Call for Papers: Special Issue on Mining Social Semantics on the Social Web
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
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. .
- Call for papers: September 2015
- Submission deadline: 14 February 2016 (extended!)
- Notification: 14 April 2016
Please use the e-mail address firstname.lastname@example.org for inquiries.
- Andreas Hotho, University of Würzburg, Germany
- Robert Jäschke, L3S Research Center, Germany
- Kristina Lerman, University of Southern California, United States