XML Keyword Search Refinement

1. XML Keyword Search Refinement 郭青松

2. Outline • Introduction • Query Refinement in Traditional IR • XML Keyword Query Refinement • My work

3. Why we need query refinement? • User express their query intention by keywords, but their don’t know how to formulate good query • Lack of experience • Too many expression forms • Unfamiliar with the system • Have no idea about the data • „Query Refinement • Refine the query and get good results

4. What is Query Refinement? • Query expansion(query reformulation) • Given an ill-formed query from the user, we refine the query and help the user to better retrieve documents. • The goal is to improve precisionand/or recall. • Example: • “cars” “car”, “automobile”, “auto”

5. XML Search • Tag + Keyword search • book: xml • Path Expression + Keyword search (CAS Queries) • /book[./title about “xml db”] • Structure query • XPath, XQuery • Keyword search (CO Queries) • “xml”

6. XML Keywords Search VS IR • IR • Flat HTML pages • Whole page returned • XML • Model(tree、graph) • Structural(semi-structural) • Semantic-based query(LCA, SLCA…) • Information fragment returned

7. Need of XML Keyword Query Refinement • Hard to know the XML content • Especially big xml document • Information fragments(LCA\SLCA) • Easily affect the results(Precision ) • Huge difference of query results • IR style refinement methods is not suitable for xml • Only content be considered • Need structure information to form a good query

8. Outline • Introduction • Query Refinement in Traditional IR • XML Keyword Query Refinement • My work

9. Tasks • Spelling Correction • Word Splitting/Word Merging • Phrase Segmentation • Word Stemming • Acronym Expansion • Add/Delete Terms • Substitution

10. Classes of Query Refinement • Relevance feedback • Users mark documents(relevant, nonrelevant) • Reweight the terms in the query • Automatic query Refinement • System analysis the relevance of documents and query, give refined query automatically • Global analysis • Local analysis

11. Relevance Feedback • Began in the 1960s • Improvement in recall and precision • Basic process as follows • The user issues their initial query • The system returns an initial result set. • The user then marks some returned documents as relevant or nonrelevant. • The system then re-weights the terms and refine the query results

12. Relevance Feedback Models • Boolean. • Terms appear in document: relevance • Vector Space. • q=(t1, t2,…, tn) d=(w1, w2,…, wn) • Probabilistic. • Relevance of a query and documents evaluate as probability • Probabilistic ranking principle

13. Rocchio algorithm for vector-space model • qm :refined query vector • q0: the original query vector • Dr :relevantdocuments, Dnr: nonrelevant documents • α, β, γ: weights attached to each term Average relevant- document vector Average non-relevant document vector

14. Globalanalysis(1) • Using all documents to compute the similarity of query q and terms in the documents • Similarity Thesaurus based

15. Globalanalysis(2) Similarity of terms Queryvector Similarityofqueryandterms Select r terms with highest sim value and adding into initial query , reformulate the new query

16. Local analysis • Local analysis: Using initial query results(especially documents front ,local documents) to refine the query • Local clustering • Clustering the term of local documents • Query refined with the relevant cluster • Similarity of terms in query and terms in documents • Local context analysis(LCA) • Get the most similar term in local documents with the query q to expanse • Similarity of q and terms in documents Company name

17. Outline • Introduction • Query Refinement in Traditional IR • XML Keyword Query Refinement • My work

18. XML Refinement Manner(1) • Query refined form • Keywords query  New Keywords Query • Treat as traditional IR problem • IR with XML Keyword search Semantics • Keywords  Structural Query • User participant • Manually(User Interactive ) • Structural Feedback • Automatic Company name

19. XML Refinement Manner (2) • Manually Refined to new Keywords Query • IR(consider the structure of xml) • Manually Transform to Structural Query • Relevance Feedback • Automatic Refined to new Keywords Query • Lu jiaheng: • AutomaticTransform to Structural Query • NLP

20. Automatic Refined to new Keywords Query(1) • Query  Refined Query • Rule based • Operation • Term merging: • Term splitting: • Term substitution: • Term deletion

21. Automatic Refined to new Keywords Query(2) • Ranking Refined query candidates set S(RQ) • Refinement cost • Cost: the step of “op” from “Q” to “RQ” • Dynamic programming • Efficient Refinement Algorithms • Avoid the multiple scan invert list • stack-based ,stack-based, short-list-eager approach • RQ candidates have the same refinement cost • Q={XML, Jim, 2001}{XML, 2001}, {Jim, 2001} or {XML, Jim}

22. NLPX • Natural Language Query (NLQ)  NEXI • NEXI(Narrowed Extended XPath I) • //A[about(//B,C)] • A: path expression, • B :relative path expression to A • C is the content requirement. • ‘about’ clause represents an individual information request.

23. NLPX—Lexical and Semantic Tagging • structural words: content requirements • boundary words: Path expression • instruction words • R :return request , S :support request. Find sections about compression in articles about information retrieval Tagged: Find/XIN sections/XST about/XBD compression/NN in/IN articles/XST about/XBD information/NN retrieval/NN

24. NLPX—Template Matching • most queries correspond to a small set of patterns • formulate grammar templates with patterns Query: Request+ Request : CO_Request | CAS_Request CO_Request: NounPhrase+ CAS_Request: SupportRequest | ReturnRequest SupportRequest: Structure [Bound] NounPhrase+ ReturnRequest: Instruction Structure [Bound] NounPhrase+ Grammar Templates Request 1 Request 2 Structural: /article/sec /articlec Content: compression information retrieval Instruction: R S Information Requests

25. NLPX—NEXI Query Production • merge the information request into NEXI query. • A[about(.,C)] • A :the request structural attribute and • C : the request content attribute. //article[about(.,information retrieval)]//sec[about (.,compression)]

26. Query generation process • Create target component • Break up the query into units • Generate initial target • combinations of input target components • Generate queries • modifying a target component • combing two components

27. Initialization • Breaks up the input query into terms • Structure( XML tags or attributes) • Content term(refer to text) • Create component • Structure term  unbound target • Content term  binding to a bound target • Probability enumeration

28. Target component and target sets Query: Papers by jennifer widom • {//author[~’jennifer widom’]} 0.6842 • {//editor[~’jennifer widom’]} 0.3150 • {//title[~’jennifer widom’]} 0.0004 • {//article} {//author[∼‘jennifer widom’]} 0.3421 • {//inproceedings} {//author[∼‘jennifer widom’]} 0.3421 • {//inproceedings} {//editor[∼‘jennifer widom’]} 0.1577 • {//article} {//editor[∼‘jennifer widom’]} 0.1577 • {//inproceedings} {//title[∼‘jennifer widom’]} 0.0002 • {//article} {//title[∼‘jennifer widom’]} 0.0002 • papers • {//article} 0.5000 • {//inproceedings} 0.5000 • Jennifer widom

29. Transformation Operators(1) • Aggregation: merge targets with same tag • {//a}, {//a[~’x’]}  {//a[~’x’]} • {//a[~’x’]} , {//a[~’y’]}  {//a[~’x y’]} • Prefix expansion: add an ancestor condition • {//b}  {//a//b} • {//b[~’x’]}  {//a//b[~’x’]} • Ordering: combine targets • {//a}, {//b}  {//a//b} or {//a[//b]} • {//a}, {//b[~’x’]}  {//a//b[~’x’]} or {//a[//b[~’x’]]}

30. Conclusion • Two stronger assumption • Keyword query non-ambiguity • Availability of XML thesaurus • Accuracy: • terms classification didn’t consider specific XML context • Time costly: • Term classification • Targets create scan the XML documents

31. Outline • Introduction • Query Refinement in Traditional IR • XML Keyword Query Refinement • My work

32. Thank You ! www.themegallery.com