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ESTER Efficient Search on Text, Entities, and Relations

ESTER Efficient Search on Text, Entities, and Relations. Holger Bast Max-Planck-Institut für Informatik Saarbrücken , Germany joint work with Alexandru Chitea, Fabian Suchanek, Ingmar Weber. Talk at SIGIR’07 in Amsterdam, July 26th.

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ESTER Efficient Search on Text, Entities, and Relations

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  1. ESTEREfficient Search on Text, Entities, and Relations Holger Bast Max-Planck-Institut für Informatik Saarbrücken, Germany joint work with Alexandru Chitea, Fabian Suchanek, Ingmar Weber Talk at SIGIR’07 in Amsterdam, July 26th

  2. ESTEREfficient Search on Text, Entities, and Relations Holger Bast Max-Planck-Institut für Informatik Saarbrücken, Germany joint work with Alexandru Chitea, Fabian Suchanek, Ingmar Weber Talk at SIGIR’07 in Amsterdam, July 26th

  3. ESTERIt’s about:Fast Semantic Search Holger Bast Max-Planck-Institut für Informatik Saarbrücken, Germany joint work with Alexandru Chitea, Fabian Suchanek, Ingmar Weber Talk at SIGIR’07 in Amsterdam, July 26th

  4. Keyword Search vs. Semantic Search • Keyword search • Query: john lennon • Answer: documents containing the words john and lennon • Semantic search • Query: musician • Answer: documents containing an instance ofmusician • Combined search • Query: beatles musician • Answer: documents containing the word beatles and an instance of musician Useful by itself or as a component of a QA system

  5. Semantic Search: Challenges + Our System 1. Entity recognition • approach 1: let users annotate (semantic web) • approach 2: annotate (semi-)automatically • our system: uses Wikipedia links + learns from them 2. Query Processing • build a space-efficient index • which enables fast query answers • our system: as compact and fast as a standard full-text engine 3. User Interface • easy to use • yet powerful query capabilities • our system: standard interface with interactive suggestions

  6. Semantic Search: Challenges + Our System 1. Entity recognition • approach 1: let users annotate (semantic web) • approach 2: annotate (semi-)automatically • our system: uses Wikipedia links + learns from them 2. Query Processing • build a space-efficient index • which enables fast query answers • our system: as compact and fast as a standard full-text engine 3. User Interface • easy to use • yet powerful query capabilities • our system: standard interface with interactive suggestions focus of the paper and of this talk

  7. In the Rest of this Talk … • Efficiency • three simple ideas (which all fail) • our approach (which works) • Queries supported • essentially all SPARQL queries, and • seamless integration with ordinary full-text search • Experiments • efficiency (great) • quality (not so great yet) • Conclusions • lots of interesting + challenging open problems

  8. Efficiency: Simple Idea 1 • Add “semantic tags” to the document • e.g., add the special word tag:musician before every occurrence of a musician in a document • Problem 1: Index blowup • e.g., John Lennon is a: Musician, Singer, Composer, Artist, Vegetarian, Person, Pacifist, … (28 classes) • Problem 2: Limited querying capabilities • e.g., could not produce list of musicians that occur in documents that also contain the word beatles • i.p., could not do all SPARQL queries (more on that later)

  9. Efficiency: Simple Idea 2 • Query Expansion • e.g., replace query word musician by disjunction musician:aaron_copland OR … OR musician:zarah_leander (7,593 musicians in Wikipedia) • Problem: Inefficient query processing • one intersection per element of the disjunction needed

  10. Efficiency: Simple Idea 3 • Use a database • map semantic queries to SQL queries on suitably constructed tables • that’s what the Artificial-Intelligence / Semantic-Web people usually do • Problem: Inefficient + Lack of control • building a search engine on top of an off-the-shelf database is orders of magnitude slower or uses orders of magnitude more space, or both • very limited control regarding efficiency aspects

  11. Efficiency: Our Approach • Two basic operations • prefix search of a special kind [will be explained by example] • join[will be explained by example] • An index data structure • which supports these two operations efficiently • Artificial words in the documents • such that a large class of semantic queries reduces to a combination of (few of) these operations

  12. Processing the query “beatles musician” position Gitanes … legend says that John Lennon entity:john_lennon of the Beatles smoked Gitanes to deepen his voice … John Lennon 0 entity:john_lennon 1 relation:is_a 2 class:musician 2 class:singer… beatles entity:* entity:*. relation:is_a . class:musician two prefix queries entity:john_lennonentity:1964 entity:liverpool etc. entity:wolfang_amadeus_mozart entity:johann_sebastian_bach entity:john_lennon etc. onejoin entity:john_lennon etc.

  13. Processing the query “beatles musician” position • Problem: entity:* has a huge number of occurrences • ≈ 200 million for Wikipedia, which is ≈20% of all occurrences • prefix search efficient only for up to ≈ 1% (explanation follows) Gitanes … legend says that John Lennon entity:john_lennon of the Beatles smoked Gitanes to deepen his voice … John Lennon 0 entity:john_lennon 1 relation:is_a 2 class:musician 2 class:singer… beatles entity:* entity:*. relation:is_a . class:musician • Solution: frontier classes • classes at “appropriate” level in the hierarchy • e.g.: artist, believer, worker, vegetable, animal, …

  14. Processing the query “beatles musician” position Gitanes … legend says that John Lennon artist:john_lennonbeliever:john_lennon of the Beatles smoked … John Lennon 0 artist:john_lennon0 believer:john_lennon 1 relation:is_a 2 class:musician… beatles artist:* artist:*. relation:is_a . class:musician two prefix queries artist:john_lennonartist:graham_greene artist:pete_best etc. artist:wolfang_amadeus_mozart artist:johann_sebastian_bach artist:john_lennon etc. first figure out: musician  artist (easy) onejoin artist:john_lennon etc.

  15. The HYB Index [Bast/Weber, SIGIR’06] • Maintains lists for word ranges (not words) able ablaze abroad abnormal abl-abt • Looks like this for person:* person:graham_greene person:john_lennon person:ringo_starr person:john_lennon person:*

  16. The HYB Index [Bast/Weber, SIGIR’06] • Maintains lists for word ranges (not words) able ablaze abroad abnormal abl-abt • Provably efficient • no more space than an inverted index (on the same data) • each query = scan of a moderate number of (compressed) items • Extremely versatile • can do all kinds of things an inverted index cannot do (efficiently) • autocompletion, faceted search, query expansion, errorcorrection, select and join, …

  17. Queries we can handle SPARQL Protocol AndRDF Query Language (yes, it’s recursive) • We prove the following theorem: • Any basic SPARQL graph query with m edges can be reduced to at most 2m prefix / join operations SELECT ?who WHERE { ?who is_a Musician ?who born_in_year ?when John_Lennon born_in_year ?when } • ESTER achieves seamless integration with full-text search • SPARQL has no means for dealing with full text search • XQuery can handle full-text search, but is not really suitable for semantic search musicians born in the same year as John Lennon more about supported queries in the paper

  18. Experiments: Corpus, Ontology, Index • Corpus: English Wikipedia (xml dump from Nov. 2006) ≈ 8 GB raw xml ≈ 2,8 million documents ≈ 1 billion words • Ontology: YAGO (Suchanek/Kasneci/Weikum, WWW’07) ≈ 2,5 million facts derived from clever combination of Wikipedia + WordNet(Entities from Wikipedia, Taxonomy from WordNet) • Our Index ≈ 1.5 billion words (original + artificial) ≈ 3.3 GB total index size; ontology-only is a mere 100 MB Note: our system works for an arbitrary corpus + ontology

  19. Experiments: Efficiency — What Baseline? • SPARQL engines • can’t do text search • and slow for ontology-only too (on Wikipedia: seconds) • XQuery engines • extremely slow for text search (on Wikipedia: minutes) • and slow for ontology-only too (on Wikipedia: seconds) • Other prototypes which do semantic + full-text search • efficiency is hardly considered • e.g., the system of Castells/Fernandez/Vallet (TKDE’07) “… average informally observed response time on a standard professional desktop computer [of] below 30 seconds [on 145,316 documents and an ontology with 465,848 facts] …” • our system: ~100ms, 2.8 million documents, 2.5 million facts

  20. Experiments: Efficiency — Stress Test 1 • Compare to ontology-only system • the YAGO engine from WWW’07 • Onto Simple : when was [person] born [1000 queries] • Onto Advanced: list all people from [profession][1000 queries] • Onto Hard : when did people die who were born in the same year as [person][1000 queries] • Note: comparison very unfair (for our system) 4 GB index 100 MB index

  21. Experiments: Efficiency — Stress Test 2 • Compare to text-only search engine • state-of-the-art system from SIGIR’06 • Onto+Text Easy: counties in [US state] [50 queries] • Onto+Text Hard: computer scientists [nationality][50 queries] • Full-text query: e.g. german computer scientists Note: hardly finds relevant documents • Note: comparison extremely unfair (for our system)

  22. Experiments: Quality — Entity Recognition • Use Wikipedia links as hints • “… following [[John Lennon | Lennon]] and Paul McCartney, two of the Beatles, …” • “… The southern terminus is located south of the town of [[Lennon, Michigan | Lennon]] …” • Learn other links • use words in neighborhood as features • Accuracy

  23. Experiments: Quality — Relevance • 2 Query Sets • People associated with [american university] [100 queries] • Counties of [american state] [50 queries] • Ground truth • Wikipedia has corresponding lists e.g., List of Carnegie Mellon University People • Precision and Recall

  24. Conclusions • Semantic Retrieval System ESTER • fast and scalable via reduction to prefix search and join • can handle all basic SPARQL queries • seamless integration with full-text search • standard user interface with (semantic) suggestions • Lots of interesting and challenging problems • simultaneous ranking of entities and documents • proper snippet generation and highlighting • search result quality • … Dank je wel!

  25. Context-Sensitive Prefix-Search • Compute completions of last query word • which together with the previous part of the query would lead to a hit • [DEMO: show a live example] • Extremely useful • autocompletion search • faceted search • error correction, synonym search, … • category search • for example, add place:amsterdam • then query place:* finds all instances of a place formal definition in the paper Isn’t the last idea enough for semantic search?

  26. DEMO • Do the following queries [live or recorded] • beatles • beatles musi • beatles musicia • beatles musician:john_lennon (or beatles entity:john_lennon)

  27. Processing the query “beatles musician” position Problem: entity:* has a huge number of occurrences • ≈ 200 million for Wikipedia = 20% of all occurrences • prefix search efficient only up XXX Liverpool [one of many documents mentioning John Lennon] … in honor of the late Beatle entity:john_lennon John Lennon 0 entity:john_lennon 1 r:is_a 2 class:musician 2 class:singer … beatles entity:* “entity:* r:is_a class:musician” Solution: Frontier set • classes high up in the hierarchy [explain more] • e.g.: person, animal, substance, abstraction, …

  28. Processing the query “beatles musician” position Liverpool [one of many documents mentioning John Lennon] … in honour of the late Beatle person:john_lennon John Lennon 0 person:john_lennon 1 is_a: 2 class:musician 2 class:singer … beatles person:* person:john_lennonperson:the_queen person:pete_best etc. “person:* r:is_a class:musician” person:wolfang_amadeus_mozart person:johann_sebastian_bach person:john_lennon etc. two prefix queries one join entity:john_lennon etc.

  29. Our Solution, Version 1 • Combination of Prefix Search + Join • Query 1: beatles entity:* entities co-occuring with beatles • Query 2: musician – entity:* entities which are musicians • Join the completion from 1 & 2 musicians co-occuring with beatles Some document about Albert Einstein … entity:einstein … Albert Einstein entity:albert_einsteinscientistvegetarianintellectual … But: unspecific prefixes (entity:*) are hard

  30. Our Solution, Version 2 • Combination of Prefix Search + Join • Query 1: translate:singer:* tells us that a singer is a musician • Query 2: beatles musician:* musicians co-occurring with beatles • Query 3: physicist – scientist:* musicians which are singers • Join the completion from 1 & 2 singers co-occurring with beatles Some document mentioning John Lennon … musician:john_lennon xyz:john_lennon … John Lennon musician:john_lennonxyz:john_lennon … [Special Doc] TRANSLATE:singer:musician

  31. Processing the query “beatles musician” position John Lennon at the Royal Variety Show in 1963, in the presence of members of the British royalty: "Those of you in the cheaper seats can clap your hands. The rest of you, if you'll just rattle your jewellery." Gitanes … legend says that John Lennon artist:john_lennonbeliever:john_lennon of the Beatles smoked … John Lennon 0 artist:john_lennon0 believer:john_lennon 1 relation:is_a 2 class:musician… beatles artist:* artist:*. relation:is_a . class:musician two prefix queries artist:john_lennonartist:queen_elisabeth artist:pete_best etc. artist:wolfang_amadeus_mozart artist:johann_sebastian_bach artist:john_lennon etc. onejoin person:john_lennon etc.

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