7,829 research outputs found
Structuring Wikipedia Articles with Section Recommendations
Sections are the building blocks of Wikipedia articles. They enhance
readability and can be used as a structured entry point for creating and
expanding articles. Structuring a new or already existing Wikipedia article
with sections is a hard task for humans, especially for newcomers or less
experienced editors, as it requires significant knowledge about how a
well-written article looks for each possible topic. Inspired by this need, the
present paper defines the problem of section recommendation for Wikipedia
articles and proposes several approaches for tackling it. Our systems can help
editors by recommending what sections to add to already existing or newly
created Wikipedia articles. Our basic paradigm is to generate recommendations
by sourcing sections from articles that are similar to the input article. We
explore several ways of defining similarity for this purpose (based on topic
modeling, collaborative filtering, and Wikipedia's category system). We use
both automatic and human evaluation approaches for assessing the performance of
our recommendation system, concluding that the category-based approach works
best, achieving precision@10 of about 80% in the human evaluation.Comment: SIGIR '18 camera-read
Eliciting New Wikipedia Users' Interests via Automatically Mined Questionnaires: For a Warm Welcome, Not a Cold Start
Every day, thousands of users sign up as new Wikipedia contributors. Once
joined, these users have to decide which articles to contribute to, which users
to seek out and learn from or collaborate with, etc. Any such task is a hard
and potentially frustrating one given the sheer size of Wikipedia. Supporting
newcomers in their first steps by recommending articles they would enjoy
editing or editors they would enjoy collaborating with is thus a promising
route toward converting them into long-term contributors. Standard recommender
systems, however, rely on users' histories of previous interactions with the
platform. As such, these systems cannot make high-quality recommendations to
newcomers without any previous interactions -- the so-called cold-start
problem. The present paper addresses the cold-start problem on Wikipedia by
developing a method for automatically building short questionnaires that, when
completed by a newly registered Wikipedia user, can be used for a variety of
purposes, including article recommendations that can help new editors get
started. Our questionnaires are constructed based on the text of Wikipedia
articles as well as the history of contributions by the already onboarded
Wikipedia editors. We assess the quality of our questionnaire-based
recommendations in an offline evaluation using historical data, as well as an
online evaluation with hundreds of real Wikipedia newcomers, concluding that
our method provides cohesive, human-readable questions that perform well
against several baselines. By addressing the cold-start problem, this work can
help with the sustainable growth and maintenance of Wikipedia's diverse editor
community.Comment: Accepted at the 13th International AAAI Conference on Web and Social
Media (ICWSM-2019
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Linking Data Across Universities: An Integrated Video Lectures Dataset
This paper presents our work and experience interlinking educational information across universities through the use of Linked Data principles and technologies. More specifically this paper is focused on selecting, extracting, structuring and interlinking information of video lectures produced by 27 different educational institutions. For this purpose, selected information from several websites and YouTube channels have been scraped and structured according to well-known vocabularies, like FOAF 1, or the W3C Ontology for Media Resources 2. To integrate this information, the extracted videos have been categorized under a common classification space, the taxonomy defined by the Open Directory Project 3. An evaluation of this categorization process has been conducted obtaining a 98% degree of coverage and 89% degree of correctness. As a result of this process a new Linked Data dataset has been released containing more than 14,000 video lectures from 27 different institutions and categorized under a common classification scheme
A Graph-structured Dataset for Wikipedia Research
Wikipedia is a rich and invaluable source of information. Its central place
on the Web makes it a particularly interesting object of study for scientists.
Researchers from different domains used various complex datasets related to
Wikipedia to study language, social behavior, knowledge organization, and
network theory. While being a scientific treasure, the large size of the
dataset hinders pre-processing and may be a challenging obstacle for potential
new studies. This issue is particularly acute in scientific domains where
researchers may not be technically and data processing savvy. On one hand, the
size of Wikipedia dumps is large. It makes the parsing and extraction of
relevant information cumbersome. On the other hand, the API is straightforward
to use but restricted to a relatively small number of requests. The middle
ground is at the mesoscopic scale when researchers need a subset of Wikipedia
ranging from thousands to hundreds of thousands of pages but there exists no
efficient solution at this scale.
In this work, we propose an efficient data structure to make requests and
access subnetworks of Wikipedia pages and categories. We provide convenient
tools for accessing and filtering viewership statistics or "pagecounts" of
Wikipedia web pages. The dataset organization leverages principles of graph
databases that allows rapid and intuitive access to subgraphs of Wikipedia
articles and categories. The dataset and deployment guidelines are available on
the LTS2 website \url{https://lts2.epfl.ch/Datasets/Wikipedia/}
Aspect-Driven Structuring of Historical Dutch Newspaper Archives
Digital libraries oftentimes provide access to historical newspaper archives
via keyword-based search. Historical figures and their roles are particularly
interesting cognitive access points in historical research. Structuring and
clustering news articles would allow more sophisticated access for users to
explore such information. However, real-world limitations such as the lack of
training data, licensing restrictions and non-English text with OCR errors make
the composition of such a system difficult and cost-intensive in practice. In
this work we tackle these issues with the showcase of the National Library of
the Netherlands by introducing a role-based interface that structures news
articles on historical persons. In-depth, component-wise evaluations and
interviews with domain experts highlighted our prototype's effectiveness and
appropriateness for a real-world digital library collection.Comment: TPDL2023, Full Paper, 16 page
DBpedia Mashups
If you see Wikipedia as a main place where the knowledge of mankind is concentrated, then DBpedia – which is extracted from Wikipedia – is the best place to find machine representation of that knowledge. DBpedia constitutes a major part of the semantic data on the web. Its sheer size and wide coverage enables you to use it in many kind of mashups: it contains biographical, geographical, bibliographical data; as well as discographies, movie meta-data, technical specifications, and links
to social media profiles and much more. Just like Wikipedia, DBpedia is a truly cross-language effort, e.g., it provides descriptions and other information in various languages. In this chapter we introduce its structure, contents, its connections to outside resources. We describe how the structured information in DBpedia is gathered, what you can expect from it and what are its characteristics and limitations.
We analyze how other mashups exploit DBpedia and present best practices of its usage. In particular, we describe how Sztakipedia – an intelligent writing aid based on DBpedia – can help Wikipedia contributors to improve the quality and integrity of articles. DBpedia offers a myriad of ways to accessing the information it contains, ranging from SPARQL to bulk download. We compare the pros and cons of these methods. We conclude that DBpedia is an un-avoidable resource for pplications dealing with commonly known entities like notable persons, places; and for others looking for a rich hub connecting other semantic resources
Crosslingual Document Embedding as Reduced-Rank Ridge Regression
There has recently been much interest in extending vector-based word
representations to multiple languages, such that words can be compared across
languages. In this paper, we shift the focus from words to documents and
introduce a method for embedding documents written in any language into a
single, language-independent vector space. For training, our approach leverages
a multilingual corpus where the same concept is covered in multiple languages
(but not necessarily via exact translations), such as Wikipedia. Our method,
Cr5 (Crosslingual reduced-rank ridge regression), starts by training a
ridge-regression-based classifier that uses language-specific bag-of-word
features in order to predict the concept that a given document is about. We
show that, when constraining the learned weight matrix to be of low rank, it
can be factored to obtain the desired mappings from language-specific
bags-of-words to language-independent embeddings. As opposed to most prior
methods, which use pretrained monolingual word vectors, postprocess them to
make them crosslingual, and finally average word vectors to obtain document
vectors, Cr5 is trained end-to-end and is thus natively crosslingual as well as
document-level. Moreover, since our algorithm uses the singular value
decomposition as its core operation, it is highly scalable. Experiments show
that our method achieves state-of-the-art performance on a crosslingual
document retrieval task. Finally, although not trained for embedding sentences
and words, it also achieves competitive performance on crosslingual sentence
and word retrieval tasks.Comment: In The Twelfth ACM International Conference on Web Search and Data
Mining (WSDM '19
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