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,j) = min D(i-1,j-1) + d(si,tj) //subst/copy D(i-1,j)+1 //insert D(i,j-1)+1 //delete COHEN M 1 2345 C 1 2345 C 2 3345 O 3 2 345 H 43 2 3 4 N 5433 3 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 D(i,j) = max 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 G = 1 d(c,c) = -2 d(c,d) = +1 COHEN M -2-3-4-5 C 0 0-2-3 C+1 0-2-3 O +2 +1 0 H -2+1 +4+3 +2 N -3 0+3 +5

8. Smith-Waterman distance - 3 D(i,j) = max 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 G = 1 d(c,c) = -2 d(c,d) = +1 COHEN M 00000 C 0 0000 C+1 0000 O 0 +2 +1 00 H 0 +4+3 +2 N 0 0+3 +5

9. Smith-Waterman distance - 5 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

10. Smith-Waterman distance in Monge & Elkan’s WEBFIND (1996) String s=A 1 A 2... A K, string t=B 1 B 2... B L sim’ is editDistance scaled to [0,1] Monge-Elkan’s “recursive matching scheme” is average maximal similarity of A i to B j:

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

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

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 TFIDFEdit Distance Cora 0.7510.839 0.721 OrgName10.9250.633 0.3660.950 Orgname20.9580.571 0.7780.912 Restaurant0.9810.827 0.9670.867 Parks0.9760.967

19. TFIDF distance for data integration Experiments with WHIRL

21. 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.

33. 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”,...)

35. Soft joins with “incompatible schemas”

37. WHIRL as a classification-learner

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

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

43. Inference in WHIRL “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,...

44. Inference in WHIRL Explode p(X1,X2,X3): find all DB tuples 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.

45. Inference in WHIRL

46. 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