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XML Full-Text Search: Challenges and Opportunities

XML Full-Text Search: Challenges and Opportunities. Sihem Amer-Yahia AT&T Labs – Research. Jayavel Shanmugasundaram Cornell University. Outline. Motivation Full-Text Search Languages Scoring Query Processing Open Issues. Motivation.

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XML Full-Text Search: Challenges and Opportunities

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  1. XML Full-Text Search: Challenges and Opportunities Sihem Amer-Yahia AT&T Labs – Research Jayavel Shanmugasundaram Cornell University VLDB Tutorial on XML Full-Text Search

  2. Outline • Motivation • Full-Text Search Languages • Scoring • Query Processing • Open Issues VLDB Tutorial on XML Full-Text Search

  3. Motivation • XML is able to represent a mix of structured and text information. • XML applications: digital libraries, content management. • XML repositories: IEEE INEX collection, LexisNexis, the Library of Congress collection. VLDB Tutorial on XML Full-Text Search

  4. XML in Library of Congresshttp://thomas.loc.gov/home/gpoxmlc109/h2739_ih.xml <bill bill-stage="Introduced-in-House"> <congress>109th CONGRESS</congress> <session>1st Session</session> <legis-num>H. R. 2739</legis-num> <current-chamber>IN THE HOUSE OF REPRESENTATIVES</current-chamber> <action> <action-date date="20050526">May 26, 2005</action-date> <action-desc><sponsor name-id="T000266">Mr. Tierney</sponsor> (for himself, <cosponsor name-id="M001143">Ms. McCollum of Minnesota</cosponsor>, <cosponsor name-id="M000725">Mr. George Miller of California</cosponsor>) introduced the following bill; which was referred to the <committee-name committee-id="HED00">Committee on Education and the Workforce</committee-name> </action-desc> </action> … VLDB Tutorial on XML Full-Text Search

  5. THOMAS: Library of Congress VLDB Tutorial on XML Full-Text Search

  6. INEX Data <article> <fno>K0271</fno> <doi>10.1041/K0271s-2004</doi> <fm> <hdr><hdr1><ti>IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING</ti> <crt> <issn>1041-4347</issn>/04/$20.00 &copy; 2004 IEEE Published by the IEEE Computer Society</crt></hdr1><hdr2><obi><volno>Vol. 16</volno>, <issno>No. 2</issno></obi> <pdt><mo>FEBRUARY</mo><yr>2004</yr></pdt> <pp>pp. 271-288</pp></hdr2> </hdr> <tig><atl>A Graph-Based Approach for Timing Analysis and Refinement of OPS5 Knowledge-Based Systems</atl><pn>pp. 271-288</pn><ref rid="K02711aff" type="aff">*</ref></tig> <au sequence="first"><fnm>Albert Mo Kim</fnm><snm> <ref aid="K0271a1“ type="prb">Cheng</ref></snm><role>Senior Member</role><aff><onm>IEEE</onm></aff></au><au sequence="additional"><fnm>Hsiu-yen</fnm><snm> Tsai</snm></au> <abs><p><b>Abstract</b>&mdash;This paper examines the problem of predicting the timing behavior of knowledge-based systems for real-… VLDB Tutorial on XML Full-Text Search

  7. Example INEX Query <inex_topic topic_id="275" query_type="CAS"> <castitle>//article[about(.//abs, "data mining")]//sec[about(., "frequent itemsets")]</castitle> <description>sections about frequent itemsets from articles with abstract about data mining</description> <narrative>To be relevant, a component has to be a section about "frequent itemsets". For example, it could be about algorithms for finding frequent itemsets, or uses of frequent itemsets to generate rules. Also, the article must have an abstract about "data mining". I need this information for a paper that I am writing. It is a survey of different algorithms for finding frequent itemsets. The paper will also have a section on why we would want to find frequent itemsets.</narrative> </inex_topic> VLDB Tutorial on XML Full-Text Search

  8. Challenges in XML FT Search • Searching over Semi-Structured Data • Users may specify a search context and return context. • Expressive Power and Extensibility • Users should be able to express complex full-text searches and combine them with structural searches. • Scores and Ranking • Users may specify a scoring condition, possibly over both full-text and structured predicates and obtain top-k results based on query relevance scores. • The language should allow for an efficient implementation. VLDB Tutorial on XML Full-Text Search

  9. XML FT Search Definition • Context expression: XML elements searched: • pre-defined XML nodes. • XPath/XQuery queries. • Return expression:XML fragments returned: • pre-defined meaningful XML fragments. • XPath/XQuery to build answers. • Search expression:FT search conditions: • Boolean keyword search. • proximity distance, scoping, thesaurus, stop words, stemming. • Score expression: • system-defined scoring function. • user-defined scoring function. • query-dependent keyword weights. VLDB Tutorial on XML Full-Text Search

  10. Outline • Motivation • Full-Text Search Languages • Scoring • Query Processing • Open Issues VLDB Tutorial on XML Full-Text Search

  11. Four Classes of Languages • Keyword search (INEX Content-Only Queries) • “book xml” • Tag + Keyword search • book: xml • Path Expression + Keyword search • /book[./title about “xml db”] • XQuery + Complex full-text search • for $b in /booklet score $s := $b ftcontains “xml” && “db” distance 5 VLDB Tutorial on XML Full-Text Search

  12. Outline • Motivation • Full-Text Search Languages • Simple Keyword Search • Tags + Keyword Search • Path Expressions + Keyword Search • XQuery + Complex Full-Text Search • Scoring • Query Processing • Open Issues VLDB Tutorial on XML Full-Text Search

  13. XRank [Guo et al., SIGMOD 2003] <workshopdate=”28 July 2000”> <title> XML and Information Retrieval: A SIGIR 2000 Workshop </title> <editors> David Carmel, Yoelle Maarek, Aya Soffer </editors> <proceedings> <paperid=”1”> <title> XQL and Proximal Nodes </title> <author> Ricardo Baeza-Yates </author> <author> Gonzalo Navarro </author> <abstract> We consider the recently proposed language … </abstract> <sectionname=”Introduction”> Searching on structured text is becoming more important with XML … <subsection name=“Related Work”> The XQL language … </subsection> </section> … <citexmlns:xlink=”http://www.acm.org/www8/paper/xmlql> … </cite> </paper> … VLDB Tutorial on XML Full-Text Search

  14. XRank [Guo et al., SIGMOD 2003] <workshopdate=”28 July 2000”> <title> XML and Information Retrieval: A SIGIR 2000 Workshop </title> <editors> David Carmel, Yoelle Maarek, Aya Soffer </editors> <proceedings> <paperid=”1”> <title> XQL and Proximal Nodes </title> <author> Ricardo Baeza-Yates </author> <author> Gonzalo Navarro </author> <abstract> We consider the recently proposed language … </abstract> <sectionname=”Introduction”> Searching on structured text is becoming more important with XML … <subsection name=“Related Work”> The XQL language … </subsection> </section> … <citexmlns:xlink=”http://www.acm.org/www8/paper/xmlql> … </cite> </paper> … VLDB Tutorial on XML Full-Text Search

  15. XIRQL [Fuhr & Grobjohann, SIGIR 2001] <workshopdate=”28 July 2000”> <title> XML and Information Retrieval: A SIGIR 2000 Workshop </title> <editors> David Carmel, Yoelle Maarek, Aya Soffer </editors> <proceedings> <paperid=”1”> <title> XQL and Proximal Nodes </title> <author> Ricardo Baeza-Yates </author> <author> Gonzalo Navarro </author> <abstract> We consider the recently proposed language … </abstract> <sectionname=”Introduction”> Searching on structured text is becoming more important with XML … <em>The XQL language </em> </section> … <citexmlns:xlink=”http://www.acm.org/www8/paper/xmlql> … </cite> </paper> … Index Node VLDB Tutorial on XML Full-Text Search

  16. Similar Notion of Results • Nearest Concept Queries • [Schmidt et al., ICDE 2002] • XKSearch • [Xu & Papakonstantinou, SIGMOD 2005] VLDB Tutorial on XML Full-Text Search

  17. Outline • Motivation • Full-Text Search Languages • Simple Keyword Search • Tags + Keyword Search • Path Expressions + Keyword Search • XQuery + Complex Full-Text Search • Scoring • Query Processing • Open Issues VLDB Tutorial on XML Full-Text Search

  18. XSearch [Cohen et al., VLDB 2003] <workshopdate=”28 July 2000”> <title> XML and Information Retrieval: A SIGIR 2000 Workshop </title> <editors> David Carmel, Yoelle Maarek, Aya Soffer </editors> <proceedings> <paperid=”1”> <title> XQL and Proximal Nodes </title> <author> Ricardo Baeza-Yates </author> <author> Gonzalo Navarro </author> <abstract> We consider the recently proposed language … </abstract> <sectionname=”Introduction”> Searching on structured text is becoming more important with XML … … </paper> <paperid=”2”> <title> XML Indexing </title> … <paperid=”2”> Not a “meaningful” result VLDB Tutorial on XML Full-Text Search

  19. Outline • Motivation • Full-Text Search Languages • Simple Keyword Search • Tags + Keyword Search • Path Expressions + Keyword Search • XQuery + Complex Full-Text Search • Scoring • Query Processing • Open Issues VLDB Tutorial on XML Full-Text Search

  20. XPath [W3C 2005] • fn:contains($e, string) returns true iff $e contains string //section[fn:contains(./title, “XML Indexing”)] VLDB Tutorial on XML Full-Text Search

  21. XIRQL [Fuhr & Grobjohann, SIGIR 2001] • Weighted extension to XQL (precursor to XPath) //section[0.6 · .//* $cw$ “XQL” + 0.4 · .//section $cw$ “syntax”] VLDB Tutorial on XML Full-Text Search

  22. XXL [Theobald & Weikum, EDBT 2002] • Introduces similarity operator ~ Select Z From http://www.myzoos.edu/zoos.html Where zoos.#.zoo As Z and Z.animals.(animal)?.specimen as A and A.species ~ “lion” and A.birthplace.#.country as B and A.region ~ B.content VLDB Tutorial on XML Full-Text Search

  23. NEXI [Trotman & Sigurbjornsson, INEX 2004] • Narrowed Extended XPath I • INEX Content-and-Structure (CAS) Queries //article[about(.//title, apple) and about(.//sec, computer)] VLDB Tutorial on XML Full-Text Search

  24. Outline • Motivation • Full-Text Search Languages • Simple Keyword Search • Tags + Keyword Search • Path Expressions + Keyword Search • XQuery + Complex Full-Text Search • Scoring • Query Processing • Open Issues VLDB Tutorial on XML Full-Text Search

  25. Schema-Free XQuery [Li, Yu, Jagadish, VLDB 2003] • Meaningful least common ancestor (mlcas) for $a in doc(“bib.xml”)//author $b in doc(“bib.xml”)//title $c in doc(“bib.xml”)//year where $a/text() = “Mary” and exists mlcas($a,$b,$c) return <result> {$b,$c} </result> VLDB Tutorial on XML Full-Text Search

  26. XQuery Full-Text [W3C 2005] • Two new XQuery constructs • FTContainsExpr • Expresses “Boolean” full-text search predicates • Seamlessly composes with other XQuery expressions • FTScoreClause • Extension to FLWOR expression • Can score FTContainsExpr and other expressions VLDB Tutorial on XML Full-Text Search

  27. FTContainsExpr //book ftcontains “Usability” && “testing” distance 5 //book[./content ftcontains “Usability” with stems]/title //book ftcontains /article[author=“Dawkins”]/title VLDB Tutorial on XML Full-Text Search

  28. FTScore Clause In any order FOR $v [SCORE $s]? IN [FUZZY] Expr LET … WHERE … ORDER BY … RETURN Example FOR $b SCORE $s in /pub/book[. ftcontains “Usability” && “testing”] ORDER BY $sRETURN <result score={$s}> $b </result> VLDB Tutorial on XML Full-Text Search

  29. FTScore Clause In any order FOR $v [SCORE $s]? IN [FUZZY] Expr LET … WHERE … ORDER BY … RETURN Example FOR $b SCORE $s in /pub/book[. ftcontains “Usability” && “testing” and ./price < 10.00] ORDER BY $sRETURN $b VLDB Tutorial on XML Full-Text Search

  30. FTScore Clause In any order FOR $v [SCORE $s]? IN [FUZZY] Expr LET … WHERE … ORDER BY … RETURN Example FOR $b SCORE $s in FUZZY /pub/book[. ftcontains “Usability” && “testing”] ORDER BY $sRETURN $b VLDB Tutorial on XML Full-Text Search

  31. XQuery Full-Text Evolution Quark Full-TextLanguage (Cornell) 2002 IBM, Microsoft,Oracle proposals TeXQuery(Cornell, AT&T Labs) 2003 XQuery Full-Text 2004 XQuery Full-Text (Second Draft) 2005 VLDB Tutorial on XML Full-Text Search

  32. Outline • Motivation • Full-Text Search Languages • Scoring • Query Processing • Open Issues VLDB Tutorial on XML Full-Text Search

  33. Full-Text Scoring • Score value should reflect relevance of answer to user query. Higher scores imply a higher degree of relevance. • Queries return document fragments. Granularity of returned results affects scoring. • For queries containing conditions on structure, structural conditions may affect scoring. • Existing proposals extend common scoring methods: probabilistic or vector-based similarity. VLDB Tutorial on XML Full-Text Search

  34. Granularity of Results • Keyword queries • compute possibly different scores for LCAs. • Tag + Keyword queries • compute scores based on tags and keywords. • Path Expression + Keyword queries • compute scores based on paths and keywords. • XQuery + Complex full-text queries • compute scores for (newly constructed) XML fragments satisfying XQuery (structural, full-text and scalar conditions). VLDB Tutorial on XML Full-Text Search

  35. Outline • Motivation • Full-Text Search Languages • Scoring • Simple Keyword Search • Tags + Keyword Search • Path Expressions + Keyword Search • XQuery + Complex Full-Text Search • Query Processing • Open Issues VLDB Tutorial on XML Full-Text Search

  36. Granularity of Results • Document as hierarchical structure of elements as opposed to flat document. • XXL [Theobald & Weikum, EDBT 2002] • XIRQL [Fuhr & Grobjohann, SIGIR 2001] • XRANK [Guo et al., SIGMOD 2003] • Propagate keyword weights along document structure. VLDB Tutorial on XML Full-Text Search

  37. <workshop> date <title> <editors> <proceedings> 28 July … XML and … David Carmel … <paper> <paper> … <title> <author> … … XQL and … Ricardo … XML Data Model Containment edge VLDB Tutorial on XML Full-Text Search Hyperlink edge

  38. XXL[Theobald & Weikum, EDBT 2002] • Compute similar terms with relevance score r1 using an ontology. • Compute tf*idf of each term for a given element content with relevance score r2. • Relevance of an element content for a term is r1*r2. • r1 and r2 are computed as a weighted distance in an ontology graph. • Probabilities of conjunctions multiplied (independence assumption) along elements of same path to compute path score. VLDB Tutorial on XML Full-Text Search

  39. Probabilistic ScoringXIRQL [Fuhr & Grobjohann, SIGIR 2001] • Extension of XPath. • Weighting and ranking: • weighting of query terms: • P(wsum((0.6,a), (0.4,b)) = 0.6 · P(a)+0.4 · P(b) • probabilistic interpretation of Boolean connectors: • P(a && b) = P(a) · P(b) VLDB Tutorial on XML Full-Text Search

  40. XIRQL Example • Query: • “Search for an artist named Ulbrich, living in Frankfurt, Germany about 100 years ago” • Data: • “Ernst Olbrich, Darmstadt, 1899” • Weights and ranking: • P(Olbrich p Ulbrich)=0.8 (phonetic similarity) • P(1899 n 1903)=0.9 (numeric similarity) • P(Darmstadt g Frankfurt)=0.7 (geographic distance) VLDB Tutorial on XML Full-Text Search

  41. d/3 d: Probability of following hyperlink d/3 d/3 PageRank [Brin & Page 1998] : Hyperlink edge w 1-d: Probability of random jump VLDB Tutorial on XML Full-Text Search

  42. d1/3 d3 d1/3 d1: Probability of following hyperlink d2: Probability of visiting a subelement d1/3 d3: Probability of visiting parent d2/2 d2/2 ElemRank [Guo et al. SIGMOD 2003] : Hyperlink edge : Containment edge w 1-d1-d2-d3: Probability of random jump VLDB Tutorial on XML Full-Text Search

  43. Outline • Motivation • Full-Text Search Languages • Scoring • Simple Keyword Search • Tags + Keyword Search • Path Expressions + Keyword Search • XQuery + Complex Full-Text Search • Query Processing • Open Issues VLDB Tutorial on XML Full-Text Search

  44. XSearch[Cohen et al., VLDB 2003] • tf*ilf to compute weight of keyword for a leaf element. • A vector is associated with each non-leaf element. • sim(Q,N): sum of the cosine distances between the vectors associated with nodes in N and vectors associated with terms matched in Q. VLDB Tutorial on XML Full-Text Search

  45. Outline • Motivation • Full-Text Search Languages • Scoring • Simple Keyword Search • Tags + Keyword Search • Path Expressions + Keyword Search • XQuery + Complex Full-Text Search • Query Processing • Open Issues VLDB Tutorial on XML Full-Text Search

  46. Vector–based ScoringJuruXML [Mass et al INEX 2002] • Transform query into (term,path) conditions: article/bm/bib/bibl/bb[about(., hypercube mesh torus nonnumerical database)] • (term,path)-pairs: hypercube, article/bm/bib/bibl/bb mesh, article/bm/bib/bibl/bb torus, article/bm/bib/bibl/bb nonnumerical, article/bm/bib/bibl/bb database, article/bm/bib/bibl/bb • Modified cosine similarity as retrieval function for vague matching of path conditions. VLDB Tutorial on XML Full-Text Search

  47. JuruXML Vague Path Matching • Modified vector-based cosine similarity Example of length normalization: cr (article/bibl, article/bm/bib/bibl/bb) = 3/6 = 0.5 VLDB Tutorial on XML Full-Text Search

  48. Query Relaxation on Structure • Schlieder, EDBT 2002 • Delobel & Rousset, 2002 • Amer-Yahia et al, VLDB 2005 VLDB Tutorial on XML Full-Text Search

  49. XML Query Relaxation[Amer-Yahia et al EDBT 2002]FlexPath [Amer-Yahia et al SIGMOD 2004] Query book • Tree pattern relaxations: • Leaf node deletion • Edge generalization • Subtree promotion info edition paperback author Dickens book book Data book edition? info info author Dickens info edition (paperback) author Charles Dickens edition paperback author C. Dickens VLDB Tutorial on XML Full-Text Search

  50. Adaptation of tf.idf to XML Whirlpool[Marian et al ICDE 2005] VLDB Tutorial on XML Full-Text Search

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