Using the Structure of DBpedia for Exploratory Search
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The document presents a framework for exploratory search using DBpedia's graph structure. It analyzes DBpedia's infobox properties and ontology graphs, finding the infobox graphs are dense and heterogeneous while the ontology graphs are sparse and tree-like. The proposed framework clusters search results according to their contexts in DBpedia's graphs to guide users. An example search for "Lisa Hannigan" is shown clustered into categories like instruments, songs, and albums. Ongoing work includes evaluating the framework through user studies and comparing to other faceted search methods.
Using the Structure of DBpedia for Exploratory Search
- 1. Using the Structure of DBpedia for Exploratory Search Speaker: Samantha Lam Supervisor: Conor Hayes
- 2. Motivating Work DBpedia - heterogeneous graph 2
- 3. Motivating Work Background Network Similarity: PathSim, NetClus, RankClus Faceted Search: Facets for refining search specific schema, (semi) supervised 3
- 4. 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? 3
- 5. 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
- 6. 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... 4
- 7. 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 4
- 9. Research Questions 1 Can we provide an effective graph-based framework that can aid exploratory search? 2 To do this, what is DBpedia’s graph structures wrt its different datasets? 6
- 10. DBpedia graphs summary Infobox properties emergent, crowd-sourced heterogeneous ‘types’ dense Infobox ontology, SKOS/Wiki Category, YAGO agreed rules is-A structure sparse, tree-like 7
- 11. DBpedia graphs summary Infobox properties emergent, crowd-sourced heterogeneous ‘types’ dense Infobox ontology, SKOS/Wiki Category, YAGO agreed rules is-A structure sparse, tree-like Infobox good for GGGGGGGGGGA Relatedness Ontology good for GGGGGGGGGGA Labelling similar items 7
- 12. Research Q1 Proposition General Framework: 8
- 13. Sample Query & Results Query: Lisa Hannigan Two methods Weighted (W) and Uniform (U), 6 clusters 9
- 14. 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 9
- 15. 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) 10
- 16. Sample Query & Results Summary: Weighted produced 4 out of 6 coherent clusters whereas Unweighted only produced 2. DBpedia Ontology labelling (see paper) provided broader labelling for messier clusters, e.g. top label was MusicalWork for mixed clusters → Categories better for more specific clusters. 11
- 17. 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? 12
- 18. Summary Investigated graph structure of DBpedia datasets Framework to utilise this finding in exploratory search, gave example results Ongoing challenge, evaluation 13
- 19. Summary Investigated graph structure of DBpedia datasets Framework to utilise this finding in exploratory search, gave example results Ongoing challenge, evaluation Thanks for listening! Questions welcome! 13