Exploratory search on topics through different perspectives with DBpedia

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Exploratory search on topics through differentperspectives with DBpedia

Nicolas Marie, Fabien Gandon, Alain Giboin, Émilie Palagi


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CONTEXTPROPOSITIONEVALUATIONCONCLUSION


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Search is only a partially solved problem [White, 2009]Ambiguous queries, natural language queries, exploratory search tasks…


10 blue links paradigm, simple, fast

Exploratory searchbottleneck

Discovery Hub


Overviews

Faceted interfaces

Results clustering

Low-cost of browsing (going back-and-forth functionalities)

Query-suggestions and refinement

Serendipitous discoveries provocation

In-session of account related memory features

Exploratory search systems are optimized to support exploratory search tasks, common functionalities:

Discovery Hub


Discovery Hub

Linked data are promising for supporting exploratory search: • new algorithms • new interaction models optimized for exploration.


Discovery Hub


MaturityDiscovery Hub


1 topic of interest => 1 entity => 1 results set to explore

1 perspective


Discovery HubTopics are complex, multifaceted,

One entity => multiple perspectives &knowledge nuances

Entourage Art. movement

Curiosities…In French culture

In American culture

I want to discover Claude Monet (painter)...


MORE

Aemoo


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Discovery Hub

The models and algorithms we proposeunveil topic knowledge nuances by allowing the exploration of topics through several perspectives.

In the graph context of linked data these perspectives correspond to different non exclusive sets of objects and relations that are informative on a topic regarding specific aspects.

Flexible querying and data processing



Refer to the papers for the complete formalization

Building perspectives thanks to spreading activation

……






Discovery Hub

3 perspective-operations to expose knowledge nuances :

• Criteria of interest specification• Controlled randomness injection• Data source selection


Discovery Hub Criteria of interest specification

, dcterms:category, ?x

, dcterms:category, ?x

Classic similarity measure

, dcterms:category, ?a | ?b | ?c |...

, dcterms:category, ?a | ?b | ?c |...

Criteria spec. similarity


Discovery Hub Criteria of interest specification


Classic – top 5 artists

« French / not impressonist » criteria specification – top 5 artists

« Not French / Impressonist » criteria specification – top 5 artists


Chosen level of randomness

Randomness injection

* r + (1-r)*

* r + (1-r)*

* r + (1-r)*

* r + (1-r)*

* r + (1-r)*

* r + (1


Discovery Hub

Local Kgram instance

Data source selection

fr.dbpedia.org/sparql

it.dbpedia.org/sparql

de.dbpedia.org/sparql

es.dbpedia.org/sparql

dbpedia.org/sparql


Discovery HubData source selection


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Evaluated algorithm versions

•Basis algorithm of Discovery Hub

•Personalized algorithm through criteria specification

•Randomized algorithm, with 0.5 threshold

•Highly randomized algorithm (Highly R.), with 1.0 threshold

Discovery Hub


• Hypothesis 1:

Users who specify their criteria of interest about a topic find the results of the search more relevant.

• Hypothesis 2:

Users who specify their criteria of interest about a topic do not find the results of the search less novel.

• Hypothesis 3:

The stronger is the level of randomness the more surprising the results are for the users.

• Hypothesis 4:

Even if the level of surprise is high, the majority of the top results are still relevant to the users.

Discovery Hub


𝐻3 𝐻𝑖𝑔ℎ𝑙𝑦 𝑅. ; 𝑆𝑢𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > 𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡]

[ " ; Suprising Relation] > " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛 > [ " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛]

𝐻2 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑧𝑒𝑑 ; 𝑆𝑢𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡]

[ " ; Surprising Relation] > [ " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛]

𝐻1 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡]

" ; 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 < [ " ; 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒]

𝐻4 𝐻𝑖𝑔ℎ𝑙𝑦 𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 (2,5)

𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 (2,5)

(Highly R. : Highly Randomized)

Discovery Hub


A « good »

result in

ESS is…

Our definitions Chosen metrics : Questions

(Likert Scale)

… A

surprising

result

A result is surprising if :

• You discovered an

unknown resource or

relation

• You discovered

something unexpected

Surprising Result

This result is suprising

?

Surprising

Relation

This relation between

the topic searched and

the result is surprising

?

… An

intersting

result

A result is interesting if :

• You think it is similar to

the topic explored

• You think you will

remind or reuse it

Interesting Result

This result is interesting

?

Distance between

the Result and the

topic searched

This result is too distant

from the topic searched

?

Discovery Hub


• 16 participants

• Phase 1 : Selection of 2 topics in a list of 20 queries randomly choose in the query log of Discovery Hub

- Information Visualization

- Serge Gainsbourg (french singer)

• Phase 2 : Specification of the categories of interest

• Phase 3 : User Test (~1h)

- Before the test

- Interview (name, age, do they know Discovery Hub ?,…)

- Presentation of Discovery Hub and the objective of the test

- Presentation of the questions and simulation

Discovery Hub


𝐻1 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡]

" ; 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 < [ " ; 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒]

H1 : Users who specify their criteria of interest about a topic find the results of the search more relevant.


𝐻2 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑧𝑒𝑑 ; 𝑆𝑢𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡]

[ " ; Surprising Relation] > [ " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛]

H2: Users who specify their criteria of interest about a topic do not find the results of the search less novel


𝐻3 𝐻𝑖𝑔ℎ𝑙𝑦 𝑅. ; 𝑆𝑢𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > 𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡]

[ " ; Suprising Relation] > " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛 > [ " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛]

(Highly R. : Highly Randomized)

H3: The stronger is the level of randomness the more surprising the results are for the users


𝐻4 𝐻𝑖𝑔ℎ𝑙𝑦 𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 (2,5)

𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 (2,5)

H4: Even if the level of surprise is high, the majority of the top results are still relevant to the users


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Discovery Hub

• We proposed a framework to enable multi-perspective exploratory search:

- Formalization

- Implementation

- Evaluation

• 3 operators : criteria spec., randomization, data selection

• Evaluations globally positive, slight adjustements needed

• Interesting propositions from the reviewers, thank you


http://semreco.inria.fr

[email protected]

Thank you ! Questions ?

Discovery Hub

http://semreco.inria.fr/

mailto:[email protected]

Technology

Exploratory search on topics through different perspectives with DBpedia