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 Career PHD Previous

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 Web 2016/03/04

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

Keyword BBC 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

Service Broadcast Channel

Scene

Shot

MediaFragment

Face

Part 1.b

Available @ http://data.linkedtv.eu/ontologies/core/

2016/03/04


Part 1.b

Locator

MediaResource

MediaFragment Annotation

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

Analysis Metadata

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

117140

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

410453

402 396 404353 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 302260 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

527481 488 469

412 412 434 419487

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 272245

186149

177 165 165143

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

204222

186

129166

131148 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

423495

451401 404

356 354 368 338

689

Thing Amount Animal Event Function Loc Organization Person Product Timex−Axis: The temporal positions of NEsy−Axis: The number of NEs

[Li_LIME'13] Dailymotion Dataset, 805 videos, 46.58% Accuracy

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

117140

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

410453

402 396 404353 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 302260 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

527481 488 469

412 412 434 419487

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 272245

186149

177 165 165143

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

204222

186

129166

131148 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

423495

451401 404

356 354 368 338

689

Thing Amount Animal Event Function Loc Organization Person Product Timex−Axis: The temporal positions of NEsy−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 28 2016/03/04

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



Generating the NSS: General Method


[Redondo_SNOW’14]

(2)

c) News Semantic Snapshot

Part 2.b

2016/03/04



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


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


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 C

A

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 X

FREQ

POP

EXP

Re-Shuffle

Original

(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+Crust CrustOnly

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 Experiment 3: Multidimensional VS Concentric

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

Data & Analytics