Semantic Spend Classification
- 1. Arivoli Tirouvingadame Principal Memberof Technical Staff, Oracle America, Inc.
- 2. Acknowledgements Sincere thanks to Keshava Rangarajan, Chief Architect, Halliburton Corporation for all the contribution and guidance, without which this research would not have been possible.
- 3. What is SpendClassification ? •Definition: Process of determining a purchase code for each spend record (Requisitions, Purchase Orders, Receipts, Invoices, etc.) from a hierarchical structure (Taxonomy). Requisitions, POs, Receipts, Invoices, etc.
- 4. Why to classifyspend ? •Once all spend transactions are classified with a standard code from a taxonomy – simple queries can be answered like •What are my top 10 spend categories ? •What is my travel spend ? •What is my spend for a given Supplier ? •What is my spend for a given Part ? •What is my spend for a given Business Unit ? •If your classification is done on a consolidated data across all systems in your organization, you get visibility across all systems with classification.
- 5. What is Taxonomy? •A simple hierarchical level of coding structure used to classify spend at different levels. Segment Family Class Commodity
- 6. What is theSpend Classification challenge ? •Categorization at source •Categorization itself is inconsistent or missing completely •Multiple disparate Taxonomies may exist in a company •Classifying into “MISCELLANEOUS” category •No standardization of Taxonomies
- 7. What is the“Categorization at source” challenge ? Exercise: Buying a work laptop and expensing via procurement X Category: Facility. Building.Hardware Category: IT.Hardware.Laptop Characteristics: •User entered, hence error-prone •No standardization across the supply chain – business units, customers, or suppliers.
- 8. What is the“inconsistent/missing Categorization” challenge ? • Category: IT.Hardware.Laptop • Category: IT.Hardware.Computers.Laptop
- 9. What is the“multiple disparate Taxonomies” challenge ? •Multiple (and disparate) taxonomies may also exist in the organization where classification could be carried out business unit-wise without regard to, or referring to, the taxonomies used in other business units. Business Unit 3 Business Unit 2 Business Unit 1 Taxonomy 3 Taxonomy 2 Taxonomy 1
- 10. What is the“MISCELLANEOUS category” challenge ? •Spend transactions are classified into the 'Miscellaneous' category, making it very difficult for business analysts to figure out which category the item should actually belong to. •Spend analytics data will then show a weighted 'Miscellaneous' category, which is incorrect and thus does not reflect a true picture of spend by categories for the organization. •Similar popular categories: OTHERS, UNCATEGORIZED
- 11. What is thestandardization of Taxonomies need ? •An enterprise may have multiple taxonomies at different levels – corporate, strategic, business unit and regional center. •Multiple taxonomies at various levels creates a number of issues when analyzing spend, therefore it is important to create or use standard taxonomies across the enterprise.
- 12. What are thetypes of Spend Classification Taxonomies ? SPEND CLASSIFICATION TAXONOMY Standard Custom
- 13. Standard Taxonomies •UNSPSC: UnitedNations Standard Products and Services Code. It is 5 level hierarchy coded as an 8-digit number. Example: •Segment 44. Office Equipment and Accessories and Supplies. • Family 10. Office machines and their supplies and accessories. • Class 15. Duplicating machines. • Commodity 01. Photocopiers. • Business Function 14. Retail.
- 14. Custom Taxonomies •If yourown coding structure is strong enough for your business, or you think your business is more acquainted with your own structure
- 15. 1) Requisitions ERP Category 2) Purchase Orders 3) Receipts 4) Invoices Procurement & Spend Analysis Item Invoice Categories Supplier Description Description Description And Attribute And Attribute And Attribute ERP Taxonomy UNSPSC Code Custom Taxonomies Data Mining Spend Classification
- 16. What is SpendAnalysis? •Process of collecting, cleansing, classifying and analyzing expenditure data with the purpose of reducing procurement. •Process of aggregating, classifying, and leveraging spend data for the purpose of gaining visibility into cost reduction, performance improvement, and contract compliance opportunities. •Enables to answer the following questions: •Who is buying ? •What ? •From whom ? •When ? •(optionally) Where ? •At what price ?
- 17. Who needs SpendAnalysis? •It is the process of organizing a company’s spend in such a way that one understand it, slice it, dice it and uncover hidden savings opportunities. •Impacts more than just the sourcing team •Spend analysis/ visibility serves three internal user community groups: •Leadership and CxOs: who need up-to-date reports to drive strategic direction •Managers, accountants: who need to drill down into a spend data set to explore specific areas of interest or track down payment specifics •Sourcing power users: who need to locate, drive, and monitor the next set of savings initiatives
- 18. What is SpendManagement? •Process in which companies control and optimize the money they spend. •Involves cutting operating and other costs associated with doing business. •Includes spend analysis, sourcing, procurement, receiving, payment settlement and management of accounts payable and general ledger accounts. •In an enterprise, spend management is managing how to spend money to best effect in order to build products and services. •Encompasses processes such as outsourcing, procurement, e-procurement, and supply chain management.
- 19. Benefits of SpendManagement •Decreasing "maverick" spend •Increase of spend economies of scale •Strategic sourcing (also called "supplier rationalization") •Sourcing optimization •Co-operative sourcing •Increase process efficiencies •Increase procurement efficiency
- 20. Life cycle ofa PO Create PO 1 Add items to PO 2 Add PO to Cart * 3 Create Document for the PO in the Cart 4 Create Requisition for the Document 5 Note: PO needs to be classified before it hits the Cart. After the Order hits the Cart, then it is too late for classification.
- 21. Classifying Spend • We have a set of pre-defined fields chosen for classification from a Purchase Order. All these fields are concatenated to form one giant string. (Note: This textual string could have multi-lingual strings.) • Lexers can be used for detecting languages. (eg: Auto lexers, World lexers) • SVM could be used for Textual mining.
- 22. Where does MachineLearning fit in? (Spend Auto-Classification) Ontology (including Spend Descriptions + other textual attributes) Taxonomies Spend transaction Spend Auto-classifier Linguistics (UIMA) + Neural Net Engine/ Text SVM Auto-Classified Spend
- 23. Training data set • To begin with, customers provide a Training data set. This is from their historic data. They take some well known data set from their most common use cases. This would constitute a good representation of their problem. • We run our logic against this training set and get the results. The results are verified. We iterate this for some cycles to tune the logic. • Repeat the same over other use cases.
- 24. Data Mining Model Create a Model Model created Enrich/Re-train Cleanse incorrect classification Support new categories (if needed)
- 25. What is NamedEntity Recognition ? •“Named-entity recognition (NER) (also known as entity identification and entity extraction) is a subtask of information extraction that seeks to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.” -- Wikipedia •Most research on NER systems has been structured as taking an unannotated block of text, such as this one • Jim bought 300 shares of Acme Corp. in 2006. •And producing an annotated block of text, such as this one: • <ENAMEX TYPE="PERSON">Jim</ENAMEX>bought<NUMEX TYPE="QUANTITY">300</NUMEX>shares of<ENAMEX TYPE="ORGANIZATION">Acme Corp.</ENAMEX> in <TIMEX TYPE="DATE">2006</TIMEX>.
- 26. Anatomy of aquery … Query = “Find Approved Status POs with High Amount”
- 27. Stemmed Entity Recognition& Linguistic Parsing yields… Search Verb: “Find” Find Approved Status POs with High Amount
- 28. Stemmed Entity Recognition& Linguistic Parsing yields… Search Verb: “Find” Attribute:Status= “Approved” Find Approved Status POs with High Amount
- 29. Stemmed Entity Recognition& Linguistic Parsing yields… Search Verb: Entity: “Find” Attribute:Type=“PO” Attribute:Status= “Approved” Find Approved Status POs with High Amount
- 30. Stemmed Entity Recognition& Linguistic Parsing yields… Search Verb: Entity: “Find” Attribute:Type=“PO” Attribute:Amount= “High” Attribute:Status= “Approved” Find Approved Status POs with High Amount
- 31. Stemmed Entity Recognition& Linguistic Parsing yields… Search Verb: Target Entity: “Find” Attribute:Type=“PO” Attribute:Amount= “High” Attribute:Status= “Approved” Find Approved Status POs with High Amount
- 32. Stemmed Entity Recognition& Linguistic Parsing yields… Search Verb: Target Entity: “Find” Attribute:Type=“PO” Having Attribute Attribute:Amount= “High” Attribute:Status= “Approved” Find Approved Status POs with High Amount
- 33. Stemmed Entity Recognition& Linguistic Parsing yields… Search Verb: Target Entity: “Find” Attribute:Type=“PO” Having Having Attribute Attribute Attribute:Amount= “High” Attribute:Status= “Approved” Find Approved Status POs with High Amount
- 34. Spend record witha Domain Ontology
- 36. OWL: attribute: string Transaction Party has a Code OWL:class has OWL:class many Role OWL:class has an plays OWL: Is A Bank attribute: string is related ID to Person Corporation OWL:class Is A OWL:class OWL:attribute: Finance number Corporation OWL:class has has First has Name ID has many many Name OWL:class OWL: Address attribute: string Last has an OWL:attribute: Account number Name OWL:attribute: has an in ID string OWL:attribute: has number Door Street City State Zip Country Number Name OWL: OWL: OWL: OWL: attribute: OWL: OWL: attribute:string attribute: string attribute: string attribute: string attribute:string string
- 37. Transaction ID:200911071234 has Party has ID: SBK has Role: S? Bank Role played by Bank has Name: Bank Of Congo has many Address has Street Name: Afrique Au Congo has Country: RDC
- 38. Transaction ID:200911071235 has Party has ID: ORP has Role: Ordering Party Role played by Person has First Name: John has Last Name: Doe has many Address Account has City: Kinshasa has Account Id: 123456 has Country: CD in Bank has Name: Bank Of Congo
- 39. Transaction Transaction ID:200911071234 ID:200911071235 has is related Party to Party has has ID: ORP has ID: SBK has Role: Ordering Party Role has Role: S? Bank Role played by Person has First Name: John played by has Last Name: Doe Address has City: Kinshasa Address has Country: CD has Street Name: has Afrique Au Congo many has Country: RDC Account has Account Id: 123456 in Bank has has Name: Bank Of Congo many
- 40. A possible solution:Pipelining approach •Flow 1: •Machine learning Pipeline: Input data is directly fed to the Machine Learning piece. •Flow 2: •Domain Ontology Pipeline: Input data is fed to a Domain Ontology. •Standardize the output from the Domain Ontology. •Machine learning Pipeline: Feed it into the Machine Learning piece. •Flow 3: •NER Pipeline: Input data is fed to a NER. •Domain Ontology Pipeline: Output from the NER is fed to the Domain Ontology. •Standardize the output from the Domain Ontology. •Machine learning Pipeline: Feed it into the Machine Learning piece. •Note: •Domain Ontology and NER Pipelines can be optionally turned on or off
- 41. 5 6 1 9 4 2 3 7 8
- 42. Stanford NER Demo http://nlp.stanford.edu:8080/ner/process Copyright © 2011, Stanford University, All Rights Reserved.
- 49. SVM Steps 1.Identify taxonomy(hierarchical or flat) to be classified against 2.Identity representative training data that has been classified to this taxonomy 3.Run training data against blank SVM model and the given taxonomy 4.Classify training data as per required taxonomy 5.Classify the data 6.Increase training population and enrich classification model 7.Recognize and realign impact of original model against fresh training data 8.Classify (manually) misclassifications into proper taxonomy nodes 9.Run step 6 through 8 until all the variations for a given domain have been recognized 10.Introduce live data 11.Repeat steps 4 and 5 for misclassifications 12.Store the result in a relational database 13.Insert data in an Ontology 14.Enable analysis using RQL or SPARQL
- 50. Open source software 1.Jena 2.Pentahohttp://www.pentaho.com/ 3. Stanford NER, http://nlp.stanford.edu/software/CRF-NER.shtml 4.Annie NER 5.GATE 6.UIMA 7.SVM, http://en.wikipedia.org/wiki/Support_vector_machine
- 51. Acknowledgements •Keshava Rangarajan, ChiefArchitect, Halliburton Corporation •Gopalan Arun, Vice President, Software Development •Ramesh Vasudevan, Senior Director, Software Development •Nagaraj Srinivasan, VP R&D, Halliburton Corporation (Landmark Graphics) •Ashish Pathak, Director, Product Management, Oracle America, Inc. •Chandra Yeleshwarapu, Director, Information Management & Platform Technologies, Halliburton Corporation •Jayesh Shah, Vice President, Product Development, Oracle America, Inc. •Rajesh Raheja, Senior Director, Applications Development, Oracle America, Inc •Stanford NER, http://nlp.stanford.edu:8080/ner/process •Prashant Mendki http://scn.sap.com/community/epm/blog/2012/03/03/your-basic-guide-to-spend- classification
- 52. References •Jena •Pentaho http://www.pentaho.com/ • StanfordNER, http://nlp.stanford.edu/software/CRF-NER.shtml •Annie NER •GATE •UIMA •SVM, http://en.wikipedia.org/wiki/Support_vector_machine •Oracle Spend Classification Process Guide, Release 7.9.6 •Lexers
Editor's Notes
- #23 We will talk about Auto-Classification and the place for Machine Learning . When a Spend transaction is added, what needs to happen is, the positioning of a spend in terms of a formal taxonomy might have to be dynamically changed. And that is not something that a person can manually do it in real time. We need an automated way of doing that. The spend transactions themselves have descriptions . When a tagging activity happens, when a review is written up , there is textual information. We could use UIMA, to pick out all the textual tokens – break them out into attributes and do Named Entity recognition. And then bring out a trained SVM engine which works on a model, that is able to pick up all the spend descriptions, and all its attributes from the Classification model, and tag it, and then position it appropriately in the Taxonomy. There are two flavors available: Neural Net Engine SVM They both have comparable performance. The bottom line is, we took in the spend Taxonomy , we took in the spend Ontology that describes the entire Spend model as well as the description of the spend - you can run it into a Neural Net Engine and then you can tag things, so that, as and when a new spend transaction is introduced, it is appropriately positioned in the Taxonomy, dynamically .