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Call for papers: Special Issue on
Mining Social Semantics on the Social Web
In recent years the amount of data available on the social web has grown
massively. Consequently, researchers have developed approaches that
leverage this social web data to tackle interesting challenges of the
semantic web. Among them are methods for learning ontologies from social
media or crowdsourcing, extracting semantics from data collected by
citizen science and participatory sensing initiatives, or for better
understanding and describing user activities.
The rich data provided by the social web can be used to learn and
construct the semantic web. This can be facilitated by learning basic
semantic relationships, e.g., between entities, or by employing more
sophisticated methods that are able to construct a complete knowledge
graph or ontology. Other methods enrich content from the social web and
link it to the semantic web.
The proposed special issue is open to all submissions that utilize data
from the social web a) with the help of semantic web technologies, b)
for inferring and extracting semantics, or c) for enriching and linking
content with/to the semantic web or existing knowledge structures like
the linked open data cloud. Any kind of data can be utilized as long as
it has a connection with the social web, e.g., tags from Flickr, tweets
from Twitter, check-ins from Foursquare, articles from Wikipedia, shared
mobile sensor data, data from participatory mapping, crowd-sourced data,
etc. Examples include approaches for inferring the semantics of tags,
extracting semantics from Wikipedia articles, or enriching tweets with
named entities. The resulting knowledge can be integrated into
structures like the linked open data cloud.
== Topics of Interest ==
We welcome original high quality submissions on (but are not restricted
to) the following topics:
- linked open data and the social web
- machine learning for the semantic web on social web data
- semantic enrichment (e.g., sentiment detection, polarity, named entity
recognition, ...) of user-generated texts (e.g., Wikipedia articles,
tweets, blogs, …)
- extraction and modelling of arguments and discourse
- never-ending language learning from user-generated content
- ontology learning from user-generated content
- semantics of social tagging (e.g., inferring semantics of tags,
identifying relationships between tags, learning ontologies from tags, ...)
- mining Wikipedia (e.g., extracting semantics from articles, semantic
enrichment of articles, inter-language analyses, mining the Wikipedia
category graph, ...)
- temporal and spatial semantics of content from the social web
- inferring semantics from user data, usage logs, mobile sensing, ...
- extracting location-based semantics from Foursquare, OpenStreetMap, ...
- leveraging crowd-sourcing for the semantic web
- capturing the semantics of user interactions
- inferring semantics from user data and usage logs
== Submissions ==
31 January 2016 - Paper submission deadline
Submissions shall be made through the Semantic Web journal website at
http://www.semantic-web-journal.net/. Prospective authors must take
notice of the submission guidelines posted at
http://www.semantic-web-journal.net/authors. Note that you need to
request an account on the website for submitting a paper. Please
indicate in the cover letter that it is for the special issue on Mining
Semantics in/from the Social Web.
Submissions are possible as full research papers or surveys. While there
is no upper limit, the paper length must be justified by content.
== Important Dates ==
- Call for papers: September 2015
- Submission deadline: 31 January 2016
- Notification: 31 March 2016
== Guest editors ==
Please use the e-mail address [log in to unmask] for inquiries.
- Andreas Hotho, University of Würzburg, Germany
- Robert Jäschke, L3S Research Center, Germany
- Kristina Lerman, University of Southern California, United States
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