This paper presents a novel approach to Linked Data exploration that uses Encyclopaedic Knowledge Patterns (EKPs) as relevance criteria for selecting, organising, and visualising knowledge. EKP are discovered by mining the linking structure of Wikipedia and evaluated by means of a user-based study, which shows that they are cognitively sound as models for building entity summarisations. We implemented a tool named Aemoo that supports EKP-driven knowledge exploration and integrates data coming from heterogeneous resources, namely static and dynamic knowledge as well as text and Linked Data. Aemoo is evaluated by means of controlled, task-driven user experiments in order to assess its usability, and ability to provide relevant and serendipitous information as compared to two existing tools: Google and RelFinder.

1. Aemoo: Linked Data Exploration
based on Knowledge Patterns
Andrea Giovanni Nuzzolese1 -Valentina Presutti1
Aldo Gangemi1,3 - Silvio Peroni2 - Paolo Ciancarini2
1. STLab, ISTC-CNR, Rome , Italy
2. Dept. of Computer Science, University of Bologna, Italy
3. LIPN, Université Paris 13, Sorbone Cité, UMR CNRS, Paris, France
October 20th 2016 - Kōbe, Japan

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Contribution
A novel approach to
Linked Data visual exploration

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• Encyclopaedic Knowledge Patterns (EKP)
• provide the core knowledge about entities of a certain type
• empirically emerge by analysing the structure of wikilinks
• human action of linking entities on the Web reflects the way humans organise
their knowledge
• EKPs are cognitively sound and evaluated with a user study
• EKPs emerging from WikipediaVs. conceptual schema proposed by users (ρ=.75)
[1] A.G. Nuzzolese, A. Gangemi, V. Presutti, and P. Ciancarini, 2011. Encyclopaedic knowledge patterns
from wikipedia links. In Proc. of ISWC 2011, pages 520–536. Springer. DOI: 10.1007/978-3-642- 25073-6_33
Background

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An EKP for philosophers

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EKPs support effective entity-centric exploratory search
Hypothesis

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• EKPs as query templates for building entity-
centric summaries and exploration
• EKPs as lenses over data
• Design driven by collective cognition schemes
• Evaluation based on user-oriented experiments
http://aemoo.org
DOI: 10.3233/SW-160222

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• Aggregates data from heterogeneous sources
• Wikipedia, DBpedia,Twitter and Google News
• Provides two complementary exploratory perspectives
• core and peculiar facts exploration

8. Homepage: keyword-based entity search
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Visualisation features

9. Entity-centric and interactive 1-degree radial graph
Entity label, type and short abstract
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Visualisation features

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Visualisation features
Related entities appear by hovering the pointeron node sets
Explanations
Customisable
data sources
Curiosity
perspective

11. Breadcrumb about exploratory path
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Visualisation features

12. Curiosity perspective: same visual widgets
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Visualisation features

13. • Metrics
• System Usability Scale (SUS)
• Relevance and unexpectedness
• Grounded theory
• User-based: 32 participants aged between 20 and 35 years
• CS students with little background in Semantic Web technologies
• Comparative analysis based on three tools:
• Aemoo
• Google: reference baseline
• RelFinder: visual exploratory search popular among Semantic Web experts
• Each participant used 2 tools during the experiment
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Evaluation

14. • Controlled experiment with three tasks involving look-up,
learning and investigation [2]
• Summarisation: building a summary of a specific subject (i.e.“Alan turing”)
• Related entities: finding as many objects of a certain type as possible, which
have a relation to the given subject
• Relation finding: finding one or more relations between two subjects by
describing their semantics
• Participants recorded the relevance and unexpectedness on 5
point scale
[2] G. Marchionini, 2006. Exploratory Search: From Finding to Understanding. Communications of
the ACM 49 (4), 41–46, DOI: 10.1145/1121949.1121979
Experimental setup

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• Well-known metrics used for evaluating the usability of a
system [3]
• Technology-independent and reliable even with a very small
sample size [4]
• Provides a two-factor orthogonal structure: Usability and
Learnability dimensions
[3] J. Brooke, 1996. SUS - A quick and dirty usability scale. Usability evaluation in industry 189 (194), 4–7
[4] J. Sauro, 2011.A practical guide to the system usability scale: Background, benchmarks & best practices.
Measuring Usability LCC.
System Usability Scale

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Results: usability
• Aemoo SUS satisfactory (> 68)
Aemoo/RelFinder p < 0.01
Aemoo/Google p > 0.1
Google/RelFinder p < 0.01
• Strong evidence supporting the hypothesis that Aemoo is
more usable than RelFinder
P-values computed with a Tukey’s HSD pairwise
comparison of the SUS scores

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• Aemoo shows a promising behaviour as far as serendipity is
concerned
• best ratio between relevance and unexpectedness
Results: relevance and unexpectedness

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Future Work
Grounded theory

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• We presented Aemoo, a tool that uses EKPs for helping
humans in visual exploratory search
• Initial hypothesis was validated in terms of usability by means
of controlled, task-driven user experiments
Conclusions and future work

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Thank you