Effective XML Keyword Search with Relevance Oriented Ranking

1. Effective XML Keyword Search with Relevance Oriented Ranking Zhifeng Bao, Tok Wang Ling, Bo Chen, Jiaheng Lu

2. Introduction • XML Keyword search • Inspired by IR style keyword search on the web • Enables user to access information in XML database • XML data modeled as a rooted, labeled tree • Recent research efforts • Efficiency • Effectiveness

3. Effectiveness • Capture user’s search intention • Identify the target that user intends to search for • Infer the predicate constraint that user intends to search via • Result ranking • Rank the query results according to their objective relevance to user search intention

4. State of the Art • Search semantics design • LCA (Lowest Common Ancestor) • Node v is a LCAof keyword set K={w1, w2,…,wk} if the sub-tree rooted at v contains at least one occurrence of all keywords in K, after excluding the sub-elements that already contain all keywords in K • SLCA (Smallest LCA) • Node v is a SLCA of keyword set K={w1, w2,…,wk} if • (1) v is a LCA of K • (2) no proper descendant of v is LCA of K • XSeek • Infers the search intention based on the concept of objects and an analysis of the matching between keyword and data node

5. State of the Art (cont) • Efficient result retrieval • Designed based on a certain search semantics • XKSearch, Multiway SLCA etc. • Result ranking • XRANK, XKSEarch, EASE • They only consider • Structural compactness of matching results • Keyword proximity • Similarity at node level

6. Problems Unaddressed Neither SLCA nor Xseek can well address keyword ambiguity • Not address the user search intention adequately! • Meaningfulness of query result • SLCA is less meaningful in many cases • Keyword Ambiguity Problems • A keyword can appear both as an xml node type and as the text value of some other nodes • A keyword can appear in the text values of different xml node types and carry different meanings

7. Meaningfulness Problems storeDB customers books ... ... book customer ... customer ... ID customer ID interests publisher title name authors ... interests ... ... ID ... interest “C 3 ” name name interests author ID author interest name “Art Smith” contact “C 4 ” “B 2 ” address “rock music” ... “Oxford” interest book “C 1 ” “Edward Martin” “Rock Davis” “art” customer no . ... “Sophia Jones” city authors ... “ 1 ” purchases street title ID ... interests name ID author author “Mary Smith” 1 ” “B purchase interest “Art Street” “fashion” “John Williams” 2 ” “C “Art of Customer “Daniel Jones” “John Martin” “street art” Interest Care” • Keyword query “rock music” • Search intention: find customers interested in “rock music” – C3 • SLCA returns:interestnode of C3

8. Keyword Ambiguity Problems storeDB customers books ... ... book customer customer ID customer ID interests publisher title name authors interests ... ... ID ... interest “C 3 ” name interests author ID author interest name “Art Smith” contact “C 4 ” “B 2 ” address “rock music” ... interest book “C 1 ” “Edward Martin” “Rock Davis” “art” no . ... “Sophia Jones” city authors “ 1 ” street title ID ... author author “Mary Smith” 1 ” “B “Art Street” “fashion” “John Williams” “Art of Customer “Daniel Jones” Interest Care” ... ... ... name “Oxford” customer ... purchases interests name ID purchase interest “C 2 ” “John Martin” “street art” • Q = “customer, interest, art” • Ambiguity 1: customer, interest; Ambiguity 2: art • Intention: find customer whose interest is art • less relevant or irrelevant result to be returned also --- C1,C3, B1’s title

9. Keyword Ambiguity (cont) Problems storeDB customers books ... ... book customer ... customer ... ID customer ID interests publisher title name authors ... interests ... ... ID ... interest “C 3 ” name name interests author ID author interest name “Art Smith” contact “C 4 ” “B 2 ” address “rock music” ... “Oxford” interest book “C 1 ” “Edward Martin” “Rock Davis” “art” customer no . ... “Sophia Jones” city authors ... “ 1 ” purchases street title ID ... interests name ID author author “Mary Smith” 1 ” “B purchase interest “Art Street” “fashion” “John Williams” “C 2 ” “Art of Customer “Daniel Jones” “John Martin” “street art” Interest Care” - How to rank C1 to C4 and B1? • Q = “customer, art” • “art” can be the value of interest node(C2, C4), name node(C3), or street node of customer(C1), or title node of book(B1) • “customer” can be tag name of customer node, or (part of) value of title of(B1)

10. Objectives & Challenges • Address the below as a single problem • Search intention identification • Query result retrieval • Result ranking • Extend original TF*IDF from text database to XML database, while capture the hierarchical structure of XML data • Challenges • How to decide which sub-tree(s) with appropriate node types can capture user desired information • How to return sub-trees of an appropriate size (i.e. contain enoughbut non-overwhelming information) • How to rank those sub-trees by their relevance

11. Challenges Difficulty in applying TF*IDF to XML • XML DB carries semantic information while text DB contains pure text information. XML TF*IDF must be aware of the underlying semantics. • All contents of XML data are stored in leaf nodes only • What is analogy of “flat document” in XML? • Sub-tree classified according to its prefix path • Normalization factor is not simply the size of sub-tree • Structure of sub-trees may also infest the ranks

12. TF*IDF Recap • Rule 1: A keyword appearing in many documents should not be regarded as more important than a keyword appearing in a few. --- IDF • Rule 2: A document with more occurrences of a query keyword should not be regarded as less important for that keyword than a document that has less. --- TF • Rule 3: A normalization factor is needed to balance between long and short documents • as Rule 2 discriminates against short documents which may have less chance to contain more occurrences of keywords.

13. Our Approach • Extend IR-style keyword search techniques (like TF*IDF) from text database to XML database, in order to capture the hierarchical structure of xml document • by analyzing the knowledge of statistics of underlying XML data • Major Contributions • Identify user’s desired search-for node and search-via node(s) in a heuristic way • Define XML TF(term frequency) and XML DF (document frequency) • Confidence Formulas for search for/via candidates • Define XML TF*IDF Similarity • Propose 3 guidelines specifically for xml keyword search • Take keyword ambiguity problems into account • Design a Keyword Search Engine XReal

14. Data Model • Value node – text values contained in leaf node • Structural node • Single-valued node type, multi-valued node type • Grouping type– all its children are of same multi-valued type storeDB customers books ... ... book customer ... customer ... ID customer ID interests publisher title name authors ... interests ... ... ID ... interest “C 3 ” name name interests author ID author interest name “Art Smith” contact “C 4 ” “B 2 ” address “rock music” ... “Oxford” interest book “C 1 ” “Edward Martin” “Rock Davis” “art” customer no . ... “Sophia Jones” city authors ... “ 1 ” purchases street title ID ... interests name ID author author “Mary Smith” “B 1 ” purchase interest “Art Street” “fashion” “John Williams” “C 2 ” “Art of Customer “Daniel Jones” “John Martin” “street art” Interest Care” • Node type- Two nodes are of same node type if they share the same prefix path /storeDB/customers/customer/name vs. /storeDB/books/book/publisher/name

15. XML TF and IDF • XML DF (document frequency) • The number of T-typed nodes that contain keyword k in their sub-trees in XML database. • Granularity of similarity measurement is sub-trees of certain node type T • XML TF (term frequency) • The number of occurrences of a keyword k in a given value node a in XML database.

16. Infer the desired search-fornode • Guidelines: A node type T is considered asa desired search for nodeif • T is intuitively related to every query keyword • XML nodes of type T should be informativeenough to contain enough relevant information • XML nodes of type T should be not overwhelmingto contain too much irrelevant information • Confidence of T as the search for node w.r.t. query q. • productinstead of sum is used to follow 1st guideline • log partdesigned to follow 3rd guideline • exponentialpart designed to follow 2nd guideline • r is a decay factor in (0,1].

17. Infer the Search-ViaNodes • Infer structural nodeto search via • Structural node n is a good candidate if it is related to as many (but not necessarily all) keywords as possible • Search via node type normally is not unique • Infer individual value nodeto search via • Statistics alone is not adequate to infer the likelihood of a value nodeas (part of) search via node • Capture keyword co-occurrence

18. storeDB customers books ... ... book customer ... customer ... ID customer ID interests publisher title name authors ... interests ... ... ID ... interest “C 3 ” name name interests author ID author interest name “Art Smith” contact “C 4 ” “B 2 ” address “rock music” ... “Oxford” interest book “C 1 ” “Edward Martin” “Rock Davis” “art” customer no . ... “Sophia Jones” city authors ... “ 1 ” purchases street title ID ... interests name ID author author “Mary Smith” 1 ” “B purchase interest “Art Street” “fashion” “John Williams” 2 ” “C “Art of Customer “Daniel Jones” “John Martin” “street art” Interest Care” Capture keyword co-occurrence • E.g. Q = “ customer, name, rock, interest, art ” • Easy to find nameand interesthave high confidence to be the search via nodes • But hard to know rockis value of name or interest, artis value of interest or name • How to differ customer C4 from C3?

19. Capture keyword co-occurrence • Proximity factors for a value node v of type kt containing keyword k • Given a query q and a certain value node v, if there are two keywords kt and k in q, s.t. kt matches the type of an ancestor node of v and k matches a keyword in v • In-Query distance • Distance between keyword k and node type kt in query q • Favors: kt appears before k • Structural distance • Depth distance between v and the nearest kt typed ancestor node of v • Value-Type distance • Max of the above two

20. Principles of XML keyword search • Principle 1 • When searching for D-typed nodes via a single-valued typeV, ideally only the values and structures nested in V-typed nodes can affect the relevance, regardless of the size of other typed nodes nested in D-typed nodes. • However, TF*IDF similarity in IR normalizes the relevance score of each document w.r.t. its size • Principle 2 – address keyword Ambiguity 2 • When searching for nodes of type D via a multi-valued typeV’, the relevance of a D-typed node which contains a query relevant V’-typed node should not be affected (i.e. normalized) too much by other query-irrelevant V’-typednodes. • Example: query “art” - C4 should not be less relevant than C1

21. Principles of XML keyword search • Principle 1 and 2 • Especially useful for interpreting pure keyword query - find search via node correctly • Principle 3 • The order of keywords in a query is important to indicate the search intention • Incorporate the search via confidence Cvia we defined before

22. XML TF*IDF Similarity TF IDF Normalization factor • To calculate the similarity between the search for node and the query q • Base case: similarity between value nodea and q • Apply original TF*IDF directly since a contains keywords only without any structure • Recursive case: similarity between structural noden and q • Based on similarities of its children c and the confidence level of c as the node type to search via

23. XML TF*IDF Similarity (cont.) Weighted sum of all n’s children’s similarity and their confidence to be the search via node Overall weight of node n w.r.t query q which essentially plays the role of anormalization factor • Recursive Case • Intuition 2.An internal node n is relevant to q, if n has a child c such that the type of c has high confidence to be a search via node w.r.t. q (i.e. large Cvia(Tc , q)), and c is highly relevant to q (i.e. large sim(q, c)). • Intuition 3.An internal node n is more relevant to q if n has more query-relevant children when all others being equal.

24. Flowchart of answering a query • Identify user search intention • Compute the confidence of all possible candidate node types and choose desired search for node Tfor • Relevance-oriented ranking • Compute XML TF*IDF similarity in a bottom-up approach from value nodes containing keywords up to nodes of type Tfor • Return a ranked list of sub-trees rooted at nodes of type Tfor • If more than one search for node type have comparable confidence, a ranked list for each search for node is returned

25. Experimental Result • Data set • DBLP, XMark, WSU, eBay • Comparison • Compare XReal with SLCA, Xseek • Equipment • Implement in Java • Run on 3.6GHz pentium IV, 1 GB memory PC with Windows XP • Berkeley DB java edition for storing keyword inverted lists and keyword frequency table

26. Search Effectiveness • Accuracy in inferring the search for node • Conducted by user survey • Tested queries contain at least one of the two ambiguity problems • Conclusion • XReal works well, especially when the search for node is not given explicitly in the query

27. Search Effectiveness • Result effectiveness • Measured by precision, recall, F-measure • Observations • XReal achieves higher precision than SLCA and Xseek for queries that contain ambiguities • XReal Performs as well as XSeek when queries have no ambiguity in XML data • XReal: Top-100 precision higher than overall precision • F-measure also shows good overall effectiveness of both XReal and XSeek

28. Ranking Effectiveness • Metrics • Number of Top-1 answers that are relevant • Reciprocal Rank (R-Rank) • Mean Average Precision (MAP)

29. Efficiency & Scalability • Compare three adoptions of indices for XReal, and SLCA • Dup • Store only the dewey id and XML TF • DupType • Stores an extra node type (i.e. its prefix path) • DupTypeNorm • Stores an extra normalization factor Wa for value node

30. XMark DBLP

31. Q&A Thank You

32. storeDB customers books ... ... book customer ... customer ... ID customer ID interests publisher title name authors ... interests ... ... ID ... interest name name interests author ID author interest name “Art Smith” contact address “rock music” ... “Oxford” interest book “C1” “Edward Martin” “Rock Davis” “art” customer no . ... “Sophia Jones” city authors ... “ 1 ” purchases street title ID ... interests name ID author author “Mary Smith” purchase interest “Art Street” “fashion” “John Williams” “Art of Customer “Daniel Jones” “John Martin” “street art” Interest Care” “C3” “C4” “B2” “B1” “C2”