FINDING QUALITY IN QUANTITY: THE CHALLENGE OF DISCOVERING VALUABLE SOURCES FOR INTEGRATION
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1 FINDING QUALITY IN QUANTITY: THE CHALLENGE OF DISCOVERING VALUABLE SOURCES FOR INTEGRATION Theodoros Rekatsinas University of Maryland Amol Deshpande, Xin Luna Dong, Lise Getoor and Divesh Srivastava
2 DATA, DATA, DATA Clean Analyze Integrate
3 DATA, DATA, DATA Clean Analyze Knowledge Bases Business Analysis Outbreak Prediction Stock Price Prediction Integrate
4 IN REALITY Clean Analyze Integrate
5 IN REALITY Clean Analyze Integrate
6 Cleaning and integrating data takes time and costs money! Things only become worse when using data from low quality sources!
7 A REAL EXAMPLE Knowledge-base construction in Google State-of-the-art automatic knowledge extraction from Web accu=30% [KV KDD`14/Sonya VLDB`14] State-of-the-art fusion on top prec=90%, recall=20% [KV KDD`14/Sonya VLDB`14] Human curation to increase accuracy and coverage Select sources carefully to focus resources!
8 Data INFLUENCING FACTORS Context
9 Data LOW QUALITY SOURCES Low coverage Biased information polarity High delays - staleness negativeneutral positive Erroneous information subjectivity objective subjective 0 1
10 CONTEXT MATTERS Context
11 WE ARE IN NEED OF Data Source Management Systems Data Source - Index the content of sources - Build quality profiles Repository Selection Engine
12 WE ARE IN NEED OF Data Source Management Systems Data Source Repository Selection Engine - Find relevant sources to user queries. - Find sources that if combined, maximize the quality of integrated data. - Explore different solutions.
13 REASONING ABOUT CONTENT Data sources have diverse data domains. Users interested in different data domains. Use a knowledge base (KB) as back-end to reason about the content of sources and user queries.
14 REASONING ABOUT CONTENT Extend KB with a Correspondence Graph. Context Clusters group instances and concepts. Detect c-clusters using latent variable learning or frequent itemset mining.
15 REASONING ABOUT QUALITY Build source quality profiles per context cluster. Compare source content with integrated content of all relevant sources.
16 SOURCE SIGHT A data source management system for news stories (events). News articles extracted from EventRegistry.com and originate from news papers, blogs, and social media. Content semantically annotated using OpenCalais by Thomson Reuters.
17 SOURCE SIGHT DEMO
18 RANKING IS NOT ENOUGH Entities: Obama, Topic: War_Conflict Source Ranking Coverage nypost.com 0.42 nymag.com 0.37 nytimes.com 0.37 csmonitor.com 0.32 cleveland.com 0.28 washingtonexaminer.com 0.23 gawker.com 0.20 democracynow.org 0.17 blogtown.portlandmercury.com 0.11 nydailynews.com 0.11
19 RANKING IS NOT ENOUGH Entities: Obama, Topic: War_Conflict Combining Sources nypost.com (ranked 1st), nymag.com (ranked 2nd) Coverage: 0.48 nypost.com (ranked 1st), business-standard.com (not in top-10) Coverage: 0.52
20 REASON ABOUT SETS Perform source selection [DSS VLDB`13, RDS SIGMOD`1 Find the set of sources that maximizes the quality of integrated data while minimizing the overall cost. But there are multiple quality metrics. Coverage, Timeliness, Bias, Accuracy How can we reason about different metrics?
21 Coverage PARETO OPTIMALITY Source selection as multi-variate optimization Goal: find pareto optimal sets of sources Accuracy
22 Coverage PARETO OPTIMALITY Source selection as multi-variate optimization Goal: find pareto optimal sets of sources Finding the pareto front is hard! Accuracy
23 SOURCE SIGHT DEMO
24 CHALLENGES The content and quality of data sources changes over time. How can we update the content and quality profiles efficiently? How can we build quality profiles (e.g., via sampling) that come with rigorous guarantees? How can we provide succinct descriptions of the source characteristics? How can we provide users with explanations? Why does this source appear in my result?
25 CONCLUSIONS Reasoning about the quality of data sources and their relevance to user queries is crucial. Data source management systems should support diverse integrations tasks and allow users to understand the quality of integrated data. We presented Source Sight a prototype data source management system. Thank you! thodrek@cs.umd.edu
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