31,149 research outputs found
WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking
We present WISER, a new semantic search engine for expert finding in
academia. Our system is unsupervised and it jointly combines classical language
modeling techniques, based on text evidences, with the Wikipedia Knowledge
Graph, via entity linking.
WISER indexes each academic author through a novel profiling technique which
models her expertise with a small, labeled and weighted graph drawn from
Wikipedia. Nodes in this graph are the Wikipedia entities mentioned in the
author's publications, whereas the weighted edges express the semantic
relatedness among these entities computed via textual and graph-based
relatedness functions. Every node is also labeled with a relevance score which
models the pertinence of the corresponding entity to author's expertise, and is
computed by means of a proper random-walk calculation over that graph; and with
a latent vector representation which is learned via entity and other kinds of
structural embeddings derived from Wikipedia.
At query time, experts are retrieved by combining classic document-centric
approaches, which exploit the occurrences of query terms in the author's
documents, with a novel set of profile-centric scoring strategies, which
compute the semantic relatedness between the author's expertise and the query
topic via the above graph-based profiles.
The effectiveness of our system is established over a large-scale
experimental test on a standard dataset for this task. We show that WISER
achieves better performance than all the other competitors, thus proving the
effectiveness of modelling author's profile via our "semantic" graph of
entities. Finally, we comment on the use of WISER for indexing and profiling
the whole research community within the University of Pisa, and its application
to technology transfer in our University
Social Interactions vs Revisions, What is important for Promotion in Wikipedia?
In epistemic community, people are said to be selected on their knowledge
contribution to the project (articles, codes, etc.) However, the socialization
process is an important factor for inclusion, sustainability as a contributor,
and promotion. Finally, what does matter to be promoted? being a good
contributor? being a good animator? knowing the boss? We explore this question
looking at the process of election for administrator in the English Wikipedia
community. We modeled the candidates according to their revisions and/or social
attributes. These attributes are used to construct a predictive model of
promotion success, based on the candidates's past behavior, computed thanks to
a random forest algorithm.
Our model combining knowledge contribution variables and social networking
variables successfully explain 78% of the results which is better than the
former models. It also helps to refine the criterion for election. If the
number of knowledge contributions is the most important element, social
interactions come close second to explain the election. But being connected
with the future peers (the admins) can make the difference between success and
failure, making this epistemic community a very social community too
A Wikipedia Literature Review
This paper was originally designed as a literature review for a doctoral
dissertation focusing on Wikipedia. This exposition gives the structure of
Wikipedia and the latest trends in Wikipedia research
Is Stack Overflow Overflowing With Questions and Tags
Programming question and answer (Q & A) websites, such as Quora, Stack
Overflow, and Yahoo! Answer etc. helps us to understand the programming
concepts easily and quickly in a way that has been tested and applied by many
software developers. Stack Overflow is one of the most frequently used
programming Q\&A website where the questions and answers posted are presently
analyzed manually, which requires a huge amount of time and resource. To save
the effort, we present a topic modeling based technique to analyze the words of
the original texts to discover the themes that run through them. We also
propose a method to automate the process of reviewing the quality of questions
on Stack Overflow dataset in order to avoid ballooning the stack overflow with
insignificant questions. The proposed method also recommends the appropriate
tags for the new post, which averts the creation of unnecessary tags on Stack
Overflow.Comment: 11 pages, 7 figures, 3 tables Presented at Third International
Symposium on Women in Computing and Informatics (WCI-2015
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