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Exploiting Context Analysis for Combining Multiple Entity Resolution Systems PowerPoint Presentation
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Exploiting Context Analysis for Combining Multiple Entity Resolution Systems

Exploiting Context Analysis for Combining Multiple Entity Resolution Systems

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Exploiting Context Analysis for Combining Multiple Entity Resolution Systems

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  1. Exploiting Context Analysis for Combining Multiple Entity Resolution Systems Zhaoqi Chen, Dmitri V. Kalashnikov, SharadMehrotra University of California, Irvine ACM SIGMOD 2009 Conference, Providence, RI, USA, June 30 – July 2, 2009 © 2009 Dmitri V. Kalashnikov

  2. Information Quality Data Processing Flow Quality of Analysis Quality of Decisions Analysis Decisions Quality of Data (Raw) Data • Quality of data is critical • $1 Billion market • Estimated by Forrester Group 2

  3. Entity Resolution Entity Resolution (ER) • One of the Information Quality challenges • Disambiguating uncertain references to objects (in raw data) Lookup • List of all objects is given • Match references to objects Grouping • No list of objects is given • Group references that corefer 3

  4. Example of Analysis on Bad Data: CiteSeer Unexpected Entries • Lets check two people in DBLP • “A. Gupta” • “L. Zhang” Analysis Decisions Raw Data • Analysis on bad data can lead to incorrect results • Fix errors before analysis Data Quality Engine CiteSeer: Top-k most cited authors DBLP DBLP

  5. Motivating ER Ensembles • Many ER solutions exist • No single ER solution is consistently the best • In terms of quality • Different ER solutions perform better in different contexts • Example: • LetKbe the true number of clusters • K is part of context • Assume that we use Agglomerative Clustering (Merging) if (K is large) then use Solution1: high threshold if (K is small) then use Solution2: low threshold • Observe that Kis unknown beforehand in this case!

  6. Graphical View of ER Problem • Virtual Connected Subgraph • Use simple techniques to create • similarity edges (or connect all refs.) • Similarity edges form VCSs • VCS properties • Virtual • Contains only similarity edges • Connected • A path exists between any 2 nodes • Subgraph • Subgraphs of the ER graph • 4. Complete • Adding more nodes/edges would violate (1) or (2) [CKM: JCDL 2007] Logically, the goal of ER is to partition each VCS correctly

  7. Problem Definition • Black boxes • Apply each to dataset • Outputs as graphs: • node - per each ref. • edges - connect each pair of references • For each edge ej, system Si makes decision dji{-1,+1} • Goal: combine • dj1,dj2, …,djn to make the final decision aj* for ej, such that the final clustering is as close to the ground truth as possible Raw Dataset Base-level ER Systems … S1 S2 SN Output of S2 Output of SN Output of S1 … Ensemble Techniques Final Result

  8. A B E F C D G A B E F A B E F C D G C D G VCS1 VCS2 Toy Example: Notation Graph ER system S1 ER system S2

  9. Naïve Solutions: Voting and Weighted Voting • Weighted Voting • Assign weight wi to each system Si • For ej count weighted decisions dji made by Si’s • Proceed like in voting Voting For each edge ej count decisions dji made by each Si: if (sum ≥ 0) then ej - positive (+1) else ej - negative (-1) Notice: if (n -1) systems perform poorly and only one performs well - the majority will win…

  10. Limitations of Weighted Voting • No matter how we choose the weights, in our running example Accuracy ≤ 56% • Problem: WV is static non-adaptive to the context

  11. Choosing Context Features • Error Features • Measure how far the prediction of a parameter by Si is different from the estimated true value of that parameter • The more the error is, the likely is that Si ’s solution is off • Combining Features • Number of Clusters (K) • K+ can help (merging ex.) • But, K+ is unknown! • Use regression to predict • K1, K2, …, Kn→ K* • Ki is # of clusters by Si • Features for edge ej: • Node Fanout • Nv+ is # of pos. edges of v • Also unknown • Use regression to predict • Nv1, Nv2,…,Nvn→ Nv* • Nvi is # according to Si • Features for edge ej: Effectiveness – should capture well which ER systems work well in the given context Generality– should be generic, not be present just in few datasets 11

  12. Training & Testing (training only)

  13. f2 ≤0.9 >0.9 d1 d2 -1 1 -1 1 d2 C=-1 C=1 C=-1 -1 1 C=1 C=-1 Approach 1: Context-Extended Classification • Three Methods • Method1: learn • Method2: • Method3: 2n features → n • Confidence in “merge” • Learn Context features:

  14. Approach 2: Context-Weighted Classification • Idea • For each Si learn model Mi of how well Si performs in context • Learn fj → cj • Algorithm • Apply Si, get dj and fj for ej • Apply Mi on fj, get c*ji and pji • pji is confidence in c*ji • vji = dji·c*ji· pji; vj = (vj1, vj2,…,vjn) • May reverse some decisions • Learn/Use vj → a*j mapping

  15. Clustering • Correlation Clustering • Once a*j{-1,+1} are known, we need to cluster • CC is designed to handle conflicts in labeling • Finds clustering that agrees the most with the labeling • CC can behave as Agglom. Clustering • Set params. accordingly • More generic scheme • Example • Simple merging will merge • CC will not • 2 negative vs. 1 positive

  16. Experimental Setup • Dataset • Web domain: [WWW’05] • Publication domain: RealPub [TODS’05] • Baseline Algorithms • BestBase - Si that produces the best overall result • Majority Voting • Weighted Voting • Three clustering-aggregation algos from [GMT05] • Standard ER ensemble [ZR05] • Base-level Systems Si • TF-IDF+merging, with different merging threshold • Feature+relationship+Correlation Clustering • Etc.

  17. Sample of Base-level systems

  18. Experiment 1: “Sanity Check” • Introduce one “perfect” base-level system that always gets perfect results • Does not exist in practice • Utilizes the ground truth (unknown, of course) • As expected, the algorithms were able to learn to use that “perfect” system, and to ignore the results of other base-level systems

  19. Comparing Various Aggregation Algorithms • WeightedERE is #1 • ExtendedERE is #2 • Both are statistically better • According to t-test  = 0.05 • Consistent improvement • 5 → 10 → 20 • Measures: FP, FB,F1 • Num. systems: 5, 10, 20 • MajorVot < BestBase • Many base-algo’s do not perform well

  20. Detailed results for 20 systems and Fp • None of the baselines is consistently better • See “BestIndiv” • That is why ER Ensemble outperforms the rest

  21. Results on RealPub • Results are similar to those on WePS data

  22. Comparing Different Combinations of Base-line Systems on Real Pub • Combination 1 • 1 Context, 3 RelER (t=0.05;0.01;0.005), and 1 RelAA (t=0.1) • Combination 2 • 3 RelER (t=0.0005;0.0001;0.00005) and 2 RelAA (t=0.01;0.001) • W_ERE #1, E_ERE #2, Comb2 > Comb1

  23. Efficiency Issues • Running time consist of • Running (in parallel) base-level systems • To get decision features • Running (in parallel) two regression classifiers • To get context features • Applying meta-classifier • Depends on the type of classifier • Usually not a bottleneck (1-5 sec on 5K to 50K data) • Applying correlation clustering • Not a bottleneck (under second) • Blocking • 1-2 order magnitude of improvement

  24. Future Work • Efficiency • How to determine which base-level systems to run • And on which parts of data • Trade efficiency for quality • Features • Look into more feature types • Improve the quality of predictions • Apply framework iteratively 24

  25. Questions? • Stella Chen SharadMehrotra www.ics.uci.edu/~sharad GDF Project www.ics.uci.edu/~dvk/GDF Dmitri V. Kalashnikov www.ics.uci.edu/~dvk