Building Knowledge Graphs in DIG
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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 in DIG Pedro Szekely and Craig Knoblock University of Southern California Information Sciences Institute dig.isi.edu
- 2. Goal USC Information Sciences Institute 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: Human Trafficking 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: Human Trafficking USC Information Sciences Institute CC-By 2.0 4 100 million pages ~ 100 Web sites help victims prosecute traffickers
- 5. Salient Statistics on Human Trafficking • 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 the Victim’s Locations > 100 million pages advertising adult services USC Information Sciences Institute CC-By 2.0 6
- 7. Example: Investigating a Reported Victim San Diego, where else? USC Information Sciences Institute CC-By 2.0 7
- 8. DIG Interface: Find the locations where a potential victim was advertised CC-By 2.0 8
- 9. Steps To Build a 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 Information Sciences 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 Build a 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 Information Sciences 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 from Text 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 2.0 14USC Information Sciences Institute
- 15. 1,000’s of Tasks (2 Cents/Sentence) CC-By 2.0 15
- 16. Performance of CRF Extractors 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
- 18. Automated Extraction input: a pile of pages Classify by Templates pages clustered by template Infer Extractor Infer Extractor Infer Extractor Infer Extractor extractor USC Information Sciences Institute CC-By 2.0 18
- 19. Unsupervised Extraction Tool CC-By 2.0 19
- 20. Extraction Evaluation Title Desc Seller 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 Build a 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 Information Sciences 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 Data to 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 Feature Alignment 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 Build a 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 Information Sciences 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 on Strong 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 Text Similarity 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 Image Similarity 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 same victim same Trafficker Unsupervised Collective Entity Resolution USC Information Sciences Institute CC-By 2.0 31
- 32. Unsupervised Collective Entity Resolution USC Information Sciences Institute CC-By 2.0 32
- 33. Steps To Build a 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 Information Sciences 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 Data Model Adult Service Offer Person Phone Web Page USC Information Sciences Institute CC-By 2.0 35
- 36. Indexing for High Performance 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 Build a 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 for Human 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
- 41. Deployed to 6 Law Enforcement Agencies and Successfully Used to Prosecute Traffickers USC Information Sciences Institute CC-By 2.0 41
- 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-chain to 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, Apache 2 License CC-By 2.0 44USC Information Sciences Institute
Notas del editor
- Karma offers suggestions on how to do the mapping
- 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 …
- Estimating text similarity is challenging – here we are emphasizing stylometric similarity; map->n-grams->jacquard similarity
- 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)
- Why is linking significant in this domain? Slide shows why.
- 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.