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Conference PaperPDF Available


LINKING EVENTS WITH MEDIA

 * September 2010

DOI:10.1145/1839707.1839759
 * Source
 * DBLP

 * Conference: Proceedings the 6th International Conference on Semantic Systems,
   I-SEMANTICS 2010, Graz, Austria, September 1-3, 2010

Authors:
Raphaël Troncy
 * EURECOM



Bartosz Malocha


Bartosz Malocha
 * This person is not on ResearchGate, or hasn't claimed this research yet.



André T. S. Fialho


André T. S. Fialho
 * This person is not on ResearchGate, or hasn't claimed this research yet.



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Citations (88)
References (11)
Figures (4)





ABSTRACT AND FIGURES

We present a large dataset composed of events descriptions together with media
descriptions associated with these events and interlinked with the larger Linked
Open Data cloud. We are constructing a web-based environment that allows users
to explore and select events, to inspect associated me-dia, and to discover
meaningful, surprising or entertaining connections between events, media and
people participating in events. The dataset is obtained from three large public
event directories (last.fm, eventful and upcoming) represented with the LODE
ontology and from large media directories (ickr, youtube) represented with
theMedia Ontology. We describe how the data has been converted, interlinked and
published following the best practices of the Semantic Web community
… 
A photo taken at the Radiohead Haiti Relief Concert described with the Media
Ontology
… 
Interface views illustrating a set of events
… 
Interface illustrating an event instance view for a Radiohead concert
… 

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Content may be subject to copyright.
Linking Events with Media
Raphaël Troncy
EURECOM
Sophia Antipolis, France
raphael.troncy@eurecom.fr
Bartosz Malocha
EURECOM
Sophia Antipolis, France
bartosz.malocha@eurecom.fr
André T. S. Fialho
∗
CWI Amsterdam
The Netherlands
andre.fialho@cwi.nl
ABSTRACT
We present a large dataset composed of events descriptions
together with media descriptions associated with these events
and interlinked with the larger Linked Open Data cloud.
We are constructing a web-based environment that allows
users to explore and select events, to inspect associated me-
dia, and to discover meaningful, surprising or entertaining
connections between events, media and people participating
in events. The dataset is obtained from three large pub-
lic event directories (last.fm, eventful and upcoming) repre-
sented with the LODE ontology and from large media direc-
tories (flickr, youtube) represented with the Media Ontology.
We describe how the data has been converted, interlinked
and published following the best practices of the Semantic
Web community.
Categories and Subject Descriptors
H.5.1 [Multimedia Information System]: Audio, Video
and Hypertext Interactive Systems; I.7.2 [Document Prepa-
ration]: Languages and systems, Markup languages, Multi/
mixed media, Standards
General Terms
Languages, Hyperlinks, Web, URI, HTTP
Keywords
Events, LODE, Media Ontology, dataset
1. INTRODUCTION
Events are a natural way for referring to any observable
occurrence grouping persons, places, times and activities
that can be described [7, 5, 1]. Events are also observable
experiences that are often documented by people through
different media (e.g. videos and photos). We explore this
∗Andr´e Fialho is also affiliated with Delft University of Tech-
nology in Delft, The Netherlands.
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page. To copy otherwise, to
republish, to post on servers or to redistribute to lists, requires prior
specific
permission and/or a fee.
I-SEMANTICS 2010 September 1-3, 2010, Graz, Austria
Copyright 2010 ACM 978-1-4503-0014-8/10/09 ...$10.00.
intrinsic connection between media and experiences so that
people can search and browse through content using a fa-
miliar event perspective. In the context of the Petamedia1
Network of Excellence, we are designing an application that
takes into account the “triple synergy”of users and their so-
cial networks, user-created content and metadata attached
to this content in an application for supporting users in in-
teracting with events.
While wishing to support such functionality, we are aware
that websites already exist that provide interfaces to such
functionality, e.g. eventful.com, upcoming.org, last.fm/events,
and facebook.com/events to name a few. These services
have sometimes overlap in terms of coverage of upcoming
events and provide social networks features to support users
in sharing and deciding upon attending events. However,
the information about the events, the social connections
and the representative media are all spread and locked in
amongst these services providing limited event coverage and
no interoperability of the description [1]. Our goal is to
aggregate these heterogeneous sources of information using
linked data, so that we can explore the information with the
flexibility and depth afforded by semantic web technologies.
Furthermore, we will investigate the underlying connections
between events to allow users to discover meaningful, en-
tertaining or surprising relationships amongst them. We
also use these connections as means of providing informa-
tion and illustrations about future events, thus enhancing
decision support.
In this paper, we present how we intend to represent de-
scription of events using the LODE ontology which ensures
interoperability among various event ontologies (Section 2).
We describe the data scraping and interlinking process as
well as a large SKOS taxonomy of event categories (Sec-
tion 3). We provide interface mockups to illustrate the types
of task support and expected functionality we will be provid-
ing in the coming weeks based on this dataset (Section 4).
Finally, we give our conclusions and outline future work in
Section 5.
2. THE LODE ONTOLOGY
The LODE ontology2is a minimal model that encapsu-
lates the most useful properties for describing events [5].
The goal of this ontology is to enable interoperable model-
ing of the “factual” aspects of events, where these can be
characterized in terms of the four Ws:What happened,
1http://www.petamedia.eu
2http://linkedevents.org/ontology/



Where did it happen, When did it happen, and Who was
involved. “Factual” relations within and among events are
intended to represent intersubjective “consensus reality” and
thus are not necessarily associated with a particular per-
spective or interpretation. This model thus allows us to
express characteristics about which a stable consensus has
been reached, whether these are considered to be empirically
given or rhetorically produced will depend on one’s episte-
mological stance. We enhance the LODE descriptions with
properties for categorizing events and for relating them to
other events through parthood or causal relations using the
Descriptions and Situations approach of the Event-Model-
F [4].
The Figure 1 depicts the metadata attached to the event
identified by 1380633 on last.fm according to the LODE on-
tology. More precisely, it indicates that an event of type
Concert has been given on the 24th of January 2010 at
20:00 PM in the Henry Fonda Theater featuring the Radio-
head rock band.
Figure 1: The Radiohead Haiti Relief Concert de-
scribed with LODE
LODE is not yet another “event” ontology per se. It has
been designed as an interlingua model that solves an inter-
operability problem by providing a set of axioms express-
ing mappings between existing event ontologies. Hence, the
ontology contains numerous OWL axioms stating classes
and properties equivalence between models such as MO [3],
CIDOC-CRM, DOLCE, SEM [6] to name a few. Therefore,
an OWL-aware agent would infer that the resource identi-
fied by dbpedia:Radiohead is a dul:Agent as described in
the Dolce Ultra Lite ontology.
3. DATA SCRAPING AND INTERLINKING
In this section, we detail how the data from events and
media directories is scraped and interlinked. Furthermore,
we describe how we build a large SKOS thesaurus of event
categories.
3.1 Event Directories
We first explore the overlap in metadata between four
popular web sites, namely Flickr as a hosting web site for
photos and videos and Last.fm, Eventful and Upcoming as
a documentation of past and upcoming events. Explicit re-
lationships between events and photos exist using machine
tags such as lastfm:event=XXX. Hence, we have been able
to convert the description of more than 1.7 million photos
which are indexed by nearly 140.000 events.
We use the Last.fm, Eventful and Upcoming APIs to con-
vert each event description into the LODE ontology (Sec-
tion 2). We mint new URIs into our own namespace for
events (http://data.linkedevents.org/event/), agents (http://data.
linkedevents.org/agent/) and locations (http://data.linkedevents.
org/location/). A graph representing an event is composed
of the type of the event, a full text description, the agents
(e.g. artists) involved, a date (instant or interval represented
with OWL Time [2]), a location in terms of both geograph-
ical coordinates and a URI denoting the venue and users
participation. A graph representing an agent or a location
is composed of a label and a description (e.g. the artist’s
biography).
Event directories have overlap in their coverage. We in-
terlink these events descriptions when they involve the same
agents at the same date or when they happen at the same
venue at the same date. We invoke additional semantic web
lookup services such as dbpedia, geonames and freebase in
order to enrich the descriptions of the agents and the loca-
tions. Hence, the agent URI which has for label “Radiohead”
is interlinked with the dbpedia URI (http://dbpedia.org/page/
Radiohead) which provides additional information about the
band such as its complete discography. This URI is declared
to be owl:sameAs another identifier from the New York
Times (http://data.nytimes.com/N12964944623934882292) which pro-
vides information about the 38 associated articles from this
newspaper to this band. The venue has also been converted
into a dbpedia URI (http://dbpedia.org/page/The_Henry_Fonda_
Theater) but has been augmented with geo-coordinates from
last.fm which was not originally present in dbpedia thus in-
creasing the amount of information available in the LOD
cloud for the benefit of all semantic web applications. We
observe that a few venues have been interlinked with the
LOD cloud. We are now investigating further linkage with
Foursquare which has a much broader coverage of event
venues. The linked data journey can be rich and long. One
of the challenges we want to address is how to visualize these
enriched interconnected datasets while still supporting sim-
ple user tasks such as searching and browsing enriched media
collections.
3.2 Media Directories
The Ontology for Media Resource currently developed by
W3C is a core vocabulary which covers basic metadata prop-
erties to describe media resources3. It also contains a formal
set of axioms defining mapping between different metadata
formats for multimedia. We use this ontology together with
properties from SIOC, FOAF and Dublin Core to convert
into RDF the Flickr photo descriptions (Figure 2). The link
between the media and the event is realized through the
lode:illustrate property, while more information about
the sioc:UserAccount can be attached to his URI. In the
Figure 1, we see that the video hosted on YouTube has for
ma:creator the user aghorrorag.
The Ontology for Media Resource can then be used to at-
tach different types of metadata to the media, such as the
duration, the target audience, the copyright, the genre, the
rating. Media Fragments can also be defined in order to
have a smaller granularity and attach keywords or formal
annotations to parts of the video. We use the patterns de-
fined in the Event-Model-F to represent the actual role of
various users for a given media: the uploader of the video,
the user who has tagged, or commented the video, etc. In
summary, using these patterns, we can extend the LODE
and Media ontologies with provenance information, making
3http://www.w3.org/TR/mediaont-10/


Figure 2: A photo taken at the Radiohead Haiti Re-
lief Concert described with the Media Ontology
the distinction between the creator of some media or event
and the creator of the association between events and media,
and even between the participants of this event.
3.3 Event Categories
Events are generally categorized in lightweight taxonomies
that provide facets when browsing event directories. We
have manually analyzed the taxonomy used in various sites,
namely facebook, eventful, upcoming, zevents, linkedin, event-
brite and ticketmaster, and used card sorting techniques in
order to build a rich SKOS thesaurus of event categories.
This SKOS thesaurus contains axioms expressing mappings
relationships with these taxonomies while the terms are de-
fined in our own namespace (http://data.linkedevents.org/category/).
The top level categories are Sports, Music, Food, Arts, Movies,
Family, Social Gathering, Community and Professional but
alignment with other classification such as the IPTC News
Codes for sports of the last.fm genres for music is also pro-
vided.
Event Agent Location Media User
Last.fm 37,647 50,151 16,471 1,393,039 18,542
Eventful 37,647 6,543 14,576 52 12
Upcoming 13,114 7,330 347,959 4,518
Table 1: Number of event/agent/location and me-
dia/user descriptions in the dataset
Overall, the dataset collected contains more than 30 mil-
lion triples. A dump is available at (http://www.eurecom.fr/
~troncy/ldtc2010/) while a temporary SPARQL endpoint is
available at (http://data.linkedevents.org/sparql).
4. APPLICATION
In this section, we briefly illustrate initial interface possi-
bilities derived from a user study described in [1]. Unsurpris-
ingly, the sketches below correspond to the basic properties
defined in the LODE ontology.
What - One prospective view is media centered and al-
lows to quickly illustrate the event through associated me-
dia. In this view we display events through a representative
images and convey different event characteristics (e.g. rele-
vance, rating, popularity, etc) with one and/or more of the
image properties, i.e., size and transparency. This approach
has been used in other applications4to represent clustered
result sets or convey sorting by size on different contexts
(Figure 3a).
4See for example http://www.jinni.com or http://www.ted.com/
talks.
Figure 3: Interface views illustrating a set of events
under: (A) media centric perspective; (B) a chrono-
logical perspective; and (C) location centric perspec-
tive
When - Ordering can also be used to represent chrono-
logical event occurrence. In fact, the time centric view can
be interpreted as the sorting of events chronologically (Fig-
ure 3b).
Where - A location centric view can be used to represent
where the events occur geographically to orient the user and
convey distance. The use of maps is commonly used to vi-
sualize such information (Figure 3c).
Who - Events are intrinsically bound to a social compo-
nent. Users want to know who will be attending to an event
when deciding to attend to it. In this context, a people
centric view would be relevant to explore the relationships
between users and events. Alternatively we can combine at-
tendance information to other views such as location, allow-
ing users to browse for friends on a map and identify their
attended events. It could also be used to provide means
of visualizing event popularity ,e.g. identify the cities hot-
spots on a map, indicate visual cues of popularity according
to number of attendees.
When representing an event instance, we show all infor-
mation needed to support the decision making process (e.g.
Figure 4). Since experiences are centered around media con-
tent, we wish to explore different media that better illustrate
the event to end-users. Some information that can support
decision making are the following.
•background information (e.g. performers, topic, genre,
price, attendance list, etc)
•subjective or computed attributes (e.g. reputation,
fun, atmosphere, audience)
•user opinions, comments and ratings (strangers and
friends)
•representative media (ads, media from past related


Figure 4: Interface illustrating an event instance view for a Radiohead concert
events, media from the audience, etc.)
Apart from the inspection of the event instance, other con-
ceptual classes (e.g. users, venues, performers, media) should
also have accessible views, so that the user can obtain more
information about these instances and explore events related
to them. In future work, we will also identify what are the
relevant associated information and how to represent navi-
gation from and to these nodes.
5. CONCLUSION AND FUTURE WORK
In this paper, we have shown how linked data technolo-
gies can be used for integrating information contained in
event and media directories. We used the LODE and Media
Ontology respectively for expressing linked data description
of events and photos. Ultimately, we aim at providing an
event-based environment for users to explore, annotate and
share media and we present some sketches of user interfaces
that we will develop in the coming weeks.
We are currently consolidating and cleaning our dataset
with more sources (e.g. YouTube videos) and more linkage
(e.g. description of recurring event, artist and venue from
hubs such as freebase or dbpedia). We intend to provide
soon user participation at events from public Foursquare
check-in or live Tweets. Our priority is also to express the
right licensing and attribution information to the data that
has been rdf-ized. Finally, we will release soon a voiD de-
scription of the complete dataset. We truly believe that
multimedia will then be finally added back to the Semantic
Web.
6. ACKNOWLEDGMENTS
The authors would like to thank Lynda Hardman (CWI),
Carsten Saathoff (WeST Institute), Ansgar Scherp (WeST
Institute) and Ryan Shaw (Berkeley University) for fruit-
ful discussions on the infrastructure and the design of the
EventMedia interfaces. The research leading to this paper
was supported by the European Commission under contract
FP7-216444, Petamedia Peer-to-peer Tagged Media.
7. REFERENCES
[1] A. Fialho, R. Troncy, L. Hardman, C. Saathoff, and
A. Scherp. What’s on this evening? Designing User
Support for Event-based Annotation and Exploration
of Media. In 1st International Workshop on EVENTS -
Recognising and tracking events on the Web and in real
life (EVENTS’10), pages 40–54, Athens, Greece, 2010.
[2] J. Hobbs and F. Pan. Time Ontology in OWL. W3C
Working Draft, 2006.
http://www.w3.org/TR/owl-time.
[3] Y. Raimond, S. Abdallah, M. Sandler, and F. Giasson.
The Music Ontology. In 8th International Conference
on Music Information Retrieval (ISMIR’07), Vienna,
Austria, 2007.
[4] A. Scherp, T. Franz, C. Saathoff, and S. Staab. F—A
Model of Events based on the Foundational Ontology
DOLCE+ Ultra Light. In 5th International Conference
on Knowledge Capture (K-CAP’09), Redondo Beach,
California, USA, 2009.
[5] R. Shaw, R. Troncy, and L. Hardman. LODE: Linking
Open Descriptions Of Events. In 4th Asian Semantic
Web Conference (ASWC’09), 2009.
[6] W. van Hage, V. Malais´e, G. de Vries, G. Schreiber,
and M. van Someren. Combining Ship Trajectories and
Semantics with the Simple Event Model (SEM). In 1st
ACM International Workshop on Events in Multimedia
(EiMM’09), Beijing, China, 2009.
[7] U. Westermann and R. Jain. Toward a Common Event
Model for Multimedia Applications. IEEE MultiMedia,
14(1):19–29, 2007.



CITATIONS (88)


REFERENCES (11)




... Exploring the intrinsic connection between structured events and media
shared on the Web has been the focus of numerous studies (Becker, Naaman, and
Gravano 2010;Troncy, Malocha, and Fialho 2010;Liu, Troncy, and Huet 2011). They
propose different techniques in the area of media classification, data
interlinking and event detection, trying to leverage the wealth of user
generated content. ...
... On the left side of the Figure 1: A showcase of Confomaton with Lanyrd and
Dog Food data main view, the user can select the main conference event or one of
the sub-events if available as provided by the Dog Food metadata corpus. In the
center, the default view is a map centered on where the event took place and the
user is also encouraged to explore potential other type of events (concerts,
exhibitions, sports, etc.) happening nearby, this data being provided by
EventMedia (Troncy, Malocha, and Fialho 2010). The What tab is media-centered
and allows to quickly see what illustrates a selected event (tweets, photos,
slides). ...

Aggregating Social Media for Enhancing Conference Experience
Article
 * Aug 2021

 * Houda Khrouf
 * Ghislain Auguste Atemezing
 * Giuseppe Rizzo
 * Thomas Steiner

A scientific conference is a type of event where attendees have a tremendous
activity on social media platforms. Participants tweet or post longer status
messages, engage in discussions with comments, share slides and other media
captured during the conference. This information can be used to generate
informative reports of what is happening, where (which specific room) and when
(which time slot), and who are the active participants. However, this
information is locked in different data silos and platforms forcing the user to
monitor many different channels at the same time to fully benefit from the
event. In this paper, we propose a framework named Confomaton that aggregates in
real-time social media shared by conference attendees and aligns it with event
descriptions. Developed with Semantic Web technologies, this framework enables
to relive past events and to follow live conferences. A demonstrator is
available at http://eventmedia.eurecom.fr/confomaton.
View
Show abstract
... To assess and feature conspicuous events, traditional media content was
often utilized [4]. Troncy et al. in [5], describes events as a characteristic
method to allude to any noticed happenings that can be described by grouping
individuals, places, times, and activities thereby making all the related events
identified by individuals. Data related to events are represented in an
assortment of forms, like news, videos, status updates, and pictures that were
taken before the occasion, during, and after specific occasions. ...

Recent trends in event detection from Twitter using multimodal data
Conference Paper
 * Nov 2022

 * Rajat Bahuguna
 * Nisha Chandran S.
 * Durgaprasad Gangodkar

A large amount of social media data hosted on platforms like Twitter, Instagram,
Facebook, etc. are event-based and hold a substantial amount of real-world data.
Event-based information can appear on any social media site in the form of news
items, images, videos, audio clips, status updates, etc. The task of event
detection refers to identifying data relevant to an event and the classification
of this relevant data to different event types. Traditional social media event
detection techniques focused mainly on a single modality as the data shared were
mostly homogenous. However, the current social media data is multimodal and
includes text, images, audio, and video clips, and geolocations. Multimodal
event detection techniques are essential for handling such heterogeneous data.
Among all the social media sites Twitter is the most popular as users share
event-related short messages and photos in real-time generating several
thousands of tweets very frequently. In this paper, we focus on providing a
comprehensive survey of event detection from social media, especially from the
widely used platform, Twitter. The survey focuses mainly on research done on
event detection using the two main modalities single and multimodality. At the
end of the paper, we discuss the relevance of multimodal event detection from
social media data which currently spans multiple dimensions.
View
Show abstract
... Topic-based categorization is the most widely used approach in the
literature, which categorizes SM events according to their topics (e.g.,
politics, sport, and so on [99]). According to the topic-based categorization
approach, an event (i.e., a topic) might be an actual event that is held in a
location and has a predefined schedule; for example, [5,87] monitor and
investigate the participants' reaction to a cultural event. ...

A multi-perspective approach for analyzing long-running live events on social
media. A case study on the “Big Four” international fashion weeks
Article
Full-text available
 * Jul 2021

 * Alireza Javadian Sabet
 * Marco Brambilla
 * Marjan Hosseini

In the last few years, thanks to the emergence of Web 2.0, social media has made
the concept of online live events possible. Users participate more and more in
long-running recurring events in social media by sharing their experiences and
desires. In the last few years, thanks to the emergence of Web 2.0, social media
has made the concept of online live events possible. Users participate more and
more in long-running recurring events in social media by sharing their
experiences and desires. This work introduces long-running live events (LRLEs),
as a type of activity that span physical spaces and digital ecosystems,
including social media. LRLEs encompass several individuals, organizations, and
brands collaborating/competing in the same event. This provides unprecedented
opportunities to understand the dynamics and behavior of event-oriented
participation, through collection and analysis of data of user behaviors enabled
by the Web platform, where most of the digital traces are left by users. What
makes this setting interesting is that the behaviors that are traced are not
focused only on one individual brand or organization, and thus allows one to
understand and compare the respective roles and influence in a defined setting.
In this paper we provide a high-level and multi-perspective roadmap to mine,
model, and study LRLEs. Among the various aspects, we develop a multi-modal
approach to solve the problem of post popularity prediction that exploits
potentially influential factors within LRLE. We employ two methods for
implementing feature selection, together with an automated grid search for
optimizing hyper-parameters in various regression methods.
View
Show abstract
... Information sharing and seeking are common on social media for certain
events occurring in the real world. According to Troncy et al., "events are a
natural way for referring to any observable occurrence grouping persons, places,
times, and activities that can be described" [102]. So, the information related
to events is often documented by people. ...

Twitter: A Survey and Framework on Event Detection Techniques
Article
Full-text available
 * Jun 2019

 * Zafar Saeed
 * Rabeeh Abbasi
 * Onaiza Maqbool
 * Guandong Xu

In the last few years, Twitter has become a popular platform for sharing
opinions, experiences, news, and views in real-time. Twitter presents an
interesting opportunity for detecting events happening around the world. The
content (tweets) published on Twitter are short and pose diverse challenges for
detecting and interpreting event-related information. This article provides
insights into ongoing research and helps in understanding recent research trends
and techniques used for event detection using Twitter data. We classify
techniques and methodologies according to event types, orientation of content,
event detection tasks, their evaluation, and common practices. We highlight the
limitations of existing techniques and accordingly propose solutions to address
the shortcomings. We propose a framework called EDoT based on the research
trends, common practices, and techniques used for detecting events on Twitter.
EDoT can serve as a guideline for developing event detection methods, especially
for researchers who are new in this area. We also describe and compare data
collection techniques, the effectiveness and shortcomings of various Twitter and
non-Twitter-based features, and discuss various evaluation measures and
benchmarking methodologies. Finally, we discuss the trends, limitations, and
future directions for detecting events on Twitter.
View
Show abstract
... The recent development in the application of social media services has
motivated many scholars to explore potential associations and patterns using
information available in different platforms. In general, events are the
real-world referents of predicative relations that unfold over space and time
[19,22]. Microblogging such as Twitter provides a rich source of information
about events, activities, products, etc. Twitter, as a form of social media, is
fast emerging in recent years. ...

Geo-spatial-based Emotions: A Mechanism for Event Detection in Microblogs
Conference Paper
Full-text available
 * Feb 2019

 * Samer Sarsam
 * Hosam Al-Samarraie
 * Bahiyah Omar

The use of emotions in microblogs to trace the occurrence of certain events and
determine their locations is an open challenge for sentiment analysis. This
study investigated the potential of detecting the geographical location of
events based on existing linkages between the types of emotion embedded in
tweets (degree of polarity) and the source location of those tweets. The
extracted tweets were clustered using K-means algorithm and a predictive model
was developed using Naïve Bayes algorithm. Then, a time series forecasting
technique was applied using linear regression analysis. This method was used to
predict the amount of emotions in association with the event of interest.
Latitude and longitude were used to evaluate the results of the linear
regression model on a real-time world map. Results showed that happy emotion
tends to be a reliable source for detecting the geographical location of an
event. This study revealed the feasibility of using the time series forecasting
approach in investigating the degree of emotions in twitter messages.
View
Show abstract
IDN Authoring -- a design case
Preprint
Full-text available
 * Jun 2023

 * Frank Nack

In this text, we consider the authoring of interactive digital narratives (IDNs)
as a system of interwoven creative processes and look at it as a design process.
The aim is to better understand the structural, aesthetic and interactive
concepts authoring has to address, how authors think about those and what that
means regarding the tools required to support authoring for IDNs. The paper
concludes with a detailed vision of an authoring environment that is considered
an open-source sandbox system, which provides the technical means to build a
functional IDN but adapts the availability of technology based on the narrative
engineer's aims, goals and skills. The environment establishes a collaboration
between itself and the narrative engineer, who's interaction on one side
focusses on the collection of material and its classification and on the other
side covers the design of the engine that facilitates the aimed for audience to
establish the stories for their information need out of the provided
proto-narrative content space.
View
Show abstract
Event Detection from Social Media Stream: Methods, Datasets and Opportunities
Conference Paper
 * Dec 2022

 * Quanzhi Li
 * Yang Chao
 * Dong Li
 * Chi Zhang

View
A Survey on Event Detection and Prediction Online and Offline Models using
Social Media Platforms
Article
 * Mar 2021

 * Poonam Tijare
 * Jhansi Rani Prathuri

The world came closer with the introduction of Social Media Platforms. The day
begins and ends with news on events happening around us. Enormous information
exchange is happening on diverse events for every second. This information gives
insight into people, lifestyle, culture, their opinion on events and etc. Event
analytics is one such thing which is evolving in recent time due to its
inevitable applications. Goal of this survey is to present about the techniques
and methodologies proposed in the area of event analytics. The work presents the
analysis of umbrella term events and associated defiance with a methodological
overview. Offline and online event detection and prediction models are explored
and the tools, techniques, their high and low points are traversed. The datasets
inclusive of benchmark datasets explored using offline and online frameworks are
presented. Our study shows that the majority research is about event detection
in offline models. Real time event detection and prediction frameworks are very
less. Another finding is the absence of a unified model to detect any kind of
event. The event analytics area is still progressing and has the potential of
gaining micro information related to varieties of events happening which leads
to future event prediction.
View
Show abstract
Real-World Events Discovering with TWIST
Chapter
 * Sep 2020

 * Natalia Vanetik
 * Marina Litvak
 * Efi Levi

Event detection in social media is a broad and well-addressed research topic,
but the characteristics and sheer volume of Twitter messages with high amounts
of noise in them make it a difficult task for Twitter. Tweets reporting
real-life events are usually overwhelmed by a flood of meaningless information.
This paper describes the TWItter event Summarizer and Trend detector (TWIST)
system that attempts to tackle these challenges by combining wavelet and text
analysis. TWIST extends the Event Detection with Clustering of Wavelet-based
Signals (EDCoW) algorithm of Weng and Lee (ICWSM 11:401–408, 2011) with the use
of text analysis of retrieved tweets. The system detects and summarizes
real-life events reported in Twitter. TWIST analyses external sources for
detected events to provide high-quality summaries with clean and meaningful
content.
View
Show abstract
Real-time Event Detection and Tracking in Microblog via Text Chain and Sentiment
time series
Conference Paper
 * Jul 2020

 * Bingxu Piao
 * Xu Wu
 * Jingchen Wu
 * Xiaqing Xie

View
Show more

Combining ship trajectories and semantics with the simple event model (SEM)
Article
Full-text available
 * Oct 2009

 * Willem Robert van Hage
 * Véronique Malaisé
 * Gerben Klaas Dirk de Vries
 * Maarten van Someren

Bridging the gap between low-level features and semantics is a problem commonly
acknowledged in the Multimedia community. Event modeling can fill the gap. In
this paper we present the Simple Event Model (SEM) and its application in a
Maritime Safety and Security use case about Situational Awareness. We show how
we abstract over low-level features, recognize simple behavior events using a
Piecewise Linear Segmentation algorithm, and model the events as instances of
SEM. We apply deduction rules, spatial proximity reasoning, and semantic web
reasoning in SWI-Prolog to derive abstract events from the recognized simple
events. The use case described in this paper come from the Dutch Poseidon
project.
View
Show abstract
What's on this evening? Designing User Support for Event-based Annotation and
Exploration of Media
Article
Full-text available
 * May 2010

 * André Fialho
 * Raphaël Troncy
 * Lynda Hardman
 * Ansgar Scherp

We present an event-based approach for users to explore, annotate and share
media. We are constructing a web-based environ-ment that allows users to explore
and select events, including discov-ering meaningful, surprising or entertaining
connections among them. We build a knowledge base of events from event
directories that will be linked to the Linked Open Data (LOD) cloud, in
conjunction with event and media ontologies. The approach is user-driven and,
having carried out initial user inquiries, we are designing interfaces that
sup-port user-identified tasks while exploring the connections between users,
multimedia content and events.
View
Show abstract
LODE: Linking Open Descriptions of Events
Conference Paper
Full-text available
 * Dec 2009
 * Lect Notes Comput Sci

 * Ryan Shaw
 * Raphaël Troncy
 * Lynda Hardman

People conventionally refer to an action or occurrence taking place at a certain
time at a specific location as an event. This notion is potentially useful for
connecting individual facts recorded in the rapidly growing collection of linked
data sets and for discovering more complex relationships between data. In this
paper, we provide an overview and comparison of existing RDFS+OWL event models,
looking at the different choices they make of how to represent events. We
describe a recommended model for publishing records of events as Linked Data. We
present tools for populating this model and a prototype of an "event directory"
web service, which can be used to locate stable URIs for events that have
occurred and to provide RDFS+OWL descriptions of them and links to related
resources.
View
Show abstract
F - A model of events based on the foundational ontology DOLCE+DnS ultralite
Conference Paper
Full-text available
 * Sep 2009

 * Ansgar Scherp
 * Thomas Franz
 * Carsten Saathoff
 * Steffen Staab

The lack of a formal model of events hinders interoper- ability in distributed
event-based systems. In this pa- per, we present a formal model of events,
called Event- Model-F. The model is based on the foundational ontol- ogy
DOLCE+DnS Ultralite (DUL) and provides com- prehensive support to represent time
and space, objects and persons, as well as mereological, causal, and cor-
relative relationships between events. In addition, the Event-Model-F provides a
exible means for event com- position, modeling event causality and event
correla- tion, and representing dierent interpretations of the same event. The
Event-Model-F is developed following the pattern-oriented approach of DUL, is
modularized in dierent ontologies, and can be easily extended by domain specic
ontologies.
View
Show abstract
The Music Ontology
Conference Paper
Full-text available
 * Sep 2007

 * Yves Raimond
 * Samer Abdallah
 * Mark Brian Sandler
 * Frederick Giasson

In this paper, we overview some Semantic Web technologies and describe the Music
Ontology: a formal framework for dealing with music-related information on the
Semantic Web, including editorial, cultural and acoustic information. We detail
how this ontology can act as a grounding for more domain-specific knowledge
representation. In addition, we describe current projects involving the Music
Ontology and interlinked repositories of musicrelated knowledge.
View
Show abstract
Time ontology in OWL
Article
 * Jan 2006

 * J.R. Hobbs
 * F. Pan

View
Toward a Common Event Model for Multimedia Applications
Article
 * Feb 2007

 * Utz Westermann
 * Ramesh Jain

Although events are ubiquitous in multimedia, no common notion of events has
emerged. Events appear in multimedia presentation formats, programming
frameworks, and databases, as well as in next-generation multimedia applications
such as eChronicles, life logs, or the Event Web. A common event model for
multimedia could serve as a unifying foundation for all of these applications
View
Show abstract
The Music Ontology In 8<sup>th&lt

 * Y Raimond
 * S Abdallah
 * M Sandler
 * F Giasson



What's on this evening? Designing User Support for Event-based Annotation and
Exploration of Media
 * Jan 2010
 * 40-54

 * A Fialho
 * R Troncy
 * L Hardman
 * C Saathoff
 * A Scherp

A. Fialho, R. Troncy, L. Hardman, C. Saathoff, and A. Scherp. What's on this
evening? Designing User Support for Event-based Annotation and Exploration of
Media. In 1 st International Workshop on EVENTSRecognising and tracking events
on the Web and in real life (EVENTS'10), pages 40-54, Athens, Greece, 2010.

Time Ontology in OWL. W3C Working Draft
 * Jan 2006
 * w3

 * J Hobbs
 * F Pan
 * Hobbs J.

J. Hobbs and F. Pan. Time Ontology in OWL. W3C Working Draft, 2006.
http://www.w3.org/TR/owl-time.

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action ou quelque chose qui a lieu à un endroit particulier pendant une certaine
période de temps. Ce concept est utile pour organiser et relier des faits
individuels, et pour découvrir de nouvelles relations entre ceux-ci, en
particulier sur le web de données. Dans cet article, nous proposons tout d'abord
une comparaison ... [Show full abstract] des différents modèles permettant de
représenter des événements, en mettant l'accent sur leur expressivité et les
choix de modélisation. Nous décrivons ensuite l'ontologie LODE qui permet de
représenter des événements dans le web sémantique. Nous présentons finalement un
prototype de service fournissant des URIs stables pour les événements et
retournant une description RDF pour chacune des dimensions les composants.
View full-text
Conference Paper
Full-text available


LODE: LINKING OPEN DESCRIPTIONS OF EVENTS

December 2009 · Lecture Notes in Computer Science
 * Ryan Shaw
 * Raphaël Troncy
 * Lynda Hardman

People conventionally refer to an action or occurrence taking place at a certain
time at a specific location as an event. This notion is potentially useful for
connecting individual facts recorded in the rapidly growing collection of linked
data sets and for discovering more complex relationships between data. In this
paper, we provide an overview and comparison of existing RDFS+OWL event models,
... [Show full abstract] looking at the different choices they make of how to
represent events. We describe a recommended model for publishing records of
events as Linked Data. We present tools for populating this model and a
prototype of an "event directory" web service, which can be used to locate
stable URIs for events that have occurred and to provide RDFS+OWL descriptions
of them and links to related resources.
View full-text

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