Using the Structure ofDBpedia for ExploratorySearch

Speaker: Samantha LamSupervisor: Conor Hayes

Motivating Work

DBpedia - heterogeneous graph

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Motivating Work

Background

Network Similarity: PathSim, NetClus, RankClus

Faceted Search: Facets for refining search

specific schema, (semi) supervised

→ good for search when user is familiar with query

→ ...but what about complete beginners?

→ Requires Exploratory Search – Unsupervised

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Motivating Work

Background

Network Similarity: PathSim, NetClus, RankClus

Faceted Search: Facets for refining search

specific schema, (semi) supervised

→ good for search when user is familiar with query

→ ...but what about complete beginners?

→ Requires Exploratory Search – Unsupervised

3

Motivating Work

Background

Network Similarity: PathSim, NetClus, RankClus

Faceted Search: Facets for refining search

specific schema, (semi) supervised

→ good for search when user is familiar with query

→ ...but what about complete beginners?

→ Requires Exploratory Search – Unsupervised

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Exploratory Search?

Given query, how to organise results in a manner that is ‘useful’,i.e. aids exploratory search

E.g. suppose you hear a song on the radio...

Solution:

Classify results according to its contexts

Why? Alleviates in-depth reading and guides user

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Exploratory Search?

Given query, how to organise results in a manner that is ‘useful’,i.e. aids exploratory search

E.g. suppose you hear a song on the radio...

Solution:

Classify results according to its contexts

Why? Alleviates in-depth reading and guides user

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Assumption

similarity ⊂ relatedness

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Research Questions

1 Can we provide an effective graph-based framework that canaid exploratory search?

2 To do this, what is DBpedia’s graph structures wrt itsdifferent datasets?

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DBpedia graphs summary

Infobox properties

emergent, crowd-sourcedheterogeneous ‘types’dense

Infobox ontology, SKOS/Wiki Category, YAGO

agreed rulesis-A structuresparse, tree-like

Infobox good forGGGGGGGGGGA Relatedness

Ontology good forGGGGGGGGGGA Labelling similar items

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DBpedia graphs summary

Infobox properties

emergent, crowd-sourcedheterogeneous ‘types’dense

Infobox ontology, SKOS/Wiki Category, YAGO

agreed rulesis-A structuresparse, tree-like

Infobox good forGGGGGGGGGGA Relatedness

Ontology good forGGGGGGGGGGA Labelling similar items

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Research Q1 Proposition

General Framework:

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Sample Query & Results

Query: Lisa Hannigan

Two methods Weighted (W) and Uniform (U), 6 clusters

Cluster 1 (W, U) instruments

Top label: (W, U) Musical instruments

Cluster 2 (W) songs (U) album and songs

Top label: (W) Songs by artist (U) Albums by artist

Cluster 3 (W) albums (U) album, music genres and songs

Top label: (W) Albums by artist (U) Music subgenres by genre

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Sample Query & Results

Query: Lisa Hannigan

Two methods Weighted (W) and Uniform (U), 6 clusters

Cluster 1 (W, U) instruments

Top label: (W, U) Musical instruments

Cluster 2 (W) songs (U) album and songs

Top label: (W) Songs by artist (U) Albums by artist

Cluster 3 (W) albums (U) album, music genres and songs

Top label: (W) Albums by artist (U) Music subgenres by genre

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Sample Query & Results

Query: Lisa Hannigan

Cluster 4 (W) mixed, (U) mixed

Top label: (W) Songs by artist (U) Missing people

Cluster 5 (W) mixed, (U) mixed

Top label: (W) Albums by artist (U)Towns and villages in the Republic of Ireland by county

Cluster 6 (W) musicians and bands, (U) musicians and bands

Top label: (W) Place of birth missing (living people) (U)Place of birth missing (living people)

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Sample Query & Results

Summary:

Weighted produced 4 out of 6 coherent clusters whereasUnweighted only produced 2.

DBpedia Ontology labelling (see paper) provided broaderlabelling for messier clusters, e.g. top label was MusicalWorkfor mixed clusters

→ Categories better for more specific clusters.

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Ongoing Challenges

Evaluation

User Study:

- compare only Weighted versus Unweighted results,different labelling methods?

Comparison:

- possible to compare against other faceted methods?

- compare with plain list for recall?

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Summary

Investigated graph structure of DBpedia datasets

Framework to utilise this finding in exploratory search, gaveexample results

Ongoing challenge, evaluation

Thanks for listening! Questions welcome!

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Summary

Investigated graph structure of DBpedia datasets

Framework to utilise this finding in exploratory search, gaveexample results

Ongoing challenge, evaluation

Thanks for listening! Questions welcome!

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