This is the final program for the 2017 International Conference on Knowledge Management.

Precision medicine is an emerging approach for disease treatment and prevention that considers individual variability in genes, environment, and lifestyle for each person. Big data analytics (BDA) using cutting-edge technologies helps to design models that can diagnose, treat and predict diseases. In Ethiopia, healthcare service delivery faces many challenges specifically in relation to prescribing the right medicine to the right patient at the right time. Thus, patients face challenges ranging from staying on treatment plans longer, and then leaving treatment, and finally dying of complications. Therefore, the aim of this paper is to explore the trends, challenges, and opportunities of applying BDA in precision medicine globally and take lessons for Ethiopia through a systematic literature review of 19 peer reviewed articles from five databases. The findings indicated that cancer in general, epilepsy, and systemic diseases altogether are areas currently getting big attention. The challenges are attributed to the nature of health data, failure in collaboration for data sharing, ethical and legal issues, interoperability of systems, poor knowledge skills and culture, and poor infrastructure. Development of modern technologies, experimental technologies and methods, cloud computing, Internet of Things, social networks and Ethiopia’s government initiative to promote private technological firms could be an …

Trauma, and its associated effects, can be conceptualized as a period of information uncertainty. The natural psychological response to trauma is a period of post-traumatic stress. Trauma occurs when an existing knowledge base has been challenged. Any event that challenges important components of an individual’s assumptive world is said to be traumatic. This post-traumatic period is akin to many theories and concepts in information science including uncertainty reduction, Everyday Life Information Seeking, Sensemaking Theory, Making Meaning and Anomalous States of Knowledge. One possible outcome after the post- traumatic period is post-traumatic growth. Researchers agree post-traumatic growth primarily occurs across one or more of the following domains: personal strength, new possibilities, relating to others, appreciation of life and spiritual change. That is, people affected by trauma tend to grow when they find new or additional paths of information certainty.

Understanding trends is helpful to identify future behaviors in the field, and the roles of people, places, and institutions in setting those trends. Although traditional clustering strategies can group articles into topics, these techniques do not focus on topics over limited timescales; additionally, even when articles are grouped, the generated results are extensive and difficult to navigate. To address these concerns, we create an interactive dashboard that helps an expert in the field to better understand and quantify trends in their area of research. Trend detection is performed using the time-biased document clustering introduced in Behpour et al. (2021) study. The developed and freely available web application enables users to detect well defined trending topics in financial journals by experimenting with various levels of temporal bias - from detecting short-timescale trends to allowing those trends to spread over longer times. Experts can readily drill down into the identified topics to understand their meaning through keywords, example articles, and time range. Overall, the interactive dashboard will allow experts in the field to sift through the vast literature to identify the concepts, people, places, and institutions most critical to the field.

Developing, implementing, and managing metadata is crucial to successful knowledge management, and academic libraries have traditionally played a central role in these activities. The Arabian Gulf countries are underrepresented in the existing research into library metadata practices. This exploratory study used semi-structured interviews of metadata managers at 8 universities with the goal of developing understanding of the current state of metadata practices, including descriptive cataloging, identity management, and knowledge organization in academic libraries of three Arabian Gulf countries (Kuwait, Oman, and Qatar), as well as potential future developments to facilitate discovery of resources. Findings provide insights into this previously under-researched area and contribute to understanding of knowledge management and risks on a global scale.

Evaluating the impact of learning from climate data, in addition to bridge data, on the performance of concrete deck condition rating prediction is critical for identifying the right data needed to enhance bridge maintenance decision making. Few studies have considered such an evaluation and utilized a small size of samples that prevent revealing the knowledge hidden within the big size of data. Although, such evaluation over big data seems quite necessary, class imbalance problem makes it challenging. To alleviate such a problem, five states, including Alabama, Iowa, New York, Pennsylvania, and South Carolina, were selected as the case study. Not only are the states located in three different climatically consistent regions defined by the National Ocean and Atmospheric Administration (NOAA), but also their concrete deck conditions ratings are somewhat balanced. To conduct the evaluation, this research developed the bridge data set pertaining to 56,288 bridges across the afore-mentioned states through employing the GIS technology. The bridge data set contains bridge data derived from National Bridge Inventory (NBI), and climate data derived from Parameter-elevation Relationships on Independent Slopes Model (PRISM) climate maps and NOAA. Then, two machine learning algorithms, including random forest and GBM, were trained - with and without climate …

This workshop is presented by members of a University of North Texas research team. First, the team will overview their experience as members of the research team and share experience in areas such as trust formation, team roles, productivity, work-life balance, faculty-students interaction, peer and faculty mentorship, dissertation preparation, and job seeking. Second, the workshop will discuss and brainstorm how this format can be implemented for organizations both with faculty-student teams and with peer-directed teams. Finally, successes and challenges are openly discussed with audience.

Global warming is the term used to describe critical environmental issues and concerns. Social media such as Twitter provides a platform for people to share information, exchange ideas, and express their opinions about current and timely issues. This study utilized contextual analysis to analyze data collected from Twitter for the hashtag "global warming" during the period 2010 & 2011. Using sentiment analysis and topic modeling, the study aimed first at assessing people's perception towards global warming issues, and second study the impact of misinformation from the standpoint of social noise on people's perception of global warming during critical environmental events. The outcome of this study helps create a better understanding of the environmental issues discussed on social media. The sentiment analysis from the data analyzed so far shows that most of the tweets were based on Twitter users' personal opinions and not science. The topic modeling results suggest that Twitter users typically tweeted when a major environmental event occurred due to global warming. Topic modeling also aids in the identification of terms that is associated with social noise. The presence of social noise suggests that misinformation does exist and spreads faster.

Building predictive models for companies often relies on inference using historical data of companies in the same industry sector. However, companies are similar across a variety of dimensions that should be leveraged in relevant prediction problems. This is particularly true for large, complex organizations which may not be well defined by a single industry and have no clear peers. To enable prediction using company information across a variety of dimensions, we create an embedding of company stocks, Stock2Vec, which can be easily added to any prediction model that applies to companies with associated stock prices. We describe the process of creating this rich vector representation from stock price fluctuations and characterize what the dimensions represent. We then conduct comprehensive experiments to evaluate this embedding in applied machine learning problems in various business contexts. Our experiment results demonstrate that the four features in the Stock2Vec embedding can readily augment existing cross-company models and enhance cross-company predictions.

Considerable research has been conducted on the topic of information overload using different approaches, from marketing and customer demand to information technologies and sciences, and even among mental health professionals. In business the critical question is how does information overload impact processes, operations, and profitability, and how can data visualization help to solve issues with data management and consumption in organizations. The ability to quickly and effectively process information and make decisions equates to organizational survival in a dynamic, knowledge-based economy where all segments of society are heavily affected by information technologies and systems and data management industries. The growing number of systems apparatuses challenges both individuals and organizations, resulting in reports of fatigue and experiences that compromise successful performance. The objective of this literature review is to discuss how data visualization tools help address information overload and optimize decision making and the business intelligence process in organizations. It concludes that data visualization, indeed, is critical in helping individuals capture, manage, organize, visualize, and present understandable data, but that decision making is affected by cognitive factors that interfere with data processing and interpretation in decision makers.

This paper presents the first results from an analysis of recent job advertisements from online platforms from English and German-speaking countries. The full texts of about 4100 job adverts carrying the terms “knowledge manager” or “knowledge management” either in in the job title or in the short description have been analyzed. The main research aim was to identify the most common KM job titles, providing a basis for further analysis of tasks and required competencies to compare the data samples. In particular, the research will address the following research questions: RQ1: What are the job titles used in job adverts related to KM? RQ2: Which tasks are mainly associated with different KM job titles (roles)? RQ3: Which competencies & skill sets are typically required in job adverts? This research inform KM practitioners and HR managers as well as the KM community about landscape of KM roles, main tasks and skills required on the labor markets in the German-speaking (AU, CH, DE) countries and three English-speaking (AUS, UK, USA) countries.

Soft skills have become increasingly important in the workplace, particularly in knowledge management (KM). Developing a knowledge culture in an organization depends on the soft skills of leaders. KM literature, however, does not often discuss the development and teaching of soft skills that can facilitate the success of knowledge management in organizations. This presentation will present the challenge of teaching and learning soft skills as well as a proposed framework to address those challenges.

Brazilian scientific and technological research in the space area covers a diversity of themes so that its results are applied in several areas, such as earth observation, fire control, climate change, telecommunications, and security, among others. By providing useful products and services for society, economy, and industry, one perceives its relevance and the need to improve Brazil's space research. Thus, given its social and economic importance, this project aims to structure, based on scientific and situated knowledge, a framework that could help to mitigate the knowledge risks identified in the diverse types of organizations and sectors that are part of the Brazilian Space Program. This work in progress will try to address this problem by providing a methodological framework based on Knowledge Governance tools and it is intended to homogenize and mitigate the knowledge risks identified in the diverse types of organizations that are part of the Brazilian Space sector. The methodological approach to be used in the research will be predominantly qualitative, with possible quantitative treatment when analyzing the collected data used to develop the framework. This paper identify, analyze and catalogue, based on the distinct categories found in the literature, the types of knowledge risks observed in organizations …

Project-based learning (PjBL) allows learners to focus on a topic of their interest and apply the presented and acquired knowledge in their current (work) practice. When applied in institutional contexts, these particularities have to be recognized for stakeholder-centered learning support. In this paper we report on identifying and designing value exchanges between concerned stakeholder groups by methodological deployment of the Value Network Analysis. The individual and collective perception of PjBL experiences could be externalized and design inputs of involved and addressed stakeholder groups could be developed in a non-disruptive way. The results give way for increasing self-efficacy of knowledge acquisition and skill development in (digital) learning environments.

Neo-learning was developed as a teaching and learning methodological platform for the Networked Corporate University (NCU) model, in order to promote experiential and expansive learning for the development of competences and facilitate the transfer of learning to work practice. The purpose of this article is to present the neo-learning as a methodological support of the Networked Corporate University model to facilitate the transfer of learning into practice. To achieve this objective, a qualitative approach was chosen for exploratory and descriptive purposes, through a narrative literature review and multiple case studies of the application of the methodology. This article presents the main approaches and theories that underlie the NCU model and the neo-learning methodology. The neo-learning cycle and the applications that validate the methodology are also presented. It is concluded that the neo-learning methodology was developed in accordance with the NCU model to support the teaching and learning processes. The methodology can be considered validated, since it has been applied in several research and extension projects, in academic and corporate, public and private universities, in undergraduate and graduate programs, for initial training, for the developing of teachers and tutors of the presential, online, and remote modalities.

In Nova Scotia, the marginalization of African Nova Scotian learners in the public education system has led to an achievement gap, where their performance in terms of literacy and numeracy in annual assessments is often below the provincial average. This research aims to use machine learning to analyze student data collected by the Nova Scotia Department of Education and Early Childhood Development to provide evidence-driven insights into the schooling outcomes of African Nova Scotian students. We expect that these insights would enable the development of an equitable education system where African Nova Scotian students are able to achieve their maximum potential and overcome the barriers that they face at school. Through collaboration with the Regional Centers of Education, the Nova Scotia Department of Education and Early Childhood Development has been collecting data to track students’ education experiences. We aim to analyze this data using machine learning algorithms to identify learning patterns and promising school characteristics that would provide an understanding of how we can reduce the achievement gap that exists among African Nova Scotian students.

Risk management is generally characterized by identification, assessment, and mitigation. In the literature on knowledge risk management (KRM), there appears to be much emphasis on taxonomies - which primarily focus on identification - and mitigation strategies, without particular attention to the evaluation phase of KRM. To address this gap, the present paper aims to systematically evaluate potential knowledge risks common at the operational level of small and medium-sized enterprises (SMEs). More significantly, in SMEs, due to their flat structure, the majority of risk is borne by operational functions. This study adopts a quantitative approach by utilizing the grey-DEMATEL technique. The results of this study reveal outsourcing risks are the most critical of all operational knowledge risks followed by communication risks and improper knowledge application. Furthermore, it is observed the risks could be categorized into effect-risk (e.g. relational risk, espionage knowledge waste, continuity risk) and cause-risk (e.g. knowledge waste, risks related to knowledge gaps, risk of using obsolete/unreliable knowledge)

By writing this paper we tried to illustrate the practical difference between KM initiatives in R&D and industry. However, the overlapping of the approaches at some certain stages are visible. The information management has been considered as an essential part of the knowledge management in both of organizations. The capture of the critical knowledge in both organizations remains a main issue even if the selected methods are different. The coaching & mentoring program which are well implemented at the IRE (HZDR), are may be difficult to realize/accomplish at the BGE. However, the pilot coaching program is planned at the BGE, but currently not available due to the “young” founding date in 2017. One of the main concerns of the BGE as an implementer, is the motivation and encouragement of all employees to share and distribute their knowledge and benefit from each other.

The iMapping technique is a visual knowledge mapping approach that is based on deep zooming and nesting and that unites the essential benefits of virtual whiteboards, mind maps and concept maps. To achieve that, it breaks the physical page metaphor and introduces an infinitely zoomable, nested canvas, to create large scalable knowledge spaces. It has been originally developed and scientifically evaluated in the context of personal knowledge management only. Infinity Maps is the state-of-the art successor of the iMapping Tool: A cloud based visual knowledge and productivity platform. It not only fully implements the visual iMapping approach but takes it to the next level, by adding real-time collaboration and sharing functionality – thus also adding the benefit of wikis. This has enabled knowledge workers to create shared knowledge spaces with hitherto unseen depth. Nested Maps with thousands of individual cards, 10 and more levels of hierarchy and zoom-factors of one to several thousand are normal. With this paper, we want to introduce Infinity Maps to the knowledge management community and showcase some real-life examples of visual Knowledge Management, from personal research over teaching and learning to corporate knowledge management.

Theses and dissertations, as a means to accumulate concepts, innovations, and implementations, are the main building blocks of the creative learning organization. In this research, we focus on a digital media-oriented graduate program in Architecture, where young researchers often derive their theses from various disciplines and encounter difficulties when exploring unfamiliar research areas. The aim of this paper is to provide a new way to explore the thematic map of knowledge generated in theses. The study proposes a bibliometric analysis method with a visualizing workflow embedded with focus+context visualization to support wayfinding in theses networks. In this study, we extracted terms from 95 design research theses across 2002 to 2022 in an applied knowledge base of a creative institution, and conducted automated data pre-processing to apply the co-occurrence analysis for thematic clusters. We present a framework around 3 abstract processes to design the interactive visualization for exploring multivariate information based on top-down and bottom-up perspectives. The results reveal that digital media-oriented theses papers cover 3 main topics and highlight the shifting pattern of the research interest. The integrated workflow can be applied to different academic works, such as syllabus, and projects, given the possibility to expand the knowledge and explore …

Conference abstracts and project reports can be a valuable source for access to information on possible risks or experiences to build on. However, these collections are often not accessible in a convenient manner. This work explores the usage of knowledge graphs to support document discovery with an automated indexing approach. Therefore, an RDF/SKOS graph is built, consisting of nearly 1400 tagging labels manually extracted from literature on knowledge graphs and organized in a thesaurus structure. The feasibility is demonstrated with a small collection of texts from the domain of library IT. The graph is published online for further use (cf. https://w3id.org/kgcm/ ). This method may be usable to explore other knowledge domains. On the other hand, the graph-building process is both intellectually and technically demanding and needs support by an interplay of cooperative and mechanical processes in the future.

WeChat group-based online medical community (WGMC) is increasingly accepted by the public because of its high efficiency, convenience, and shared advantages in seeking medical resources. However, the problem to separate a complete dialogue relationship from the chat records is arising because efficient online community management is based on clear dialogue relationships and clear topics. To solve the problem, we proposed a hybrid three-stages BERT method to recognize the dialogue relationships in "Home of Love" --- a central nervous system tumor online healthy community WeChat group. First, based on the social support theory, a multi-layer BiLSTM model is proposed to classify the conversation scenes into five classes. Then, two domain adaptation methods for transfer learning are designed to optimize the BERT pre-training model for specific tasks using the "Haodaifu" as the training corpus. Finally, a hybrid BERT method based on the text scene information and the pre-training model is proposed to recognize the dialogue relationships, and its feasibility is verified by manual labeling. The results show that adding more prior knowledge to the dialogue recognition model by extracting the social support scene information can effectively improve the classification ability and stability of the model.

The study in this paper aims to describe the mechanism of knowledge recall in the context of organizational memory. We approached a startup involved in the development of a digital marketplace and observed how the founders interacted with the accumulated knowledge. With observation as the research method and longitudinal data collected, we found the founding group in the way they understood organizational memory. There are three mechanisms of remembering organizational memory including identification, adaptation, and correction. The identification mechanism is the highest level of recall ability followed by adaptation and correction. This study implies that founders with different backgrounds have different ways of remembering organizational memory.

To provide solutions to the world’s global challenges, there is an urgent demand for wise organizations, wise leadership, wise workers, and most importantly, for wise actions. Since the mid- 1990s, Knowledge Management (KM) as a discipline and practice has emerged internationally and it went through several phases of development. Similarly, in the last four decades, we experience a growth in wisdom research, and in intense discourses about Wisdom Management (WM) as a possible venue for dealing with wicked problems. The dilemma is, whether the present phase of KM would be able to address the global problems of the world. Therefore, this paper seeks to answer the question if WM will complement or replace KM. The purpose is to compare KM and WM. The research methodology is an explorative approach of their main characteristics and concepts in a selected range of literature. The findings are presented in a framework to show the similarities and differences of KM and WM. Keywords: Knowledge Management (KM), Wisdom Management (WM), phases of KM, wisdom economy, wise leaders, wisdom workers, global problems of the world.