Collaborative Knowledge Acquisition Research Articles

Analysis of Service Quality Factors of the Regional Planning Agency of Aceh

Objective: This study examines how collaborative public innovation, transformational leadership, knowledge acquisition, employee competencies, innovative climate, and organizational performance affect service quality at the Regional Planning Agency of Aceh. Methods/Analysis: The study used quantitative analysis, qualitative interviews, and surveys. The data were analyzed using the SERVQUAL model and scatter analysis approaches. The correlation between variables was analyzed by Spearman rho correlation, while Kruskal-Wallis analyzed the significance between variables. Finding: The SERVQUAL model shows the Regional Planning Agency of Aceh provides good quality services; 98% of respondents gave an excellent rating, and 12% were good. Scatter analysis shows that the agency consistently meets expectations. Spearman rho correlation analysis shows that all determinant factor variables have a strong relationship with improving service quality and are also significant between variables (p<0.05). Novelty/Improvement: Improving the quality of the organization (Regional Planning Agency of Aceh) is primarily determined by the knowledge acquisition factor, which is followed by collaborative public innovation, transformational leadership, employee competencies, and an innovative climate. Doi: 10.28991/ESJ-2023-07-05-017 Full Text: PDF

Personal Ontologies for Knowledge Acquisition and Sharing in Collaborative PrOnto Framework

This paper summarizes our preliminary experiences with implementing some of the ideas lying behind the concept of creative environment. Research group at the National Institute of Telecommunications has developed a prototype framework for collaborative knowledge acquisition and sharing, called PrOnto. At the moment the artifacts we organize and share are typical sources of scientific knowledge, namely journal papers and web pages. In PrOnto we introduce two interrelated explicit levels of knowledge representation: keywords and ontological concepts. Each user of the framework maintains his own ontological profile, consisting of concepts and each concept is, in turn, by subjective user’s decision, related to a set of weighted keywords that define its meaning. Furthermore, dedicated indexing engine is responsible for objectively establishing correspondence between documents and keywords, or in other words, the measure of representativeness of the keyword to document’s content. Developing an appropriate knowledge model is a preliminary step to share it efficiently. We believe that higher level representation facilitates exploration of other people’s areas of interest. PrOnto gives an opportunity to browse knowledge artifacts from the conceptual point of view of any user registered in the system. The paper presents the ideas behind the PrOnto framework, gives an outline of its components and finalizes with a number of conclusions and proposals for futuren enhancements.

Enhancing knowledge construction processes within multicriteria decision analysis: The Collaborative Value Modelling framework

This paper proposes a new Collaborative Value Modelling framework, that combines Delphi and multicriteria decision conferencing, to build widely informed evaluation models. Multicriteria Decision Analysis (MCDA) is commonly used to help decision-makers and other stakeholders in complex evaluation contexts. Further to the technical soundness and meaningfulness of the methods and tools used, it is critical to design adequate social processes to promote shared understanding around key evaluation issues while capturing multiple stakeholders’ values and perspectives. Multicriteria decision conferencing processes have been typically adopted for collaborative modelling using MCDA methods in decision conferences with relatively small groups. Such a socio-technical approach has proven to be effective, in a variety of contexts, in creating a collaborative environment that enables surfacing individual beliefs, identifying common concerns, managing eventual value conflicts and promoting agreement in group model building. But, extending this framework to broader participatory contexts requires a different design of the social process, in order to ensure that model building captures the full panoply of points of view. This challenge can be tackled by enhancing multicriteria decision conferencing with an all-embracing (Web-)Delphi participatory process. We depart from the existing collaborative knowledge acquisition methodology to design, with the Delphi method, a participatory knowledge construction process that elicits and analyses individual judgemental knowledge from a (very) large and diverse number of stakeholders. The knowledge acquired is then digested by a small group of key-players, in a subsequent decision conferencing, to collaboratively develop a widely informed multicriteria evaluation model. This new Web-Delphi-decision conferencing social setting has been tested already in real complex evaluation contexts using a specific multicriteria method, the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH), to develop a variety of value modelling activities. We call this socio-technical design the Collaborative Value Modelling framework. Here, we describe its real use to support the construction of value functions, focusing on how the judgemental knowledge collected flows between the participatory and collaborative stages of the framework. Results validate that enhancing MACBETH decision conferencing with an ex ante Web-Delphi process fosters higher participation and collaboration in multicriteria modelling.

A method for collaborative knowledge acquisition and modeling enabling the development of a knowledge-based configurator of robot-based automation solutions

When it comes to the development of knowledge-based configurators, there is a lack of procedures for the collaborative work on knowledge acquisition and modeling. However, the division of work is inevitable, especially in interdisciplinary application domains such as the configuration of automation solutions. Therefore, this paper presents a practicable method, enabling the collaborative development of a knowledge-based configurator across various partners and locations. The developed method is divided into eight steps: Firstly, economically relevant use cases are identified so that the necessary human and financial resources are released by the management. Secondly, several best practices, in this case successfully implemented robotic-based automation solutions, are collected. In the third step, generic, cross-case topological elements are extracted. Building on the relevant topological elements, constraints for the formation of the configuration logic are derived in the fourth step. In the fifth step, the required attributes are deduced from constraints and implemented within the data schema. The integration of the constraints, as well as the data schema in the configuration framework, takes place in the sixth step. In the seventh step, concrete components, e.g. individual end effectors, robot arms or controllers, are collected and prepared according to the data schema and integrated into the configurator. In the eighth and last step, the configurator is tested by comparing given configurations with known best practices. By deriving new constraints from the identified discrepancies and by analyzing new best practices, the resulting configurator can then be optimized iteratively. In addition, an auxiliary tool to support single steps of the method is introduced. For validation purposes, a suitable configuration model for robot-based automation cells was designed, distinguishing between scenario, product, processes and resources.

The Flipped Classroom for Teaching Millennials: A Competency-Based Pedagogical Approach

This paper describes a competency-based pedagogical framework as the basis for designing a flipped classroom for millennials taking an undergraduate course on sustainable business. Its theoretical underpinnings are culled from the literatures on learner empowerment, epistemological belief systems, and the social construction of knowledge. The desired learning outcomes of this pedagogical framework include the development of cognitive competencies, collaborative knowledge acquisition skills, and motivational skills. In a flipped classroom of an undergraduate sustainability course, students acquired these competencies and skills to use intelligently information about sustainability which they found on the Internet. The paper describes in some detail how the course was taught in an actual class to impart such competencies and skills. The flipped-classroom pedagogy allows for better student learning outcomes by incorporating student collaboration, and by fostering critical thinking and ethical reasoning. The paper demonstrates how teacher and student benefit from the competency-based pedagogical approach within a flipped-classroom format. This approach moves beyond the traditional giver and receiver format to a more fellow-explorer format.

Leveraging social networks in the adoption of mobile technologies for collaboration

Mobile devices such as tablets, smart phones, and portable computers are connecting users in a myriad of contexts. Social networks can be a benefit for individuals in the knowledge acquisition process, and group dynamics disclosed by Social Network Analysis provides a good basis for studying how mobile collaboration is affected by social networks. This study focuses on two aspects of social network mechanism, namely eigenvector centrality and network reciprocity. The assumption is that social networks influence mobile technology use behaviors, and can be optimized to leverage mobile collaboration. A series of controlled field experiments involving 327 groups were conducted to evaluate the differences between two modes of mobile collaborative settings: individual non-collaborative vs. group collaborative. Results indicate that by leveraging social networks in a mobile platform, study participants were able to positively heighten their collaborative knowledge acquisition process through enhanced group interactions and enjoyment.

WebProtégé: A collaborative ontology editor and knowledge acquisition tool for the Web

In this paper, we present WebProtégé-a lightweight ontology editor and knowledge acquisition tool for the Web. With the wide adoption of Web 2.0 platforms and the gradual adoption of ontologies and Semantic Web technologies in the real world, we need ontology-development tools that are better suited for the novel ways of interacting, constructing and consuming knowledge. Users today take Web-based content creation and online collaboration for granted. WebProtégé integrates these features as part of the ontology development process itself. We tried to lower the entry barrier to ontology development by providing a tool that is accessible from any Web browser, has extensive support for collaboration, and a highly customizable and pluggable user interface that can be adapted to any level of user expertise. The declarative user interface enabled us to create custom knowledge-acquisition forms tailored for domain experts. We built WebProtégé using the existing Protégé infrastructure, which supports collaboration on the back end side, and the Google Web Toolkit for the front end. The generic and extensible infrastructure allowed us to easily deploy WebProtégé in production settings for several projects. We present the main features of WebProtégé and its architecture and describe briefly some of its uses for real-world projects. WebProtégé is free and open source. An online demo is available at http://webprotege.stanford.edu.

Collaborative knowledge acquisition for the design of context-aware alert systems

To present a framework for combining implicit knowledge acquisition from multiple experts with machine learning and to evaluate this framework in the context of anemia alerts. Five internal medicine residents reviewed 18 anemia alerts, while 'talking aloud'. They identified features that were reviewed by two or more physicians to determine appropriate alert level, etiology and treatment recommendation. Based on these features, data were extracted from 100 randomly-selected anemia cases for a training set and an additional 82 cases for a test set. Two staff internists assigned an alert level, etiology and treatment recommendation before and after reviewing the entire electronic medical record. The training set of 118 cases (100 plus 18) and the test set of 82 cases were explored using RIDOR and JRip algorithms. The feature set was sufficient to assess 93% of anemia cases (intraclass correlation for alert level before and after review of the records by internists 1 and 2 were 0.92 and 0.95, respectively). High-precision classifiers were constructed to identify low-level alerts (precision p=0.87, recall R=0.4), iron deficiency (p=1.0, R=0.73), and anemia associated with kidney disease (p=0.87, R=0.77). It was possible to identify low-level alerts and several conditions commonly associated with chronic anemia. This approach may reduce the number of clinically unimportant alerts. The study was limited to anemia alerts. Furthermore, clinicians were aware of the study hypotheses potentially biasing their evaluation. Implicit knowledge acquisition, collaborative filtering and machine learning were combined automatically to induce clinically meaningful and precise decision rules.

Reports on the 2006 AAAI Fall Symposia

The American Association for Artificial Intelligence was pleased to present the AAAI 2006 Fall Symposium Series, held Friday through Sunday, October 13-15, at the Hyatt Regency Crystal City in Washington, DC. Seven symposia were held. The titles were (1) Aurally Informed Performance: Integrating Ma- chine Listening and Auditory Presentation in Robotic Systems; (2) Capturing and Using Patterns for Evidence Detection; (3) Developmental Systems; (4) Integrating Reasoning into Everyday Applications; (5) Interaction and Emergent Phenomena in Societies of Agents; (6) Semantic Web for Collaborative Knowledge Acquisition; and (7) Spacecraft Autonomy: Using AI to Expand Human Space Exploration.

A case-based collaborative knowledge acquisition tool

Abstract We describe a fault tolerant client-server application that supports collaboration between knowledge engineers for developing case-based expert systems. The server holds knowledge bases and perfonns cases acquisition-verification, cases maintenance, cases reasoning, multimedia acquisition, and security checking. The clients are a number of specialized components which have the following functionalities: creating new knowledge base, acquiring cases, attaching multimedia files to cases, defining users for security checking, and deducting solutions using generic reasoning component for the purpose of application testing

Class library implementation of an open architecture knowledge support system

Object-oriented class libraries offer the potential for individual researchers to manage the large bodies of code generated in the experimental development of complex interactive systems. This article analyses the structure of such a class library that supports the rapid prototyping of a wide range of systems including collaborative networking, shared documents, hypermedia, machine learning, knowledge acquisition and knowledge representation, and various combinations of these technologies. The overall systems architecture is presented in terms of a heterogeneous collection of systems providing a wide range of application functionalities. Examples are given of group writing, multimedia and knowledge-based systems which are based on combining these functionalities. The detailed design issues of the knowledge representation server component of the system are analysed in terms of requirements, the current state of the art, and the underlying theoretical principles that lead to an effective object-oriented implementation. It is shown that modeling the server through intensional algebraic semantics leads naturally to an open-architecture class library into which new data types may be plugged as required without change to the basic deductive engine. It is concluded that the development of a principled class library targeted on complex interactive applications does empower the individual researcher in the rapid prototyping of experimental systems. However, it is noted that much of the power of the approach stems from the cumulative evolution of the class library through successive applications, and hence the results may not generalize to team projects where greater rigidity is required in the class library in order to facilitate project management and inter-member coordination.

Collaborative knowledge acquisition

The increasing complexity of expert systems applications dictates the involvement of many experts in building those systems. Most existing knowledge acquisition techniques are not appropriate for knowledge acquisition from multiple experts. A collaborative approach to acquiring knowledge from a team of experts is developed based on our experience of using Group Support Systems (GSS) for strategic planning, crisis management, and knowledge acquisition. A collaborative environment consists of hardware, software, facility, people, procedures, and facilitation. Techniques for collaborative knowledge acquisition include brainstorming, Nominal Group Technique, Delphi technique, focus group interviews, group repertory grid analysis, and voting. A dimensional analysis framework is used to analyze tools for collaborative knowledge acquisition with regard to three dimensions: the problem-solving process supported, the structure of information generated, and the interaction patterns among participants. Process models including planning, identification, classification, group repertory grid analysis, and verification are developed to facilitate the collaborative knowledge acquisition activities. This paper concludes with observations of a study employing this approach and implications to future research.