1 / 46

Distance functions and IE -2

Distance functions and IE -2. William W. Cohen CALD. Announcements. March 25 Thus – talk from Carlos Guestrin (Assistant Prof in Cald as of fall 2004) on max-margin Markov nets 9:30 am in NSH 1507 open to public - tell your friends! Datasets:

shona
Download Presentation

Distance functions and IE -2

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Distance functions and IE -2 William W. Cohen CALD

  2. Announcements • March 25 Thus – talk from Carlos Guestrin (Assistant Prof in Cald as of fall 2004) on max-margin Markov nets • 9:30 am in NSH 1507 • open to public - tell your friends! • Datasets: • some public extraction data is (I hope readable) on /afs/cs/project/extract-learn/repository • Writeups: • nothing today • “distance metrics for text” – three papers - due next Monday, 3/22

  3. Record linkage: definition • Record linkage: determine if pairs of data records describe the same entity • I.e., find record pairs that are co-referent • Entities: usually people (or organizations or…) • Data records: names, addresses, job titles, birth dates, … • Main applications: • Joining two heterogeneous relations • Removing duplicates from a single relation

  4. The data integration problem • Control flow (modulo details about querying • Extract (author, department) pairs from DB1 • Extract (department ,www server) pairs from DB2 • Execute the two-step plan to get paper: • author -> department -> wwwServer • two steps means matching (linking, integrating, deduping, ....) department names in DB1/DB2 • issues are completely different if user is executing a one-step plan: • one-step plan is retrieval

  5. String distance metrics: Levenshtein • Edit-distance metrics • Distance is shortest sequence of edit commands that transform s to t. • Simplest set of operations: • Copy character from s over to t • Delete a character in s (cost 1) • Insert a character in t (cost 1) • Substitute one character for another (cost 1) • This is “Levenshtein distance”

  6. Computing Levenshtein distance – 4 D(i-1,j-1) + d(si,tj) //subst/copy D(i-1,j)+1 //insert D(i,j-1)+1 //delete D(i,j) = min A trace indicates where the min value came from, and can be used to find edit operations and/or a best alignment (may be more than 1)

  7. Smith-Waterman distance - 2 0 //start over D(i-1,j-1) - d(si,tj) //subst/copy D(i-1,j) - G //insert D(i,j-1) - G //delete D(i,j) = max G = 1 d(c,c) = -2 d(c,d) = +1

  8. Smith-Waterman distance - 3 0 //start over D(i-1,j-1) - d(si,tj) //subst/copy D(i-1,j) - G //insert D(i,j-1) - G //delete D(i,j) = max G = 1 d(c,c) = -2 d(c,d) = +1

  9. c o h e n d o r f m 0 0 0 0 0 0 0 0 0 c 1 0 0 0 0 0 0 0 0 c 0 0 0 0 0 0 0 0 0 o 0 2 1 0 0 0 2 1 0 h 0 1 4 3 2 1 1 1 0 n 0 0 3 3 5 4 3 2 1 s 0 0 2 2 4 4 3 2 1 k 0 0 1 1 3 3 3 2 1 i 0 0 0 0 2 2 2 2 1 dist=5 Smith-Waterman distance - 5

  10. Smith-Waterman distance in Monge & Elkan’s WEBFIND (1996) • String s=A1 A2 ... AK, string t=B1 B2 ... BL • sim’ is editDistance scaled to [0,1] • Monge-Elkan’s “recursive matching scheme” is average maximal similarity of Aito Bj:

  11. Results: S-W from Monge & Elkan

  12. Affine gap distances • Smith-Waterman fails on some pairs that seem quite similar: William W. Cohen William W. ‘Don’t call me Dubya’ Cohen Intuitively, a single long insertion is “cheaper” than a lot of short insertions Intuitively, are springlest hulongru poinstertimon extisn’t “cheaper” than a lot of short insertions

  13. Affine gap distances - 2 • Idea: • Current cost of a “gap” of n characters: nG • Make this cost: A + (n-1)B, where A is cost of “opening” a gap, and B is cost of “continuing” a gap.

  14. D(i-1,j) - A IS(i-1,j) - B Best score in which si is aligned with a ‘gap’ IS(i,j) = max Best score in which tj is aligned with a ‘gap’ D(i,j-1) - A IT(i,j-1) - B IT(i,j) = max Affine gap distances - 3 D(i-1,j-1) + d(si,tj) IS(I-1,j-1) + d(si,tj) IT(I-1,j-1) + d(si,tj) D(i-1,j-1) + d(si,tj) //subst/copy D(i-1,j)-1 //insert D(i,j-1)-1 //delete D(i,j) = max

  15. Affine gap distances - 4 -B IS -d(si,tj) -A D -d(si,tj) -A -d(si,tj) -B IT

  16. Affine gap distances – experiments (from McCallum,Nigam,Ungar KDD2000) • Goal is to match data like this:

  17. Affine gap distances – experiments (from McCallum,Nigam,Ungar KDD2000) • Hand-tuned edit distance • Lower costs for affine gaps • Even lower cost for affine gaps near a “.” • HMM-based normalization to group title, author, booktitle, etc into fields (as in Borkar et al)

  18. Affine gap distances – experiments

  19. TFIDF distance for data integration Experiments with WHIRL

  20. Three ways to deal with output of IE systems • Method 1. • Do the best you can at mapping the output into a conventional database (or KR system) with a natural schema (info about people, events, etc) • Answer any questions with the existing DB • Method 2. • Given a query, try and see how much the answer can be constrained by information derived from IE (somehow or other • Probably requires some sort of uncertain reasoning.

  21. Birds: r(birdName,soundDescription) and 5 short descriptions of sounds (“an owl hooting”) • Movies r(movieName,review) and 5 long, 5 short plot descriptions (“sci-fi comedy”, “serious czech movie”, ...)

  22. Soft joins with “incompatible schemas”

  23. WHIRL as a classification-learner

  24. Classification with unlabeled “Background” instances Example: instances are paper titles, background instances are paper abstracts

  25. Classifying short newswire headlines Very very short examples Very short examples

  26. “Best-first” search: pick state s that is “best” according to f(s) Suppose graph is a tree, and for all s, s’, if s’ is reachable from s then f(s)>=f(s’). Then A* outputs the globally best goal state s* first, and then next best, ... Inference in WHIRL

  27. Explode p(X1,X2,X3): find all DB tuples <p,a1,a2,a3> for p and bind Xi to ai. Constrain X~Y: if X is bound to a and Y is unbound, find DB column C to which Y should be bound pick a term t in X, find proper inverted index for t in C, and bind Y to something in that index Keep track of t’s used previously, and don’t allow Y to contain one. Inference in WHIRL

  28. Inference in WHIRL

  29. Summary • WHIRL finds the top k answers to a query • Queries tend to be easy because either they’re • unconstrained (e.g. 2-way similarity join) => easy to find 100 or so “good” answers • highly constrained (e.g. restricted sim join, multi-way join, classification query, ....) => easy to present all the “reasonable” answers to a user • Data integration usually considers matching two lists of entity descriptions in the abstract • unconstrained, sometimes under constrained (what is a match to the end user?) – i.e., we don’t know what the final query, and hence final constraints, will turn out to be. • this is evaluated a lot in experiments, but in an ideal world it would not the “wrong” problem

More Related