An overview of recent works on entitiy linking and retrieval in large corpora, specifically bibliographic data. The works address both traditional Linked Data and knowledge graphs as well as data extracted from Web markup, such as the Web Data Commons.
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Semantic Linking & Retrieval for Digital Libraries
1. Backup
Semantic Linking & Retrieval for Digital Libraries
Dr. Stefan Dietze
11.02.2016
Institut für Informatik/Universität Bonn
29/03/16 1Stefan Dietze
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 ...
29/03/16 2Stefan Dietze
3. 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 ...
29/03/16 3Stefan Dietze
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]
29/03/16 4Stefan Dietze
5. 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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7. 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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9. 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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10. 29/03/16 10Stefan Dietze
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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12. 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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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
16. 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).
29/03/16 16Stefan Dietze
17. 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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18. 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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20. 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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21. 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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23. 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 ...
29/03/16 23Stefan Dietze
24. 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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25. 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
26. 29/03/16 26Stefan Dietze
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
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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 ...
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
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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
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