Semantic E-CommerceUse Cases in Enterprise Web Applications

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

● 3-years project funded by Federal Ministry of Education and Research (BMBF)○ 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)

Semantics

● Semantic web○ Coined by TimBL in 2001

○ Extension of the web for a web of data that can be processed by machines

○ Based on common - structured - data formats, standardized by W3C - most fundamentally RDF

○ Since then rarely implemented but lately more evolving - not least because of its importance for SEO

● Linked data○ Coined by TimBL in 2006 in connection with the semantic web

○ Method of publishing structured data enabling to interlink it and make it better queryable

○ Built upon standard technologies such as HTTP, RDF, URI

○ Data structured by entities of certain vocabularies, identified by URIs

○ Entities can be related with any other entities and the relations are expressed by their URIs

○ Already big and constantly growing number Linked Open Datasets available

Business Data Integration

Business Data Integration - Problem

● (Web-) IT infrastructure 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

Business Data Integration - Problem

● Required to exchange data 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 or 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 assets are used

● Often further complex business rules

● Mostly vendor specific formats and services

CMS

Shop

DAM

PIM CRM

ERP

Business Data Integration - Today's 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

○ Formats mostly transformed from source to target directly → System dependencies reintroduced

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 - 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 - Solution

Content Augmentation - 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

CMS

Shop

DAM

PIM CRM

ERP

● Business data integration most fundamental use case, even

only enabling the other ones for e-commerce companies with

multiple applications

● LEDS technology stack laid out to work with data lake and

close-by applications as those from the other use cases

Thank you!

Please send me your feedback:

christian.opitz@netresearch.de

T: +49 341 47842 211

F: +49 341 47842 29

Netresearch GmbH & Co. KG

Nonnenstraße 11d - 04229 Leipzig