Multiple Knowledge Sources Research Articles

Threshold-Enhanced PROMETHEE Group Decision Support under Uncertainties

As threshold determination is arduous when using PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) to make decisions, this study proposes a comprehensive PROMETHEE-based group decision support for covering the uncertainties of threshold determination, risk preferences, and the evaluation itself. To reduce the difficulty on threshold determination, it explicitly offers statistical aggregation of individuals’ indifference and preference thresholds so as to obtain benefits from multiple sources of knowledge and experience. Three typical combinations are characterized that reflect conservative, balanced, and aggressive group preferences. We also derive 6 properties to illustrate the effects of threshold changes on preference changes. Despite variances among the individual rankings of alternatives, the rank differences within the group decisions do converge by the illustrative example. A larger interval of a conservative group preference generates more diverse ranks versus the other two group preferences. Moreover, PROMETHEE III has a power on managing inaccurate measurement. The introduction of S-shaped functions has the benefit of fitting behavioral decision making under uncertainty. Integrating these approaches together for a decision support, our proposal is less affected by rank variance and coherent with traditional group PROMETHEE under differentiated decision power.

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

Knowledge sources, innovation objectives, and their impact on innovation performance: Quasi‐replication of Leiponen and Helfat (2010)

AbstractResearch SummaryThe well‐known study by Leiponen and Helfat (2010) examined how firms increased their odds of innovation success by pursuing multiple parallel innovation objectives and knowledge sources. We replicate their study across geographic and temporal contexts using three sets of data: from Finland (N = 884) in 2006–2008 and from other European countries in two different time periods (N = 36,480 in 2006–2008 and 24,326 in 2008–2010). Our results largely corroborate their core findings of the direct effects. We also find no evidence of diminishing returns to the breadth of innovation objectives, and we find at best slight evidence of diminishing returns to the breadth of knowledge sources. However, we find evidence of a significant negative interaction between knowledge sources and innovation objectives, contrary to Leiponen and Helfat's (2010) finding of a lack of evidence in a positive interaction. The new evidence of a negative interaction effect suggests that the positive effect of breadth in knowledge sources can also diminish contingent upon the breadth of innovation objectives.Managerial SummaryCompanies are increasingly searching for new sources of knowledge while simultaneously pursuing multiple strategic innovation objectives. The results of our analysis from two multicountry datasets show that increased number of knowledge sources and strategic objectives are likely to result in higher innovation performance, that is, increased revenue from new products or increased likelihood of new product introduction. However, simultaneously pursuing greater number of strategic objectives while seeking knowledge from a greater number of sources may not result in greater innovation success. Consequently, managers should either focus on a narrow set of objectives while increasing their search breadth or pursue a broader set of objectives while focusing on fewer sources of knowledge. There is evidence of slight reduction in the ability to introduce new products when firms pursue more than eight knowledge sources. Otherwise, there is no evidence of diminishing returns of knowledge sources on revenue from new products or diminishing returns of objectives on innovation performance.

A safe space for local knowledge sharing in sustainable tourism: an organisational justice perspective

Knowledge is recognised as having more value when it is created and shared amongst stakeholders. Applying knowledge management principles can assist tourism organisations in achieving a competitive advantage. Importantly though, one-way, top-down transfer of knowledge, particularly in developing countries, can result in ignoring valuable local knowledge and compromise sustainable development objectives. Certainly, when considering the context of sustainable tourism, relying solely on more powerful stakeholders’ knowledge inputs to make decisions can result in injustice and undermine sustainability values. Given the importance of justice principles to sustainable tourism, this study applies the concept of organisational justice with a focus on the three dimensions of distributive, procedural and interactional justice to examine local knowledge sharing in tourism development. To address issues of justice and fairness in the context of sustainable tourism development, this study proposes a safe space framework to facilitate knowledge sharing in a local context that is based on recognition of diversity in social patterns and embedding multiple forms and sources of knowledge. This conceptual paper contributes to the framing of justice principles with regards to local knowledge sharing and discusses practical implications for how different claims of justice by local actors can be addressed in sustainable tourism development.

Knowledge-based engineering in the context of railway design by integrating BIM, BPMN, DMN and the methodology for knowledge-based engineering applications (MOKA)

Designing railway infrastructure is a knowledge-intensive task. Although there are a number of mature design authoring systems available, their support for dynamically incorporating domain-specific engineering knowledge is very limited. At the same time, a standardized digital representation of railway engineering knowledge (such as building codes and best practice) does not exists. To overcome this deficiency, this paper proposes the use of Knowledge Based Engineering (KBE) to automate routine design tasks by considering multiple knowledge sources. In this scenario, KBE is used to support a Railway design authoring system. To ensure maximum transparency in the design of the developed KBE application, graphical ‘Business Process Model and Notation’ (BPMN) has been used in combination with ‘Decision Model and Notation’ (DMN) to formalize the underlying engineering knowledge. The KBE application has been developed according to the Methodology for Knowledge-Based Engineering Applications (MOKA). An evaluation of the BPMN/DMN approach shows that it meets up to 58% of the acceptance criteria found in the literature. In addition, BPMN and DMN can already be used in the early capture phase of MOKA and its workflows can be developed into an executable KBE application in the subsequent phases. The results of the test example discussed here show that time savings of up to 97.5% can be achieved in the execution of the KBE application.

Strengthening Conservation Agriculture innovation systems in sub-Saharan Africa: lessons from a stakeholder analysis

ABSTRACT Complexity of African agrarian systems necessitates that agricultural research and development transition to agricultural innovation system [AIS] approaches. While AIS perspectives are embraced across sub-Saharan Africa, engagement of stakeholders in agricultural research and development processes as espoused in AIS paradigm remains limited. This paper aims to analyze key stakeholders in the AIS in Malawi using the case of Conservation Agriculture [CA]. We analyze roles, organizational capacity and collaboration of stakeholders in Malawi’s CA innovation system. Although Government has the most extensive role, NGOs dominate the national CA agenda, while smallholder farmers remain passive recipients of CA interventions. Many CA promoters lack technical and financial capacity, and pursue limited collaboration, which diminish prospects of inclusive stakeholder engagement. While insufficient resources lead to inadequate technical support to smallholders, the limited collaboration hinders integration of programmes, multiple sources of innovation and knowledge required to foster social learning and sustainability of CA. Our findings indicate a need to: (1) strengthen understanding of AIS approaches among CA innovation system stakeholders; (2) build stronger partnerships in CA research and development by strengthening stakeholder platforms and social processes; (3) strengthen collaboration advisory mechanisms to facilitate knowledge-sharing, resource mobilization and joint programme implementation with strengthened feedback loops.

Multi-source brain computing with systematic fusion for smart health

With the progress of artificial intelligence, big data and functional neuroimaging technologies, brain computing has rapidly advanced our understanding of brain intelligence and brain disorders. We argue that existing data analytical methods have become insufficient for brain computing when dealing with multiple brain big data sources, because such methods mainly focus on flattening strategies and fail to work well for systematic understanding of the constituent elements of cognition, emotion and disease, as well as the intra- and inter-relations within and among themselves. To address this problem, we present in this paper a novel multi-source brain computing platform by Data-Brain driven systematic fusion. First, we formalize a series of behaviors surrounding the Brain Informatics-based investigation process, and present a conceptual model to systematically represent content and context of functional neuroimaging data. Then, we propose the systematic brain computing framework with multi-aspect fusion and inference to understand brain specificity and give uncertainty quantification, as well as its inspiration and applications for translational studies on brain health. In particular, a graph matching-based task search algorithm is introduced to help systematic experimental design and data sampling with multiple cognitive tasks. The study increases the interpretability and transparency of brain computing findings by inferring and testing multiple hypotheses taking into consideration the effect of evidence combination. Finally, multiple sources of knowledge (K), information (I) and data (D) are driven by a KID loop as the thinking space to inspire never-ending learning and multi-dimensional interactions in the connected social–cyber–physical spaces. Experimental results have demonstrated the efficacy of the proposed brain computing method with systematic fusion.

Stacked authorship attribution of digital texts

In computational authorship attribution (AA) – the task of identifying the author of a given text based on a set of possible candidates – existing differences across domains, languages or input settings may require using knowledge from multiple sources, ranging from surface character patterns to deeper semantics and others. Moreover, since increasing the model complexity may easily lead to overfitting, sources of this kind have to be selected judiciously according to each particular input. Based on these observations, this article introduces a novel approach to AA consisting of stacked classifiers built from multiple knowledge sources - words, characters, part-of-speech n-grams, syntactic dependencies, word embeddings and more - that are dynamically included in the AA model according to the relevant input. In doing so, we would like to show that a stacking approach not only outperforms previous work in the field, but also that dynamic model selection outperforms the use of any of the individual components alone. The current model - called DynAA - is evaluated in a number of AA scenarios covering multiple languages, domains and input sizes, and is shown to generally outperform a number of baseline alternatives, including convolutional neural networks, BERT and others.

The role of western‐based scientific, Indigenous and local knowledge in wildlife management and conservation

Abstract Managers of wildlife are faced with decisions and issues that are increasingly complex, spanning natural and human dimensions (i.e. values, preferences, attitudes). A strong evidence base that includes multiple forms and sources of knowledge is needed to support these complex decisions. However, a growing body of literature demonstrates that environmental managers are far more likely to draw on intuition, past experience or opinion to inform important decisions rather than empirical evidence. We set out to assess how decision‐makers and other potential knowledge users (a) perceive, evaluate and use western‐based scientific, Indigenous and local knowledge and (b) the extent to which social, political and economic considerations challenge the integration of different forms of evidence into decision‐making. In 2018, we interviewed members from natural resource management branches of Indigenous governments (n = 4) and parliamentary governments (n = 33), as well as representatives from nongovernmental stakeholder groups (n = 28) involved in wildlife management and conservation in the Canadian province of British Columbia. Contrary to studies that suggest evidence‐based conservation and management are rare, respondents described relying heavily on multiple forms of knowledge. Results revealed that western science is used near‐unanimously, procured from internal (i.e. institutional) sources slightly more than external ones (i.e. peer‐reviewed journals, management agencies in other jurisdictions). However, we found Indigenous and local knowledge use to be much less than western scientific knowledge (approximately half as much) despite being highly valued. Perceived challenges to applying Indigenous and local knowledge include a lack of trust, hesitancy to share knowledge (particularly from Indigenous communities), difficulties in assessing reliability and difficulties discerning knowledge from advocacy. Despite high (and relatively diverse) evidence use, more than 40% of respondents perceived a diminishing role for evidence in final decisions concerning wildlife management and conservation. They associated this with decreases in institutional resources and capacity and increases in socio‐economic and political interference. We encourage transformative change in wildlife management enabling decision‐makers to draw upon multiple forms of knowledge. This transformative change should include direct involvement of knowledge holders, co‐assessment of knowledge and transparency in how (multiple forms of) evidence contribute to decision‐making. A free Plain Language Summary can be found within the Supporting Information of this article.

Knowledge as an Organizational Asset for Managing Complex Projects: The Case of Naval Platforms

Knowledge management (KM) involves learning from past experiences to avoid or correct scope misalignments, quality deviations, safety problems, time delays and/or cost overruns. KM is frequently materialized as a risk management (RM) plan. An RM plan allows for anticipating, avoiding, mitigating, or reducing potential problems impacting project performance. However, despite their high complementarity, KM and RM are not the same, nor share the same purpose. In the advent of the fourth industrial revolution, managing complex projects involves many KM-related challenges, such as differential competitiveness enhancement and value chain streamlining. Naval platforms are complex projects that require the integration of multiple sources of knowledge and information. They also need to keep on integrating latest digital technology innovations in their production processes. In this context, streamlining the requirements management may become a differential asset for project stakeholders of naval platforms. Namely, enhancing requirements management can make the customers’ needs easier to meet, shorten the projects duration, reduce costs, optimize resources, and allow for higher flexibility. However, requirements management has KM as pre-requisite and RM as consequence. Unfortunately, potential synergies between KM and RM have remained largely unexplored in the project management literature, and so has requirements management as a potential bridge between both concepts. In this paper, a holistic model for shipbuilding organizations linking KM and RM is proposed. The model draws from existing KM and RM models while considering organizational factors, technological platforms, and competitiveness factors. A case study of a naval platform showing the model’s applicability is provided. It is shown how the model can allow shipbuilding companies to sustain a competitive advantage by facilitating more robust decision making in dynamic project environments. Furthermore, the model also facilitates the identification of the companies’ core competences to reach and keep a strong position in current global markets.

Generation of reusable learning objects from digital medical collections: An analysis based on the MASMDOA framework.

Learning Objects represent a widespread approach to structuring instructional materials in a large variety of educational contexts. The main aim of this work consists of analyzing the process of generating reusable learning objects followed by Clavy, a tool that can be used to retrieve data from multiple medical knowledge sources and reconfigure such sources in diverse multimedia-based structures and organizations. From these organizations, Clavy is able to generate learning objects that can be adapted to various instructional healthcare scenarios with several types of user profiles and distinct learning requirements. Moreover, Clavy provides the capability of exporting these learning objects through standard educational specifications, which improves their reusability features. The analysis proposed is conducted following criteria defined by the MASMDOA framework for comparing and selecting learning object generation methodologies. The analysis insights highlight the importance of having a tool to transfer knowledge from the available digital medical collections to learning objects that can be easily accessed by medical students and healthcare practitioners through the most popular e-learning platforms.

Learning in Faculty Development: The Role of Social Networks

Faculty development is increasingly acknowledged as an important aspect of health professions education. Its conceptualization has evolved from an individual skills training activity to contemporary notions that draw on an organizational model. This organizational model recognizes relationships and networks as important mediators of knowledge mobilization. Although such conceptual advancements are critical, we lack empirical evidence and robust insights into how social networks function to shape learning in faculty development. The purpose of this study was to understand how informal professional social networks influence faculty development learning in the health professions. This study used a qualitative social network approach to explore how teaching faculty's relationships influenced their learning about teaching. The study was conducted in 2018 in an undergraduate course at a Canadian medical school. Eleven faculty participants were recruited, and 3 methods of data collection were employed: semistructured interviews, participant-drawn sociograms, and demographic questionnaires. The social networks of faculty participants influenced their learning about teaching in the following 4 dimensions: enabling and mobilizing knowledge acquisition, shaping identity formation, expressing vulnerability, and scaffolding learning. Faculty developers should consider faculty's degree of social embeddedness in their professional social networks, as our study suggests this may influence their learning about teaching. The findings align with recent calls to conceptually reorient faculty development in the health professions as a dynamic social enterprise.

KGEL: A novel end-to-end embedding learning framework for knowledge graph completion

Knowledge graphs (KGs) have recently become increasingly popular due to the broad range of essential applications in various downstream tasks including intelligent search, personalized recommendations, intelligent financial data analytics, etc. During an automated construction of a KG, the knowledge facts from multiple knowledge sources are automatically extracted in the form of triples, and these observed triples are used to derive new unobserved triples for KG completion (also known as link prediction). State-of-the-art link prediction methods are known to be primarily KG embedding models, among which tensor factorization models have recently drawn much attention due to their scalability and expressive feature embeddings, and hence, perform well for link prediction. However, these embedding models consider each KG triple individually and fail to capture the useful information present in the neighborhood of a node. To this end, we propose a novel end-to-end KG embedding learning framework that consists of an encoder of a dual weighted graph convolutional network, and a decoder of a novel fully expressive tensor factorization model. The proposed encoder extends weighted graph convolutional network to generate two rich and high quality embedding vectors for each node by aggregating information from the neighboring nodes. The proposed decoder has a flexible and powerful tensor representation form of the Tensor Train decomposition that takes benefit of the two representations of each node in its embedding space to accurately model the KG triples. We also derive a bound on the size of the embeddings for full expressivity and show that our proposed tensor factorization model is fully expressive. Additionally, we show the relationship of our tensor factorization model to previous tensor factorization models. The experimental results show the effectiveness of the proposed framework that consistently marks performance gains over several previous models on recent standard link prediction datasets.

Understanding the reading process: Insights from a second grade English language learner

The objective of this paper is to make a theoretically informed judgement about how a child processes a text in a reading activity. A second-grade English language learner participated as the subject of this study and was observed for five times of one-on-one guided reading condition (30 minutes each). The observation focused on what happened when the child read, how and what changes occurred over time. We also focused on the individual differences and the role of knowledgeable others in helping the child processed the texts. We took notes on observable behaviors in reading processing, including monitoring activities, self-correction behaviors, and problem-solving strategies. The findings suggest some critical arguments, including (1) reading is a problem-solving activity involving multiple knowledge sources, (2) self-correction in reading is as a sign of progress, and (3) reading progress from novice to expert occurs in different path system. DOI: 10.26905/enjourme.v4i2.4036

A topic modeling framework for spatio-temporal information management

Real-time processing and learning of conflicting data, especially messages coming from different ideas, locations, and time, in a dynamic environment such as Twitter is a challenging task that recently gained lots of attention. This paper introduces a framework for managing, processing, analyzing, detecting, and tracking topics in streaming data. We propose a model selector procedure with a hybrid indicator to tackle the challenge of online topic detection. In this framework, we built an automatic data processing pipeline with two levels of cleaning. Regular and deep cleaning are applied using multiple sources of meta knowledge to enhance data quality. Deep learning and transfer learning techniques are used to classify health-related tweets, with high accuracy and improved F1-Score. In this system, we used visualization to have a better understanding of trending topics. To demonstrate the validity of this framework, we implemented and applied it to health-related twitter data from users originating in the USA over nine months. The results of this implementation show that this framework was able to detect and track the topics at a level comparable to manual annotation. To better explain the emerging and changing topics in various locations over time the result is graphically displayed on top of the United States map.

Including indigenous knowledge in species distribution modeling for increased ecological insights.

Indigenous knowledge systems hold detailed information on current and past environments that can inform ecological understanding as well as contemporary environmental management. Despite its applicability, there are limited examples of indigenous knowledge being incorporated in species distribution models, which are widely used in the ecological sciences. In a collaborative manner, we designed a structured elicitation process and statistical framework to combine indigenous knowledge with survey data to model the distribution of a threatened and culturally significant species (greater bilby or mankarr [Macrotis lagotis]). We used Martu (Aboriginal people of the Australian western deserts) occurrence knowledge and presence data from track-based surveys to create predictive species distribution models with the Maxent program. Predictions of species distribution based on Martu knowledge were broader than those created with survey data. Together the Martu and survey models showed potential local declines, which were supported by Martu observation. Both data types were influenced by sampling bias that appeared to affect model predictions and performance. Martu provided additional information on habitat associations and locations of decline and descriptions of the ecosystem dynamics and disturbance regimes that influence occupancy. We concluded that intercultural approaches that draw on multiple sources of knowledge and information types may improve species distribution modeling and inform management of threatened or culturally significant species.

The Bristol Green Capital Partnership: an exemplar of reflexive governance for sustainable urban development?

The theory of ‘reflexive governance’ prioritises process over outcomes and advocates participatory procedures, valorising multiple sources of knowledge, mutual learning and continuous evolution of organisational forms. Through the lens of reflexive governance, we explore the evolution of Bristol Green Capital Partnership, which, since 2008, has played a pivotal role in animating the environmental debate within Bristol, especially in the European Green Capital programme (2015). We conclude that the BGCP represents a ‘bounded’ example of reflexive governance, one that exhibits advanced forms of deliberation and coproduction but, ultimately, addresses too narrow a constituency of environmental, business and green activist interests.

Semantically-enhanced information retrieval using multiple knowledge sources

Classical or traditional Information Retrieval (IR) approaches rely on the word-based representations of query and documents in the collection. The specification of the user information need is completely based on words figuring in the original query in order to retrieve documents containing those words. Such approaches have been limited due to the absence of relevant keywords as well as the term variation in documents and user’s query. The purpose of this paper is to present a new method to Semantic Information Retrieval (SIR) to solve the limitations of existing approaches. Concretely, we propose a novel method SIRWWO (Semantic Information Retrieval using Wikipedia, WordNet, and domain Ontologies) for SIR by combining multiple knowledge sources Wikipedia, WordNet, and Description Logic (DL) ontologies. In order to illustrate the approach SIRWWO, we first present the notion of Labeled Dynamic Semantic Network (LDSN) by extending the notions of dynamic semantic network and extended semantic net based on WordNet (and DAML ontology library). According to the notion of LDSN, we obtain the notion of Weighted Dynamic Semantic Network (WDSN, intuitively, each edge in WDSN is assigned to a number in the [0, 1] interval) and give the WDSN construction method using Wikipedia, WordNet, and DL ontology. We then propose a novel metric to measure the semantic relatedness between concepts based on WDSN. Lastly, we investigate the approach SIRWWO by using semantic relatedness between users’ query keywords and digital documents. The experimental results show that our proposals obtain comparable and better performance results than other traditional IR system Lucene.

Stakeholder participation in IPBES: connecting local environmental work with global decision making

ABSTRACT The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services(IPBES) strengthens the science-policy interface by producing scientific assessments on biodiversity and ecosystem services to inform policy. IPBES fosters knowledge exchange across disciplines, between researchers and other knowledge holders, practitioners, societal actors and decision makers working at different geographic scales. A number of avenues for participation of stakeholders across the four functions if IPBES exist. Stakeholders come from diverse backgrounds, including Indigenous Peoples and local communities, businesses, and non-governmental organization. They represent multiple sources of information, data, knowledge, and perspectives on biodiversity. Stakeholder engagement in IPBES seeks to 1. communicate, disseminate, and implement the findings of IPBES products; 2. Develop guidelines for biodiversity conservation within member countries; and 3. create linkages between global policy and local actors – all key to the implementation of global agreements on biodiversity. This paper reflects on the role of stakeholders in the first work programme of IPBES (2014–2018). It provides an overview of IPBES processes and products relevant to stakeholders, examines the motivation of stakeholders to engage with IPBES, and explores reflections by the authors (all active participants on the platform) for improved stakeholder engagement and contributions to future work of the platform.

The Male Role as “King of the Family”: Barriers to Vasectomy Uptake

Vasectomy is a long-acting male-centred family planning method that is accepted globally as a safe, low-cost procedure. However, the vasectomy uptake remains low in Africa and birth rates remain high, particularly in rural areas. In Tanzania, family planning education and service delivery are currently women-centred. Vasectomies to date have not been positively perceived in African societies including urban Tanzania, owing to the adherence to strong patriarchal belief systems and the social role of men. This study aimed to explore rural men’s perceptions and beliefs regarding barriers to vasectomy uptake. Significant barriers to vasectomy uptake were the lack of knowledge, expectations around men’s gendered role, religious beliefs, and social stigma. The participants were 51 purposively selected married men aged 35 to 61 years. Four focus group discussions and 20 in-depth interviews were conducted. Five main themes were extracted, namely men as the kings of their families; multiple sources of knowledge causing confusion; gender roles in family planning; cultural and religious beliefs; and stigma related to failure to produce children after vasectomy. Overall, men expressed the view that vasectomy was a challenge to their role as “king of their family”. Engaging in targeted media releases, using community billboards, and providing grassroots-based health education delivered by male “elimisha rika” (peer educators) at community level are recommended as awareness raising strategies in communities.