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 , Italy2. 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 Wikipedia Vs. 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

Homepage: keyword-based entity search8

Visualisation features

Entity-centric and interactive 1-degree radial graphEntity 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

Breadcrumb about exploratory path

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Visualisation features

Curiosity perspective: same visual widgets

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Visualisation features

• 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 9

Evaluation

• 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 < 0.01Aemoo/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 WorkGrounded 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