Mining and Understanding Activities and Resources on the Web

Mining and Understanding (Learning)
Activities and Resources on the Web
Stefan Dietze, Besnik Fetahu, Ujwal Gadiraju
L3S Research Center, Hannover, Germany
14/07/16 1Stefan Dietze, Besnik Fetahu, Ujwal Gadiraju

Research areas
 Web science, Information Retrieval, Semantic Web, Social Web
Analytics, Knowledge Discovery, Human Computation
 Interdisciplinary application areas: digital humanities,
TEL/education, Web archiving, mobility
Some projects
L3S Research Center
14/07/16 2
 See also: http://www.l3s.de

“Intelligent Access to Information” / L3S
14/07/16 3Stefan Dietze, Besnik Fetahu, Ujwal Gadiraju

Team & current projects
LA4S LearnWeb
14/07/16 4
GlycoRec
Ran Yu
Ujwal Gadiraju
Besnik Fetahu
Stefan Dietze

14/07/16 5
AFEL – Analytics for Everyday (Online) Learning
Figure courtesy of Mathieu d‘Aquin

14/07/16 6
AFEL – Analytics for Everyday Learning
Apply and Evaluate
- WP1 -
Data
Capture
- WP3 -
Visual
Analytics
- WP5 -
Use Cases and
Evaluation
Collect & Enrich Data
Detect and Model User &
Learning Activities
Analyse Learning Behaviour
- WP2 -
Data
Enrichment
- WP4 -
Cognitive
Modelling
Figure courtesy of Mathieu d‘Aquin

14/07/16 7
Entities/notions, e.g.:
• Learning
• ... Resource
• ... Activity
• ... Performance
• Knowledge
• Competence
• ....
Learning Activities
- WP2 -
Data
Enrichment
- WP4 -
Cognitive
Modelling

14/07/16 8
Entities/notions, e.g.:
• Learning
• ... Resource
• ... Activity
• ... Performance
• Knowledge
• Competence
• ....
Learning Activities
- WP2 -
Data
Enrichment
- WP4 -
Cognitive
Modelling
Understanding informal/micro learning on the Web (e.g. Social Web) – Challenges:
 Absence of competence indcators/assessments etc ?
 Measuring/detecting progress/competence etc, i.e. distinguish good/bad performance ?
 Understanding learning activities => understanding of learning resources and involved entities
 Heterogeneity and scale of data/activities/documents to consider (i.e. the Web)
 ...

14/07/16 9
Overview
Mining & understanding (learning) resources on the Web:
 “Extracting entity-centric knowledge/learning resources from Web Documents“ (Stefan)
 “Automated Wikipedia Entity Enrichment with News Sources” (Besnik)
Mining & understanding (learning) activities on the Web
 Predicting/measuring „competence“: “Behavioral Methods for Improving the Effectiveness of
Microtask Crowdsourcing" (Ujwal)
Learning Activities
- WP2 -
Data
Enrichment
- WP4 -
Cognitive
Modelling

14/07/16 10
Understanding knowledge resources on the Web
Apple
Digital Revolution
Steve Jobs
IT Company
Bank
Jobs Biopic/Movie
Person
 Detecting (salient) entities in Web
resources/documents
 NLP-based named entity
recognition and disambiguation
(Babelfy, DBpedia Spotlight etc)
 Usually uses background
knowledge graphs
(eg DBpedia/Wikipedia, Linked
Data)
Band
?

Web documents vs structured entity-centric knowledge graphs
14/07/16 11
Unstructured Web documents
Linked Data & Knowledge Graphs
 The Web: approx. 46.000.000.000.000 (46 trillion)
Web pages indexed by Google
vs
 Linked Data & Knowledge Graphs: structured
entity-centric data, approx. 1000 datasets & 100
billion statements (DBpedia, etc)
 Linking entities (NED/NER) from documents:
 Computational complex
 Error-prone
 Issues with less popular entities
(example: regional news sites)
 Knowledge graphs less dynamic than Web
documents

 Markup: entity-centric data embedded in the Web
(30% of all Web documents in 2015)
 Using W3C standards (RDFa, Microdata,
Microformats)
 Schema.org: inititative from
Google, Yahoo, Bing, Yandex to
push common vocabulary
 Same order of magnitude as Web itself with respect
to scale and dynamics
(as opposed to knowledge graphs, DBpedia et al)
 Rich source of knowledge and data going beyond
existing knowledge bases (eg Wikipedia)
Entity-centric data on the Web: Web markup (schema.org)
14/07/16 12
Entity
node2 publisher Pearson Education
node2 publisher Elsevier
node2 published 03-01-2014
Unstructured Web documents
Linked Data & Knowledge Graphs
Embedded Markup (schema.org)
Entity
node1 name French Grammar advanced
node1 publisher The Open University
node1 publisher Nature
node1 datePublished 1956
node1 datePublished 1953

1
10
100
1000
10000
100000
1000000
10000000
1 51 101 151 201
count(log)
PLD (ranked)
# entities # statements
Example: entity markup of learning resources on the Web
 “Learning Resources Metadata Intiative (LRMI)”:
schema.org vocabulary for annotation of learning
resources (informal, formal, etc)
 Approx. 5000 PLDs in “Common Crawl”
 LRMI-Adaptation on the Web (WDC) [LILE16]:
 2014: 30.599.024 quads, 4.182.541 resources
 2013: 10.636873 quads, 1.461.093 resources
14/07/16 13
Power law distribution across providers
4805 Provider / PLDs
Taibi, D., Dietze, S., Towards embedded markup of learning resources
on the Web: a quantitative Analysis of LRMI Terms Usage, in
Companion Publication of the IW3C2 WWW 2016 Conference, IW3C2
2016, Montreal, Canada, April 11, 2016

Entity-centric markup on the Web: challenges
14/07/16 14
Characteristics Example
Coreferences
18.000 results for <„Iphone 6“, type, s:Product>
(8,6 quads on average) in CommonCrawl
Redundancy <s, schema:name, „Iphone 6“> occurring 1000 times in CC
Lack of links Largely unlinked entity descriptions
Errors
(typos & schema
violations, see Meusel
et al [ESWC2015])
Wrong namespaces, such as http://schma.org
Undefined types & predicates:
9,7 %, less common than in LOD
Confusion of datatype and object properties:
<s1, s:publisher, „Springer“>, 24,35 % object property issues vs 8%
in LOD
Data property range violations: e.g. literals vs numbers
(12,6% vs 4,6 in LOD)
 Why not using markup as knowledge graph of entities involved in (learning) resources (similar to
DBpedia/Wikipedia)?

 Improving understanding of resources: consolidating entity-
centric Web data for a given document/resource/entity?
 Markup as distributed knowledge graph/base, e.g. to augment
existing knowledge bases (eg DBpedia/Wikipedia) ?
Data fusion for consolidating entity centric Web markup
14/07/16 15
Yu, R., Gadiraju, U., Zhu, X., Fetahu, B., S. Dietze, Entity
summarisation on structured web markup. In The
Semantic Web: ESWC 2016 Satellite Events. Springer,
2016.
Yu, R., Gadiraju, U., Zhu, X., Fetahu, B., S. Dietze, Fact
Selection for data fusion on structured web markup.
ICDE2017, IEEE International Conference on Data
Engineering, in progress.
Query
iPhone 6, type:(Product)
Entity Description
brand Apple Inc.
weight 129
date 30.09.2015
manufacturer Foxconn
Storage 16 GB
<e1, s:name, „Iphone 6“>
<e2, s:brand, „Apple Inc.“>
<e3, s:brand, „Apple“> <e4, s:weight, 127>
<e5, s:releaseDate, „1.12.1972“>
Web (crawl)
(i.e. billions of entites/facts)

A supervised ML approach to select entity facts from the Web
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 Fact/entity retrieval: BM25 entity retrieval model on markup index (Common Crawl)
 Fact selection: supervised ML classifier (SVM), using 3 feature categories (relevance, authority, clustering)
 Experiments on Common Crawl: products, movies, books (approx. 3 billion facts)
1. Retrieval
2. Fact selection
New Queries
Foxconn, type:(Organization)
Cupertino, type:(City)
Apple Inc., type:(Organization)
(trained SVM classifier)
Entity Description
brand Apple Inc.
weight 129
date 30.09.2015
manufacturer Foxconn
Storage 16 GB
Query
iPhone 6, type:(Product)
Candidate Facts
node1 brand _node-x
node1 brand Apple Inc.
node1 weight 129
node2 weight 172
node2 manufacturer Foxconn
node3 releasedate 01.12.1972
node3 manufacturer Foxconn
Web page
markup
Web (crawl)
approx. 125.000 facts for „iPhone6“

14/07/16 19
Evaluation & results
Performance
 Outperforms baselines (BM25F, CBFS)
 Strong variance across types/queries
 Average precision from 75% – 98 %

14/07/16 20
Evaluation & results: markup vs DBpedia/Wikipedia
Can markup augment existing Knowledge Graphs?
 Comparison of obtained facts with existing
knowledge bases (DBpedia/Wikipedia)
 „new“: fact not existing in DBpedia
(eg a book‘s releaseDate in Wiki/DBpedia)
 „new-p“: property not existing in DBpedia
(eg a book‘s release countries)
 „existing“: fact already in DBpedia
 On average approx. 60% new facts
Performance
 Outperforms baselines (BM25F, CBFS)
 Strong variance across types/queries
 Average precision from 75% – 98 %

14/07/16 21
Conclusions
 Data fusion on markup as means to extract
rich descriptions of entities in Web documents
 Understanding semantics of activities and
resources (particularly learning resources)
 Markup: rich source of entity centric data
(30% of the Web, i.e. 16 trillion Web pages)
 Potential training data for NED/NER
approaches
 Potential for augmenting existing knowledge
graphs/bases (DBpedia/Wikipedia et al)
Learning Activities

14/07/16 22
Next
 “Extracting entity-centric knowledge/learning
resources from Web Documents“ (Stefan)
 “Automated Wikipedia Entity Enrichment with News
Sources” (Besnik)
 Predicting/measuring „competence“: “Behavioral
Methods for Improving the Effectiveness of Microtask
Crowdsourcing" (Ujwal)
Learning Activities

Outline
Wikipedia Entity
Enrichment
Besnik Fetahu, Katja Markert, Avishek Anand: Automated News Suggestions for Populating Wikipedia Entity Pages. CIKM 2015: 323-332
14/07/16Stefan Dietze, Besnik Fetahu, Ujwal Gadiraju

Introduction
• Human fatalities: 10k vs 1.8k losses
• Estimated damages: $4.5 vs. $108 billions
• ‘Odisha cyclone’ has no coverage in the
entity location ‘Odisha’
• ‘Hurricane Katrina’ finds broad coverage in
entity location `New Orleans’
New Orleans
Odisha
Hurricane Katrina
Odisha Cyclone

Introduction
• Entities comprise of facts and statements supported by external
references!
• News as authoritative sources with emerging facts and events.
• Delay between the reporting of an event in news and its
inclusion in entity pages1
• Incomplete section structure for long—tail entities
• Several implications on real-world applications that make use of
Wikipedia, e.g. KB maintenance, entity disambiguation etc.
Besnik Fetahu, Abhijat Anand, Avishek Anand: How much is Wikipedia lagging behind news?. WebSci 2015

Motivation: News Density in Wikipedia
• Citation templates (‘news’,
‘books’, ‘web’, ‘journal’ etc.)
• ~60% vs. 20% ‘web’ and
‘news’ citations
• On average there are ~6.5
news citations per entity
• On average a news article is
assigned to ~1.3 entities
• The most cited news article
is cited by 81 entities
Besnik Fetahu, Abhijat Anand, Avishek Anand: How much is Wikipedia lagging behind news?. WebSci 2015

Problem Definition
news
Pub.date: tk
entity pages
Rev.date: tk-1
news article
• news title
• headline
• paragraphs
• named entities
entity page
• section template
• categories
• entities (anchors)
• …..
suggest news n to entity e ?
specify the section in e for n
suggest news n to entity e ?

Automated news suggestion to entity pages
feature extraction
Some half a million people were evacuated
from the southeastern Indian coast as
Cyclone Phailin, a tropical storm from the
Bay of Bengal, bore down on India. The
states of Orissa and Andhra Pradesh, both
of which have large coastal populations, were
on high alert ahead of the storm’s expected
arrival.
entities
news article
sections
wikipedia
entity page
article entity
placement
Odisha
Bay of Bengal Phailin
Task#1
one classifier per
entity type
article section
placement
[state]:geography
[city]:climate
…
Task#2

Article—Entity Placement
Task#1

News Suggestion Attributes: Task#1
Entity Salience
Nikola Tesla
Elon Musk
Larry Page
John B. Kennedy
Entity Salience: Relative Entity Frequency
• reward entity appearing throughout the text
• reward entity appearing in the top paragraphs
• weigh an entity w.r.t its co-occurring entities
Tesla is a central
concept in the given
news article

Relative Entity Authority
Elias TabanHillary Clinton
Relative Entity Authority
• entities with `low authority’ have lower
entry barrier for a news article
• a news article in which an entity co-
occurs with `high authority’ entities
conveys news the importance
• entity authority as an a priori probability
or any centrality based measure

Novelty & Redundancy
previously added news articles
• novelty is measured w.r.t previously added news articles
in an entity page
• major events have wide coverage in news media
• place the news article into the correct section
Novelty and Redundancy Measure

Article—Section Placement
Task#2

Task#2: Section—template Generation
Germanwings Adria Lufthansa
• Section templates per entity type
• Pre-determined number of main
sections
• Canonicalize sections
• Generate `complete’ section
templates based on similar entities
• Cluster based on the X—means[3]
algorithm
[3] D. Pelleg, A. W. Moore, et al. X-means: Extending k-means
with efficient estimation of the number of clusters. In ICML,
pages 727–734, 2000.

Task#2: Overall news—section fit
• What is the best section to append a given news article?
• measure overall similarity between n and the pre-computed sections in
the section templates
• Similarity aspects between news articles and sections
• Topic similarity (LDA models over the sections and news documents)
• Syntactic similarity
• Lexical similarity
• Entity—based similarity (overlap of named entities)
• Frequency

Evaluation Strategy
What comprises of the ground-truth for such a task?
Challenges
• `Invasive’: add news articles and wait for a time period until it is either accepted or
deleted by the Wikipedia editors
• Long tail vs. trunk entities: long tail entities might not be of particular interest to
editors, hence, many `false positives’ will go unnoticed.
• Crowdsourcing: Challenging to find knowledgable workers for long-tail entities
Approach
•Use already referenced news articles from entity pages
•Avoid the uncertainty of judgements and expertise of crowd workers
•Non-invasive approach for entity pages
•Reusable test bed for similar approaches

Experimental Setup
Distribution of news articles, entities,
and sections across the years
Datasets Evaluation Plan
• train at years [to, ti], test at (ti, tk]
• P/R/F1 metrics
Baselines
Task#1: AEP
• B1: AEP based on Dunietz and Gillick
• B2: AEP if entity appears in the news title
Task#2: ASP
• S1: AES based on max similarity to one of the sections
• S2: AES to the most frequent section

Task#1: Article—Entity Placement
Performance
Robustness
Feature Analysis
Number Instances

Task#2: Article—Section Placement

• Two—stage news suggestion approach for Wikipedia entity pages
• Model and define what makes a good news suggestion
• Model functions for salience, relative authority, novelty and section placement defined as attributes
for a ‘good news suggestion’
• Entity profile expansion
• Extensive evaluation over 350k news articles, 73k entity pages and for the different Wikipedia
states between 2009 and 2014.
• A publicly available and reusable test bed for similar tasks
Conclusions

Next
Sources” (Besnik)
Learning Activities

42
Crowdsourcing - A Brief Introduction
* 42
Portmanteau of "crowd " and "outsourcing,"
first coined by Jeff Howe in a June 2006
Wired magazine article.
Accumulating small
contributions from
each crowd worker to
solve a bigger
problem.

43
Crowdsourcing - The Means to Many Ends
* 4314/07/16Stefan Dietze, Besnik Fetahu, Ujwal Gadiraju

44
The Paid Crowdsourcing Paradigm
❏ Small monetary rewards in exchange for completing short tasks online
❏ Entertainment-driven workers primarily seek diversion by taking up
interesting, possibly challenging tasks
❏ Money-driven workers mainly attracted by monetary incentives
❏ A crowdsourcing platform acts as a marketplace for such tasks
❏ About five million tasks are completed per year at 1-5 cents each
❏ Some jobs can contain more than 300K tasks

45
Microtask Crowdsourcing Platforms as Online Social
Environments
Crowd worker as a learner in an atypical learning environment :
❏ No information regarding the background, knowledge, or skills
of a worker.
❏ Short nature of crowdsourced microtasks, workers face an
‘on-the-fly’ learning situation.
❏ Comparable to experiential learning and microlearning.
❏ In many cases, workers have no time to apply their gained
experience.
❏ Often for single use, high % of new requesters.
Training Workers for Improving Performance in
Crowdsourcing Microtasks. Ujwal Gadiraju, Besnik
Fetahu, Ricardo Kawase. ECTEL 2015; Toledo, Spain.
Crowd Workers as Learners

46
Challenges
○ Diverse pool of workers
○ Wide range of behavior
○ Various motivations
Ross, J., Irani, L., Silberman, M., Zaldivar, A. and Tomlinson, B.
Who are the crowdworkers?: shifting demographics in mechanical
turk. In CHI'10 Extended Abstracts on Human factors in computing
systems. ACM.
Kazai, Gabriella, Jaap Kamps, and Natasa Milic-Frayling. The face of
quality in crowdsourcing relevance labels: demographics,
personality and labeling accuracy. Proceedings of CIKM’12. ACM.
Quality Control in Crowdsourcing

47
➢ Typically adopted solution to
prevent/flag malicious activity
:
Gold-Standard Questions
➢ Flourishing crowdsourcing
markets, advances in
malicious activity
“workers with ulterior motives, who either simply sabotage
a task, or provide poor responses in an attempt to quickly
attain task completion for monetary gains”
Need to understand workers
behavior and types of malicious
activity.
Malicious Workers

48
Malicious Workers - Behavioral Patterns in a Survey
Ineligible
Workers (IW)
Fast Deceivers
(FD)
Rule Breakers
(RB)
Smart Deceivers
(SD)
Gold Standard
Preys (GSP)
Instruction: Please attempt this microtask ONLY IF you have
successfully completed 5 microtasks previously.
Response: ‘this is my first task’
eg: Copy-pasting same text in response to multiple questions, entering
gibberish, etc.
Response: ‘What’s your task?’ , ‘adasd’, ‘fgfgf gsd ljlkj’
Instruction: Identify 5 keywords that represent this task
(separated by commas).
Response: ‘survey, tasks, history’ , ‘previous task yellow’
Instruction: Identify 5 keywords that represent this task
(separated by commas).
Response: ‘one, two, three, four, five’
These workers abide by the instructions and provide valid
responses, but stumble at the gold-standard questions!
Understanding Malicious Behavior in Crowdsourcing
Platforms: The Case of Online Surveys. Ujwal Gadiraju,
Ricardo Kawase, Stefan DIetze, Gianluca Demartini. CHI
2015; Seoul, Korea.

49
Workers Behavioral Patterns - Experimental Results

50
Automatic Classification of Worker Type
Image Transcription & Information Findings Tasks

51
Low-level features through
keystroke & mouse-tracking
❏ timeBeforeInput
❏ timeBeforeClick
❏ tabSwitchFreq
❏ windowToggleFreq
❏ openNewTabFreq
❏ totalMouseMovements
❏ scrollUpFreq
❏ scrollDownFreq
❏ . . .
Competent Worker
Fast Deceiver
Crowd Anatomy: Behavioral Traces for Crowd Worker
Modeling and Pre-selection. Ujwal Gadiraju, Gianluca
Demartini, Ricardo Kawase, and Stefan Dietze. (Under
Review at AAAI HCOMP 2016. Austin, Texas, USA.
Capturing Behavioral Traces ⇒ Behavioral Patterns

52
Worker Behavioral Patterns
❏ Multitaskers
❏ Divers & Feelers
❏ Wanderers
❏ Copy-Pasters & Typers
❏ . . .
Worker Types
❏ Competent Workers
❏ Diligent Workers
❏ Ineligible Workers
❏ Fast Deceivers
❏ Smart Deceivers
❏ Rule Breakers
❏ Incompetent Workers
❏ Sloppy Workers
Automatic Worker Type
Classification
Behavioral Traces for
Crowd Worker Modeling
and Pre-selection
Capturing Behavioral Traces ⇒ Behavioral Patterns

53
Evaluation of Automatic Worker Type Classification
Supervised Machine Learning
Model
❏ Automatic classification at scale
❏ Random forest classifier
❏ Classifiers evaluated using 10-fold
cross validation
❏ Information Finding & Content
Creation Tasks
Evaluation for Information Finding Tasks

54
Benefit of Automatic Worker Type Classification
Information Finding
Tasks (finding
middle names)
Content Creation
Tasks
(image transcription)
PRE-SELECTION
OF DESIRED
WORKER TYPES

55
Task Turnover Time
“the amount of time required to acquire the full set of
judgments from crowd workers, thereby completing and
finalizing a task considering pre-defined criteria (such as
qualification tests or pre-selection)”

56
Task Turnover Time
Information Finding
Tasks (finding
middle names)
Content Creation
Tasks
(image transcription)

57
Cognitive Theories & Entailing Data
Paradox of Choice in the Crowd
❏ Many available platforms and tasks
❏ Overload of choices for workers
❏ Detrimental effects on decision
making (psychology & social theory
works)
❏ Workers settle for less suitable tasks
❏ More capable workers are deprived
of an opportunity to work on suitable
tasks
❏ Overall effectiveness of the
crowdsourcing paradigm decreases
Typically Adopted Solution:
Crowd Worker Pre-selection

58
The Dunning-Kruger Effect
❏ Cognitive bias: Incompetent
individuals depict inflated self-
assessments and illusory superiority.
❏ Incompetence in a particular domain
reduces the metacognitive ability of
individuals to realize it.
❏ Incompetent individuals cognitively
miscalibrate by erroneously assessing
oneselves, while competent
individuals miscalibrate by
erroneously assessing others.

60
Self-Assessments for Pre-selection of Crowd Workers
❏ Crowd workers often lack awareness about their true level of
competence
❏ Novel worker pre-selection method based on self-assessments
& performance
Evaluation in
a Sentiment
Analysis Task
Worker
Performance Data
Using Worker Self-Assessments for Competence-based
Pre-Selection. Ujwal Gadiraju, Besnik Fetahu, Ricardo
Kawase, Patrick Siehndel and Stefan Dietze. (Under
Review at ACM CSCW 2017. Portland, Oregon, USA.

14/07/16 61
Summary
Sources” (Besnik)
Learning Activities

14/07/16 62
Thank you!
• http://www.l3s.de
• http://stefandietze.net
• http://l3s.de/~fetahu
• http://www.l3s.de/~gadiraju/

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