1.
Backup
Semantic Linking & Retrieval for Digital Libraries
Dr. Stefan Dietze
11.02.2016
Institut für Informatik/Universität Bonn
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2.
Stefan Dietze
Overview: research/application context
Information (types)
 Bibliographic (meta)data
 Research information
 Educational (meta)data
 Web & social data
Stakeholders
 Archival organisations
 Digital libraries
 Publishers
 Resource providers/
consumers
Domains
 Life Sciences
 Computer Science
 Learning Analytics
 ...
Data-centric tasks
 Publishing, preservation, annotation, crawling, search, retrieval ...
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Overview: contents
Introduction & motivation
Publishing, linking and profiling
 Publishing & linking (bibliographic) data
 Dataset profiling & linking
Retrieval & search
 Entity retrieval in large graphs
 Embedded (bibliographic) Web data
 Entity summarisation from Web markup
Outlook and future directions
Stefan Dietze
Information (types)
 Bibliographic (meta)data
 Research information
 Educational (meta)data
 Web & social data
Stakeholders
 Archival organisations
 Digital libraries
 Publishers
....
Domains
 Life Sciences
 Computer Science
 Learning Analytics
 ...
Data-centric tasks
 Publishing, preservation, annotation, crawling, search, retrieval ...
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4.
Introduction & motivation
Publishing, linking and profiling
 Publishing & linking (bibliographic) data
 Dataset profiling & linking
Retrieval & search
 Entity retrieval in large graphs
 Embedded (bibliographic) Web data
 Entity summarisation from Web markup
Outlook and future directions
Overview: contents
knowledge graphs and linked data
beyond LD: embedded semantics
[ESWC13, ESCW14]
[ISWC15]
[WebSci13, SWJ15]
Stefan Dietze
Information (types)
 Bibliographic (meta)data
 Research information
 Educational (meta)data
 Web & social data
Stakeholders
 Archival organisations
 Digital libraries
 Publishers
....
Domains
 Life Sciences
 Computer Science
 Learning Analytics
 ...
Data-centric tasks
 Publishing, preservation, annotation, crawling, search, retrieval ...
[ongoing]
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Linked Data diversity: example library & scholarly data
 Linked Data: W3C standards & de-facto standard for sharing data on the Web (roughly 1000 datasets, 100 bn
triples), adopted specifically by library/GLAM sector & life sciences
 Strong focus on established knowledge graphs, e.g. Yago, DBpedia, Freebase (still)
Vocabularies/Schemas
 BIBO, Bibliographic Ontology
 BIRO, Bibliographic Reference Ontology
 CITO, Citation Typing Ontology
 SPAR vocabularies (incl. CITO, BIRO)
 SWRC (Semantic Web Dogfood)
 Functional Req. for Bibliographic Records (FRBR)
 Nature Publishing Group Ontology
 mEducator Educational Resources
 ....
Datasets
 EUROPEANA
 British Library
 Deutsche-, Französische-, Spanische
Nationalbibliotheken
 Nature Publishing Group
 Hochschulbibliothekszentrum NRW
 Elsevier Scholarly Publications
 TED Talks
 mEducator Linked Educational Resources
 Open Courseware Consortium
 LAK Dataset
 ...
Initiatives
 W3C Library Linked Data Incubator Group
 Linked Library Data group on DataHub
 LinkedUniversities.org
 LinkedEducation.org
 W3C Linked Open Education Community Group
 ...
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?
?
? ?? ?
Challenge: efficient search for suitable resources & datasets
 „Quality“: currency, dynamics, accessibility [Buil-Aranda2013],
correctness [Paulheim2013], schema compliance [Hogan2012]
 Domains/topics: which datasets/resources address topic XY (e.g.
„microbiology“) ?
 Types: statistical data, bibliographic resources, AV resources,
scholarly publications?
 Links: related datasets?
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Data publishing, linking and profiling: LinkedUp
Dataset
Catalog/Registry
http://data.linkededucation.org/linkedup/catalog/
 LinkedUp project (FP7 project: L3S, OU, OKFN, Elsevier, Exact Learning solutions)
 LinkedUp Catalog: largest collection of LD/Open Data for educationally relevant resources (approx. 50 Datasets)
 Original datasets published with key content providers, automatically extracted metadata
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8.
Dietze, S., Kaldoudi, E., Dovrolis, E., Giordano, D.,
Spampinato, C., Hendrix, M., Protopsaltis, A., Taibi, D., Yu,
H. Q. (2013), Socio-semantic Integration of Educational
Resources – the Case of the mEducator Project, in
Journal of Universal Computer Science (J.UCS), Vol. 19,
No. 11, pp. 1543-1569.
Dietze, S., Taibi, D., Yu, H. Q., Dovrolis, N., A Linked
Dataset of Medical Educational Resources, British
Journal of Educational Technology (BJET), Volume 46,
Issue 5, pages 1123–1129, September 2015.
mEducator: medical educational resources
 EC-funded eContentPlus project (2009-2012)
 Exploratory search through semantic and clustering techniques
 Lifting/enriching/clustering medical metadata
 Common vocabularies (MESH, SNOMED, Bioportal etc)
 mEducator dataset: first Linked Data corpus of enriched OER
metadata, used by number of applications
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LAK Dataset: facilitating scientometrics
Concept ofType #
Reference npg:Citation 7885
Author foaf:Person 1214
Conference Paper swrc:InProceedings 652
Organization foaf:Organization 365
Journal Paper bibo:Article 45
Proceedings Volume swrc:Proceedings 15
Journal Volume bibo:Journal 9
 Cooperation of
 Linked Data corpus of „Learning Analytics“publications
of last 5 years (~ 800 publications)
 Metadata, full-text & automated linking
(DBLP, SWDF, DBpedia)
 Wide adoption (http://lak.linkededucation.org)
1. Data extraction & vocabulary definition
2.3. Applications & analysis Entity co-reference resolution & linking
Facilitating Scientometrics in Learning Analytics and
Educational Data Mining - the LAK Dataset, Dietze, S.,
Taibi, D., D’Aquin, M.,Semantic Web Journal, 2015.
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LinkedUp Catalog: dataset index & registry, federated searchn a
nutshell “Federated queries” through schema mappings
 Dataset accessability
 Linking & topic profiling
Schema/Types

11.
Co-occurence of
types
(in 146 datasets:
144 vocabularies,
588 overlapping
types, 719
predicates)
Assessing the Educational Linked Data Landscape,
D’Aquin, M., Adamou, A., Dietze, S., ACM Web Science
2013 (WebSci2013), Paris, France, May 2013.
po:Programme
yov:Video
?
bibo:Book
Schema analysis & mapping
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typeX
typeX
Co-occurence after
mapping
(201 frequently
occuring types,
mapped into 79 types)
bibo:Film
bibo:Document
po:Programme
bibo:Book
foaf:Document
yov:Video
typeX
Assessing the Educational Linked Data Landscape,
D’Aquin, M., Adamou, A., Dietze, S., ACM Web Science
2013 (WebSci2013), Paris, France, May 2013.
Schema analysis & mapping
Co-occurence of
types
(in 146 datasets:
144 vocabularies,
588 overlapping
types, 719
predicates)
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13.
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http://data.linkededucation.org/linkedup/catalog/
LinkedUp Catalog: dataset index & registry, federated searchn a
nutshell “Federated queries” through schema mappings
 Dataset accessability
 Linking & topic profiling
Dataset topic
profiles

14.
contains
yov:Video
<yo:Video …>
<dc:title> Lecture 29 –
Stem Cells </dc:title>
…
</yo:Video…>
Yovisto Video
db:Medicine
db:Rudolf
Virchow
db:Cell
Biology
 Linking entities/datasets through combination of (i)
„semantic (graph-based) connectivity score (SCS)“ (based
on Katz centrality) and „co-occurence-based measure
(CBM)“ (similar to Normalised Google Distance)
 Evaluation: outperforming Explicit Semantic Analysis (ESA)
SCS = 0.32
CBM = 0.24
Data(set) interlinking
bibo:Book
British Library Book
<bibo:Book …>
<bibo:title>Über den Hungertyphus</.>
<bibo:creator>Rudolf Virchov</…>
</bibo:Book…>
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).
?
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db:Cell
(Biology)
db:Cell(Micro-
processor)
Stefan Dietze

15.
db:Biology
db:Cell biology
Dataset
Catalog/Registry
yov:Video
<yo:Video …>
<dc:title>Lecture 29 –
Stem Cells</dc:title>
…
</yo:Video…>
Yovisto Video
 Extraction of representative (DBpedia) categories („topic profile“) for arbitrary datasets
 Technically trivial, but scalability issues: LOD Cloud 1000+ datasets with <100 billion RDF statements
 Efficient approach: sampling & ranking for balance between scalability and precision /recall
Scalable profiling of datasets
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).
db:Cell
(Biology)
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db:Cell
(Biology)
Stefan Dietze

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Efficient dataset profiling
1. Sampling of resources
(random sampling, weighted sampling, resource
centrality sampling)
2. Entity- & topic-extraction (NER via DBpedia Spotlight,
category mapping & -expansion)
3. Normalisation & ranking (graph-based models such as
PageRank with Priors, HITS with Priors & K-Step Markov)
 Result: weighted dataset-topic profile graph
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).
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Search & exploration of datasets through topic profiles
in a nutshell Applied to entire LOD cloud/graph
 Visual exploration of extracted RDF dataset profiles
(datasets, topics, relationships)
 Evaluation results: K-Step Markov (10% sampling size)
outperforms baselines (LDA, tf/idf on entire datasets)
http://data-observatory.org/lod-profiles/
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Search: entity retrieval on large structured datasets?
in a nutshell
Challenges
 How to efficiently retrieve related entities/resources for given query ?
 Explicit entity links (owl:sameAs etc) are sparse yet important to facilitate state of the art methods
(eg BM25F, Blanco et al, ISWC2011)
 Query type affinity?
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??
Large dataset/crawl
e.g. LinkedUp dataset graph, LIVIVO dataset, BTC2014
entities related to <James D. Watson>
?
BTC2014

19.
Entity retrieval: approach
in a nutshell
(I) Offline processing (clustering to address link sparsity)
1. Feature vectors (lexical and structural features)
2. Bucketing: per type (LSH algorithm)
3. Clustering: X-means & Spectral clustering per bucket
Improving Entity Retrieval on Structured Data,
Fetahu, B., Gadiraju, U., Dietze, S., 14th International
Semantic Web Conference (ISWC2014), Bethlehem,
US, (2015).
(II) Online processing (retrieval)
1. Retrieval & expansion:
a) BM25F results
b) expansion from clusters (related entities)
2. Re-Ranking
(context terms & query type affinity)
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Dataset
 BTC2014 (1.4 billion triples)
 92 SemSearch queries
Methods
 Our approaches: XM: Xmeans, SP: Spectral
 Baselines B: BM25F, S1: Tonon et al [SIGIR12]
Conclusions
 XM & SP outperform baselines
 Clustering to remedy link sparsity
 Relevance to query crucial
Improving Entity Retrieval on Structured Data,
Fetahu, B., Gadiraju, U., Dietze, S., 14th International
Semantic Web Conference (ISWC2014), Bethlehem,
US, (2015).
Entity retrieval: evaluation
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Introduction & motivation
Publishing, linking and profiling
 Publishing & linking (bibliographic) data
 Dataset profiling & linking
Retrieval & search
 Entity retrieval in large graphs
 Embedded (bibliographic) Web data
 Entity summarisation from Web markup
Outlook and future directions
Overview: contents so far
29/03/16 21Stefan Dietze
[ESWC13, ESCW14]
[ISWC15]
[WebSci13, SWJ15]
Outcomes & impact ?

22.
Tangible outcomes / impact
Open Datasets
Applications
Vocabularies & Schemas
Initiatives & Working Groups
VOL
+ vocabularies for educational resource & service modeling
 W3C Community Group
„Open Linked Education“
 DCMI Task Force on LRMI
 W3C Schema Bib Extend Group
 Tutorial & workshop series on
Linked Data & Learning
 LinkedUniversities, LinkedEducation.org
 KEYSTONE WG „Search and Profiling of LD“
 ….
http://linkeduniversties.org
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Introduction & motivation
Publishing, linking and profiling
 Publishing & linking (bibliographic) data
 Dataset profiling & linking
Retrieval & search
 Entity retrieval in large graphs
 Embedded (bibliographic) Web data
 Entity summarisation from Web markup
Outlook and future directions
Overview: contents
beyond LD: embedded semantics
Stefan Dietze
Information (types)
 Bibliographic (meta)data
 Research information
 Educational (meta)data
 Web & social data
Stakeholders
 Archival organisations
 Digital libraries
 Publishers
....
Domains
 Life Sciences
 Computer Science
 Learning Analytics
 ...
Data-centric tasks
 Publishing, preservation, annotation, crawling, search, retrieval ...
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 The Web: approx. 46.000.000.000.000 (46 trillion) Web pages indexed
by Google
vs
 Linked Data: approx. 1000 datasets & 100 billion statements
- different order of magnitude wrt scale & dynamics
 Other „semantics“ (structured facts) on the Web?
The Web as a knowledge base: semantics on the Web?
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 Embedded markup (RDFa, Microdata, Microformats) for
interpretation of Web documents (search, retrieval)
 Arbitrary vocabularies; schema.org used at scale:
(700 classes, 1000 predicates)
 Adoption on the Web: 26 %
(2014 Google study of 12 bn Web pages)
 “Web Data Commons” (Meusel & Paulheim [ISWC2014])
• Markup from Common Crawl (2.2 billion pages):
17 billion RDF quads
• Markup in 26% of pages, 14% of PLDs in 2013
(increase from 6% in 2011)
 Same order of magnitude as “the Web”
Embedded semantics: Web page markup & schema.org
<div itemscope itemtype ="http://schema.org/Movie">
<h1 itemprop="name">Forrest Gump</h1>
<span>Actor: <span itemprop=„actor">Tom Hanks</span>
<span itemprop="genre">Drama</span>
...
</div>
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RDF statements
node1 actor _node-x
node1 actor Robin Wright
node1 genre Comedy
node2 actor T. Hanks
node2 distributed by Paramount Pic.
node3 actor Tom Cruise
node3 distributed by Paramount Pic.
Stefan Dietze

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Characteristics Example
Coreferences
18.000 results for <„Iphone 6“, type, s:Product>
(8,6 quads on average)
Redundancy
<s, schema:name, „Iphone 6“> occuring 1000
times in WDC2013
Lack of links Largely unlinked entity descriptions / subgraphs
Errors
(typos & schema
violations, see
Meusel et al
[ESWC2015])
Wrong namespaces, such as http://schma.org
Undefined types & predicates:
9,7 % in WDC, 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% in WDC vs 4,6 in LOD)
Using markup as global knowledge base - state of the art
 Glimmer (http://glimmer.research.yahoo.com):
entity retrieval (BM25F) on WDC dataset
[Blanco, Mika & Vigna, ISWC2011]
 Challenges: specific characteristics of markup data

27.
 Goal: obtaining entity summary (or entity-centric knowledge graph) for given query ?
 Tasks: document annotation, knowledge base augmentation, semantic enrichments
Using markup as global knowledge base/graph?
Web page
markup
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Query
Nucleic Acids, type:(Article)
Entity Summary/Graph
Name
Molecular structure of nucleic
acids
author
James D. Watson
Francis Crick
publisher Nature
datePublished 1953
Web crawls, WDC or large (domain-specific) crawls:
e.g. publishers, universities, libraries etc

28.
Candidate Facts
node1 name
Molecular structure
of nucleic acids
node1 author James D. Watson
node1 publisher Nature
node1 datePublished 1956
node1 datePublished 1953
node2 name Francis Crick
node2 name Cricks
 Extract (domain-specific) knowledge bases and knowledge graphs for digital libraries
 Experiments on WDC data: 87,6 % MAP, coverage: on average 57% additional facts compared to DBpedia
Ongoing work: entity summarisation from markup data
Query
Nucleic Acids, type:(Article) 1. Retrieval
2. Fact selection
Entity Summary/Graph
Name
Molecular structure of nucleic
acids
author
James D. Watson
Francis Crick
publisher Nature
datePublished 1953
29/03/16 28
New Queries
James D. Watson, type:(Person)
Francis Crick, type:(Person)
Nature, type:(Organization)
Stefan Dietze
Web crawls, WDC or large (domain-specific) crawls:
e.g. publishers, universities, libraries etc
Web page
markup
(clustering, heuristics, trained classifier)

29.
1
10
100
1000
10000
100000
1000000
10000000
1 51 101 151 201
count(log)
PLD (ranked)
# entities # statements
Unprecedented source of bibliographic data
 Metadata about scholarly articles
(s:ScholarlyArticle): 6.793.764 quads, 1.184.623
entities, 429 distinct predicates (in WDC / 1 type
alone)
 Top 5 domains: Springer, MDPI, BMJ,
diabetesjournals.org, mendeley.com,
Biodiversitylibrary.org
Domains, topics, disciplines?
 Life Sciences and Computer Science predominant
 Top-10 article titles
 Most important publishers/journals, libraries
represented
=> Domain-specific & targeted crawls
= unprecedented source of data
Embedded data for digital libraries / life sciences?
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30.
Knowledge graphs and LD
(Yago, Freebase, Pubmed, DBLP etc)
Entity
node1 name
Molecular structure of
nucleic acids
node1 author James D. Watson
node1 publisher Nature
node1 datePublished 1956
node1 datePublished 1953
Future work: improving entity-centric tasks for digital libraries
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Entity
node2 name Francis Crick
node2 name Cricks
node2 born 1916
Stefan Dietze
• Web data as knowledge resource
• Background knowledge/structured data
• Training data & ground truths
• ....
Embedded
data
Unstructured (Web)
documents
Linked Data
Improving data-centric tasks for large
(bibliographic/life sciences) corpora, eg LIVIVO
• KB construction & augmentation
• Document annotation
• Entity recognition, disambiguation, interlinking
• Search & retrieval ...

31.
Acknowledgements: team
 Besnik Fetahu (L3S)
 Ivana Marenzi (L3S)
 Ujwal Gadiraju (L3S)
 Eelco Herder (L3S)
 Ran Yu (L3S)
 Ricardo Kawase (L3S)
 Pracheta Sahoo (L3S, IIT India)
 Bernardo Pereira Nunes (L3S, PUC Rio)
+ external collaborators
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32.
References (presented work)
Dietze, S., Taibi, D., D’Aquin, M., Facilitating Scientometrics in Learning Analytics and Educational Data Mining - the LAK Dataset,
Semantic Web Journal, 2016.
Dietze, S., Kaldoudi, E., Dovrolis, E., Giordano, D., Spampinato, C., Hendrix, M., Protopsaltis, A., Taibi, D., Yu, H. Q. (2013), Socio-
semantic Integration of Educational Resources – the Case of the mEducator Project, in Journal of Universal Computer Science (J.UCS),
Vol. 19, No. 11, pp. 1543-1569.
Dietze, S., Taibi, D., Yu, H. Q., Dovrolis, N., A Linked Dataset of Medical Educational Resources, British Journal of Educational
Technology (BJET), Volume 46, Issue 5, pages 1123–1129, September 2015.
Gadiraju, U., Demartini, G., Kawase, R., Dietze, S. Human beyond the Machine: Challenges and Opportunities of Microtask
Crowdsourcing. In: IEEE Intelligent Systems, Volume 30 Issue 4 – Jul/Aug 2015.
Gadiraju, U., Kawase, R., Dietze, S, Demartini, G., Understanding Malicious Behavior in Crowdsourcing Platforms: The Case of Online
Surveys. ACM CHI Conference on Human Factors in Computing Systems (CHI2015), April 18-23, Seoul, Korea.
Fetahu, B., Gadiraju, U., Dietze, S., Improving Entity Retrieval on Structured Data, 14th International Semantic Web Conference
(ISWC2014), Bethlehem, US, (2015).
Fetahu, B., Dietze, S., Nunes, B. P., Casanova, M. A., Nejdl, W., A Scalable Approach for Efficiently Generating Structured Dataset Topic
Profiles, 11th Extended Semantic Web Conference (ESWC2014), Crete, Greece, (2014).
D’Aquin, M., Adamou, A., Dietze, S., Assessing the Educational Linked Data Landscape, ACM Web Science 2013 (WebSci2013), Paris,
France, May 2013.
Nunes, B. P., Dietze, S., Casanova, M.A., Kawase, R., Fetahu, B., Nejdl, W., Combining a co-occurrence-based and a semantic measure
for entity linking, in: The Semantic Web: Semantics and Big Data, Proceedings of the 10th Extended Semantic Web Conference
(ESWC2013), Lecture Notes in Computer Science Vol. 7882, Springer Berlin Heidelberg, 2013.
http://www.stefandietze.net
29/03/16 32Stefan Dietze

33.
Selected related work
Entity retrieval
 Alberto Tonon, Gianluca Demartini, and Philippe Cudré-Mauroux. Combining Inverted Indices and Structured
Search for Ad-hoc Object Retrieval. In: 35th Annual ACM SIGIR Conference (SIGIR 2012), Portland, Oregon,
USA, August 2012.
 Roi Blanco, Peter Mika, Sebastiano Vigna: Effective and Efficient Entity Search in RDF Data. International
Semantic Web Conference (ISWC) 2011, pages 83-97.
Embedded markups & Web Data Commons
 Robert Meusel, Petar Petrovski, Christian Bizer: The WebDataCommons Microdata, RDFa and Microformat
Dataset Series. Proceedings of the 13th International Semantic Web Conference (ISWC 2014), RBDS Track,
Trentino, Italy, October 2014.
 Robert Meusel and Heiko Paulheim: Heuristics for Fixing Common Errors in Deployed schema.org Microdata.
Proceedings of the 12th Extended Semantic Web Conference (ESWC 2015), Portoroz, Slovenia, May 2015
Linked Data quality
 Carlos Buil-Aranda, Aidan Hogan, Jürgen Umbrich Pierre-Yves Vandenbussch, SPARQL Web-Querying
Infrastructure: Ready for Action?, International Semantic Web Conference 2013, (ISWC2013).
 Paulheim H., Bizer, C., Type Inference on Noisy RDF Data, Semantic Web – ISWC 2013, Lecture Notes in
Computer Science Volume 8218, 2013, pp 510-525
 Hogan, A., Umbrich, J., Harth, A., Cyganiak, R., Polleres, A., Decker., S., An empirical survey of Linked Data
conformance. Journal of Web Semantics 14, 2012
29/03/16 33Stefan Dietze

34.
Thank you
29/03/16 34Stefan Dietze
• http://stefandietze.net
• http://data.l3s.de
• http://data.linkededucation.org/linkedup/catalog