text: People learn whenever and wherever possible, and whatever they like or encounter--Mathematics, Drama, Art, Languages, Physics, Philosophy, and so on. With the bursting of knowledge, evaluation of one's possession of knowledge becomes increasingly difficult. There are a lot of demands to evaluate one's understanding of a piece of knowledge. Assessment of understanding of knowledge is conventionally through tests or interviews, but they have some limitations such as low-efficiency and not-comprehensive. This paper proposes a method called Understanding Tree to estimate one's understanding of knowledge, by keeping track of his her learning activities. It overcomes some limitations of traditional methods, hence complements traditional methods.
  @cite_30: The problem of finding someone who might be able to help with a particular task or knowledge area exists everywhere, be it in groups of students or corporate settings. Time and effort are spent looking for relevant information when another person in the community could easily provide assistance. We have chosen to tackle this problem. Our approach to addressing the problem of finding people who can help is to use software agents to assist the search for expertise and mediate the information exchange process. Issues of availability, user profiling, privacy and incentives are involved. We chose the Java Programming domain for initial implementation and testing of the system. Other researchers have taken stabs at this problem, most without the use of agent technology. We are building agents, called Expert Finders, to help people find help.
  @cite_14: This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.
  @cite_29: The rising popularity of social media in the enterprise presents new opportunities for one of the organization's most important needs--expertise location. Social media data can be very useful for expertise mining due to the variety of existing applications, the rich metadata, and the diversity of user associations with content. In this work, we provide an extensive study that explores the use of social media to infer expertise within a large global organization. We examine eight different social media applications by evaluating the data they produce through a large user survey, with 670 enterprise social media users. We distinguish between two semantics that relate a user to a topic: expertise in the topic and interest in it and compare these two semantics across the different social media applications.
  @cite_3: The Answer Garden supports knowledge sharing in two intertwined ways: by making relevant information retrievable and by mediating access to people with knowledge. We present a case study in which the Answer Garden approach was applied to encourage knowledge sharing in maintenance engineering of a steel mill. The results show that the sheer amount of drawings and the long history of chaning classification schemes challenge the Answer Garden apporach as well as domain-specific needs for technically mediated communication. Moreover, the given divison of labor and organizational micro-politics prevent the Answer Garden apporach from encouraging knowledge sharing. Based on these expriences, design directions for knowledge management systems are pointed out. Finally, the results of the study are related to a recent controversy on technology support for expertise location.
  @cite_19: In this paper we describe two systems designed to connect users to distributed, continuously changing experts and their knowledge. Using information retrieval, information extraction, and collaborative filtering techniques, these systems are able to enhance corporate knowledge management by overcoming traditional problems of knowledge acquisition and maintenance and associated (human and financial) costs. We describe the purpose of these two systems, how they work, and current deployment in a global corporate environment to enable end users to directly discover experts and their knowledge.
  @cite_16: 
  @cite_20: This article describes automated tools for increasing organizational awareness within a global enterprise. The MITRE Corporation is the context for this work; however, the tools and techniques are general and should apply to a wide variety of distributed, heterogeneous organizations. These tools provide awareness of team members and materials in virtual collaboration environments as well as support for automated discovery of distributed experts. The results are embodied in 3 systems: MITRE's Collaborative Virtual Workspace (CVW), Expert Finder, and XpertNet. CVW is a place-based collaboration environment that enables team members to find one another and work together. Expert Finder is an expert skill finder that exploits the intellectual products created within an organization to support automated expertise identification. XpertNet addresses the problem of detecting extant or emerging classes of expertise without a priori knowledge of their existence. Both Expert Finder and XpertNet combine to detect and ...
  @cite_12: Knowledge Management (KM) is a diffuse and controversial term, which has been used by a large number of research disciplines. CSCW, over the last 20 years, has taken a critical stance towards most of these approaches, and instead, CSCW shifted the focus towards a practice-based perspective. This paper surveys CSCW researchers' viewpoints on what has become called knowledge sharing' and expertise sharing'. These are based in an understanding of the social contexts of knowledge work and practices, as well as in an emphasis on communication among knowledgeable humans. The paper provides a summary and overview of the two strands of knowledge and expertise sharing in CSCW, which, from an analytical standpoint, roughly represent generations' of research: an object-centric' and a people-centric' view. We also survey the challenges and opportunities ahead.
  @cite_17: 
summary: Expertise is one's expert skill or knowledge in a particular field. Expertise finding is the use of tools for finding and assessing individual expertise @cite_16 @cite_19 @cite_30 . As an important link of knowledge sharing, expertise finding has been heavily studied in many research communities @cite_12 @cite_17 @cite_20 @cite_14 @cite_3 @cite_29 . Many sources of data have been exploited to assess an individual's expertise, such as one's publications, documents, emails, web search behavior, other people's recommendations, social media etc. Knowledge Model and Understanding Tree provide a new source of data to analyze one's expertise -- one's learning history about a subject, which is more comprehensive and straightforward than other data sources, because one's expertise is mainly obtained through learning (Including Informal Learning", which occurs through the experience of day-to-day situations, such as a casual conversation, play, exploring, etc.)