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.
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.)
@cite_30 @cite_14 @cite_29 @cite_3 @cite_19 @cite_16 @cite_20 @cite_12 @cite_17
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The automatic search for knowledgeable people in the scope of an organization is a key function which makes modern Enterprise search systems commercially successful and socially demanded. A number of effective approaches to expert finding were recently proposed in academic publications. Although, most of them use reasonably defined measures of personal expertise, they often limit themselves to rather unrealistic and sometimes oversimplified principles. In this thesis, we explore several ways to go beyond state-of-the-art assumptions used in research on expert finding and propose several novel solutions for this and related tasks. First, we describe measures of expertise that do not assume independent occurrence of terms and persons in a document what makes them perform better than the measures based on independence of all entities in a document. One of these measures makes persons central to the process of terms generation in a document. Another one assumes that the position of the person’s mention in a document with respect to the positions of query terms indicates the relation of the person to the document’s relevant content. Second, we find the ways to use not only direct expertise evidence for a person concentrated within the document space of the person’s current employer and only within those organizational documents that mention the person. We successfully utilize the predicting potential of additional indirect expertise evidence publicly available on the Web and in the organizational documents implicitly related to a person. Finally, besides the expert finding methods we proposed, we also demonstrate solutions for the tasks from related domains. In one case, we use several algorithms of multi-step relevance propagation to search for typed entities in Wikipedia. In another case, we suggest generic methods for placing photos uploaded to Flickr on the World map using language models of locations built entirely on the annotations provided by users with a few task specific extensions. Expert finding is a task of finding knowledgeable people on a given topic. State-of-the-art expertise retrieval algorithms identify matching experts based on analysis of textual content of documents experts are associated with. While powerful, these models ignore social structure that might be available. In this paper, we develop a Bayesian hierarchical model for expert finding that accounts for both social relationships and content. The model assumes that social links are determined by expertise similarity between candidates. We demonstrate the improved retrieval performance of our model over the baseline on a realistic data set. One of the key challenges for users of social media is judging the topical expertise of other users in order to select trustful information sources about specific topics and to judge credibility of content produced by others. In this paper, we explore the usefulness of different types of user-related data for making sense about the topical expertise of Twitter users. Types of user-related data include messages a user authored or re-published, biographical information a user published on his her profile page and information about user lists to which a user belongs. We conducted a user study that explores how useful different types of data are for informing human's expertise judgements. We then used topic modeling based on different types of data to build and assess computational expertise models of Twitter users. We use We follow directories as a proxy measurement for perceived expertise in this assessment. Our findings show that different types of user-related data indeed differ substantially in their ability to inform computational expertise models and humans's expertise judgements. Tweets and retweets a#x2014; which are often used in literature for gauging the expertise area of users a#x2014; are surprisingly useless for inferring the expertise topics of their authors and are outperformed by other types of user-related data such as information about users' list memberships. Our results have implications for algorithms, user interfaces and methods that focus on capturing expertise of social media users. Recently expertise retrieval has received increasing interests in both academia and industry. Finding experts with demonstrated expertise for a given query is a nontrivial task especially from a large-scale Web 2.0 systems, such as question answering and bibliography data, where users are actively publishing useful content online, interacting with each other, and forming social networks in various ways, leading to heterogeneous networks in addition to the large amounts of textual content information. Many approaches have been proposed and shown to be useful for expertise ranking. However, most of these methods only consider the textual documents while ignoring heterogeneous network structures or can merely integrate with one additional kind of information. None of them can fully exploit the characteristics of heterogeneous networks. In this paper, we propose a joint regularization framework to enhance expertise retrieval by modeling heterogeneous networks as regularization constraints on top of document-centric model. We argue that multi-typed linking edges reveal valuable information which should be treated differently. Motivated by this intuition, we formulate three hypotheses to capture unique characteristics for different graphs, and mathematically model those hypotheses jointly with the document and other information. To illustrate our methodology, we apply the framework to expert finding applications using a bibliography dataset with 1.1 million papers and 0.7 million authors. The experimental results show that our proposed approach can achieve significantly better results than the baseline and other enhanced models.