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Building Knowledge Graphs in DIG
This document discusses building knowledge graphs using DIG (Distributed Information Graphs) to integrate heterogeneous data sources. It describes the steps involved, including data acquisition, feature extraction, mapping to an ontology, entity resolution, graph construction, and deployment. As a use case, DIG has been used to build a knowledge graph from over 100 million web pages related to human trafficking to help law enforcement identify victims and prosecute traffickers.
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Building Knowledge Graphs in DIG
- 1. Building knowledge graphs inDIG Pedro Szekely and Craig Knoblock University of Southern California Information Sciences Institute dig.isi.edu
- 2. Goal USC Information SciencesInstitute CC-By 2.0 2 raw w messy w disconnected clean w organized w linked hard to query, analyze & visualize easy to query, analyze & visualize
- 3. Use Case: HumanTrafficking USC Information Sciences Institute CC-By 2.0 3 raw w messy w disconnected clean w organized w linked hard to query, analyze & visualize easy to query, analyze & visualize
- 4. Use Case: HumanTrafficking USC Information Sciences Institute CC-By 2.0 4 100 million pages ~ 100 Web sites help victims prosecute traffickers
- 5. Salient Statistics on HumanTrafficking • Profits per Year: $32 Billion • Average Age of Entry To Prostitution in the US: 14 • PIMP’s Profit Per Victim Per Year: $150,000 • Advertising Budget On the Web:$45 Million CC-By 2.0 5USC Information Sciences Institute
- 6. Task: Tracking theVictim’s Locations > 100 million pages advertising adult services USC Information Sciences Institute CC-By 2.0 6
- 7. Example: Investigating aReported Victim San Diego, where else? USC Information Sciences Institute CC-By 2.0 7
- 8. DIG Interface: Findthe locations where a potential victim was advertised CC-By 2.0 8
- 9. Steps To Builda DIG USC Information Sciences Institute CC-By 2.0 9 Crawling Extraction Data Acquisition Mapping To Ontology Entity Linking & Similarity Knowledge Graph Deployment Query & Visualization Elastic Search Graph DB schema.org geonames Data Acquisition Feature Extraction Feature Alignment Entity Resolution Graph Construction User Interface Data Acquisition
- 10. Data Acquisition USC InformationSciences Institute CC-By 2.0 10 downloading relevant data batch w real-time Web pagesw Web service w database w CSV w Excel w XML w JSON
- 11. Steps To Builda DIG USC Information Sciences Institute CC-By 2.0 11 Crawling Extraction Data Acquisition Mapping To Ontology Entity Linking & Similarity Knowledge Graph Deployment Query & Visualization Elastic Search Graph DB schema.org geonames Data Acquisition Feature Extraction Feature Alignment Entity Resolution Graph Construction User Interface
- 12. Feature Extraction USC InformationSciences Institute CC-By 2.0 12 from raw sources to structured data • trainable text extractors • extraction from structured Web pages • image features • PDF extractor
- 13. Feature Extraction fromText USC Information Sciences Institute CC-By 2.0 13 “YOU don't wanna miss out on ME :) Perfect lil booty Green eyes Long curly black hair Im a Irish,Armenian and Filipino mixed princess :) ❤ Kim ❤ 7○7~7two7~7four77 ❤ HH 80 roses ❤ Hour 120 roses ❤ 15 mins 60 roses” name: Kim eye-color: green hair-color: black phone: 707-727-7477 rate: $60/15min $80/30min $120/60min
- 14. 20 Examples CC-By 2.014USC Information Sciences Institute
- 15. 1,000’s of Tasks(2 Cents/Sentence) CC-By 2.0 15
- 16. Performance of CRFExtractors 80 10 18 99 91 94 0 20 40 60 80 100 120 Precision Recall F Regular Expressions DIG 80 6 12 99 73 84 0 20 40 60 80 100 120 Precision Recall F Regular Expressions DIG Eyes Hair USC Information Sciences Institute CC-By 2.0 16
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- 18. Automated Extraction input: a pile of pages Classify by Templates pages clustered by template Infer Extractor Infer Extractor Infer Extractor Infer Extractor extractor USCInformation Sciences Institute CC-By 2.0 18
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- 20. Extraction Evaluation Title DescSeller Date Price Loc Cat Member Since Expires Views ID Perfect 1.0 (50/50) .76 (37/49) .95 (40/42) .83 (40/48 ) .87 (39/45 ) .51 (23/45) .68 (34/50) 1.0 (35/35) .52 (15/29) .76 (19/25) .97 (35/36 ) Pretty Good 1.0 (50/50) .98 (48/49) .95 (40/42) .83 (40/48 ) .98 (44/45 ) .84 (38/45) .88 (44/50) 1.0 (35/35) .55 (16/29) 1.0 (25/25) 1.0 (36/36 ) 10 websites, 5 pages each fields USC Information Sciences Institute CC-By 2.0 20
- 21. Steps To Builda DIG USC Information Sciences Institute CC-By 2.0 21 Crawling Extraction Data Acquisition Mapping To Ontology Entity Linking & Similarity Knowledge Graph Deployment Query & Visualization Elastic Search Graph DB schema.org geonames Data Acquisition Feature Extraction Feature Alignment Entity Resolution Graph Construction User Interface
- 22. Feature Alignment USC InformationSciences Institute CC-By 2.0 22 from multiple schemas to a common domain schema - CSV, Excel - Database tables - Web services - Extractors - Nomenclature - Spelling Multiple Schemas
- 23. Karma: Mapping Datato Ontologies Services Relational Sources Karma { JSON-LD } Hierarchical Sources Schema.org USC Information Sciences Institute CC-By 2.0 23 karma.isi.edu
- 24. Karma Solves FeatureAlignment CC-By 2.0 24USC Information Sciences Institute Provenance Domain Schema took ~30 minutes to align the output of the Stanford name extractor
- 25. Feature Alignment Statistics •5 contractors provided data • ~ 15 datasets • > 30 Karma models • > 200 million records • 1 hour processing in 20 node Hadoop cluster CC-By 2.0 25USC Information Sciences Institute
- 26. Steps To Builda DIG USC Information Sciences Institute CC-By 2.0 26 Crawling Extraction Data Acquisition Mapping To Ontology Entity Linking & Similarity Knowledge Graph Deployment Query & Visualization Elastic Search Graph DB schema.org geonames Data Acquisition Feature Extraction Feature Alignment Entity Resolution Graph Construction User Interface
- 27. Entity Resolution USC InformationSciences Institute CC-By 2.0 27 merging records that refer to the same entity missing data incorrect data scale (~50 million records) currently working on techniques to address
- 28. Entity Resolutuion onStrong Attributes AdultService-1 Person-1 Offer-1 availableAt seller phone 619-319-7315 Santa Barbara hairColor red price 250/hour startDate 2014-12-07 eyeColor blue name Jessica itemProvided Offer-2 Person-2 availableAt Washington DC phone seller email price 250/hour startDate 2014-05-28 AdultService-2 eyeColor blue name Jessica itemProvided USC Information Sciences Institute CC-By 2.0 28
- 29. Linking Using TextSimilarity E M I LY SEXY. ** wHiTe/lATin girl ** bUsTy SWEET. LoTs Of fUn. Call Me. O_U_T_C___A___L_L_S LAY LA SEXY. ** wHiTe girl ** bUsTy SWEET. LoTs Of fUn. Call Me. O____U____T____C___A___L____L____S L I LA SEXY. ** WhiTe girl ** bUsTy SWEET. LoTs Of fUn. Call Me. O_U_T_C___A___L_L_S USC Information Sciences Institute CC-By 2.0 29
- 30. Linking Using ImageSimilarity CC-By 2.0 30USC Information Sciences Institute 100 Million Images Technology: Deep Learning
- 31. AdultService-1 Person-1 Offer-1 availableAt seller phone 619-319-7315 Santa Barbara hairColor red price 250/hour startDate 2014-12-07 eyeColor blue name Jessica itemProvided Offer-2 Person-2 availableAt Washington DC phone seller email price 250/hour startDate 2014-05-28 AdultService-2 eyeColor blue name Jessica itemProvided samevictim same Trafficker Unsupervised Collective Entity Resolution USC Information Sciences Institute CC-By 2.0 31
- 32. Unsupervised Collective Entity Resolution USCInformation Sciences Institute CC-By 2.0 32
- 33. Steps To Builda DIG USC Information Sciences Institute CC-By 2.0 33 Crawling Extraction Data Acquisition Mapping To Ontology Entity Linking & Similarity Knowledge Graph Deployment Query & Visualization Elastic Search Graph DB schema.org geonames Data Acquisition Feature Extraction Feature Alignment Entity Resolution Graph Construction User Interface
- 34. Graph Construction USC InformationSciences Institute CC-By 2.0 34 assembling the data for efficient query & analysis - ElasticSearch: scalable, efficient query - graph databases: network analytics - NoSQL: scalable analytics - bulk loading: massive data imports - real-time updates: live, changing data
- 35. Elastic Search DataModel Adult Service Offer Person Phone Web Page USC Information Sciences Institute CC-By 2.0 35
- 36. Indexing for HighPerformance Knowledge Graph Queries Avg. Query Times in Milliseconds Single User Query Load 1.2 billion triples State of the Art Graph Database (RDF) DIG indexing deployed in ElasticSearch USC Information Sciences Institute CC-By 2.0 36
- 37. Steps To Builda DIG USC Information Sciences Institute CC-By 2.0 37 Crawling Extraction Data Acquisition Mapping To Ontology Entity Linking & Similarity Knowledge Graph Deployment Query & Visualization Elastic Search Graph DB schema.org geonames Data Acquisition Feature Extraction Feature Alignment Entity Resolution Graph Construction User Interface
- 40. DIG Deployment forHuman Trafficking USC Information Sciences Institute CC-By 2.0 40 - 100 million Web pages - Live updates (~5,000 pages/hour) - ElasticSearch database (7 nodes) - Hadoop workflows (20 nodes) - District Attorney - Law Enforcement - NGOs
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- 42. DIG Applications Human Trafficking large,real users Material Science Research 70,000 paper abstracts (built in 1 week) Arms Trafficking Identify illegal sales Patent Trolls Identify patent trolls Cyber Attacks Predict cyber attacks from dark web data CC-By 2.0 42USC Information Sciences Institute
- 43. Conclusions • Complete tool-chainto build domain-specific knowledge graphs • Integrates heterogeneous data: web pages, databases, CSV, web APIs, images, etc. • Scales to ~100 million pages, ~3 billion facts • Deployed to law enforcement USC Information Sciences Institute CC-By 2.0 43
- 44. Questions? dig.isi.edu Open Source, Apache2 License CC-By 2.0 44USC Information Sciences Institute
Editor's Notes
- #24 Karma offers suggestions on how to do the mapping
- #29 Simplest kind of linking we do – linking based on strong, explicit attributes (phones, emails, websites, etc.) So person-1 and person-2 might be the same person … but can we find more attributes to improve our confidence …
- #30 Estimating text similarity is challenging – here we are emphasizing stylometric similarity; map->n-grams->jacquard similarity
- #31 Clever scheme for storing pair-wise similarities in a database that can be updated incrementally (so we can bypass hashing that leverages elastic search w/lucene)
- #32 Why is linking significant in this domain? Slide shows why.
- #36 There is some clever tricks We produce json documents rooted on the classes we care about .. Contain enough of the graph-neighborhood so that keyword queries can work so that I can search for an adultservice using a phone number even though the phone number is really part of the seller. Or search all offers that have the same phone number. Basically copying over some content.