Call for Papers

Important Dates

  • Deadline for paper submission: Thursday, 26 March 2015, 23:59 (Hawaiian time)
  • Notification of acceptance/rejection: Thursday, 16 April 2015
  • Deadline for camera-ready version: Thursday, 30 April 2015

Motivation
In recent years, researchers in several communities involved in aspects of information science have begun to realise the potential benefits of assigning an important role to events in the representation and organisation of knowledge and media-benefits which can be compared to those of representing entities such as persons or locations instead of just dealing with more superficial objects such as proper names and geographical coordinates. While a good deal of relevant research for example, on the modeling of events has been done in the semantic web community, much complementary research has been done in other, partially overlapping communities, such as those involved in multimedia processing, information extraction, sensor processing and information retrieval research. However, these areas often deal with events with a different perspective. The attendance of DeRiVE 2011, DeRiVE 2012 and DeRiVE 2013 proved that there is a great interest from many different communities in the role of events. The results presented in there also indicated that dealing with events is still an emerging topic. The goal of this workshop is to advance research on the role of events within the information extraction and semantic web communities, both building on existing work and integrating results and methods from other areas, while focusing on issues of special importance for the semantic web.

Topics
We have defined questions for the two main directions that characterise current research into events on the semantic web. Orthogonal to that, we have identified a number of application domains in which we will actively seek contributions.

Question 1: How can events be detected and extracted for the semantic web?

  • How can events be detected, extracted and/or summarized in particular types of content on the web, such as calendars of public events, social media, semantic wikis, and regular web pages?
  • What is the quality and veracity of events extracted from noisy data such as microblogging sites?
  • How can a system recognise a complex event that comprises several sub-events?
  • How can a system recognise duplicate events?

Question 2: How can events be modelled and represented in the semantic web?

  • How are events currently represented on the Web? In particular, how deployed is the schema.org Event class? Should scheduled events versus breaking events be represented the same way?
  • To what extent can the many different event infoboxes of Wikipedia be reconciled? How to deal with the numerous Timeline of xxx topics in knowledge bases?
  • How can existing event representations developed in other communities be adapted to the needs of the semantic web? To what extent can/should a unified event model be employed for different types of events?
  • How do social contexts (Facebook, Twitter, etc.) change the implicit content semantics?

Application Domains
Research into detection (question 1) and representation (question 2) of events is being implemented in various application domains. We encourage submissions about the visualization of events, search and browsing of event data, and interaction with event data within a particular domain. This will contribute to a discussion on the possibly different requirements of models and tools in these domains. Known application domains that we target are:

  • Personal events
  • Cultural and sports events
  • Making something out of “raw” events
  • Historic events and events in news and other media
  • Scientific observation events
  • Supply chain events

Submissions
Submissions should not exceed 10 pages and are to be formatted according to Springer LNCS guidelines and submitted to https://www.easychair.org/conferences/?conf=derive2015. Papers should be submitted in PDF format. The workshop proceedings will be published online through CEUR-WS.

 

 

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