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Christian Opitz | Semantic E-Commerce - Use Cases in Enterprise Web Applications

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Christian Opitz | Semantic E-Commerce - Use Cases in Enterprise Web Applications

  1. 1. WWW.LEDS-PROJEKT.DE SEMANTIC E-COMMERCE USE-CASES IN ENTERPRISE WEB APPLICATIONS CHRISTIAN OPITZ 13. September 20161
  2. 2. BACKGROUND • Christian Opitz • Head of Business Development and Innovation at Netresearch • Project manager, consultant, web developer, designer, entrepreneur since 2007 • Netresearch • Leipzig based E-Commerce-Specialist founded in 1998 • Serves global enterprises in building and maintaining web platforms and shops • Develops and maintains Shop Integrations for several payment and shipping providers 13. September 20162
  3. 3. LEDS • Linked Enterprise Data Services: • Integration and Management of background knowledge, enterprise and open data • Monitoring of the data access and quality • Data evolution • Content analysis of unstructured text documents • Scalable, topic-oriented and personalized search • Iteratively tested in the domains of e-commerce and e-government. • 4 industry partners (brox, Ontos, Lecos, Netresearch) and 2 research partners (Universität Leipzig, TU Chemnitz) • 3-years project funded by Federal Ministry of Education and Research (BMBF) 13. September 20163
  4. 4. BUSINESS DATA INTEGRATION 13. September 20164
  5. 5. BUSINESS DATA INTEGRATION: PROBLEM • (Web-) IT infrastructure mostly consisting of various applications for specific domains: • Enterprise Resource Planning (ERP) Holds basic product information like SKU and stock availability • Shop Systems Presentation of products to the customer, checkout, order tracking interface • Content Management Systems (CMS) Corporate website, additional information, landing pages • Customer Relationship Management (CRM) Management of all customer and lead related activities and information • Product Information Management (PIM) Management of product information by channel (website, shop, print catalogues etc.) • Digital Asset Management (DAM) Management of files, their conversions and metadata 13. September 20165
  6. 6. BUSINESS DATA INTEGRATION: PROBLEM • Required to exchange data with each other based on business rules – f.i.: • PIM requires the basic product information (like SKU) from ERP and asset data from DAM • Shop requires stock information from ERP, product data from PIM, assets from DAM and eventually customer data and price rules from CRM • ERP must be notified when products were ordered in shop • CRM must be notified on customer and lead activities and data like signups and orders from shop or CMS • CMS requires assets from DAM, customer data from CRM and product data from PIM • DAM should know where in PIM, shop or CMS each asset is used • Often further complex business rules • Mostly vendor specific formats and services 13. September 20166
  7. 7. BUSINESS DATA INTEGRATION: PROBLEM • Todays approaches: • Wiring applications directly: • With existing or self developed adapters/connectors for each system • Costly when no existing adapters available • Introducing further dependencies • Hindering upgrades • Inflexible: Changing business rules often requires changes in several systems • Using middleware: • ETL (extract, transform, load) software allows to handle huge amounts of data • ESB (enterprise service bus) software allow to orchestrate web services based on concrete business rules • Affordable existing solutions from vendors like Talend, Pentaho or MuleSoft • Extensive or expensive integration: Steep learning curves, standard scenarios good kept secrets 13. September 20167
  8. 8. BUSINESS DATA INTEGRATION: SOLUTION • Enterprise Data Lake: • Reflects all relevant business data from several applications and domains • Vendor specific semistructured data transformed into structured, linked data using suitable vocabularies • Structured data stored in triplestore • Data can be queried from any domain mixed with data from any other domain • ETL/ESB middleware orchestrates data flow between applications via Data Lake • Other applications can use and manipulate the data without having to know the actual source 13. September 20168
  9. 9. BUSINESS DATA INTEGRATION: SOLUTION • Benefits • Vendor and application independency: • Structured data reflection of applications vendor specific data allows to replace a system in the stack by only implementing the data transformation for the new one • Flexibility: • Any applications can work with data lake without having to care about the sources and targets • Easy integration of other linked data sources and applications • Insights: • Whole business data universe available to Business Intelligence applications • Business critical questions can be answered quickly by reports based on any data from the lake 13. September 20169
  10. 10. CONTENT AUGMENTATION 13. September 201610
  11. 11. CONTENT AUGMENTATION: PROBLEM • Writing, updating and linking editorial content with further or related information is a time consuming process • Crucial – especially for e-commerce companies • Time to publishing • Quality • Quantity … influence visibility on the web • Regular publishing to social networks and timely react on trending topics is vital but mostly requires a dedicated social media manager 13. September 201611
  12. 12. CONTENT AUGMENTATION: SOLUTION • Using background knowledge to enrich and link contents • Editor assistance: • Editors input is mined for ontologies • Editor is presented with the ontologies along with the available background knowledge • Editor can choose to include the background knowledge – eventually paraphrased (into title or longdesc attributes, foot notes, parentheses, inserted sentences, blocks, asides or even new landing pages) • Automated augmentation: • Include background knowledge for ontologies mined from existing contents • Use background knowledge to link with other, suitable contents • Automated publishing: • Post suitable contents to social networks for trending topics based on background knowledge • Enrich existing content with trending keywords 13. September 201612
  13. 13. CONTENT AUGMENTATION: BENEFITS • Benefits • Easier editing work flow • Less user fluctuation by keeping them reading on the site • Increased visibility in search engines • Reduced social media management effort • Quicker and wider social network coverage 13. September 201613
  14. 14. MASTER DATA MANAGEMENT 13. September 201614
  15. 15. MASTER DATA MANAGEMENT: PROBLEM • Conception and modelling of product data is an extensive process • Product categorization and linking • Defining attributes: • Decide on type • Configure enumerations and validations • Modelling common attributes by product classes (attribute sets) • Requires shop and content management, marketing and editorial knowledge + knowledge of the particular field of the products • Mistakes can lead to bad visibility in search engines and higher bounce rates in the shop 13. September 201615
  16. 16. MASTER DATA MANAGEMENT: SOLUTION • Use existing, semantic product information on the web: • Find semantic product data on existing websites by available information (f.i. title, product class, SKU) • Web Data Commons Dataset could be used to find the websites providing appropriate data • Suggest product class, attributes, attribute sets and related products • Product manager can then choose to adopt them selectively • Eventually regularly recrawl the semantic web for updated information and notify the product manager • Benefits: • Reduced product information management effort • Reduced time to market for resellers • Eye on the market / up to date product information 13. September 201616
  17. 17. SEMANTIC SEARCH
  18. 18. SEMANTIC SEARCH: PROBLEM • Search queries for terms that are not in the index won’t give results even when there is something in the index that correlates • Example: • A toy retailer sells Corgi toy cars on his web shop • A user on the web shop searches for “Matchbox” • Unless the retailer explicitly mentioned “Matchbox” in the product descriptions the search won’t give results 13. September 201618
  19. 19. SEMANTIC SEARCH: SOLUTION • Invoke background knowledge from linked open data sources while indexing or actually searching • Match it with the search term or the background knowledge for it • On the example: • The search engine can find out that “Matchbox” relates to toy cars and can then find the Corgi cars (when it indexed “toy cars” along with “corgi” previously) • Benefits: • Better search results or results at all • No need to manually provide keywords for the index on which items should be found • When using the data lake, other linked data than open data is available to search against 13. September 201619
  20. 20. RECOMMENDATION ENGINE
  21. 21. RECOMMENDATION ENGINE: PROBLEM • Providing web shop visitors with related products (up-/cross-selling) usually done by: • Manually linking the related products • time consuming • Error-prone • Inflexible – changes usually also time consuming • Use more or less extensive and successful algorithms (f.i. “show products with the same category which are more expensive”) • Either not giving satisfying results • Or extensive work required to implement them • Or expensive to use those of specialized vendors 13. September 201621
  22. 22. RECOMMENDATION ENGINE: SOLUTION • Automatically link related products based on background knowledge • Semantic search can be utilized • Linking rules could/should also invoke data from other domains than the product information (f.i. product history of customers buying this product from CRM, stock data from ERP) • Benefits: • No need to manually link products, develop custom algorithms or costly implement existing ones 13. September 201622
  23. 23. SUMMARY
  24. 24. SUMMARY • Business data integration most fundamental use case, even only enabling the other ones for e-commerce companies with multiple applications • LEDS technology stack layed out to work with data lake and support close-by applications as those from the other use cases

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