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Improving Graph Based Entity Resolution with Data Mining and NLP
- 1. Improving Graph Based Entity Resolution Using Data Mining and NLP
- 2. Hello, I’m David Bechberger Architect and Developer ● Distributed systems ● High performance low latency big data platforms ● Graph Databases ● Teach and Mentor fellow developers www.bechbergerconsulting.com www.bechberger.com @bechbd www.linkedin.com/in/davebechberger
- 4. What is Entity Resolution The process of linking digital entities in data to real world entities.
- 5. I am known by many names but you may call me: ● Data referencing ● Record Linkage ● Canonicalization ● Coreference resolution ● Merge/purge ● Entity Clustering ● ….
- 6. Why is it Hard? ● Structured versus Unstructured ● Name Ambiguity ● Typos/Transposition/Data Errors ● Missing/Incomplete Data ● Changing Data ● Abbreviations
- 7. Two types of ER problems Ones with canonical data Ones without canonical data
- 8. Typical Entity Resolution Steps ● Deduplication ● Canonicalization/Standardization ● Blocking/Clustering ● Linking Records
- 9. Wait, I thought we were talking about graphs?
- 10. Example Graph Entity Resolution Problems ● Master Data Management ● Linking Customers ● Recommendation Engines ● Intrusion Detection ● Fraud analysis
- 11. What are we talking about today?
- 12. How can Data Mining/NLP help? ● String Similarity ● Named Entity Recognition ● Shingling ● Active/Machine Learning
- 13. How can graphs help? ● Aggregating Traversals ● Pattern Matching ● Inferring Relationships ● Path ● Clustering
- 14. Example - Product Catalogs
- 15. Problem - Matching Product Data ● Product catalog data from Amazon and Google* ● Already deduplicated ● ~1300 Amazon Products, ~3200 Google Products ● Contains a list of perfect matches for testing against *Datasets from Database Leipzig Group and is available at: https://dbs.uni- leipzig.de/de/research/projects/object_matching/fever/benchmark_datasets_for_entity_resolution
- 16. Goal Match Amazon data with Google data to build out the basis for a master data management solution
- 17. What are we starting with? Title Manufacturer Description clickart 950 000 - premier image pack (dvd-rom) broderbund ca international - arcserve lap/desktop oem 30pk computer associates oem arcserve backup v11.1 win 30u for laptops and desktops learning quickbooks 2007 intuit learning quickbooks 2007 eu063av aba microsoft windows xp professional hp eu063av aba : usually ships in 24 hours... ID Title Description Origin NameManufacturer built_by Product
- 18. How are we going to get there? 1. Bipartite and Pattern Matching 2. Iteratively add attributes to data 3. Try and match on weighted attributes
- 20. Bipartite Graph Matching ● Matched on exact titles ● Found 216 matches Quick Book Turbo Tax
- 21. Bipartite Graph Matching g.V().hasLabel("product").group(). by(values('title').fold()) .unfold() . filter( select(values).count(local).is(gt(1)) )
- 22. Graph Pattern Matching Quick Book Turbo Tax Intuit Corp Intuit built_by built_by ● Matched on manufacturer + fuzzy match on title ● Found 354 matches
- 23. Graph Pattern Matching g.V().hasLabel(‘product’).or( .group().by(values(‘manufacturer’).fold()).unfold() .filter( select(values).count(local).is(gt(1)) ), match( __.as('a').has('origin', 'amazon').as('amazon'), __.as('amazon'),has(‘title’, V().has('origin','google') .values(‘title’)).as('google'), __.as('amazon') .has('title',tokenFuzzy(V().has('origin',’google') .values(‘title’)) .values('title'), 2)) ) )
- 25. Find Manufacturers in Amazon data ● Fuzzy match to find unique ● Create and link nodes to unique manufacturers ● Found 227 manufacturers Intuit Intuit Corp Quick Book Intuit Corp built_by built_by CanonicalOriginal
- 26. Find Manufacturers in Google data ● ~7% had manufacturers (232/3229) ● 224 products matched existing manufacturers ● Found 8 more unique manufacturers
- 27. Validate Canonical Manufacturers ● Review and validate canonical data ● Add edges between data that represent the same entity Sony Sony Corp Intuit Corp Intuit is_same_asis_same_as
- 28. Build out the Canonical Manufacturer graph ● Found 235 unique manufacturers ● 14 aliases ● Canonical Manufacturers added to graph with aliases Intuit Corp Intuit is_same_as Micro soft Sony
- 29. What’s our graph look like now? Intuit Intuit Corp Intuit Corp Quick Book Intuit Turbo Tax Micro soft Sony is_same_as built_by built_bybuilt_by built_by
- 30. Manufacturer Pattern Matching ● Added Manufacturer Traversal into Pattern Match ● Found 534 matches Intuit Intuit Corp Intuit Corp Quick Book CanonicalOriginal Intuit Turbo Tax
- 31. Graph Pattern matching g.V().hasLabel(‘product’).or( .group().by(values(‘manufacturer’).fold()).unfold() .filter( select(values).count(local).is(gt(1)) ), match( __.as('a').has('origin', 'amazon').as('amazon'), __.as('amazon'),has(‘title’, V().has('origin','google') .values(‘title’)).as('google'), __.as('amazon') .has('title',tokenFuzzy(V().has('origin',’google') .values(‘title’)) .values('title'), 2)) ), V().repeat(out().hasLabel( within(‘built_by’, ‘is_same_as’))).limit(3))
- 32. ~41% ● Found 534 of 1300
- 33. Use NLP/Data Mining to add attributes
- 34. A quick word on Similarity Measurements ● Many different algorithms, each solves a different problem ● Know your data ● Research the options and ● Choose the right one for your data
- 35. Most Google Data Missing Manufacturer Or is it? Example: eu063av aba microsoft windows xp professional - license and media - 1 user - cto - english
- 36. Named Entity Recognition Process of classifying entities in strings into known categories microsoft xbox 360: forza motorsport 2 sony playstation 2: karaoke revolution: american idol bundle ibm(r) viavoice(r) advanced edition 10
- 37. Damereau-Levenstein Distance ● Measures the edit distance between two strings ● Handles insertions, deletions, transposition and substitutions Sony Snoy Snyo 1 2 2
- 38. Add distance attribute Intuit Intuit Corp Intuit Quick Book built_by Canonical distance:2 distance:3
- 39. Find similarity between titles Amazon Title Google Title ms visual studio 2011 plus video studio 11 plus Spiderman 3 ps2 activision 81935 spiderman 3 ps2 kids power fun for girls Topic entertainment kids power fun for girls
- 40. Jaccard Index ● Set similarity measures between finite sets (A, B) ● Works on n-Grams ● Calculated as Intersection over Union “J(A,B) = |A∩B|/|A⋃B|” N=1 (Unigram) This is a sentence this, is, a, sentence N=2 (Bigram) This is a sentence this is, is a, a sentence N=3 (Trigram) This is a sentence this is a, is a sentence
- 41. A = Dragon Natural Speaking 9.0 B = Dragon Natural 9.0 Professional A ⋃ B = 5 A ∩ B = 3 Jaccard Index = ⅗ = 0.60 Jaccard Index A B Dragon Natural Speaking 9.0 Professional
- 42. Add jaccard attribute Quick Book Turbo Tax Intuit Corp Intuit built_by built_by jaccard:0.6
- 43. Find similarity between descriptions ● Use TF-IDF finds the relative importance of words in a document ● Cosine similarity compares two vectors and gives the similarity between them
- 44. TF = # of times a word appears # words in a document IDF = # of documents # of documents with term TF-IDF Word TF-IDF Score unique 4.43 bag 4.34 original 2.945 professional 1.336 log( )
- 46. Add cosine_similarity attribute Quick Book Turbo Tax Intuit Corp Intuit built_by built_by cosine_similarity:0.75
- 47. Putting it all together
- 48. What does our graph looks like now? Intuit Corp Intuit is_same_as Quick Book Turbo Tax Intuit Corp Intuit built_by built_by distance:2 distance:2 distance:3 distance:3 jaccard:0.6cosine_similarity:0.75
- 49. Aggregating Traversal ● Aggregate all the values into a weighted sum* ● Highest sum was most likely Value = cosine_similarity + jaccard + (manufacturer simplest traversal path where distance is <=2 and path length is <=3) *For this talk I used evenly weighted values, in practice this needs calculated
- 50. What does our traversal look like? Intuit Corp Intuit Quick Book Turbo Tax Value = cosine_similarity + jaccard + (traversal paths <3)
- 51. So how did we do?
- 52. ~87% ● Found 1130 of 1300 ● ~1.2% error rate
- 53. Where do we go from here?
- 54. Clustering/Blocking ● N-squared comparisons are expensive ● Blocking and Clustering limit comparisons to only those likely to match
- 55. Improve NLP/Data Mining Techniques ● Tune algorithms ● Find accurate weighing with Active Learning ● Locality Sensitive Hashing
- 56. Toolkits I used? Apache Commons - https://commons.apache.org/ Java String Similarity - https://github.com/tdebatty/java-string-similarity Apache OpenNLP - https://opennlp.apache.org/ Apache Tinkerpop - http://tinkerpop.apache.org/
Notas del editor
- Test text for sizing
- Not an architect that just draws boxes and lines, I get my hands dirty by actually helping to build these things
- What this means is resolving data from one or more datasets into a canonical representation of that entity. E.g. I have facebook, linkedin, google, twitter etc but there is only one singular entity that is me. Entity resolution is the process of taking each of those disparate data sources and linking them to the singular real world me entity. Entity Resolution is not a new problem, its one that has become more important as we get more and more representation of yourself and we want mine interesting data from them
- Deduplication, Record Linkage, Data referencing, Canonicalization, Coreference resolution, Merge/purge, Object identification, Entity clustering, Object consolidation, Identity uncertainty, Reference reconciliation
- Its not if you have structured/clean and consistent data, but in reality it isnt Dave versus David Mispelled names Missing items Wife changed name
- Canonical Examples - Countries of the world (195), Fortune 500 companies Non-canonical examples - probably the most common, the canonical list has to be made from the data Examples are: people, places, products
- Not going to talk about Dedupe or blocking clustering A little bit on canonicalization but mostly on linking records
- MDM - Getting master data from multiple systems Customers - linking customers from multiple different internal systems (email, chat, phone) Rec engines - Linking sales and product data across divisions Intrustion detection - linking IP spoofs to the same person Fraud - Linking fraudulent transactions on multiple cards to same person
- Combining the best of Graph techniques with standard data mining and NLP techniques to provide a better outcome
- Lots of different String similarity - The process of comparing two strings and finding out how similar/dissimilar they are Named Entity Recognition - Process of classifying entities in text into predefined categories Shingling - process of tokenizing data to gauge similarity
- Aggregating Traversals - Using traversals to calculate weighed sums Pattern Matching - find patterns Inferring relationships Path traversals
- g.V().hasLabel("product"). group(). by(values('title').fold()). unfold(). filter(select(values).count(local).is(gt(1))).count()
- g.V().hasLabel("product"). group(). by(values('title').fold()). unfold(). filter(select(values).count(local).is(gt(1))).count()
- g.V().hasLabel("product"). group(). by(values('title').fold()). unfold(). filter(select(values).count(local).is(gt(1))).count()
- You may wonder why we added unique manufacturers from the google data to our graph if we aren’t matching on them
- g.V().hasLabel("product"). group(). by(values('title').fold()). unfold(). filter(select(values).count(local).is(gt(1))).count()
- NER works by using labelled training set data to determine entities Used canonical manufacturers as training set data Input the titles
- Good for comparing shorter string segments like names
- TF-IDF turns each document into a vector of numbers Values are then normalized using the dot product Cosine similarity compares the normalized vectors
- Produces a normalized vector of relative importance of words
- Similar scores are close to 1 Unrelated scores are close to 0 Opposites are close to -1
- Summed up the distance between items with cosine similarity, jaccard index and simplest path traversal where distance<=2 and length<=3
- Locality Sensitive Hashing - create hash codes for data to find others most like it
- Apache Commons for cosine-similarity and Jaccard Index Java Similairty for Damerau-Levensthein OpenNLP - for tokenizing and NER Tinkerpop for traversals