Semantic e commerce
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Transcript of Semantic e commerce
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:
T: +49 341 47842 211
F: +49 341 47842 29
Netresearch GmbH & Co. KG
Nonnenstraße 11d - 04229 Leipzig