Government Linked Data Tables Automatically Generating Government Linked Data from Tables Varish Mulwad University of Maryland, Baltimore County November 5, 2011 Dr. Tim FininDr. Anupam Joshi

What ? 2

State FIPS County FIPS GroupLabelValue Alabama1Macon87Farms with Black or African American operators Value of sales of grains, oil seeds, dry beans, and dry peas (farms) 5 Arizona….Navajo…. Arkansas5Union 139Farms with women principal Operators Total value of agricultural products sold (farms) 56 California6Humboldt23…….19 AdministrativeRegion Map literals as values of properties dbpedia-owl:state Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion 3

State FIPS County FIPS GroupLabelValue Alabama1Macon87Farms with Black or African American operators Value of sales of grains, oil seeds, dry beans, and dry peas (farms) 5 Arizona….Navajo…. Arkansas5Union 139Farms with women principal Operators Total value of agricultural products sold (farms) 56 dgtwc:. is rdfs:label of dbpedia-owl:AdminstrativeRegion. [ a dgtwc:DataEntry; dbpedia-owl:state dbpedia:Alabama; dbpedia:FIPS county code 000; dbpedia:Federal Information Processing Standard state code 001; dbpedia-owl:ethnicGroup “Farm with women principal dbpedia-owl:number dgtwc:. is rdfs:label of dbpedia-owl:AdminstrativeRegion. [ a dgtwc:DataEntry; dbpedia-owl:state dbpedia:Alabama; dbpedia:FIPS county code 000; dbpedia:Federal Information Processing Standard state code 001; dbpedia-owl:ethnicGroup “Farm with women principal dbpedia-owl:number 6444]. All this in a completely automated way !! Contribution Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion 4

Why ? 5

Tables are everywhere !! … yet … The web – 154 million high quality relational tables [1] Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion 6

Evidence–based medicine Figure: Evidence-Based Medicine - the Essential Role of Systematic Reviews, and the Need for Automated Text Mining Tools, IHI 2010 The idea behind Evidence-based Medicine is to judge the efficacy of treatments or tests by meta-analyses or reviews of clinical trials. Key information in such trials is encoded in tables. However, the rate at which meta-analyses are published remains very low … hampers effective health care treatment … # of Clinical trials published in 2008 # of meta analysis published in

> 400,000 raw and geospatial datasets ~ < 1 % in RDF Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion 8

Current Systems – Require users to have knowledge of the Semantic Web – Do not automatically link to existing classes and entities on the Semantic Web / Linked Data cloud – RDF data in some cases is as useless as raw data – Majority of the work focused on relational data where schema is available – Web tables systems use ‘semantically poor knowledge bases’ Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion 9

Dataset Number of Farms Farms with women principal operators Alabama <rdf:type rdf:resource=“ /data-gov-twc.rdf#DataEntry”/> 6444 Number of Farms Farms with women principal operators Alabama <rdf:type rdf:resource=“ /data-gov-twc.rdf#DataEntry”/> Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion 10

How ? 11

Preliminary work / Baseline system Analysis and Evaluation of baseline “Domain Independent” Framework grounded in graphical models and probabilistic reasoning 12 Building a table interpretation framework Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion

The System’s Brain (Knowledgebase) Yago Wikitology 1 – A hybrid knowledgebase where structured data meets unstructured data 1 – Wikitology was created as part of Zareen Syed’s Ph.D. dissertation Syed, Z., and Finin, T Creating and Exploiting a Hybrid Knowledge Base for Linked Data, volume 129 of Revised Selected Papers Series: Communications in Computer and Information Science. Springer. 13

The Baseline System 14

T2LD Framework Predict Class for Columns Linking the table cells Identify and Discover relations T2LD Framework 15 Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion

Predicting Class Labels for column State Alabama Arizona Arkansas California Class Instance Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion 1. Alabama 2.Alabama_(band) 3.Alabama_(people) 1. Alabama 2.Alabama_(band) 3.Alabama_(people) {dbpedia-owl:Place, dbpedia- owl:AdministrativeRegion,yago:S tatesOfTheUnitedStates, dbpedia-owl:Band, yago:NativeAmericanTribes …} {dbpedia-owl:Place, yago:StatesOfTheUnitedStates, dbpedia-owl:Film, …. ….. ….. } {……………………………………………… ……………. } dbpedia-owl:Place, dbpedia- owl:AdministrativeRegion,yago:StatesOfTheUnitedStates, dbpedia- owl:Band, yago:NativeAmericanTribes,dbpedia-owl:Film... 16

Linking table cells to entities Macon + County + Alabama Farms with Black or African American operators dbpedia- owl:AdministrativeRegio n Macon + County + Alabama Farms with Black or African American operators dbpedia- owl:AdministrativeRegio n 1. Macon County, Alabama 2. Macon County, Illinois 1. Macon County, Alabama 2. Macon County, Illinois Classifier 1 – SVM Rank (Ranks the set of entities) Classifier 1 – SVM Rank (Ranks the set of entities) Classifier 2 – SVM (Computes Confidence) Link to the top ranked entity Don’t link 17 Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion

Identify Relations State Alabama Arizona Arkansas California County Macon Navajo Union Humboldt Rel ‘A’ Rel ‘A’, ‘C’ Rel ‘A’, ‘B’, ‘C’ Rel ‘A’, ‘B’ 18 Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion

Generating a linked RDF dgtwc:. is rdfs:label of dbpedia-owl:AdminstrativeRegion. [ a dgtwc:DataEntry; dbpedia-owl:state dbpedia:Alabama; dbpedia:FIPS county code 000; dbpedia:Federal Information Processing Standard state code 001; dbpedia-owl:ethnicGroup “Farm with women principal dbpedia-owl:number dgtwc:. is rdfs:label of dbpedia-owl:AdminstrativeRegion. [ a dgtwc:DataEntry; dbpedia-owl:state dbpedia:Alabama; dbpedia:FIPS county code 000; dbpedia:Federal Information Processing Standard state code 001; dbpedia-owl:ethnicGroup “Farm with women principal dbpedia-owl:number 6444]. 19 Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion

Evaluation of the baseline system 20

Dataset summary Number of Tables15 Total Number of rows199 Total Number of columns56 (52) Total Number of entities639 (611) * The number in the brackets indicates # excluding columns that contained numbers 21 Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion

Evaluation # 1 (MAP) Compared the system’s ranked list of labels against a human–ranked list of labels Metric - Average Precision (a.p.) [Mean Average Precision gives a mean over set of queries] Commonly used in the Information Retrieval domain to compare two ranked sets 22 Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion

Evaluation # 1 (MAP) MAP = System Ranked: 1. Person 2. Politician 3. President Evaluator Ranked: 1. President 2. Politician 3. OfficeHolder 23 Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion

Accuracy for Entity Linking Overall Accuracy: % 24 Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion

Lessons Learnt Sequential System – Error percolated from one phase to the next Current system favors general classes over specific ones (MAP score = 0.411) Largely, a system driven by “heuristics” Although we consider evidence, we don’t do assignment jointly Predict Class for Columns Linking the table cells Identify and Discover relations T2LD Framework 25 Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion

KB a,b,c,… m,n,o,… x,y,z,… Probabilistic Graphical Model / Joint Inference Model KB Domain Knowledge – Linked Data Cloud / Medical Domain / Open Govt. Domain Query Linked Data A “Domain Independent” Framework 26

Joint Inference over evidence in a table Probabilistic Graphical Models 27

Parameterized graphical model C1 C2 C3 R11R12R13R21R22R23R31R32 R33 Function that captures the affinity between the column headers and row values Row value Variable Node: Column header Captures interaction between column headers Captures interaction between row values Factor Node Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion 28

Challenges 29

Challenges - Literals Population 690, , , ,000 Age Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion Population / Profit ? Age / Percentage ? Use evidence from the rest of the table to decide 30

Challenges - Metadata Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion 31

More Challenges ! Sampling and Interpretation – Data set 1425 has > 400,000 rows ! Human in the Loop Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion 32

Conclusion Presented a framework for inferring the semantics of tables and generating Linked data Evaluation of the baseline system show feasibility in tackling the problem Work in progress for building framework grounded in graphical models and probabilistic reasoning Working on tackling challenges posed by tables from domains such as the medical and open government data Introduction  Related Work  Baseline  Results  Joint Inference  Conclusion

References 1.Cafarella, M. J.; Halevy, A. Y.; Wang, Z. D.; Wu, E.; and Zhang, Y Webtables:exploring the power of tables on the web. PVLDB 1(1):538–549 2.M. Hurst. Towards a theory of tables. IJDAR,8(2-3): , D. W. Embley, D. P. Lopresti, and G. Nagy. Notes on contemporary table recognition. In Document Analysis Systems, pages , Wang, Jingjing, Shao, Bin, Wang, Haixun, and Zhu, Kenny Q. Understanding tables on the web. Technical report, Microsoft Research Asia, Venetis Petros, Halevy Alon, Madhavan Jayant, Pasca Marius, Shen Warren, Wu Fei, Miao Gengxin, and Wu Chung. Recovering semantics of tables on the web. In Proc. of the 37th Int'l Conference on Very Large Databases (VLDB), Limaye Girija, Sarawagi Sunita, and Chakrabarti Soumen. Annotating and searching web tables using entities, types and relationships. In Proc. of the 36th Int'l Conference on Very Large Databases (VLDB),

Thank You ! Questions 35