A Semantically Enriched Recommendation & Visualization Approach for Academic Literature

JCDL’18 Doctoral Consortium
A Semantically Enriched
Recommendation & Visualization
Approach for Academic Literature
Corinna Breitinger*
Information Science Group &
Human Computer Interaction Group
University of Konstanz
*Sponsored by SIGIR Student Travel Grant

Short Bio
2
National Institute of Informatics
Tokyo
(2014)
Bachelor of Science
UC Berkeley, California
(2011)
(2011-2014)
6/7/18
Master of Science
Linnaeus University, Sweden
(2016)
PhD Research: Information Science /
Human Computer Interaction
University of Konstanz
@BreitingerC – Semantically Enriched Recommendations for Academic Literature
PhD Stipend

Outline
• Introduction & Problem Setting
• Research Objective & Research Tasks
• Background
• Prior Work
• Planned Research
6/7/18 3@BreitingerC – Semantically Enriched Recommendations for Academic Literature

Recommender Systems
46/7/18 @BreitingerC – Semantically Enriched Recommendations for Academic Literature

Academic Recommender Systems
Annual publication volume on
“Research Paper Recommender Systems’’
Prior Work: J. Beel, B. Gipp, S. Langer, and C. Breitinger, “Research-paper recommender
systems: a literature survey’’ International Journal on Digital Libraries, pp. 1-34, 2015.

76/7/18

Problem
1. Existing content-based filtering recommendation approaches
focus on:
• textual similarity
• bibliometrics (citation counts, venue’s citation counts, etc.)
à Do not take into account the variety of semantic markers that
are especially prevalent in STEM literature:
• Academic citations
• Mathematical identifiers and equations
• Figures, graphs, and images
2. List-based visualizations of recommendation sets fail to communicate
the presence of such semantic features

Academic Literature Contains Valuable
Text-independent Semantic Information
96/7/18
(a)1
(b)
Mathematical expressions:
Citation-based similarity:
1 Source: https://citeplag.org/compare/110389/136117
(a) (b)
Figures / Graphs / Tables:

Text-based measures
• Identifying textual similarity (string-
based similarity measures)
• Considering semantic similarity, e.g.
synonyms (knowledge-based similarity)
Citation-based measures
• Citation Proximity Analysis (CPA)
[Gipp & Beel, 2009]
• Citation-based Plagiarism Detection
(CbPD) [Gipp, Meuschke, Breitinger,
2014]
Mathematical Language
• Measures from Mathematical
Information Retrieval (MIR) –
extracting semantic meaning from
mathematical content
[Schubotz et al., 2016]
Figures/ Images/ Graphs
• Measures to assess the similarity of
images & diagram understanding
• Feature-point methods
• SIFT, SURF, BRISK
• Perceptual hashing
• pHash, minHash
Semantic Similarity Measures to be
Considered

Research Objective
Conceive, implement, and evaluate a
semantically-enriched recommendation
approach that considers text-independent
semantic markers (e.g. academic citations,
mathematical formulae, and figures) to
improve the recommendation of academic
literature in the STEM fields.

Research Tasks
1. Review today’s literature recommendation approaches:
• What is being done to improve recommendation
for less text-heavy literature from the STEM fields?
• What are the special requirements for literature
recommendation in the STEM field?
2. Conceive & design a recommendation approach that
takes into account semantic markers prevalent in STEM
literature (formulas, figures, and citations).
3. Implement the novel approach in a literature
recommender system.
6/7/18 @BreitingerC – Semantically Enriched Recommendations for Academic Literature 12

Research Tasks
4. Derive appropriate weights for the different similarity
measures depending on the user (his/ her information
need) and the research discipline
• e.g. math-heavy publications should place a higher weight
on formulas and a lower weight on text-based similarity
5. Conceive visualization concepts to support the user in
sense-making of the recommended literature sets.
6. Evaluate the recommender system
• User studies

Background: Recommender Systems
6/7/18 14@BreitingerC – Semantically Enriched Recommendations for Academic Literature
• Recommendation approaches:
• User-based approaches, e.g. collaborative filtering (CF)
• Content-based filtering (CBF)
• Combination of approaches
• Academic Literature Recommendation:
• In a review of 62 approaches for research paper recommendation,
we found the majority of reviewed systems (55%) used content-based
approaches for recommending related academic literature [Beel et al.,
2016]
Prior Work
Beel, J., Gipp, B., Langer, S., and Breitinger, C. Research-paper Recommender Systems: A Literature Survey. International
Journal on Digital Libraries 17, 4 (2016), 305–338.

6/7/18 15
Prior Work
B. Gipp, N. Meuschke, and C. Breitinger, “Citation-based Plagiarism Detection: Practicability on a Large-scale Scientific Corpus” Journal
of the American Society for Information Science and Technology (JASIST), vol. 65, iss. 2, pp. 1527-1540, 2014.
N. Meuschke, C. Gondek, D. Seebacher, C. Breitinger, D. Keim, and B. Gipp, “An Adaptive Image-based Plagiarism Detection Approach”
in Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), Fort Worth, USA, 2018.
à Applied to: academic plagiarism detection (PD) use case
à Potential for: Recommending semantically relevant academic literature
Background: Semantic Similarity
Measures

Prior Work: Citation-based Approaches
Doc C
Doc E
Doc D
Section 1
This is an exampl etext withreferences to different documents for illustratingtheusageof
citation analysis for plagiari sm detection. This is an exampl etext withreferences to
different documents for illustrati ng the usage of citationanalysis forplagiarism detection .
This is ain-text citation [1].This is an exampl etext withreferences to different documents
for illustrating the usage of citation analysis for plagiari sm detection . This is an exampl e
text withreferenc es to differentdocuments fori llustratingthe usage ofci tation analysis
for plagiarism detection.
Section 2
Another in-text citation [2].tThis is anexample text with references todifferent
documents for illustrati ng the usage of citationanalysis forplagiarism detection. This is an
ex ampletext with references to different documents for illustrati ng the usageof citation
anal ysis for plagiarism detection. This is arepeated in-text citation [1].
This is an exampl etext withreferences to different documents for illustratingtheusageof
citation analysis for plagiari sm detection. This is an exampl etext withreferences to
different documents for illustrati ng the usage of citationanalysis forplagiarism detection .
Setion 3
A third in-text citation [3].This is an exampl etext withreferences to different documents
for illustrating the usage of citation analysis for plagiari sm detection . This is an exampl e
text withreferenc es to differentdocuments fori llustratingthe usage ofci tation analysis
for plagiarism detection. a final i n-text-citation[2].
References
[1]
[2]
[3]
Document B
This is an exampl etext withreferences to different documents for illustratingtheusage
ofci tation analysis for plagi arism detection. This is ain-text citation [1].This is an
ex ampletext with references to different documents for illustrati ng the usageof citation
anal ysis for plagiarism detection. Another exampl efor ani n-text citation [2].
ofci tation analysis for plagi arism detection.
ofci tation analysis for plagi arism detection. This is an exampl etext withreferences to
different documents for illustrati ng the usage of citationanalysis forplagiarism
detection. This is an exampl etext withreferences to different documents for illustrating
the usage ofcitation analysi s for pl agiarism detection.
ofci tation analysis for plagi arism detection. This is an exampl etext withreferences to
detection. Here’s a third in-text citation [3].This is an exampl etext withreferences to
detection.
ofci tation analysis for plagi arism detection.
Document A
References
[1]
[2]
[3]
EDC DECDC
Citation Pattern Citation Pattern
Doc A Doc B
Ins.EIns.DC
DECDC
Pattern Comparison
Doc A
Doc B
Prior Work
Gipp, B., Meuschke, N., and Breitinger, C. Citation-based Plagiarism Detection: Practicability on a Large-scale Scientific Corpus.
Journal of the American Society for Information Science and Technology (JASIST) 65, 8 (2014), 1527–1540.
à Applied to the PD use case
• Citation Proximity
Analysis (CPA)
• CbPD Approaches
• Longest Common
Citation Sequence
(LCCS)
• Greedy Citation
Tiling (GCT)
• Citation Chunking
(Cit-Chunk)

Prior Work
B. Gipp, N. Meuschke, C. Breitinger, M. Lipinski, and A. Nürnberger. Demonstration of the First Citation-based Plagiarism Detection
Prototype. Proc. Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR), pages 1119–1120, 2013.
B. Gipp, N. Meuschke, and C. Breitinger, “Citation-based Plagiarism Detection: Practicability on a Large-scale Scientific
Corpus” Journal of the American Society for Information Science and Technology (JASIST), vol. 65, iss. 2, pp. 1527-1540, 2014.
• A visualization concept for citation-based similarity: CitePlag

Prior Work
M. Schwarzer, C. Breitinger, M. Schubotz, N. Meuschke, and B. Gipp, “Citolytics - A Link-based Recommender System for Wikipedia”
in Proceedings of the 11th ACM Conference on Recommender Systems (RecSys), 2017.
Citolytics: A link-based
Recommender System
• CPA approach for the first time
applied to the recommendation
use case

Ongoing: Citation-based Approaches
• Currently: User study of
link-based recommendation
performance
• Findings:
àlink-based vs. text-based
recommendations address
different information needs
àSerendipitous vs. expected
àExpert vs. novice
Citolytics: A link-based
Recommender System

Prior Work: Image-based Similarity
Prior Work
N. Meuschke, C. Gondek, D. Seebacher, C. Breitinger, D. Keim, and B. Gipp, “An Adaptive Image-based Plagiarism Detection
Approach” in Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), Fort Worth, USA, 2018.
• Images/ charts / diagrams are highly valuable text-
independent features [Kembhavi et al., 2016]
• Variations in the representation of images must
still be perceived as semantically similar
• PD approach by Meuschke et al. integrates
perceptual hashing with newly developed
similarity assessments for images, such as
ratio hashing and position-aware OCR text
matching
• Photographs & diagrams à perceptual hashing
(+OCR matching)
• Bar charts à ratio hashing

Prior Work: Image-based Similarity
@BreitingerC – Semantically Enriched Recommendations for Academic Literature 21
• ‘Adaptive process’ for image-based semantic similarity identification:
6/7/18
Prior Work
N. Meuschke, C. Gondek, D. Seebacher, C. Breitinger, D. Keim, and B. Gipp, “An Adaptive Image-based Plagiarism Detection
Approach” in Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), Fort Worth, USA, 2018.

Related Work
[1] N. Meuschke, M. Schubotz, F. Hamborg, T. Skopal, and B. Gipp. 2017. Analyzing Mathematical Content to Detect Academic
Plagiarism. In Proc. CIKM.
[2] M. Schubotz, L. Kraemer, N. Meuschke, F. Hamborg, and B. Gipp, “Evaluating and Improving the Extraction of Mathematical
Identifier Definitions” in Proceedings of the 8th International Conference of the CLEF Association (CLEF 2017), 2017.
Related Work: Mathematical Formula
Analysis
Mathematical Information Retrieval (MIR)
• Meuschke et al. demonstrated the benefit of analyzing the
similarity of mathematical expressions to improve detection
of heavily disguised academic plagiarism [1]
• Schubotz et al. improved the extraction of math identifier
definitions from the surrounding text –> results in a better
understanding of the semantic meaning of a given formula [2]

Applying Mathematical Formula
Analysis to Recommendations
• Consider Formula Semantics
• the semantic meaning of a mathematical identifier or an entire
mathematical equation (e.g. defined by the accepted name for
the identifier or equation)
• Consider Formula Patterns
• Sequence of several mathematical formulas contained in a STEM
document, which holds meaning (e.g. a derivation or proof)
à The consideration of mathematical formula concepts and patterns
can improve content-based recommendation approaches for STEM
literature

Semantically-enriched Literature
Recommendations
Task 1: Creation of suitable recommendation corpus
• PMC OAS
• Life Sciences and Biomedical field
• arXiv
• Physics, Mathematics, Statistics, Quantitative Biology,
Computer Science, and Electrical Engineering
à ensure a broad and representative spectrum of STEM fields

Recommendations
Task 2: Adapt and combine the presented semantic
similarity analysis approaches (currently applied to
the PD use case) to suit the recommendation use
case
• Adjust parameters and test weightings to tailor
existing approaches to STEM literature
recommendation (and visualization)

Recommendations
Related Work
J. Beel, A. Aizawa, C. Breitinger, and B. Gipp, “Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia,” in Proceedings of the
ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), 2017.
J. Beel, B. Gipp, S. Langer, M. Genzmehr, E. Wilde, A. Nuernberger, and J. Pitman, “Introducing Mr. DLib, a Machine-readable Digital
Library” in Proceedings of the 11th ACM/IEEE Joint Conference on Digital Libraries (JCDL`11), Ottawa, Canada, 2011.
Task 3: Implement the semantically-enriched approach in a
literature recommendation system
• Make use of framework
provided by Mr.DLib

Recommendations
Task 4:
Conceive a suitable
visualization concept
• Quick identification &
navigation of relevant
semantic markers

Recommendations
Task 5: Evaluation
• Hypothesis: taking into consideration semantic markers (citations,
formulas) and similarity of images improves content-based literature
recommendation & exploration for STEM literature
• Comparison of different weighting schemes (textual similarity, citation-
based similarity, formula-based similarity, image-based similarity)
• Expert user study:
• Usefulness of recommendations given pre-defined information needs
• Easy of exploring and identifying relevant semantic features

Completed Research
• Implementation of a link-based recommendation approach
as first proof of concept
• Citolytics - applied to the Wikipedia corpus
• System requirements engineering study completed
• to be submitted: Conference on Human Information Interaction
and Retrieval (CHIIR)
• Current:
• creation of a representative STEM literature dataset to be used
for recommendation generation

Summary
Contributions:
1) Improve content-based recommendations by taking into
consideration the similarity of semantic markers (citations,
formulas) and figures
à valuable for STEM literature recommendation!
2) Conceive a visualization concept, with the help of user
studies, to aid users in more quickly identifying and
navigating through these otherwise hard-to-identify forms
of semantic relevance contained in academic literature
recommendations

Prior Work
Semantic document similarity analysis
B. Gipp, N. Meuschke, and C. Breitinger, “Citation-based Plagiarism Detection: Practicability on a Large-scale Scientific
Corpus.” Journal of the American Society for Information Science and Technology (JASIST), vol. 65, iss. 2, pp. 1527-1540,
2014.
B. Gipp, N. Meuschke, C. Breitinger, M. Lipinski, and A. Nuernberger, “Demonstration of Citation Pattern Analysis for
Plagiarism Detection.” In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in
Information Retrieval, Dublin, UK, 2013.
Meuschke, Gipp, and Breitinger. CitePlag: A Citation-based PDS Prototype. Proc. Int. Plagiarism Conf.,2012.
User Modeling
J. Beel, S. Langer, G. M. Kapitsaki, C. Breitinger, and B. Gipp, “Exploring the Potential of User Modeling based on Mind
Maps,” in User Modeling, Adaptation and Personalization – 23rd International Conference, UMAP 2015, Dublin, Ireland,
2015, pp. 3-17.
Recommender Systems
J. Beel, B. Gipp, S. Langer, and C. Breitinger, “Research-paper recommender systems: a literature survey,” International
Journal on Digital Libraries, pp. 1-34, 2015.
Trusted timestamping
C. Breitinger. “Using the Blockchain of Cryptocurrencies to Encourage Open Discussion and Sharing of Ideas”. Master
thesis: Linnaeus University, Sweden. June 2016.

References
[Beel et al. 2016] Beel, J., Gipp, B., Langer, S., and Breitinger, C. Research-paper Recommender Systems: A
Literature Survey. International Journal on Digital Libraries 17, 4 (2016), 305–338.
[Gipp & Beel, 2009] B. Gipp and J. Beel, “Citation Proximity Analysis (CPA) – A New Approach for Identifying Related
Work Based on Co-Citation Analysis,” in Proceedings of the 12th International Conference on
Scientometrics and Informetrics (ISSI’09), Rio de Janeiro, Brazil, 2009.
[Gipp, Meuschke, Breitinger, 2014] Gipp, Meuschke, Breitinger. “Citation-based Plagiarism Detection: Practicability
on a Large-scale Scientific Corpus”. Journal of the American Society for Information Science
and Technology, 65 (2): 1527–1540, 2014.
[Kembhavi et al., 2016] Kembhavi, Aniruddha, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali
Farhadi. "A Diagram Is Worth A Dozen Images." arXiv preprint arXiv:1603.07396 (2016).
[Meuschke et al. 2018] N. Meuschke, C. Gondek, D. Seebacher, C. Breitinger, D. Keim, and B. Gipp, “An Adaptive
Image-based Plagiarism Detection Approach” in Proceedings of the ACM/IEEE-CS Joint
Conference on Digital Libraries (JCDL), Fort Worth, USA, 2018.
[Schubotz et al. 2016] M. Schubotz, A. Grigorev, M. Leich, H. S. Cohl, N. Meuschke, B. Gipp, A. S. Youssef, and V.
Markl, “Semantification of Identifiers in Mathematics for Better Math Information Retrieval,”
in Proceedings of the 39th Int. ACM SIGIR Conference on Research and Development in
Information Retrieval, 2016.

Thank you!
Contact:
corinna.breitinger@uni.kn
@BreitingerC

A Semantically Enriched Recommendation & Visualization Approach for Academic Literature

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