Efficient Interactive Fuzzy Keyword Search

Efficient Interactive Fuzzy Keyword Search ShengyueJi, Guoliang Li, JianhuaFeng , Chen Li University of California, Irvine WWW 2009 1 Dec 2011 Presentation @ IDB Lab. Seminar Presented by Jee-bum Park

Outline • Introduction • Indexing Methods • Single Keyword • Multiple Keywords • Experiments • Conclusions

Introduction • http://searchenginewatch.com/article/2128218/Google-Searchers-Use-Autocomplete-Most-Ignore-Google-Instant-Eye-Tracking-Study

Introduction • A typical directory-search form

Introduction • Interactive fuzzy search

Introduction • “interactive, fuzzy search” • Interactive • The system searches for the best answers on the flyas the user types in a keyword query • Fuzzy • The system tries to find relevant records that include words similar to the keywords in the query, even if they do not match exactly

Indexing Methods • List

Indexing Methods • List • Typed “li”

Indexing Methods • List • Typed “lu”

Indexing Methods • Trie 0: \0 10: l 1 4 11: i 14: u 12: n 13: u 15: i 16: s 3, 4 5 7

Indexing Methods • Trie • Typed “li” 0: \0 10: l 1 4 11: i 14: u 12: n 13: u 15: i 16: s 3, 4 5 7

Single Keyword

Edit distance Single Keyword 0 1 2 • Example • Query = “nlis”, edit distance threshold = 2 0: \0 10: l 1 4 11: i 14: u 12: n 13: u 15: i 16: s 3, 4 5 7

Edit distance Single Keyword 0 1 2 • Initial state: “” • Query = “nlis”, edit distance threshold = 2 0: \0 10: l 1 4 11: i 14: u 12: n 13: u 15: i 16: s 3, 4 5 7

Edit distance Single Keyword 0 1 2 • Typed: “n” • Query = “nlis”, edit distance threshold = 2 0: \0 10: l 1 4 11: i 14: u 12: n 13: u 15: i 16: s 3, 4 5 7

Edit distance Single Keyword 0 1 2 • Typed: “nl” • Query = “nlis”, edit distance threshold = 2 0: \0 10: l 1 4 11: i 14: u 12: n 13: u 15: i 16: s 3, 4 5 7

Edit distance Single Keyword 0 1 2 • Typed: “nli” • Query = “nlis”, edit distance threshold = 2 0: \0 10: l 1 4 11: i 14: u 12: n 13: u 15: i 16: s 3, 4 5 7

Edit distance Single Keyword 0 1 2 • Typed: “nlis” • Query = “nlis”, edit distance threshold = 2 0: \0 10: l 1 4 11: i 14: u 12: n 13: u 15: i 16: s 3, 4 5 7

Multiple Keywords • Challenges in multiple keywords • Intersection of multiple lists of keywords • Each prefix query keyword has • Multiple predicted complete keywords • The union of the lists of predicted keywords includes potential answers • The union lists of multiple query keywords need to be intersected in order to compute the answers to the query • Cache-based incremental intersection

Multiple Keywords • HYB (H. Bast, I. Weber. Type Less, Find More: Fast Autocompletion Search with a Succinct Index. In SIGIR 2006) • The intersections can be computed in • The union can be computed in • Total time complexity W’ = { iphone, ipv4, ipv6 } D ∩ Dw = D’ = { 21, 172, 308, 759 }

Multiple Keywords • Forward lists

Experiments • DBLP • It included about one million computer science publication records • Authors, title, conference or journal name, year, page numbers, URL • MEDLINE • It had about 4 million latest publication records related to life sciences and biomedical information • Authors, their affiliations, article title, journal name, journal issue

Experiments • Computing prefixes similar to a keyword

Experiments • List intersection of multiple keywords

Efficient Interactive Fuzzy Keyword Search