What's all the data about? - Linking and Profiling of Linked Datasets

What‘s all the data about –
profiling and interlinking Web datasets
Stefan Dietze
L3S Research Center
27/03/14 1Stefan Dietze

Recent work on Linked Data exploration/discovery/search
 Entity interlinking & dataset interlinking recommendation
 Dataset profiling
 Data consistency & conflicts
Research areas
 Web science, Information Retrieval, Semantic Web & Linked
Data, data & knowledge integration (mapping, classification,
interlinking)
 Application domains: education/TEL, Web archiving, …
Some projects
Introduction
http://www.l3s.de/
Stefan Dietze 27/03/14 2
 See also: http://purl.org/dietze

…why are there so few datasets actually used?
 Date reuse and in-links focused on trusted „reference
graphs“ such as DBpedia, Freebase etc
 Long tail of LD datasets which are neither reused nor linked
to (LOD Cloud alone 300+ datasets, 50 bn triples)
 Explanations?
Linked Data is awesome, but...
27/03/14
 „HTTP-accessibility“
(SPARQL, URI-dereferencing)
 „Structure“ & „Semantics“
(=> shared/linked vocabularies)
 „Interlinked“
 „Persistent“
Hm,
really?
Stefan Dietze

Linked data is more diverse than we think
SPARQL Web-Querying Infrastructure: Ready for Action?,
Carlos Buil-Aranda, Aidan Hogan, Jürgen Umbrich Pierre-Yves
Vandenbussch, International Semantic Web Conference 2013,
(ISWC2013).
SPARQL endpoint availability over time [Buil-Aranda et al 2013]
Accessibility of datasets?
 Less than 50% of all SPARQL endpoints actually responsive
at given point of time
 “THE” SPARQL protocol? No, but many variants & subsets
 …
Shared vocabularies & schemas, but:
 …still very heterogeneous [d’Aquin, WebSci13]
 …data partially messy and not conformant
(RDFS, schemas) [HoganJWS2012]
 …even widely used reference datasets such as
DBpedia noisy [Paulheim2013]
Co-occurence graph of data
types in 146 datasets: 144
Vocabularies, 588 highly
overlapping types, 719
Properties
Assessing the Educational Linked Data Landscape, D’Aquin, M.,
Adamou, A., Dietze, S., ACM Web Science 2013 (WebSci2013), Paris,
France, May 2013.
Type Inference on Noisy RDF Data, Paulheim H., Bizer, C. Semantic
Web – ISWC 2013, Lecture Notes in Computer Science Volume 8218,
2013, pp 510-525
An empirical survey of Linked Data conformance. Hogan, A., Umbrich,
J., Harth, A., Cyganiak, R., Polleres, A., Decker., S., In the Journal of Web
Semantics 14: pp. 14–44, 2012Stefan Dietze

What about data consistency?
Inconsistency and Incompleteness of Linked Datasets – a
Case Study, Yuan, W., Demidova, E., Dietze, S., Zhu, X., Web
Science 2014, WebSci14, under review.
27/03/14

Too many/diverse datasets, too little information
Stefan Dietze 27/03/14
?
?
? ?? ?
 Which datasets are useful & trustworthy for case
XY (eg „learning about the solar system“) ? Which
topics are covered?
 Types: which datasets describe statistics, videos,
slides, publications etc?
 Currentness, dynamics, accessability/reliability,
data quantity & quality?

Data curation and dataset profiling
Dataset
Catalog/Registry
 Catalog of data: classification of
datasets according to resource
types, disciplines/topics, data
quality, accessability, etc
 Infrastructure for
distributed/federated querying
describes
 Which datasets are useful & trustworthy for case
XY (eg „learning about the solar system“) ? Which
topics are covered?
 Types: which datasets describe statistics, videos,
slides, publications etc?
 Currentness, dynamics, accessability/reliability,
data quantity & quality?

db:Astro. Objects
Dataset profiling: what’s all the data about
Dataset
Metadata
BIBO
AAISO
FOAF
contains
Entity disambiguation &
linking [ESWC13]
Topic profile extraction
[WWW13, ESCW14]
db:Astronomy
db:Astro. Objects
Dataset
Catalog/Registry
yov:Video
po:Programme
BBC Programme
<po:Programme …>
<po:Series>Wonders of the Solar System</.>
<po:Actor>Brian Cox</…>
</po:Programme…>
<yo:Video …>
<dc:title>Pluto & the
Dwarf Planets</dc:title>
…
</yo:Video…>
Yovisto Video
bibo:Fil
bibo:Fi
bibo:Film
Schema mappings
[WebSci13]

Schemas/vocabularies on the Web: XKCD 927
https://xkcd.com/927/

Schema assessment and mapping
Co-occurence of
data types
(in 146 datasets:
144 Vocabularies,
588 highly
overlapping types,
719 Properties)
Assessing the Educational Linked Data Landscape,
D’Aquin, M., Adamou, A., Dietze, S., ACM Web Science
2013 (WebSci2013), Paris, France, May 2013.
<po:Programme …>
<po:title>Secret Universe –
The Life of the Cell</po:title>
…
</po:Programme…>
BBC Programme
<sioc:Item …>
<label>Viral diseases &
bacteria</title>
…
</sioc:Item ….>
SlideShare Set
po:Programme
sioc:Item
?
http://datahub.io/group/linked-education

Schema assessment and mapping
Co-occurence of
data types
(in 146 datasets:
144 Vocabularies,
588 highly
overlapping types,
719 Properties)
Co-occurence after
mapping into most
frequent schemas
(201 frequent types
mapped into 79
classes)
Assessing the Educational Linked Data Landscape,
D’Aquin, M., Adamou, A., Dietze, S., ACM Web Science
2013 (WebSci2013), Paris, France, May 2013.
bibo:Slideshow
bibo:Film
bibo:Document
<po:Programme …>
<po:title>Secret Universe –
The Life of the Cell</po:title>
…
</po:Programme…>
BBC Programme
<sioc:Item …>
<label>Viral diseases &
bacteria</title>
…
</sioc:Item ….>
SlideShare Set
po:Programme
sioc:Item

LinkedUp Data Catalog
in a nutshell http://datahub.io/group/linked-education
http://data.linkededucation.org/linkedup/catalog/
 RDF (VoID) dataset catalog: browse &
query distributed datasets
 Live information about endpoint
accessibility
 Federated queries using type mappings
http://datahub.io/group/linked-education

<yo:Video 8748720>
…
</yo:Video 8748720>
Video
<sioc:Item 2139393292>
<title>Planetary motion
& gravity</title>
…
</sioc:Item 2139393292>
Slideset
Topics/categories addressed?
Relatedness of resources/entities?
(types, semantics)
<po:Programme519215>
<po:Series>Wonders of the Solar
System</po:Series>
<po:Episode>Emp. of the Sun</po:Episode>
<po:Actor>Brian Cox</po:Actor>
</po:Programme519215 >
Programme
Combining a co-occurrence-based and a semantic measure
for entity linking, B. P. Nunes, S. Dietze, M.A. Casanova, R.
Kawase, B. Fetahu, and W. Nejdl., ESWC 2013 - 10th Extended
Semantic Web Conference, (May 2013).
A Scalable Approach for Efficiently Generating
Structured Dataset Topic Profiles, Fetahu, B., Dietze, S.,
Nunes, B. P., Casanova, M. A., Nejdl, W., 11th Extended
Semantic Web Conference (ESWC2014), Crete, Greece, (2014).
Challenge: semantics of resources/datasets?
15Stefan Dietze 27/03/14

<yo:Video 8748720>
…
</yo:Video 8748720>
Video
System</po:Series>
Programme
Data disambiguation (for linking & profiling)
Brian Cox?
Sun?
Pluto?
16Stefan Dietze 27/03/14

db:Pluto
(Dwarf
Planet)
db:Astrono-
mical Objects
db:Sun
Data disambiguation using background knowledge
„Semantic relatetedness“ of resources?
db:Astronomy
17
System</po:Series>
Programme
<sioc:Item 2139393292>
& gravity</title>
…
</sioc:Item 2139393292>
Slideset
<yo:Video 8748720>
…
</yo:Video 8748720>
Video

db:Pluto
(Dwarf
Planet)
db:Astrono-
mical Objects
<yov:Lecture8748720>
<title>Pluto & the Dwarf
Planets</title>
…
< yov:Lecture8748720>
Online Lecture
db:Astronomy
 Computation of connectivity scores
between resources/entities
 Method: combination of a
 (i) semantic (graph-based) connectivity
score (SCS) with
 (ii) a Web co-occurence-based measure
(CBM) (similar to NGD)
 For (i): adaptation of Katz-Index from SNA
for (linked) data graphs (considering path
number and path lengths of transversal
properties)
db:Sun
SCS = 0.32
CBM = 0.24
http://purl.org/vol/doc/
http://purl.org/vol/ns/
Combining a co-occurrence-based and a semantic
measure for entity linking, B. P. Nunes, S. Dietze, M.A.
Casanova, R. Kawase, B. Fetahu, and W. Nejdl., ESWC 2013
- 10th Extended Semantic Web Conference, (May 2013).
Entity linking: semantic relatedness
<sioc:Item 2139393292>
& gravity</title>
…
</sioc:Item 2139393292>
Slideset
System</po:Series>
Programme

Entity linking: evaluation
 Evaluation based on USA Today News items (80.000 entity pairs)
 Manually created gold standard
(1000 entity pairs)
 Baseline: Explicit Semantic Analysis (ESA)
=> CBM/SCS: „relatedness“; ESA: „similarity“
Precision/Recall/F1 for SCS, CBM, ESA.
Combining a co-occurrence-based and a semantic
measure for entity linking, B. P. Nunes, S. Dietze, M.A.
Casanova, R. Kawase, B. Fetahu, and W. Nejdl., ESWC 2013
- 10th Extended Semantic Web Conference, (May 2013).

db:Astrono-
mical Objects
db:Astronomy
db:Sun
 Extracting representative metadata („topic profile“) for each dataset
 Ranking of most representative (DBpedia) categories (= topics); applied to all responsive LOD datasets
 Scalability vs representativeness: sampling & ranking for good scalability/accuracy balance
DBpedia category graph
Dataset profiling: what‘s the data about?
Structured Dataset Topic Profiles, Fetahu, B.,
Dietze, S., Nunes, B. P., Casanova, M. A., Nejdl, W.,
11th Extended Semantic Web Conference
,(ESWC2014), Crete, Greece, (2014).
System</po:Series>
Programme

Dataset profiling: approach
1. Sampling of resource instances
(random sampling, weighted sampling, resource
centrality sampling)
2. Entity and topic extraction (NER via DBpedia
Spotlight, category mapping and expansion)
3. Normalisation and ranking (using graphical-
models such as PageRank with Priors, HITS with
Priors and K-Step Markov)
=> Result: weighted dataset-topic profile graph
Structured Dataset Topic Profiles, Fetahu, B.,
Dietze, S., Nunes, B. P., Casanova, M. A., Nejdl, W.,
11th Extended Semantic Web Conference
(ESWC2014), Crete, Greece, (2014).

Dataset profiling: exploring LOD datasets/topics
in a nutshell http://data-observatory.org/lod-profiles/
 Automatic extraction of dataset “topics” [ESWC2014]
 Visualisation & exploration of dataset-topic graph
(datasets, topics, relationships)
 Includes all (responsive) datasets of LOD Cloud

Dataset profiling: results evaluation
NDCG (averaged over all datasets) .
Datasets & Ground Truth
 Yovisto, Oxpoints, LAK Dataset, Semantic Web
Dogfood
 Crowd-sourced topic indicators from datasets
(keywords, tags)
 Manual mapping to entities & category extraction
(ranking according to frequency)
Baselines
 1) LDA, 2) tf/idf (applied to entire datasets)
 Topic extraction according to our approach,
weighting/ranking based on term weight
Measure
 NDCG @ rank l
 Performance (time/NDCG) for different sampling
strategies/sizes etc

dbp:Category:Royal_Medal_winners
dbp:Category:1955_births
dbp:Category:People_from_London
dbp:Category:Buzzwords
dbp:Category:Web_Services
dbp:Category:HTTP
dbp:Category:Unitarian_Universalists
dbp:Category:World_Wide_Web
What have these categories in common?

Diversity of category profile for a single paper
Berners-Lee, Tim; Hendler, James, Ora Lassila (2001). "The Semantic Web".
Scientific American Magazine.
person
document
dbp:Tim_Berners-Lee
dbp:Category:1955_births
dbp:Category:People_from_London
dbp:Category:Buzzwords
dbp:Semantic_Web
dbp:Category:Semantic_Web
dbp:Category:Web_Services
dbp:Category:HTTP
dbp:Category:Unitarian_Universalists
first-level categories (dcterms:subject)
dbp:Category:World_Wide_Web
dbp:Category:Royal_Medal_winners

 DBpedia category graph not an ideal “topic” vocabulary:
 Broad and noisy
 “Categories” vs “topics” (for capturing disciplines, thesauri
like UMBEL or UNESCO Thesaurus seem better suited)
 Hierarchy ?
 Filtering of certain partitions of category graph (too generic
categories etc)
 Mixing categories across resource types (document, person)
creates “perceived noise”
 But: broadness is useful as general vocabulary for
categorisation of all sorts of resource types
Dataset profiling: some lessons learned

http://data-observatory.org/led-explorer/
 Type specific views on datasets/
categories
 “Document” (foaf:document)
 “Person “ (foaf:person)
 “Course” (aaiso:course)
 Currently applied to datasets in
LinkedUp Catalog only (as
schema mappings already
available here)
Type-specific exploration of dataset categories

Dataset interlinking recommendation
Candidate datasets for interlinking?
34
t
Linkset1
Linkset2
Problem
 Given dataset t, ranking datasets from D
according to probability score (di, t) to
contain linking candidates (entities)
 Features:
 Vocabulary overlap
 Existing links (SNA)
 Datasets more likely to contain linking
candidates if they (a) share common
schema elements, or (b) already link to t
or datasets t links to (friend of a friend)
Conclusions
 Roughly 60% MAP for both approaches
 Future work: quantity of links, more
remote links, extraction of dataset links
rather than data from DataHub
Lopes, G.R., Paes Leme, L.A.P., Nunes, B.P., Casanova, M.A.,
Dietze, S., Recommending Tripleset Interlinking through a
Social Network Approach, The 14th International Conference
on Web Information System Engineering (WISE 2013),
Nanjing, China, 2013.
Paes Leme, L. A. P., Lopes, G. R., Nunes, B. P., Casanova,
M.A., Dietze, S., Identifying candidate datasets for data
interlinking, in Proceedings of the 13th International
Conference on Web Engineering, (2013).
Rank
1 DBLP
2 ACM
3 OAI
4 CiteSeer
5 IBM
6 Roma
7 IEEE
8 Ulm
9 Pisa
?
?

Stefan Dietze 27/03/14 37
Success models:
data & applications
 LinkedUp Challenge
to identify innovative
tools & applications
 Evaluation methods
and approaches
“LinkedUp” – Linking Web Data (for Education)
L
Data linking & curation
Technology transfer
& community-building
 Collecting & exposing open
data
=> LinkedUp Data Catalog
 Profiling and linking of Web
Data for education
=> educational data graph
[ESWC2013], [ISWC2013],
 Disseminating knowledge &
building communities
(educators, computer
scientists, data engineers)
 Gathering stakeholder
feedback: use cases, and
requirements
http://linkedup-challenge.org/#usecases
http://linkedup-project.eu/events
http://www.linkedup-challenge.org/
http://data.linkededucation.org
European suport action to
advance take-up of open
data & related technologies
http://www.linkedup-project.eu

17/09/2013 38
Who we areL
LinkedUp Network
LinkedUp Consortium
LinkedUp Advisory Board

LinkedUp Challenge: using open data (for learning)
 Open Data Competition to promote tools and applications that analyse / integrate (Linked)
Web data
 Organised by LinkedUp project over 2 years (“Veni”, “Vidi”, “Vici”) with 40.000 EUR awards
 Veni Competition - 22 submissions, 8 shortlisted for presentation at Open Knowledge
Conference (17 September, Geneva Switzerland)
http://linkedup-challenge.org

 Open & focused track(s)
 Final events at ESWC2014
(May, Crete)
 Open Track only
 Final events at OKCon 2013
(September 2013, Geneva)
 Open track & focused tracks
 Submission details and calls to be
released soon
 Final events at ISWC2014
(October, Riva del Garda, Italy)
May –September 2013 October 2013 – May 2014 May 2014 – October 2014
?

The Veni shortlist & winners
DataConf.
KnowNodes
Mismuseos
ReCredible
YourHistory
27/03/14
http://www.globe-town.org/
WeShare - 3rd price / people‘s choice
GlobeTown - 2nd price
http://seek.cloud.gsic.tel.uva.es/weshare/
http://www.polimedia.nl/
PoliMedia – 1st price

data.l3s.de – a DataHub for the L3S

Learning Analytics & Knowledge Dataset & Challenge
Facilitating Research on Learning Analytics and EDM
a nutshell
http://lak.linkededucation.org/
http://lak.linkededucation.org/
LAK Dataset (450 publications in RDF/R)
 ACM International Conference on Learning Analytics and
Knowledge (LAK) (2011-13)
 International Conference on Educational Data Mining (2008-13)
 Journal of Educational Data Mining (2008-12)
LAK Data Challenge
 Analyse, explore correlate the LAK Dataset
 At ACM LAK 2014 (April 2014, Indianapolis)

KEYSTONE COST ACTION
http://www.keystone-cost.eu/
 Research network focused on distributed search,
dataset profiling, to Semantic Web, Databases, etc.
 Running 2013-2017
 WG1: Representation of structured data sources
 WG2: Keyword search
 WG3: User interaction and query interpretation
 WG4: Research integration, showcases,
benchmarks, and evaluations
 Open to new members (even beyond Europe)
 Joint workshops (eg PROFILES2014 @ ESWC2014)

Ongoing/future work … and some upcoming events
Linked Data evolution, preservation, consistency
 In RDF graphs (eg LOD Cloud), „all“ nodes are connected
 LD preservation: which datasets to preserve (direct links
or even more distant neighbours)?
=> semantic relatedness as guidance for scalable
preservation strategies /data enrichment
 Link correctness in evolving LD
 Investigating impact of changes on link correctness
(weekly LOD crawls over 1 year time span)
 Application: informed preservation strategies
 Conflict detection and LD quality (link quality, impact of
conflicts in distant nodes)
 PROFILES workshop @ ESWC2014
(http://keystone-cost.eu/profiles2014)
 26 May 2014, Crete, Greece
 Linking User Data 2014 at UMAP2014
(http://liud.linkededucation.org)
 Deadline: 1 April
 Online Learning & LD Tutorial at WWW2014
(http://www2014.kr/)
 07 April, Seoul

Thank you!
WWW
See also (general)
 http://linkedup-project.eu
 http://linkededucation.org
 http://data.l3s.de
http://purl.org/dietze
See also (data)
 http://data.linkededucation.org
 http://data.linkededucation.org/linkedup/catalog/
 http://lak.linkededucation.org
 Besnik Fetahu (L3S)
 Bernardo Pereira Nunes (PUC Rio)
 Marco Casanova (PUC Rio)
 Luiz Andre Paes Leme (PUC Rio)
 Giseli Lopes (PUC Rio)
 Davide Taibi (CNR, IT)
 Mathieu d’Aquin (Open University, UK)
 and many more…
Acknowledgements

What's all the data about? - Linking and Profiling of Linked Datasets

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