1. Indexing Strategies for the Linguist’s Search Engine Aaron Elkiss and Philip Resnik UMIACS

2. Why a Linguist’s Search Engine? Goal for linguists: Use naturally occurring data to support theories “Bag of word” searches not sufficient Structural searches of parse trees would be better

3. Constituency Parse

4. Database Must permit real-time interaction Must permit large-scale searches Must allow search on linguistic criteria Interface Must have linguist-friendly “look and feel” Must minimize learning/ramp-up time Must be reliable Must evolve with real use A Web Search Tool for the Ordinary Working Linguist

5. Querying Parse Trees Find all trees containing a particular subtree We use Query by Example to edit an example sentence to the structure we’re interested in

6. Query Properties Typically concerned with structure near the leaves of the tree Relationship can be ancestorship rather than immediate dominance

7. LSE Design Criteria Must permit arbitrary structural searches –multiple branches with wildcards in realtime on a large collection of sentences –1GB scaling up to 10GB or more

8. Existing Techniques Convert data to a relational model Streaming techniques (tgrep2 (Rohde), XSQ (Chawathe et al.)) Index, but permit only simple searches (DataGuides – Widom et al.) Indexing techniques work best with a simple schema

9. Goals Must handle a dataset with a very large schema – 17 million paths from root to terminal –Xmark 1GB has 2.4 million –Path lengths also longer in LSE –Set of paths from root to preterminal fixed in Xmark, grows without bound in LSE Must handle queries with wildcards well Must retrieve all results (100% recall)

10. Assumptions Indexing can be slow (overnight) Doesn’t need to support online update Can overgenerate results – < 100% precision –Use tgrep2 as a filter

11. Baseline Solution VIST: A dynamic index method for querying XML data by tree structures (Wang et al (IBM Watson), SIGMOD 2003) Suffix-tree based approach Indexes structure and content together Supports branching queries well

12. Suffix Trees Index all suffixes of a given string

13. Structure Encoded Sequences Represent each node in DFS order with the complete path from the root to the node One parse tree = one document = one structure encoded sequence S1 S_S1 NP_S_S1 NNP_S_S1 Jared_NNP_NP_S_S1 VP_S_S1 VBD_S_S1 laughed_VBD_VP_S_S1

14. VIST Trees Insert structure encoded sequences instead of suffixes of a string

15. Node Identification (DFS order / node ID, number of descendants) = (n, d) DFS order uniquely identifies a node with number of descendants, identifies which nodes are descendants of a given node can produce without using a lot of memory using perl and UNIX sort utility (0,12) (1,11) (2,10) (3,4) (4,3) (5,2) (6,1) (7,0) (8,4) (10,2) (11,1) (12,0) (9,3)

16. VIST Indexes Two Btree indexes using BerkeleyDB Structural Sequence Index Document Index

17. Structural Sequence Index Structural Sequence Element  (n, d) –S1  (0,12) –VP_S_S1  (5,2), (10,2) (0,12) (1,11) (2,10) (3,4) (4,3) (5,2) (6,1) (7,0) (8,4) (10,2) (11,1) (12,0) (9,3)

18. Document Index documents inserted at node ID of last element (0,12) (1,11) (2,10) (3,4) (4,3) (5,2) (6,1) (7,0) (8,4) (10,2) (11,1) (12,0) (9,3) 7  12 

19. Search (0,12) (1,11) (2,10) (3,4) (4,3) (5,2) (6,1) (7,0) (8,4) (10,2) (11,1) (12,0) (9,3) Query: Select everything matching the first branch of the query Order of branches in query is important For each item, recurse on items that match the next branch and are descendants in the tree - those with [n 2, n 2 + d 2 ] contained in [n 1, n 1 + d 1 ] [3,7] contains [5,7]

20. Recursion Base Case After the last branch of the query Retrieve documents with descendant node IDs (0,12) (1,11) (2,10) (3,4) (4,3) (5,2) (6,1) (7,0) (8,4) (10,2) (11,1) (12,0) (9,3) 7 

21. Peculiarities of VIST Precision is not 100%! Query matches both these documents

22. Problematic Query - Wildcards Wildcards can still be a problem –Recursion isn’t deep but can be very wide –End up looking at same nodes over and over again with different wildcard instantiations from previous branches

23. Problematic Query - Wildcards For every way we instantiate the first branch robot_nn_np_vp_vp_s_vp_s_sbar_vp_s_vp_s_sbar_vp_s_vp_s_s1 robot_nn_np_vp_vp_s_vp_vp_s_s1 robot_nn_np_vp_vp_s_vp_vp_s_sbar_np_pp_adjp_vp_s_sbar_vp_vp_ s_sbar_np_s1 … 254 more we have to look at every way to instantiate the second branch laughs_vbz_vp_vp_s_sbar_np_pp_np_pp_vp_s_s1 laughs_vbz_vp_vp_s_sbar_vp_s_s_s1 laughs_vbz_vp_vp_s_sbar_vp_s_s1 … 98 more

24. Problematic Query – Common Terminal VIST’s structural index actually stores terminal length root … preterminal the 6 S1 S VP FRAG X DT to find instantiated prefixes of structural sequence elements We’d look for JJR 5 S1 S VP FRAG X

25. Problematic Query – Common Terminal To find structural sequence elements like the_DT_X_FRAG_… we have to look at every element with the terminal ‘the’ 220284 for the_… vs. 121 for the_DT_X_frag_…

26. Solution Overview Ignore insufficiently selective query branches Reorder processing of query branches Different ordering for structural index Create in-memory tree for the query Memoization of nodes matching subtree of query

27. Ignore query branches Generate statistics for each pair of tokens Calculate estimated selectivity of each branch Discard insufficiently selective branches Use tgrep2 as filter Still problematic:

28. Reorder query branches Start processing with most selective branch Join to proceeding branches, then following branches

29. Reorder structural index Store as terminal preterminal … root the DT X FRAG VP S S1 Immediately find paths with particular suffix Terminals occurring in similar contexts are clustered together

30. Reorder structural index Now we have to look at every JJR_X_FRAG_… instead of just those with the same prefix as the_DT_X_FRAG_… But we’ll only do so once, and only keep those the_DT_X_FRAG_… and JJR_X_FRAG_… who have matching prefixes

31. Create Query Tree Keep relevant instantiations of each branch in memory S1_*_NP_*_robot robot_NN_NP_NP_S_SBAR_S_X_X_S1 robot_NN_NP_NP_S_SBAR_VP_FRAG_S1 robot_NN_NP_NP_S_SBAR_VP_S_S_S1 S1_*_VP VP_S_S1 *_laughs laughs_VBZ_VP_VP_S_SBAR_NP_PP_NP_PP *_us us_PRP_NP VP_VP_S_SBAR_NP_PP_NP_PP_VP_S_S1 *_laughs laughs_VBZ *_us us_PRP_NP

32. Subtree Memoization S1_*_NP_*_robot robot_NN_NP_NP_S_SBAR_S_X_X_S1 (1,15) (30,10) S1_*_VP VP_S_S1 *_laughs laughs_VBZ_VP_VP_S_SBAR_NP_PP_NP_PP (5,5) VP_VP_S_SBAR_NP_PP_NP_PP_VP_S_S1 *_laughs laughs_VBZ (20,0) S1_*_VP_*_laughs (5,5) (20,0) Create sorted list of all nodes for a particular branch of the query

33. Subtree Memoization S1_*_VP VP_S_S1 *_laughs laughs_VBZ_VP_VP_S_SBAR_NP_PP_NP_PP (5,5) (10,0) *_us us_PRP_NP (6,0) us_PRP_NP_NP (50,0) VP_VP_S_SBAR_NP_PP_NP_PP_VP_S_S1 *_laughs laughs_VBZ (20,20) *_us us_PRP_NP (60,0) S1_*_VP_*_us / VP_S_S1 (6,0) (50,0) Specifier for memoized list includes wildcard instantiations S1_*_VP_*_us / VP_VP_S_SBAR_NP_PP_NP_PP_VP_S_S1 (60,0)

34. Evaluation Original VIST scalability XMark LSE data

35. Original VIST scalability Random queries over a synthetic data set From Haixun Wang, Sanghyun Park, Wei Fan, and Philip S Yu. VIST: A dynamic index method for querying XML data by tree structures. In SIGMOD, 2003. http://citeseer.nj.nec.com/wang03vist.html

36. Evaluation - VIST Scales extremely well for Xmark qn vs. qnc – cached vs. non-cached Queries – same form as XPath queries from original VIST paper Q1: /site//item[location=‘US’]/mail/date[text=’12/15/1999’] (3.7s) Q2: /site//person/*/city[text=‘Pocatello’] (2.5s) Q3: //closed_auction[*[person=‘person1’]]/date[text=’12/15/1999’] (4.1s)

37. Evaluation - LSE Need more data Queries – two forms of a real LSE query Q1: Q2:

38. Evaluation – Index Size

39. Future Directions Reimplement this + original VIST in C Scale up to 10gb Improved query planning Ranking & efficient top-k results Investigate usefulness for structural search of HTML documents

40. HTML Structural Search Similar properties to LSE data –no fixed schema –no maximum path depth “Whole Web” search probably not yet feasible

41. Ranking & efficient top-k results Assign score to possible result –Closer to matrix level = higher score? Look for results with highest score first

42. Improved Query Planning “Dynamic Ignorance” –choose whether to use a query branch based on wildcard instantiations Full reordering of query branches

43. Acknowledgments Philip Resnik, of course! Saurabh Khandelwal – tree editor Doug Rohde – tgrep2 This work is supported by NSF ITR grant IIS0113641.