semantic e-commerce - use cases in enterprise web applications

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SEMANTIC E-COMMERCE USE-CASES IN ENTERPRISE WEB APPLICATIONS

CHRISTIAN OPITZ

13. September 2016 1

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


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)


BUSINESS DATA INTEGRATION


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



• 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



• 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


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


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


CONTENT AUGMENTATION


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


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


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


MASTER DATA MANAGEMENT


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


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


SEMANTIC SEARCH

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


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


RECOMMENDATION ENGINE

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


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


SUMMARY

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

semantic e-commerce - use cases in enterprise web applications

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