMT solver.To understand the status of Python version compatibility, we conduct an empirical study on Python version compatibility on 8,502 libraries. The study reveals that 80% of Python libraries do not declare Python version constraints in most versions. We also find that installation errors are strongly related to Python versions. To evaluate HELP, we conduct the experiment on 495 sampled installation failures. The results show that HELP can effectively resolve 263 installation failures, 42% more than the baseline approach. Especially, HELP provides a more comprehensive diagnosis when encountering configuration-related installation failures. Besides, HELP is more efficient than pip at predicting installation failures (30X speedups), which may save much effort if the installation cannot succeed.

Climate futures and development pathways: A journey from terrorism to tourism in the Khyber-Pakhtunkhwa Province of Pakistan

Achieving equitable development that supports climate mitigation in an uncertain future remains a daunting task, particularly for post-conflict societies. In this paper, we present results from a local stakeholder participatory scenario workshop investigating emerging priorities for anticipatory action, with respect to future climate impacts, in the vulnerable conflict-affected Khyber-Pakhtunkhwa Province of Pakistan. Importantly, our findings reveal that stakeholders were less focused on addressing climate impacts, and more interested in local development initiatives to enhance tourism offerings and the regional national park network. The novelty of our work lies in the use of participatory scenarios to both initiate a process of healing and provide a forum for the co-production of future narratives focused on sustainable and inclusive development. However, this first involves mending the social fabric that ties individuals and communities together in an inclusive and informed way. As such, our work adds to the scholarship of environmental peacebuilding, creating a local context-specific knowledge base using a bottom-up approach to understand a shared future. The case study presented here highlights the perspectives of policymakers from the global south and brings local stakeholders together in a space where top-down management approaches often dominate.

Unveiling Indigenous Traditional Knowledge (ITKs) Using Different Available Plants by the Local People of South Sikkim, India

Northeast India is biologically diverse regions, among which the South Sikkim district possesses unique rural landscapes enhanced with endless waves of farming areas and terraced hills. Within this region, three main indigenous groups—Nepali, Lepcha and Bhutia—coexist, contributing to the cultural diversity. Over the years, these communities have cultivated a traditional knowledge based on which they are greatly dependant on diverse plant resources for both protective measures and medicinal practices. These traditional practices encourages a strong local knowledge base. These practices include local plants namely utis (Alnus nepalensis), chilauni (Schima wallichii), wild fern, titepati (Artemisia vulgaris) and many more. However, despite the rich diversity of these cultural practices connected with nature, there is a lack of detailed exploration and documentation for better understanding traditional practices and ethno-medicinal knowledge upheld by these communities. The absence of such documentation underscores a significant gap in our understanding of their cultural heritage, as these ITKs can be further integrated with science to make contribution for adaptive management to many environmental and health issues. Therefore, this study is conducted with the aim to unreveal the potential of these traditional practices and ethno-medicinal knowledge preserve among the diverse communities of South Sikkim, which can help to shed light on their cultural heritage and to recognize their invaluable contributions.

Pathways and Interactions for Integrating Mechanisation into Sustainable Agricultural Production: The Case of Rice Production in Asutsuare, Ghana

Environmentally sustainable small-scale rice production mechanisation is a feasible intervention to help enhance yields and reduce food insecurity. Using machinery for rice production can help small farmers economically and promote sustainability through agroecological principles. The study analyses machinery ownership models and suggests stakeholder interactions for sustainable rice production. The study uses primary data from a field survey of 320 farmers within Asutsuare, a rice production hub in Southern Ghana, and secondary data from various sources. Four different ownership models have been proposed and evaluated. The cooperative-owned machinery (COM) model, with a sharing of the initial investment capital outlay for the machinery acquisition, and the individual ownership model, where the farmer owns and offers hiring services to other farmers (the FOHM-2B and FOHM-2T models) were the most economically viable models. The study also identifies necessary stakeholder engagement and pathways for affordable, sustainable, mechanised small-scale rice production. The models and interactions can promote machinery ownership and strengthen social connections in the community. This local knowledge base can help expand the use of machinery within the community. These models and interactions can be replicated easily in Sub-Saharan African farming communities with similar dynamics. This will improve mechanised farming throughout the continent.

Why a proactive research culture is necessary for advancing social sciences in Sri Lanka?

This editorial points to the need for developing a positive research culture for advancing social science research in Sri Lanka. This is not to say that a positive attitude towards research is absent among social scientists and the scientific community in general in Sri Lanka. On the contrary, there is an emerging recognition that social science inputs are necessary for tackling the various challenging problems facing the country today. However, what is lacking is a firm faith in building a local social science knowledge base for exploring various approaches for understanding critical issues facing the country, constant debates about relevant issues and a critical engagement with the social, economic, and political environment in which we find ourselves. This is what I call a “proactive research culture” where we constantly turn to new research for generating new knowledge and finding answers to serious problems affecting the current and future generations.

SMART Chatbots in the E-learning Domain: A Systematic Literature Review

Integrating Artificial Intelligence (AI) technologies implied significant growth in variousdomains. Furthermore, many companies integrate AI technologies into their products toenhance the quality of their services. Chatbots are among the AI technologies widely used inseveral areas, especially E-learning. Chatbots support learners in their learning processes byhelping them to find the appropriate answers to their questions. We aim to conduct a systematicliterature review (SLR) to uncover the use of AI chatbots to offload teachers from repetitive andmassive tasks. This article surveys the literature over the period 2016–2022 on the use of AIchatbots in the E-learning domain as they automatically answer learners’ questions. Thus, weidentify, collect, and synthesize multiple research studies on the application of AI chatbots in theE-learning field. Based on the renowned frameworks, PRISMA and PICO, we have succeeded in(1) Developing our research questions and (2) Automatically implementing a solution based onPython language to analyze selected papers, highlighting research gaps, and opening new windowsto guide our future works. Our study shows that chatbots effectively interact with learners.However, there are some drawbacks: (1) Educational chatbots are still limited in their localKnowledge Base (KB), which makes them unable to answer students’ questions correctly. Thus,Chatbot’s KB needs to be extended through external sources, enabling the chatbot to update itsKB over time, making it rich and saving time. (2) Lack of reliable external sources to enrich thechatbot’s KB and make it up to date. (3) Lack of educational chatbots with smart services suchas speech recognition and sentiment analysis to boost the user experience and make learningeasier. In our SLR, we discuss these limitations and propose some solutions to fill the gap.

Measuring disparities in access to district and referral hospitals in the city of Kigali, Rwanda

The study focused on the identification of theoretical and methodological constructs that can be used to analyse and improve the spatial performance of public health service delivery systems and the development of a corresponding spatial-analysis and GIS-based planning approach using the city of Kigali as a case study. Access to health care as a multi-tiered system was measured by using five dimensions of access, namely geographic accessibility, availability, affordability, acceptability and accommodation. Geo-statistical analysis was performed to measure and evaluate access to the district and referral health care. For geographic access, the travel time for every trip is approximated for the two main modes of transport (walking and public transport). To realistically represent the friction of distance, the street network was categorised into several road classes for which different travel speeds are estimated for each transport mode on the basis of local knowledge. A factor analysis model was used for availability, affordability, acceptability, and accommodation dimensions. Different indicators were evaluated in various units and scales and standardized to see how satisfaction levels varied with each dimension. Based on the type of indicator, for the cost or benefit, the score was zero (0) or one (1). One (1) was considered a positive impact, while zero (0) was considered a negative impact. Summary scores were developed for all indicators by combining more than one indicator into a single value. The created summary score also ranges from 0 to 1, and the higher the score, the better the access attainment. To find areas that need intervention, the comparison of all five indicators and the scores of access for each indicator was made. The dimensions of geographic accessibility and availability appeared to be the most problematic. The district health care dimensions have revealed a low performance level in geographic accessibility (0.64) and a high-performance level in affordability (0.94). The referral health care dimensions have revealed a low performance level of geographic accessibility (0.17) and a high-performance level of acceptability (0.92). Health planners and policy makers will commonly require such a planning method, particularly in the developing world where spatial contexts are highly dynamic as a result of rapid urbanisation.

Does external linkage stimulate innovation capacity? The analysis based on “dual‐pipelines” framework

AbstractDrawing on “buzz‐and‐pipeline” theory, we develop a framework termed “dual‐pipelines” to discover how the local innovation capacity can benefit from transnational and domestic introduced technology. We find that the intensity of transnational and domestic patent introduction both have an inverted U‐shaped relationship with local innovation capacity. Also, the relatedness and similarity of domestic technology introduced are advantageous to local innovation. However, the transnational introduced technologies are only beneficial if they perform similarly to the local knowledge base while having no significant effect if merely showing relatedness. Finally, transnational and domestic technology introductions complement each other in promoting local innovation capacity.

Ausländische Direktinvestitionen und lokale Wissensbasis: Die Einbettung von Niederlassungen in die deutsche duale Ausbildung

Dual vocational education and training systematically combines the learning of apprentices (in vocational schools) or students (in applied universities) with the ‘learning venue’ of the company. The dual vocational education and training exists in Germany, Austria and Switzerland and stands for regional competitiveness as well as for job opportunities of young people on the local labour market. Outside of these three countries, the dual system hardly exists, but dual practices are subject of international skill transfer policies. While this transfer is well researched, it is hardly known to what extent (in mirror image) subsidiaries of foreign companies take part in the dual system of German speaking countries. This question was examined, by focussing on subsidiaries of multinational companies in Germany. Theoretically, the contribution relates to research on local knowledge bases and the embeddedness of foreign subsidiaires. The study pursues a qualitative method. The results show that the local training managers have freedom for decision-making and use this in favour of getting involved in dual vocational education and training. However, the effort turns out to be high. Dual activities take place primarily as a strategy for adapting to the institutional environment, not as a way of shaping the environment. The local stakeholders are mostly reactive in this regard. This points to possible future fields of research on local knowledge bases and local embeddedness and, with regard to practice, to the relevance of mutual coordination.

The temporal value of local scientific expertise

Abstract Inventors located in a vibrant scientific community enjoy faster access to relevant publications. The key role of the local scientific community is to provide access to global knowledge and not necessarily to feed the colocated inventors with direct inputs. We develop the logic for these effects and provide empirical tests using dyads of publication and patent data. We develop a model that links scientific knowledge, codified through publication, to industry’s inventive activity. Our analysis includes three key steps. First, we characterize the knowledge profile of local expertise for French NUTS3 jurisdictions. Second, we match scientific publications to industrial patents to consider the specific scientific knowledge useful to industrial invention. Finally, we examine the extent to which the presence of a local related scientific knowledge base provides utility for local inventions. Specifically, we consider the ability of local inventions to more rapidly avail themselves of scientific knowledge when located in a munificent location. We find that location reduces the timing to access to relevant worldwide publications by almost 1 year. Thus, colocation with scientific experts provides inventors a timing advantage by allowing earlier exploitation of recent global scientific discoveries.

Drivers of territorial servitization: An empirical analysis of manufacturing productivity in local value chains

A recent stream of literature has observed the tendency for advanced services, and particularly knowledge-intensive business services (KIBS), to grow near local manufacturing value chains, supporting a trajectory of growth based on territorial servitization (TS). However, it is still unclear what affects the coupling between KIBS and manufacturing. The paper addresses this research gap by analysing how local drivers of TS moderate the effect of KIBS co-location on manufacturing productivity. To test our hypotheses, we use a functional geographic unit characterized by a well-defined local value chain, i.e., Labour Market Areas (LMAs). The empirical examination of 611 Italian LMAs for the period 2014–2018 provides evidence that human capital availability is a key determinant of KIBS' contribution to manufacturing productivity. Importantly, our analysis shows that human capital impacts variously on TS outcomes, depending on the local setting and industry type. On the one hand, high levels of education help unleash the benefits of proximity to KIBS for manufacturers, especially in urban areas typically hosting high concentrations of knowledge-intensive sectors and supporting infrastructures. On the other hand, medium levels of education play a key role in non-urban areas specialized in traditional, low-tech manufacturing sectors. By helping untangle the drivers of positive TS outcomes, our results are informative for scholars and policymakers interested in developing specific policies to promote the renaissance of local manufacturing. The study may also help manufacturing firms gain a deeper understanding of the conditions allowing them to effectively benefit from TS and increase productivity. • TS varies based on the type of local setting and knowledge base of the area. • We employ a panel analysis on Italian LMAs to test our hypotheses. • KIBS affect manufacturing productivity variously depending on contextual factors. • Urban and Made-In-Italy LMAs rely on high and medium education levels respectively. • In Heavy Manufacturing LMAs, firms own internal capabilities to benefit from KIBS.

Digital gravity? Firm birth and relocation patterns of young digital firms in Germany

AbstractThis paper analyses the spatial patterns of young (<10 years) digital firms in Germany between 2008 and 2017 on county level. Determinants of firm birth locations as well as relocations are considered jointly to understand differences in location choices within firms' life cycles. I match commercial register data of 107,321 firms with county‐level administrative data to capture local characteristics. Using an OLS model with fixed effects, I find that the local knowledge base—that is, universities, research institutes, and colocated incumbents—are significant key determinants of digital firm birth when controlling for a host of local characteristics. My results indicate that for five firms per 1000 inhabitants, there is around one firm birth. Second, using a fixed effects gravity model for the analysis of relocations, I find that the most dominant explanatory factor for firm relocation across specifications is distance, that is, relocation costs. Relocation flows are more than twice as high to neighboring counties relative to other locations which shows that digital firms are not as footloose as their business model may suggest. Jointly, my results reflect economic activity's regional persistence, particularly for new firms. My paper provides evidence for policies targeting homogenous digital clusters based on strong colocation and that digital economic activity is not shifted over long distances, but the regional entrepreneurship capital is crucial for local growth.

Distributed Knowledge Inference Framework for Intelligent Fault Diagnosis in IIoT Systems

Knowledge inference using knowledge graphs is one of the recent research trends for fault diagnosis. As complex Industrial Internet of Things (IIoT) systems evolve to incorporate distributed subsystems, each subsystem may have local measurements interpreted by a local Knowledge Base (KB). In this work, we propose a distributed fault diagnosis framework with distributed path-based reasoning. In contrast to centralized reasoning that requires merging of the distributed KBs, the proposed framework employs a centralized coordinator to transfer the model among decentralized agents and allows an agent to handover training paths for others to continue training by filling in missing links using their local KBs. Our work is the very first distributed knowledge inference framework to reason among distributed fault diagnosis systems to tackle the KB isolation, scalability and flexibility issues of centralized fault diagnosis systems. To validate our framework with IIoT datasets, we build a fault knowledge graph from the Tennessee Eastman (TE) process simulations. Evaluations show that our framework outperforms the non-cooperative distributed reasoning method in terms of prediction accuracy. With the TE process dataset, the mean reciprocal rank is improved from 0.1612 to 0.19 with transferred models, and further to 0.206 with hand-over queries between agents.

What network orientation supports the development of a Resilient City? Evidence from the innovation systems of eighty-seven cities

It is critical to understand how and why a city's economy rises or falls, especially when an abrupt change is triggered. Not all cities are able to maintain economic sustainability and resilience at the same time they support industrial vigour and innovation activities. This study reviews data collected from eighty-seven cities worldwide to understand how they were able to regain and extend their technological venturing momentum and reinforce their resilience following adverse economic events. It examines patenting indexes derived from the regional innovation system (RIS) of each city. Our difference-in-differences analysis identifies the innovation dynamics of cities in OECD countries, revealing their ability to lay a local knowledge base and to command cross-regional knowledge networks. Our study found that this highly resilient framework surprisingly demonstrated a clear downtrend after the Dotcom Bubble crisis. In contrast, many cities in BRICS and developing countries are relatively behind in terms of both their ability to command the local knowledge base and progress in developing regional and interregional networks. Our resilience analysis breaks new ground in the understanding of regional system orientations and the development context required for a city to maintain resiliency and support its ability to quickly recover from an adverse event.