Pay-as-you-go Reconciliation in Schema Matching Networks
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The document discusses a framework for pay-as-you-go reconciliation in schema matching networks, highlighting the need for user feedback to improve matching quality amidst uncertainty. It introduces a probabilistic schema matching network (PSMN) model that quantifies uncertainty and guides user interaction to optimize correspondence accuracy. Experimental results demonstrate the effectiveness of a heuristic-guided strategy in reducing user effort and enhancing precision and recall compared to traditional methods.
Pay-as-you-go Reconciliation in Schema Matching Networks
- 1. Pay-as-you-go Reconciliation in Schema Matching Networks Nguyen Quoc Viet Hung1, Nguyen Thanh Tam 1, Zoltán Miklós2, Karl Aberer1, Avigdor Gal3, and Matthias Weidlich4 1 École Polytechnique Fédérale de Lausanne 2 Université de Rennes 1 3 Technion – Israel Institute of Technology 4 Imperial College London
- 2. ICDE | 2014 2 Schema Matching - Where? Schema matching is the process of establishing correspondences between the attributes of schemas, for the purpose of data integration Large enterprises Cloud WWW Collaborative Systems P2P Networks
- 3. Private PhD Thesis Defense | 12.2013 3 Schema Matching Network A network of schemas that are matched against each other Traditional approach: Mediated schema Our approach: Schema Matching Network S1 S2 S3 S1 S2 S3 Require consensus on schema Updated Frequently
- 4. ICDE | 2014 4 Pay-as-you-go Reconciliation Reconciliation is the process of asking human user to give feedback on correspondences. Need of reconciliation: automatic techniques use heuristics results are inherently uncertain s1: EoverI s2: BBC s3: DVDizzy a4: productionDate a1: releaseDate a3: availabilityDate c4 c2 c1 c3 c5 a2: screeningDate Attribute names are quite similar automatic matching tools often fail to identify the correct correspondences. Instantiation Selective matching Uncertainty Reduction Pay‐as‐you‐go reconciliation Incrementally improve matching quality with minimal user effort Instantiate a single trusted set of correspondences
- 5. ICDE | 2014 5 System Overview General approach: 1. Develop a probabilistic matching network (pSMN) can measure the overall uncertainty of the network 2. Reduce network uncertainty: guide user feedback with minimal effort 3. Instantiate a selective matching: maintain a good set of attribute correspondences to make the system available at any time
- 6. ICDE | 2014 6 Outline Probabilistic Schema Matching Network (pSMN): Model Computation Uncertainty Reduction Instantiation of the selective matching Experimental results Conclusion and future work
- 7. ICDE | 2014 7 pSMN - Modeling Schema matching network is modeled as a quadruple N ൌ ܵ, ܩ௦, Γ, ܥ, ܲ ܵ – set of schemas ݏ ܩ௦ ‐ interaction graph: represents the connections in the networks. ܥ – set of attribute correspondences Γ – set of integrity constraints An integrity constraint is the formulation of natural properties 1‐1 constraint Cycle constraint (transitivity) Etc. ܲ ൌ ሼpୡሽ – a set of probabilities. Each probability is associated with a correspondence ܿ ∈ ܥ.
- 8. ICDE | 2014 8 pSMN - Computing Probability of a correspondence Semantics: indicate the correctness of these correspondences Source: integrity constraints and user input. Idea: a correspondence that involves many violations has a high chance of being problematic. Computation: Step 1: construct all possible matching instances Ω ൌ ሼIଵ, … , I୬ሽ. Matching instance is a maximal set of correspondences satisfying all integrity constraints and user input. Step 2: compute by the formula: ൌ #௧ ௦௧௦ ௧ # ௦௦ ௧ ௦௧௦ (i.e. ൌ ሼூ∈ஐ:∈ூሽ ஐ ) Challenge: probability computation has a high complexity We use non‐uniform sampling and a view‐maintenance technique to approximate the probability efficiently. Network Uncertainty: quantify the uncertainty of pSMN based on entropy: ܪ ܥ ൌ െ log ሺ1 െ ሻ logሺ1 െ ሻ ∈
- 9. ICDE | 2014 9 Outline Probabilistic Schema Matching Network (pSMN): Model Computation Uncertainty Reduction Instantiation of the selective matching Experimental results Conclusion and future work
- 10. ICDE | 2014 10 Reduce Network Uncertainty Goal: guide user to give feedback with minimal user effort Problem (UNCERTAINTY MINIMIZATION WITH LIMITED EFFORT BUDGET). Given a probabilistic matching network 〈ܵ, ܥ, ܩ, Γ, ܲ〉 and a budget of user effort ݇, find a set of correspondences ܥᇱ ⊆ ܥ with ܥᇱ ݇, such that ܪሺܥ, ܲሻ is minimal.
- 11. ICDE | 2014 11 Approach – Use heuristic ordering Idea: feed users the correspondences with highest information‐gain first. Information gain: the uncertainty reduction before and after validation: ܫܩ ܿ ൌ ܪ ܥ െ ܪሺܥ|ܿሻ ܪ ܥ ܿ : expected network uncertainty when knowing the true value of c Two possible solutions: {c1,c2,c3} and {c1,c4,c5}. Ask c1 first the network is unchanged no uncertainty reduction. Ask c2 first only 1 solution left the network becomes certain. SA SB SC c3 c4 c5 c1 c2 SA SB SC c5 c3 c4 c1 c2 SA SB SC c3 c1 c2
- 12. ICDE | 2014 12 Instantiate a selective matching Goal: Maintain a single trusted set of correspondences Goodness measurement of a set of correspondences ܫ ⊆ ܥ: Repair distance: information loss of eliminating some correspondences to guarantee integrity constraint Δ ܫ ൌ ܥ ∖ ܫ Likelihood: represents the collective correctness of correspondences: ݑ ܫ ൌ ෑ ∈ூ Instantiation problem: given a schema matching network, identify a set of correspondences ܫ ⊆ ܥ with minimal repair distance (w.r.t. ܥ) and maximal likelihood.
- 13. ICDE | 2014 13 Approach The instantiation problem is NP‐complete use heuristic approach Algorithm: Step 1: Initialization ‐ Pickup a sampled matching instance with minimal repair distance Step 2: Optimization – Randomized local search Repair Distance Likelihood minimal repair distance + maximal likelihood I0 Iopt randomized local search matching instances: satisfy all constraints non‐sampled instance sampled instance sampled + minimal repair distance
- 14. ICDE | 2014 14 Outline Probabilistic Schema Matching Network (pSMN): Model Computation Uncertainty Reduction Instantiation of the selective matching Experimental results Conclusion and future work
- 15. ICDE | 2014 15 Experiment – Dataset and Setting Datasets: Business Partner: schemas from enterprise systems Purchase Order: purchase order e‐business schemas University Application Form: schemas from Web interfaces of American university application forms WebForm: schemas from Web forms of different domains Thalia: schemas describing university courses Metrics: Precision: measures quality improvement at each user interaction step ݅, with G being the exact match. ܲ ൌ ሺD୧ ∩ ܩሻ/|D୧| User effort: the percentage of feedback steps relative to the size of the matcher output. ܧ ൌ ݅/|ܥ|
- 16. Efficiency of guiding strategy on uncertainty reduction Goal: compare between guiding vs. non‐guiding strategy on uncertainty reduction Evaluation procedure: ICDE | 2014 16 Increases user effort Upon each user input, measure the network uncertainty and precision Interesting finding: heuristic ordering strategy achieves savings of up to 48% user effort compared to random ordering.
- 17. ICDE | 2014 17 Efficiency of guiding strategy on instantiation Goal: compare between guiding vs. non‐guiding strategy on instantiation Evaluation procedure: Increases user effort Measure the precision and recall of the instantiated matching Interesting finding: heuristic ordering strategy outperforms the baseline with an average difference of 15% (precision) and 14% (recall).
- 18. ICDE | 2014 18 Conclusions We introduce the concept of schema matching networks and probabilistic matching networks We define a model for pay‐as‐you‐go reconciliation on top of matching networks. We propose a guiding technique to reduce network uncertainty and a heuristic approach to instantiate a selective matching. Through experiments with real‐world schemas, our guiding strategy outperforms the baseline: Saving user effort by up to 48% Increasing precision (15%) and recall (14%)
- 19. ICDE | 2014 19 Future Work Generalizing pay‐as‐you‐go reconciliation for crowdsourced models: Business process matching Ontology alignment
- 20. ICDE | 2014 20 THANK YOU Q&A