Semantically Capturing and Representing News Stories on the Web

Semantically Capturing
and Representing News
Stories on the Web
José%Luis%Redondo%García

Jluisred.github.io @peputo

Outline
 Semantic
Annotation of
News’ Context
Original artwork by Matt Might
http://matt.might.net/articles/phd-school-in-pictures/
  TOWARDS A SEMANTIC
MULTIMEDIA WEB
i.  Media annotation
ii.  A multimedia model
iii.  Semantic media
exploitation
  CONTEXTUALIZING NEWS
STORIES
i.  The News Semantic
Snapshot (NSS)
ii.  The multidimensional
nature of the entity
relevance
iii.  A concentric model for NSS
generation
iv.  NSS in the consumption of
News
Future CareerPHDPrevious
1 2

Outline
Semantically Capturing and Representing News Stories on the Web 3
 Part II: Semantic
Annotation of News’
Context
Multidimensional Relevancy
NSS Generation
Concentric Model
NSS Gold Standard
News Prototypes
2016/03/04

The Use Case: Contextualizing News
http://www.bbc.com/news/world-europe-23339199#t=34.1,39.8
(Media Fragment URI 1.0)
Edward
Snowden
(NE over Subtitles)
Sarah Harrison
WikiLeaks Editor Airport in Moscow
Sheremetyevo
2016/03/04

2016/03/04

Research Questions
 Q1: How can multimedia content be semantically
annotated and seamlessly connected with other resources on
the Web?
 Q2: Can those semantic annotations and linked media
resources bring value for the exploitation and consumption
of multimedia content?
 Q3: Is it possible to automatically contextualize news
stories with background information so they can be
effectively interpreted by humans and machines?
2016/03/04

Part 1
Towards a Semantic Multimedia
Web
1
Q.1, Q.2
2016/03/04

“
Bringing Multimedia to the Web
Why?
  Make video a first citizen of the Web
  Make video universally accessible and
shareable at different granularities
(segments)
  Benefit from the vast knowledge already
present on the Web
2016/03/04

Semantic Annotation
  Alfonseca, E. and Manandhar. An
unsupervised method for General Named Entity
Recognition and Automated Concept Discovery
  Mendes, P., Jakob, M. and Garcia-Silva,
A and Bizer, C. DBpedia spotlight: shedding
light on the web of documents
  Shinyama, Y. and Sekine, S. Named entity
discovery using comparable news articles
  Chang, S-F, Manmatha, R and Chua, T-S.
Combining text and audio-visual features in
video indexing
  Wang, Richard C. and Cohen, William W.
Iterative Set Expansion of Named Entities Using
the Web
  Talukdar, P-P., Brants, T., Liberman, M.
and Pereira, F. A. Context Pattern Induction
Method for Named Entity Extraction
Multimedia Modeling
  MPEG-7 http://mpeg.chiariglione.org/
standards/mpeg-7/mpeg-7.htm
  TV-Anytime http://tech.ebu.ch/tvanytime
  Synchronized Multimedia Integration
Language https://www.w3.org/TR/REC-smil/
  Media Fragment URI 1.0 specification
(W3C) http://www.w3.org/TR/media-frags
◉  Synote: http://linkeddata.synote.org
◉  Ninsuna: http://ninsuna.elis.ugent.be/
  BBC Programmes Ontology http://
www.bbc.co.uk/ontologies/programmes/
2009-09-07.shtml
  Schema.org (SchemaDotOrgTV) http://
www.w3.org/wiki/WebSchemas/
  Ontology for Media Resources https://
www.w3.org/TR/mediaont-10/
  Web Annotation https://www.w3.org/TR/
annotation-model/
State of the Art & Related Work
Part
1
Named
Entity
Multimodal
Expansion
 2016/03/04

Multimedia
Annotations
  Automatic annotation: 300 hours/min YouTube video
  What is inside the video? multimodal approach
  Semantic annotations, leveraging on Web
Resources: more human-like operations
1.a
2016/03/04

1 ontology http://nerd.eurecom.fr/ontology
2 API http://nerd.eurecom.fr/api/application.wadl
3 UI http://nerd.eurecom.fr
Multimedia Annotation: Named Entity Recognition
nerd:Product
S-Bahn
nerd:Person
Obama
nerd:Person
Michelle
nerd:Location
Berlin
http://data.linkedtv.eu/media/e2899e7f#t=840,900
Part
1.a
https://github.com/giusepperizzo/nerdml
ML
[Rizzo_LREC’14]
2016/03/04

Other documents
similar to DS
b) Expanded Entities
a) Entities from Seed Document DS
Multimedia Annotation: Named Entity Expansion
[Redondo_SNOW’14]
Part
1.a
2016/03/04

Multimedia Annotation: Expansion Pipeline
[Redondo_SNOW’14]
Part
1.a
Available @ http://linkedtv.eurecom.fr/entitycontext/api/
2016/03/04

Multimedia Annotation: Multimodal Approach
 Text:
○ Keyword Extraction
○ Topic Recognition
○ From Textual Visual Cues to LSCOM Concepts
 Visual:
○ Visual Concept Detection (LSCOM)
○ Shot Segmentation
○ Scene Segmentation
○ Optical Character Recognition (OCR)
○ Automatic Speech Recognition (ASR)
○ Face Detection and Tracking
○ …
14
Multimedia
Knowledge
Model
Part
1.a
Semantically Capturing and Representing News Stories on the Web2016/03/04

Multimedia
Model
  Explicitly represent video and its annotations
  At the level of fragments
  Based on well-known vocabularies, flexible and
extensible while being Linked Data compliant
1.b
2016/03/04

Multimedia Model: LinkedTV Model
Annotation
Concept
KeywordBBC Ontology +
SchemaDotOrgTV
ANALYSIS RESULTS (Support for segmentation)
Media
Fragments URI
1.0 (W3C)
LSCOM
Ontology for Media
Resources (W3C)
BROADCAST DATA
Web Annotations
(W3C)
EXTERNAL DATASETS
Entity
NERD
Provenance
Ontology for
Provenance
Management
Programme
Brand
Series
Episode
Version Broadcast
ServiceBroadcast Channel
Scene
Shot
MediaFragment
Face
Part
1.b
Available @ http://data.linkedtv.eu/ontologies/core/
2016/03/04

Part
1.b
Locator
MediaResource
MediaFragmentAnnotation
Entity
URL (hyperlink)
Type
OffsetBasedString
Multimedia Model: LinkedTV Model
2016/03/04

Multimedia Model: TV2RDF Service
Part
1.b
Content Publisher
RDF
Conversion + NERD
TV2RDF
AnalysisMetadata
RDF
Triplestore
Available @ http://linkedtv.eurecom.fr/tv2rdf/
2016/03/04

Exploiting
Knowledge
  Leverage on the Model & Annotations for
advanced mining tasks
  Probe the value of multimodal approach:
Evaluation on standard corpora
1.c
2016/03/04

Part
1.c
Exploitation: Enriching
oa:Annotation
rbbaktuell_20120809
nerd:Location
Berlin
Illustrate seed video [Milicic_WWW'13]
2016/03/04

Exploitation: Enriching Services & Prototypes
Part
1.c
Name URL Published @
MediaCollector http://linkedtv.eurecom.fr/api/mediacollector/search/ [Rizzo_SAM’12]
MediaFinder http://mediafinder.eurecom.fr/ [Milicic_WWW’13]
Italian Elections 2013 http://mediafinder.eurecom.fr/story/elezioni2013 [Milicic_ESWC’13]
TVEnricher http://linkedtv.eurecom.fr/tvenricher/api/ [LinkedTV_D2.6’14]
TVNewsEnricher http://linkedtv.eurecom.fr/newsenricher/api/ [Redondo_ESWC’14]
2016/03/04

Exploitation: Classifying videos
Part
1.c
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
1.fun channel
0
17
85 85
96 106 114
78
117
140
188
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
2.tech channel
0
410
453
402 396 404
353 364 344 374
571
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
3.sport channel
0
192
298 301 288 291 302
260 270
361
231
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
4.news channel
0
527
481 488 469
412 412 434 419
487
792
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
5.creation channel
0
259 272
245
186
149
177 165 165
143
205
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
6.lifestyle channel
0
1128
786
563 525 475 519 465 501 467
1567
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
7.shortfilms channel
0
169216431567156714971234121410991025
4268
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
8.music channel
0
204
222
186
129
166
131
148 137 125
169
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
9.other channel
0
423
495
451
401 404
356 354 368 338
689
Thing Amount Animal Event Function Loc Organization Person Product Time
x−Axis: The temporal positions of NEs
y−Axis: The number of NEs
[Li_LIME'13]Dailymotion Dataset, 805 videos, 46.58% Accuracy0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
4.news channel
0
527
481 488 469
412 412 434 419
487
792
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
5.creation channel
0
259 272
245
186
149
177 16
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
7.shortfilms channel
0
169216431567156714971234121410991025
4268
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
8.music channel
0
204
222
186
129
166
131
14
Thing Amount Animal Event
x−Axis: The temporal positions of NEs
y−Axis: The number of NEs
Temporal
distribution of
entity types
2016/03/04

Exploitation: Promoting Media Fragments
Part
1.c
Available @ http://linkedtv.eurecom.fr/HyperTED
[Redondo_ISWC’14]
2016/03/04

Evaluation: Multimodal @ Mediaeval 2013
Part
1.c
~ 1697h of BBC video data, 2323 videos
 Different TV shows
(news, sports, politics…)
from 2012
 Subtitles and ASR
(English)
 Output of some visual
algorithms: shot and face
detection
Anchor
Search Task Hyperlinking Task
Query
T/V
v1 v2 v3 vn v1 v2 v3 vn
va
2016/03/04

Evaluation: Multimodal @ Mediaeval 2013
Part
1.c
Annotations Processing Time Type
Visual Concept Detection (151) 20 days on 100 cores Visual **
Scene Segmentation 2 days on 6 cores Visual
OCR 1 day on 10 cores Visual
Keywords Extraction 5 hours Textual **
Named Entities Extraction 4 days Textual
Face detection and Tracking 4 days on 160 cores Visual
 Data Indexing:
◉  Lucene & Solr
◉  Granularities: Shot, Scenes, Sliding Windows…
◉  Multimodality
 Query Formulation:
◉  Search: Text + Visual Cues + Visual Concept
Mapping, MLSCOM
◉  Hyperlink: Subtitles, Keywords, LSCOM
concepts (MoreLikeThis)
Approach:
2016/03/04

0.19 MRR
(Mean R. Rank)
Evaluation: Mediaeval 2013 Results
Part
1.c
Search Task
Hyperlinking Task
[Sahuguet_MediaEval’13]
0,72 P10
2016/03/04

Evaluation: Mediaeval 2014 Results
Part
1.c
Search Task
[Hoang_MediaEval’14]
Hyperlinking Task
  Changes in 2014 edition:
◉  New Dataset from BBC: 2686 hours and 3520 videos
◉  No Visual Cues on Search Queries
◉  New Approach: 22% MAP improvement in 2013 Dataset
0.71 P10
0.67 P10
2016/03/04

“
Narrowing down…
From Multimedia Content to
News Items
Semantically Capturing and Representing News Stories on the Web 282016/03/04

Part 2
Semantically Contextualizing
News Stories
2
Q.3
2016/03/04

Wolfgang Schäuble
Finance Minister Ruling Party in Ger.
Christian Democratic
Union
Part
2
2016/03/04

Semantic News Annotation
  N. Fernandez, J. A. Fisteus, L. Sanchez, and G. Lopez. Identityrank: Named
entity disambiguation in the news domain.
  S. Chabra. Entity-centric summarization: Generating text summaries for graph
snippets.
  A. Fuxman, P. Pantel, Y. Lv, A. Chandra, P. Chilakamarri, M. Gamon, D.
Hamilton, B. Kohlmeier, D. Narayanan, E. Papalexakis, and B. Zhao.
Contextual insights
  N. Kanhabua, R. Blanco, and M. Matthews. Ranking related news predictions.
  N. K. Tran, A. Ceroni, N. Kanhabua, and C. Niederee. Back to the past:
Supporting interpretations of forgotten stories by time-aware re-contextualization.
  N. K. Tran, A. Ceroni, N. Kanhabua, and C. Niederee. Time-travel translator:
Automatically contextualizing news articles.
  T. Stajner, B. Thomee, A.-M. Popescu, M. Pennacchiotti, and A. Jaimes.
Automatic selection of social media responses to news.
State of the Art & Related Work
Part
2
Graph
Named Entities
in News
Contextualizing
News
Relevancy of
Entities
2016/03/04

Semantic
Snapshot of
News (NSS)
  Definition and Motivation
  A Gold Standard of News Entities
2.a
2016/03/04

Going deep
down…
It is always challenging
What is on top:
Entities explicitly appearing
in the documents
Laura Poitras
Anatoly Kucherena
Edward Snowden
Part
2.a
The News Semantic Snapshot (NSS)
2016/03/04

The News Semantic Snapshot (NSS)
Part
2.a
News Semantic Snapshot
(NSS)[Redondo_ICWE’15]
2016/03/04

The News Semantic Snapshot: Gold Standard
Part
2.a
 High Level of detail, significant human Intervention: (Experts
in the news domain + users)
 Entities in 5 Dimensions: (Visual & Text)
(1) Video Subtitles
(2) Image in the video
(4) Suggestions of an expert
(5) Related articles
USER SURVEY
“We don't have any extradition treaty with Russia.
Broadly speaking our policy remains the same: that
we'd like him returned
(3) Text in the video
image
(2)
(3)
(1)
[Romero_TVX’14]
2016/03/04

The News Semantic Snapshot: Gold Standard
Part
2.a
Play with the data and help us to extend it at:
https://github.com/jluisred/
NewsConceptExpansion/wiki/Golden-Standard-
Creation
25
2016/03/04

Automatically
Generating
the NSS
2.b
  The Selection problem
  Approaches: frequency-based, multidimensional, concentric
  Experiments and Results
2016/03/04

b) Expanded
Entities
Generating the NSS: General Method
[Redondo_SNOW’14]
(2)
c) News Semantic Snapshot
Part
2.b
2016/03/04

b) Expanded
Entities
Generating the NSS: Entity Expansion
[Redondo_SNOW’14]
(2)
Part
2.b
2016/03/04

Generating the NSS: Expansion’s Settings
Part
2.b
Query:
-  Title
-  5 W’s over Subtitles Entities
Web sites to be crawled:
-  Google
-  L1 : A set of 10 internationals
English speaking newspapers
-  L2 : A set of 3 international
newspapers used in GS
Temporal Window:
-  1W:
-  2W:
Annotation filtering
-  Schema.org
[Redondo_ICWE’15]
Parameters:
2016/03/04

b) Expanded
Entities
a) Entities DS
Generating the NSS: Expansion’s Settings
[Redondo_SNOW’14]
(2)
Part
2.b
Recall (E. Expansion) =
0.91
Recall (NER on Subtitles) =
0.42
2016/03/04

b) Expanded
Entities
a) Entities DS
Generating the NSS: Selection
(2)
Part
2.b
[Redondo_SNOW’14]
2016/03/04

Generating the NSS: The Selection problem
Part
2.b
(NSS)
0
N
FIdeal(ei)
(NSS)
FX(ei)
=?Expansion
2016/03/04

Generating the NSS: Measures
Part
2.b
1  Precision / Recall @ N
-  Popular
-  Easy to interpret
2  Mean Normalized Discounted Cumulative Gain
(MNDCG) @ N:
-  Considers ranking
-  Relevant documents at the top positions
3  Compactness for Recall R:
-  Compromise between: Recall and NSS size
2016/03/04

Generating the NSS: Compactness Example
Part
2.b
Recall: 22/33 = 0.66
Sa = 27
Sb = 33
Sc = 54
Sa = 27
Sb = 33
Sc= 54
(NSS)
A B CA
B
C
> >
2016/03/04

Generating the NSS: The Approaches
Part
2.b
1  Frequency-Based Ranking
-  Leverages on biggest sample provided by expansion
-  Prioritizes representativeness
2  Multidimensional Entity Relevance
Ranking
-  Relevancy of entities is ground on different dimensions
3  Concentric Based Approach
-  Core / Crust model
-  Alleviates the problem of dealing with many dimensions
[Redondo_SNOW’14]
[Redondo_ICWE’15]
[Redondo_KCAP’15A]
2016/03/04

Generating the NSS: (1) Frequency-Based
Part
2.b
[Redondo_SNOW’14]
A
2016/03/04

Generating the NSS: (2) Multidimensional
Part
2.b
[Redondo_ICWE2015]
2016/03/04

Part
2.b
POPULARITY (FPOP) EXPERT RULES (FEXP)
49
-  Based on Google Trends
-  w = 2 months
-  µ + 2*σ (2.5%)
Example:
-  [ Location, = 0.43]
-  [ Person, = 0.78]
-  [ Organization, = 0.95 ]
-  [ < 2 , = 0.0 ]
Generating the NSS: (2) Multidimensional
2016/03/04

Experiment 1: Frequency VS Multidimensional
Part
2.b
20 x 4 x 4 =
320 formulas
2016/03/04

Part
2.b
  News Entity Expansion & Dimensions ! Generate NSS
  Frequency-based score: 0.473 MNDCG @ 10
  Best score: 0.698 MNDCG @ 10
•  Collection:
•  CSE (Google + 2W + Schema.org)
•  Ranking:
•  Expert Rules
•  Popularity
Multidimensional Nature of the NSS
2016/03/04

Part
2.b
(NSS)
FREQ
0
(NSS)
F(Laura Poitras) = 2
F(Glenn Greenwald) = 1
2016/03/04

Part
2.b
(NSS)
(Expansion)
FREQ POP EXP
+ + =
(NSS)
2016/03/04

Experiment 2: Multidimensional ++
Part
2.b
1.  Exploit Google relevance (+1.80%)
2.  Promote subtitle entities (+2.50%)
3.  Exploit named entity extractor’s
confidence (+0.20%)
4.  Interpret popularity dimension (+1.40%)
5.  Performing clustering before filtering
(-0.60%)
- NO SIGNIFICANT IMPROVEMENT -
NMDCG @ 10:
2016/03/04

Experiment 2: Multidimensional ++
Part
2.b
Tune
Function XFREQ POP EXP Re-ShuffleOriginal
(NSS)
2016/03/04

Part
2.b
MNDCG:
•  Too focused on success at first positions (decay Function)
•  NSS intends to be flexible, ranking is application-dependent
COMPACTNESS:
•  Prioritizes coverage over ranking while minimizing NSS size
Re-thinking the problem: measures
2016/03/04

Part
2.b
Duality in news entity spectrum:
•  Representative entities:
•  Driving the plot of the story
•  Relevant entities
•  Related to former via specific reasons
•  Exploit the entity semantic relations
Suggested by Expert?
Informative?
Unexpected?
Interesting?
Explicative?
Re-thinking the problem: dimensions
2016/03/04

Part
2.b
Generating the NSS: (3) Concentric Approach
 Core
•  Representative entities
•  Spottable via frequency
dimensions
•  High degree of
cohesiveness
 Crust
•  Attached to the Core via
semantic relations
•  Agnostic to relevancy
nature:
informativeness,
interestingness, etc.
[Redondo_KCAP2015A]
2016/03/04

Part
2.b
Generating the NSS: (3) Core Creation
a) Spot representative entities:
Frequency Dimension
(NSS)
b) Cohesiveness (DBpedia)
2016/03/04

Part
2.b
Generating the NSS: (3) Crust Creation
The number of Web
documents talking
simultaneously about a
particular entity e and the
Core: ?
2016/03/04

Experiment 3: Multidimensional VS Concentric
Part
2.b
1.  Entity Frequency
○  Core1: Jaro-Winkler > 0.9
○  Core2: Frequency based on Exact String matching
2.  Cohesiveness:
○  Everything is Connected Engine, Skb(e1, e2) > 0.125
Everything is Connected
Engine:
https://github.com/mmlab/eice
Concentric Core:
2016/03/04

Part
2.b
1.  Candidates for CRUST generation:
○  Ex1: 1° ICWE2015 by R*(50): L2+Google, F3 1W, Gauss+ POP
○  Ex2: 2° ICWE 2015 by R*(50): L2+Google, F3 1W, Freq + POP
2.  Function for attaching entities to CORE:
○  SWEB(ei, Core) over Google CSE, default configuration
Concentric Crust:
2016/03/04

Part
2.b
Combining CORE and CRUST:
Core+CrustCrustOnly
2016/03/04

Part
2.b
36.9% more compact than Multidimensional
(NSS’s size decrease)
IdealGT: size of SSN according to Gold Standard
(2*2*2 + 2) Runs
2016/03/04

Part
2.b
NSS
Gold
Standard
Fukushima Disaster 2013
2016/03/04
n=22

Multidimensional
Concentric
Part
2.b
2016/03/04

Part
2.b
NSS: Suitable model for news applications ?
2016/03/04

Consuming
the Concentric
NSS
2.c
  News consumption phases
  The NSS for feeding news prototypes
2016/03/04

Part
2.c
NSS Consumption: News Prototypes
… short
summaries,
previews,
hotspots …
… advanced
graphs and
diagrams,
timelines, in-
depth summaries
…
… second screen
apps, slideshows,
info-boxes …
2016/03/04

Part
2.c
NSS Consumption: Consumptions Phases
The Before The During The After
2016/03/04

Part
2.c
NSS Consumption: Phases VS Layers
[Redondo_KCAP’15B]
2016/03/04

Conclusions
& Future Work
  Publications
  References
2016/03/04

Conclusions
a.  Applied NER and NED as semantic annotation techniques in the
multimedia domain
b.  Developed other techniques such as Named Entity Expansion or
Visual Concept Mapping
c.  LinkedTV model to harmonize annotations into the Linked Data Web
Q1: How can multimedia content be semantically
annotated and seamlessly connected with
other resources on the Web?
Q2: Can those semantic annotations and linked
media resources bring value for the exploitation
and consumption of multimedia content?
a.  Exploiting multimedia semantic techniques: enriching, highlighting
media fragments (hotspots), classifying videos…
b.  Evaluation of multimodal approaches via Mediaeval 2013/2014
2016/03/04

Conclusions
a.  Proposed the NSS model and a Gold Standard
b.  The multidimensional nature of the entity relevance
•  Gaussian function, popularity, experts rules…
c.  Concentric model better reproduces the NSS:
•  Better Compactness: 36.9% over BAS01 (similar recall, smaller size)
•  Core/Crust brings up relevant entities without having to deal with
fuzzy dimensions
d.  NSS better supports the news consumption phases:
(Before, During, After)
Q3: Is it possible to automatically contextualize news
stories with background information so they can
be effectively interpreted by humans and
machines?
2016/03/04

Future Work
•  [S] Publish generated NSS on the Web (Linked Data)
•  [S] Extend the Gold Standard:
•  From 5 to 23 videos, concentric based model for candidate selection
•  Submission to TOIS
•  [S] Not depending on “big players” for retrieving
knowledge during the expansion phase
(Terrier VS Google experiments)
2016/03/04

Future Work
•  [M] Using the power of crowdsourcing in Gold Standard
creation
•  Increase size of the Gold Standard without involving
experts
•  Consider different levels of entity relevancy
•  [M] Supervised techniques: Learn to Rank
•  Features in entities: surface forms, URL’s, types…
•  Features in documents, sources, and other provenance
information
2016/03/04

Future Work
•  [L] Spot not only the strength of the relationships
between Crust and the Core, but also the predicates
Editor in WikiLeaks
Generating
Explanations
analyzing documents
considered in Sweb
2016/03/04

Future Work
•  [L] Not having to rely on “Big Players” during Crust
generation:
•  Continuous indexing
•  Better curated white lists
•  Fresher structured databases: DBpedia events
•  [L] Reusing concentric model in context-related tasks:
•  Name Entity Extraction/Disambiguation
"  As another feature similar to BagOfWords, Word2vec…
•  Exploratory Searches
"  Diversity, serendipity…
++
[Steiner_ICWE’15]
2016/03/04

José Luis Redondo García
http://jluisred.github.io
@peputo
http://github.com/jluisred
“my small dent in
the vast ocean of
knowledge…”
Ph.D.
questions?

Publications
Journals
•  Redondo Garcia J. L and Adolfo Lozano-Tello: OntoTV: an Ontology Based System for the
Management of Information about Television Content. International Journal of Semantic
Computing, 6(01), 111-130, 2012.
Conferences
•  Redondo Garcia J. L., Rizzo G., Troncy R. (2015) Capturing News Stories Once, Retelling
a Thousand Ways. In: 8th International Conference on Knowledge Capture (K-CAP'15),
Palisades, NY, USA.
•  Redondo Garcia J. L., Rizzo G., Troncy R. (2015) The Concentric Nature of News
Semantic Snapshots: Knowledge Extraction for Semantic Annotation of News Items. In: 8th
International Conference on Knowledge Capture (K-CAP'15), Palisades, NY, USA.
Best Paper Award
•  Redondo Garcia J. L., Rizzo G., Romero L. P., Hildebrand M., Troncy R. (2015) Generating
Semantic Snapshots of Newscasts using Entity Expansion. In: 15th International Conference
on Web Engineering (ICWE'15), Rotterdam, the Netherlands.
•  Rizzo G., Steiner T., Troncy R., Verborgh R., Redondo Garcia J. L. and Van de Walle R.
(2012), What Fresh Media Are You Looking For? Extracting Media Items from Multiple Social
Networks. In (ACM Multimedia) International Workshop on Socially-Aware Multimedia
(SAM'12), Nara, Japan
Journals (2), Conferences (6), Workshops(5), Demo/Poster(7)
2016/03/04

References
[Redondo_KCAP’15B] Capturing News Stories Once, Retelling a Thousand Ways
[Redondo_KCAP’15A] The Concentric Nature of News Semantic Snapshots
[Redondo_ICWE’15] Generating Semantic Snapshots of Newscasts using Entity Expansion
[Redondo_ISWC’14] Finding and sharing hot spots in Web Videos
[Redondo_ESWC’14] Augmenting TV Newscasts via Entity Expansion
[Redondo_SNOW’14] Describing and Contextualizing Events in TV News Show
[LinkedTV_D2.6’14] LinkedTV Framework for Generating Video Enrichments with Annotations
[Romero_TVX’14] LinkedTV News: A dual mode second screen companion for web-enriched news broadcasts
[Hoang_MediaEval’14] LinkedTV at MediaEval 2014 Search and Hyperlinking Task
[Rizzo_LREC’14] Benchmarking the Extraction and Disambiguation of Named Entities on the Semantic Web
[Li_LIMe'13] Enriching Media Fragments with Named Entities for Video Classification
[Milicic_WWW'13] Live Topic Generation from Event Streams
[Milicic_ESWC’13] Tracking and Analyzing The 2013 Italian Election
[Sahuguet_MediaEval’13] LinkedTV at MediaEval 2013 Search and Hyperlinking Task
[Rizzo_SAM’12] What Fresh Media Are You Looking For? Extracting Media Items from Multiple Social
Networks
2016/03/04

Semantically Capturing and Representing News Stories on the Web

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