This is a back to office report for WWW 2014 and LDOW 2014

1. LDOW & WWW 2014
Back to office report
Dongpo Deng
!
April 23, 2014

2. Itinerary
• April 7 Taipei —> Seoul
• April 8 Linked Data on the Web 2014
• April 9 - 11 WWW 2014
• April 12 Seoul —> Taipei

3. Hotel
• Clean
• Good facilities
• Reasonable price
• Kitchen and washing machine
• Close to metro (10 mins on foot)
• Noise till to 2 pm
• Tiny room
• Small desk
Inn the City Serviced Residence, COEX
(COEX城市服務公寓旅館)
!
424 USD for 5 nights

4. Food

5. Sightseeing

8. LDOW 2014

9. • Session 1: Introduction and Keynote
• [09:00-09:10] Workshop Introduction (Christian Bizer, Tom Heath,
Sören Auer, Tim Berners-Lee)
• [09:10-10:00] Keynote: Schema.org Update (Ramanathan Guha)
• [10:00-10:30] Discussion
• [10:30-11:00] Coffee Break
• Session 2: Integration
• [11:00-11:20] RML: A Generic Language for Integrated RDF
Mappings of Heterogeneous Data
• [11:20-11:40] Knowledge Base Augmentation using Tabular Data
• [11:40-12:00] AIDA-light: High-Throughput Named-Entity
Disambiguation
• [12:00-12:20] Web-Scale Querying through Linked Data Fragments

10. • Session 3: Exploration
• [14:00-14:20] DBpedia Viewer – An Integrative Interface for
DBpedia leveraging the DBpedia Service Eco System
• [14:20-14:40] Linked Data Query Wizard: A Novel Interface for
Accessing SPARQL Endpoints
• [14:40-15:00] Programmable Analytics for Linked Open Data
• [15:00-15:20] Will Linked Data Benefit from Inverse Link Traversal?
• Session 4: Linked Data Applications
• [16:00-16:20] daQ, an Ontology for Dataset Quality Information
• [16:20-16:40] Publishing L2TAP Logs to Facilitate Transparency and
Accountability
• [16:40-17:00] Weaving the Web(VTT) of Data
• [17:00-17:20] Social Web Meets Sensor Web: Linked crowdsourced
observation data
• [17:20-17:40] Application of the Linked Data Visualization Model on
Real World Data from the Czech LOD Cloud

11. Keynote: Schema.org Update
by Ramanathan Guha
• Background of Schema.org
development
• One vocabulary understood by all
search engines
• It’s a vocabulary.Not the vocabulary.
• Google Knowledge Graph
• Pinterest Rich Pins
• Mapping entities across sites

12. Web-Scale Querying through Linked Data
Fragments

13. DBpedia Viewer – An Integrative Interface
for DBpedia leveraging the DBpedia
Service Eco System
http://live.dbpedia.org/

14. Social Web Meets Sensor Web: From
User-Generated Content to
Linked Crowdsourced Observation Data
Dong-Po Deng1,3, Guan–Shuo Mai2, Tyng-Ruey Chuang1,
Rob Lemmens3, Kwang–Tsao Shao2
!
1Institute of Information Science and 2Biodiversity Research Center,
Academia Sinica, Taiwan
3Faculty of Geo–Information Science and Earth Observation (ITC),
University of Twente, Netherlands

22. Submission Statistics
• 11 subject areas with 22 area chairs
!
• Abstracts: 905
• Submissions: 645
• Accepts: 84 (13%)
• Suggested as posters: 91

23. Statistics per Country
• Countries: 49 (submissions), 23 (accepts)
Country #Paper
USA 246
China 79
UK 40
Germany 38
India 25
Japan 24
Italy 20
Singapore 19
Korea 16
Country #Paper
Ecuador 100%
Belgium 63%
Portugal 50%
Israel 36%
Spain 24%
Denmark 22%
Hong Kong 21%
US 20%
Finland 19%

24. Statistics per track
Area Submitted Accepted Accept Rate
Behavioural Analysis and Personalization 74 14 18.9%
Content analysis 71 8 11.3%
Crowd Phenamena 43 8 18.6%
Internet, Economics & Monetization 35 8 22.9%
Security, Privacy, Trust, and Abuse 55 7 12.7%
Semantic Web 51 6 11.8%
Social Networks and Graph Analysis 126 13 10.3%
Software Infrastructure, Performance, Scalability, and Availablity 23 3 13.0%
User Interface, Human Factors, and Smart Devices 33 3 9.1%
Web mining 91 8 8.8%
Web Search 43 6 14.0%
Total 645 84 13.0%

25. Review Process
• Reviewers:
• 3 program chairs
• 22 Track chairs
• 422 PC members
• 265 external reviewers
• All paper got 3+ reviews
• Ravi Kumar’s gradin scheme: +6, +3 -2, -4, -6
• Two-day TPC meeting at Google

26. Best paper awards candidates

27. A glance of WWW program
• 2 workshop/tutorial days + 3 days main conference
• 19 workshops
• WI&C2014, Vertical Search Relevance, SOCM2014, SIPLEX2014,
WebQuality 2014, WS-REST2014, #Microposts2014, SNOW II,
DEOS’14, Big Graph Mining, MSM 2014, 4th Temporal Web Analytics,
LDOW2014, SRS2014, WOW2014, 2nd PHDA 2014, COOL’2014,
WebET 2014, LSNA, BigScholar 2014,
• 6+1 tracks
• Research, Industry, Developers, W3C, Web Science, PhD Symposium,
Demo/Poster
• 3 keynotes
• Large Graph Mining: Patterns, Cascades, Fraud Detection, and
Algorithms (Christos Faloutsos)
• Taming the Web (Jong-Deok Choi)
• Organizing the Digital World to Empower People to Do More, Know
More, and Be More (Qi Lu)
• 4 panels

28. Keynote #1
http://tinyurl.com/owz46dy

29. Some notes
• Patterns in graphs
• Patterns
• Anomaly/ fraud detection
• Graph understanding
• Time-evolving graph; tensor
• Cascades and immunization

30. Keynote #3
• Dr. Qi Lu (Microsoft)
• Organizing the Digital World to Empower People to Do More,
Know More, and Be More

47. Plenary Penal
WWW: The next 25 years

48. • TimBL
• toward decentralized system
• web science is social thing
• social piece will be reborn
• Mary Ellen Zurko
• mobilized entities for creating context
• connect everything in next 25 years web
Photo from @jure

49. • Yong Hak Kim
• how web change our society
in past 25 years-->
democracy
• evolution of education and
age
• Social divided

50. • Unna Huh
• E-learning
• with having big data advances tech., we can make better
social welfare.
• One million data leaking scandal private data security
• Prabhakar Raghavan
• more social sciences needed when we look at future of
the web(instead of leaving it to comp science)

51. • James Hendler
• Take wikipedia as an example. It’s needed to think of
• 1.which tech. we should keep going
• 2. web services easy to develop ?
• 3. social machine human creativity social web activity
• make world to be a better place
• education uses web tech. help and affect young generation
• the internet is connected more than we thought

52. • Questions from a audience (Facebook)
• opposite to connectivity, the next should be though of setting up the
cyber border
• Mary: Keep web open
• TimBL:
• what AI give the social status
• Fight to keep the web neutral, open, decentralised - not just on
technology/cool stuff - it's a social system says
• encouraging us to make the web a decentralised system again, keep
binding on open standards
• web is needed to re-decentralized

53. Talks I attended
• 4/9 14:00 - 15:30 Research Track Crowdsourcing 1
• Quizz: Targeted Crowdsourcing with a Billion (Potential) Users
• Community-Based Bayesian Aggregation Models for Crowdsourcing
• The Wisdom of Minority: Discovering and Targeting the Right
Group of Workers for Crowdsourcing
• 4/9 16:00 17:30 Industry Track
• Analyzing Behavioral Data for Improving Search Experience at
Yandex
• Twitter’s Recommendation System: Algorithms, Architectures, and
Applications

54. • 4/10 11:00 - 12:30 Content analysis 1 - Entities
• Discovering Emerging Entities with Ambiguous Names
• Effective Named Entity Recognition for Idiosyncratic Web
Collections
• Deduplicating a Places Database
• 4/10 14:00 - 15:20 Web Science 2 Communities and Network 1
• The Semantic Evolution of Online Communities
• User Churn in Focused Q&A Sites: Characterizations and Prediction
• Evolution of Reddit: From the Front Page of the Internet to a Self-
referential Community?
• A Study of the Online Profile of Enterprise Users in Professional
Social Network

55. • 4/10 16:00 - 17:20 Web Science 3 Communities and Network 2
• Analysis of Local Online Review Systems as Digital Word-of-Mouth
• Modelling Patient Engagement in Peer-to-Peer Healthcare
• Songrium: A Music Browsing Assistance Service with Interactive
• Web Science Meets Big Smog: Using Web Data to Analyze China’s Smog
Disaster
• 4/11 11:00 - 12:30
• Web Science 4: Structure on the Web
• Graph Structure in the Web — Revisited.
• Reachable Subwebs for Traversal-Based Query Execution
• Information Network or Social Network? The Structure of the Twitter
Follow Graph
• Mining Triadic Closure Patterns in Social Networks

60. Given two place names n1 and n2, and an approximate
geographical location containing both of them, determine if the
two names can refer to the same entity.

62. Methods
• A novel generative language model for places that identifies the core of
a place name. E.g., it identifies that in “Fresca’s Peruvian Restaurant”,
Fresca’s is the core and Peruvian Restaurant is the description.
• A language model that incorporates the spatial context, e.g., the
presence of nearby landmarks, parks, malls, airports, major streets
and the current city.
• The spatial model helps us with distinguishing “Newpark Mall Gap
Outlet” from “Newpark Mall Sears Outlet” and matching/non-
matching Central Park and Central Park Cafe.
• A place name similarity measure that incorporates both core-word
detection and spatial context to substantially improve precision and
recall for place name matching.

67. http://songrium.jp/

68. Demo
http://freebase-easy.cs.uni-freiburg.de/
Easy access to the freebase dataset

69. SemFacet: Semantic Faceted Search over
Yago
http://www.cs.ox.ac.uk/isg/tools/SemFacet/
http://semantic-facets.cs.ox.ac.uk:8080/QueryNavigation/

70. Exploring the Web of Spatial Data
with Facete
http://cstadler.aksw.org/facete2-geoknow-demo-y1/
https://github.com/GeoKnow/Facete2

71. Business cards
• Christian Bizer
• Tom Heath
• ASKW
• Claus Stadler
• Leipzig Univ.
• Dimitris Kontokostas
• AIST
• 後藤真孝 (Masataka Goto)
• 濱崎雅弘 (Hamsaki Masahiro)
• 台⼤大資⼯工 : 李龍豪、︑⿈黃信輔、︑許裕明
• 中研院 : 陳昇瑋