Type-Aware Entity Retrieval
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The document discusses type-aware entity retrieval, focusing on the transition from traditional information retrieval to entity-oriented search for satisfying complex information needs. It outlines research questions related to representing, combining, and estimating type-based information using various type taxonomies and experimental setups. The findings suggest different approaches and models for type representation and the integration of type and textual information in entity retrieval tasks.
Type-Aware Entity Retrieval
- 1. Type-aware Entity Retrieval Darío Garigliotti IAI Universitetet i Stavanger (UiS) NTNU - March 3, 2016
- 2. From Information Retrieval to Entity Retrieval • Traditional Information Retrieval recently extended to an Entity-oriented Search • Satisfaction of more complex information needs • Current support on search engines
- 3. From Information Retrieval to Entity Retrieval
- 4. From Information Retrieval to Entity Retrieval
- 5. Countries where one can pay with the euro Entity Retrieval
- 6. Countries where one can pay with the euro • Related entities (via a relation or predicate) • Types or categories or classes Entity Retrieval
- 7. Countries where one can pay with the euro Impressionist art museums in The Netherlands • Related entities (via a relation or predicate) • Types or categories or classes Entity Retrieval
- 8. Countries where one can pay with the euro Impressionist art museums in The Netherlands • Related entities (via a relation or predicate) • Types or categories or classes Entity Retrieval
- 9. Countries where one can pay with the euro Impressionist art museums in The Netherlands • Related entities (via a relation or predicate) • Types or categories or classes Entity Retrieval
- 10. Entity Retrieval Evaluated tasks • Entity ranking (given a textual query and target categories) • List completion (given Q and entity examples, and? types) • Related entity finding (given entity E, relation R and type T) e.g. E = "Schumacher", R = "His teammates when he was on Ferrari", T = "Person" from Q = "Schumacher teammates when he was on Ferrari"
- 11. Type-aware entity retrieval Our research questions 1. How to represent type-based information? 2. How to combine type-based and textual information? 3. How to estimate type-based information?
- 12. Type-aware entity retrieval RQ2. How to combine type-based and textual information? • Basics: term-based models • A variety of related tasks across the literature • Entity retrieval approaches • Where to look for entities? How to find them? How to rank them? • Major model families • Common main insight: types help!
- 13. Type-aware entity retrieval RQ1. How to represent type-based information? • Dimensions we identified • type taxonomies • hierarchical structure • dataset version • Minimal concerning in the related work
- 14. Type taxonomies • We consider four well-known type taxonomies Type system Wikipedia DBpedia Freebase YAGO #types 753,524 591 1719 568,672 #top-level types NA 58 92 61 #most-specific-level types 753,524 472 1626 549,623 depth NA 7 2 19 entities w/ type 4.12M 3.24M 3.77M 2.89M avg #types/entity 4.02 6.30 9.57 16.44
- 15. Type representation • We consider different ways of modeling type assignments: Top level, most specific level, and path-to-top r e r e r e
- 16. Experimental setup • Our experimental environment looks like this: Term-based representation Query model Entity model Query model Entity model Type-based representation P(e|q) / P(q|e)P(e) p(t|✓T e )p(t|✓T q ) KL(✓T q ||✓T e ) p(t0 | ✓T 0 e )p(t0 | ✓T 0 q ) KL(✓T 0 q k ✓T 0 e ) P(q | e) = (1 )P(✓T 0 q | ✓T 0 e ) + P(✓T q | ✓T e )
- 17. Experimental setup • Term-based component: Mixture of LM method • We obtain combinations of these elements: • Type taxonomies • Models • Type-based representations
- 18. Ingredients • Model instantiations for • M1 (Mixture): • M2 (Multiplicative): • M3 (Filtering): P(e | q) / P(q | e)P(e) P(q | e) = (1 )P(✓T 0 q | ✓T 0 e ) + P(✓T q | ✓T e ) P(q | e) = P(✓T 0 q | ✓T 0 e )P(✓T q | ✓T e ) P(✓T q | ✓T e ) 2 {0, 1}
- 19. Ingredients • Query model for the type-based representation is provided by a target types oracle P(t|✓T q ) Query: guitar origin blues DBpedia Types: <dbo:Album>: 4 <dbo:MusicalArtist>: 43 ... Freebase Types: <fb:music.group_member>: 34 <fb:people.deceased_person>: 17 ... Wikipedia Categories: <dbpedia:Category:Blues_musicians_from_New_Orleans,_Louisiana>: 2 <dbpedia:Category:Blues_songs>: 2 ...
- 20. Ingredients • Our experimental environment looks like this: Query model Entity model Type-based representation P(e|q) / P(q|e)P(e) p(t|✓T e )p(t|✓T q ) KL(✓T q ||✓T e ) P(q | e) = (1 )P(✓T 0 q | ✓T 0 e ) + P(✓T q | ✓T e )
- 21. Ingredients • Entity model for the type-based representation is a distribution estimated through the entity types Query: guitar origin blues Relevant entities: <dbpedia:The_Merle_Travis_Guitar> <dbpedia:Blues_Breakers_with_Eric_Clapton> <dbpedia:Poor_Boy_Blues> ... ... Freebase Types: ... DBpedia Types: <dbo:Album> <dbo:MusicalWork> ... ... Freebase Types: ... Wikipedia Categories: <Category:1950_albums> <Category:Merle_Travis_albums> ...
- 22. Results (1) RQ1. How to represent type-based information? Type representation - Model M1 MAP 0 0.058 0.115 0.173 0.23 all assigned types most specific level path-to-top top level YAGO Freebase Wikipedia DBpedia Type representation - Model M2 MAP 0 0.045 0.09 0.135 0.18 all assigned types most specific level path-to-top top level YAGO Freebase Wikipedia DBpedia Type representation - Model M3 MAP 0 0.055 0.11 0.165 0.22 all assigned types most specific level path-to-top top level YAGO Freebase Wikipedia DBpedia
- 23. Results (2) RQ2. How to combine type-based and textual information? Combining information - All assigned types MAP 0 0.06 0.12 0.18 0.24 YAGO Freebase Wikipedia DBpedia M1 M2 M3 Combining information - Most-specific-level types MAP 0 0.06 0.12 0.18 0.24 YAGO Freebase Wikipedia DBpedia M1 M2 M3
- 24. Future work RQ3: How to estimate type-based information? Term-based representation Query model Entity model Query model Entity model Type-based representation P(e|q) / P(q|e)P(e) p(t|✓T e )p(t|✓T q ) KL(✓T q ||✓T e ) p(t0 | ✓T 0 e )p(t0 | ✓T 0 q ) KL(✓T 0 q k ✓T 0 e ) P(q | e) = (1 )P(✓T 0 q | ✓T 0 e ) + P(✓T q | ✓T e )
- 25. Future work • Main focus will be on query typing, but eventually on entity typing as well • How to take the best from different type taxonomies