Making the Conceptual Layer Real via HTTP based Linked Data

Open Conceptual Data Models
Making the Conceptual Layer Real
via
HTTP based
Linked Data (aka. Linked Data)
© 2008 OpenLink Software, All rights reserved

Situation Analysis
Linked Data Vision:
The transition of the HTTP based Webs (Intranet, Extranet, or Internet)
from a Webs of Linked Documents
to Webs of interlinked Structured Data Items (aka: entities, data objects, resources)
Concurrent trend in the IT industry:
A recognition of the benefits of conceptual data models over logical data models
The Big Question:
To what extent does Linked Data support conceptual level data models ?

Open Conceptual Data Models
Topics:
Conceptual & Logical Data Models
Conceptual Models for the Semantic Web
Realizing Conceptual Models through Ontologies & Linked Data
Virtuoso’s RDF based Linked Data Views
ADO.NET Data Services & the Entity Data Model

Data Model Layers
Physical
How data is physically represented on disk
Logical (aka logical schema)
Expresses problem domain in terms of data management technology (tables / columns)
e.g. relational schema
Conceptual (aka conceptual schema)
Purely semantic description of problem space
Describes things (entities), their characteristics (attributes) & associations between things (relationships)

Logical Data Model
Most prominent of the three data model types
Main focus of database driven applications
Due to pervasiveness of relational database driven applications within the enterprise and across the Web
Weaknesses
Impedance mismatch
Loss of semantics during development process
Heterogeneous databases & interoperability

Logical Data Model Weaknesses
Impedance Mismatch
SQL expresses queries in terms of tables / views
=> targets logical schema
Normalization fragments the data model
Entities & their attributes may be split across several tables
Navigation between objects requires relational joins over two or more tables
Table rows must be reconstituted into higher level conceptual entities
Conceptual level data model is desirable to:
Remove impedance mismatch
Isolate application from changes to logical data model
Provide framework for productive human level interaction

Loss of Semantic Fidelity During Development
Process:
Develop conceptual model (E-R modelling)
Transform to logical model for implementation
DBMS generates physical model
Problems:
Each move to a lower level model depreciates semantic fidelity of the higher level model
Conceptual Model semantics fragmented across schema / business rules / application code
Application & Users must understand logical data model
Must be hardcoded or inferred (imperfectly) from system tables

Heterogeneous Databases & Interoperability
Logical data model
Describes problem domain in terms of tables/columns
Requires costly table joins to navigate model
Application
Exposed to specifics of a particular vendor’s RDBMS
In heterogeneous database environment , must handle
Different SQL dialects
Different schemas
No explicit data model. No explicit semantics.
Interoperability/integration = perpetual problem for IT depts

Conceptual Models for Linked Data Webs
Explosion of User Generated Data from Web 2.0 applications and their Data Silos is driving the recognition of the need to move from logical to conceptual models, exemplified by:
Microsoft’s Entity Data Model / Entity Framework
W3C’s Semantic Web Project which includes powerful technologies for this paradigm shift such as:
Resource Description Framework (RDF Data Model and Data Representation Formats)
Web Ontology Language (OWL)
SPARQL (Query Language, RESTful Interface, and Query Result Serialization Formats)

Benefits of Conceptual Models
More faithfully represents human view of domain of interest
Conceptual model & semantics
Explicit & available globally
Not implicit & fragmented across business logic / UI etc
Better / explicit semantics facilitates move from “search” to “esoteric precision find”
Much easier heterogeneous data integration
User Generated Data is inherently heterogeneous & disparately located

Application Areas – Present & Future
Social Media, eCommerce, Distributed Collaborative Apps.
Require shareable, standards-based, cross-platform conceptual views of data
Data portability
Needed as users maintain multiple points of presence & identity across – blogs, social network accounts etc.
Open business models
Require exchange & integration of large amounts of data
Scientific research – sharing of knowledge & findings
Requires transparent access to distributed heterogeneous data
Requires database integration using global schema
Autonomous intelligent agents
Free humans from large-volume data processing

Semantic Web Project Technologies
These technologies offer:
Ontologies
For representing common semantics
Spanning databases, applications, enterprises, on-line communities
Deliver shared conceptual model
Provide common schemas (Dublin Core, FOAF, SIOC, GoodRelations etc)
Common Semantics (Ontologies) & Common Data Representation (RDF)
Enable cross data source querying using SPARQL
Data across several databases (or data spaces) can be meshed, expanded, and explored
Querying using proprietary APIs unnecessary
Brute force data merging via code is unnecessary
Open Data Formats, Platform Independence, Common Models
Facilitate data portability, accessibility, and integration.

Realizing Conceptual Models
Ontologies
Provide the building blocks for conceptual models
Define the concepts and their relationships in a domain of interest (or world view)
Describing Classes & Properties – Ontology Languages
RDFS
Introduces the notions of concepts (classes) & instances
OWL
Adds more vocabulary for describing:
relations between classes
cardinality
richer typing of properties, etc.

Goodness of Fit
RDF was designed from the ground up as a metadata data model
RDF / RDFS / OWL work directly at the level of conceptual models
Conceptual model terminology matches RDF/OWL terminology
Concepts, entities, attributes, relationships.
A natural fit!
RDF lends itself naturally to describing conceptual models.

Semantic Expressivity Comparison
Data Definition Language (DDL)-based Relational Model
Relationship between two entities isn’t explicit
Foreign key relating two rows in separate tables doesn’t express the nature of the relationship
Semantics must often be inferred from table definitions
RDF-based Conceptual Model
Relationship between two entities is stated explicitly by predicate in subject-predicate-object triple
Semantic expressivity of RDF/RDFS/OWL is much better than DDL
Has richer semantic content than equivalent DDL-based logical/relational model

Global Granular Information Sharing
Traditional Logical/Relational Data Model
Schema described by DDL is internal to DBMS
Primary keys identifying an individual table row (i.e. entity instance) not globally unique, not easily usable outside host DBMS
Gives rise to ‘data silos’
RDF’s use of Generic HTTP-based URIs
Externalises the data and schema
Makes both globally accessible & scalable
Provides globally unique IDs for entities/relations/classes
A vehicle for granular, global information sharing down to the equivalent of the record level.

Linked Data – What is It?
A method for exposing, sharing & connecting data on HTTP based Data Networks.
A term coined by Tim Berners-Lee that describes a RESTful mechanism for HTTP based Data Access & Manipulation by Reference
A record level HTTP based Open Data Access & Connectivity mechanism
A richer hyperlinking mechanism that takes us from Hypertext Links (Document to Document) to Hyperdata Links (Data Item to Data Item).

Linked Data – Why Is It Important
It exposes the compound nature of Data Containers (e.g., Documents) such that
Data Containers are uniquely identified & referenceable
Data Items within Data Containers are uniquely identified & referencable
It provides a conceptual model oriented Open Data Access & Connectivity mechanism
It delivers a powerful mechanism for meshing disparate and heterogeneous data sources.

Linked Data Benefits – Data Exploration
Natural Navigation Through Typed Links
RDF entities (instance data, classes, and properties) are identified by dereferencable HTTP URIs
Navigating from one data item to another is easy via:
Single LINK click from any HTTP user agent commences data item relationship navigation
Linked Data Browers such as OpenLink Data Explorer
Relational/Logical Model
Cumbersome
Requires SQL joins + typically Object-Relational mapping
e.g. in C# : track = lennonAlbum.Tracks[“Imagine”]

Linked Data Benefits - Aggregatable Data
Often desirable to have an integrated view of all the data available about an item or topic
Database Realm
Integration problematic, difficult to combine logical schemas
Semantic Web
Data aggregation is easy: every resource has a unique URI
Individual items can be linked
Conceptual models can be linked
Cross-domain links enrich domain knowledge
Different facets of the same data item may be described by different URIs minted by different authors
Can be linked. e.g. owl:sameAs, rdf:type predicates
May expose facts not directly represented in any one source

Linked Data Benefits - Self Describing Data
Resource Description Framework (RDF)
A technology for creating self-describing Web resources
Data Item’s type definition ‘accompanies’ it via rdfs:type relations
An RDF based data can be queried using SPARQL without knowing anything beforehand about the data definition (schema comes last in this realm)
Provides the basis for powerful deductive data exploration tools
Logical / Relational Schema
Users / applications need a detailed understanding of the schema to use and navigate the data
Application’s knowledge of the schema typically hardcoded
Ad-hoc end-user data exploration potentially error prone

Linked Data Benefits - SPARQL
If a user agent has no built-in knowledge of a particular Data Item, it can dereference its Generic HTTP URI to obtain such information
The Power of SPARQL
Discover what sorts of things a data source contains
select distinct ?URI ?ObjectType where { ?URI a ?ObjectType }
Determine all the properties of an data item’s class
select * where { <http://my.org/resourceTypes/Department> ?property ?hasValue }
Determine all the properties and values of an data item instance
DESCRIBE <http://my.org/resource/Accounts>
No prior knowledge of the RDF data source is needed

Virtuoso - Linked Data Generation Options
Conceptual layer insulates Linked Data consumers from RDFization infrastructure & data source heterogeneity

Virtuoso RDF based Linked Data Views
Expose relational model data as RDF graph model data
Provide the means to move from a logical model to a conceptual model view
Available for querying through SPARQL or SPASQL (SPARQL embedded in SQL)
No physical regeneration of relational data
RDF Views =
Virtuoso RDF Meta-Schema (MSL) +
Meta-Schema Language
MSL =
A domain specific, declarative language for mapping a logical SQL data model to a conceptual RDF data model

© 2008 OpenLink Software, All rights reserved Northwind Demo Database: RDF View Definition Extract prefix northwind: <http://www.openlinksw.com/schemas/northwind#> … create iri class northwind:Customer <http://^{URIQADefaultHost}^/Northwind/Customer/%U#this> (in customer_id varchar not null) … alter quad storage virtrdf:DefaultQuadStorage … from Demo.demo.Customers as customers from Demo.demo.Orders as orders … { Demo.demo.Customers Northwind RDF View Definition create virtrdf:NorthwindDemo as graph iri (“http://^{URIQADefaultHost}^/Northwind”) { … northwind:Customer(customers.CustomerID) a foaf:Organization as virtrdf:Customer-CustomerID ; northwind:companyName customers.CompanyName as … ; … northwind:fax customers.Fax as virtrdf:Customer-fax . … } } northwind:Customer(orders.CustomerID) northwind:has_order northwind:Order(orders.OrderID) as virtrdf:Order-has_order . Customer ID Company Name Contact Name Contact Title Address City Postal Code Country Phone Fax

Northwind Demo Database: Customer Table to RDF data item Mapping © 2008 OpenLink Software, All rights reserved Orders Table Customer ID Company Name Contact Name Contact Title Address City Postal Code Country Phone Fax ALFKI Alfreds Futterkiste Maria Anders Sales Represe-ntative Obere Str. 57 Berlin 12209 Germany 030 - 0074321 030 - 0076545 companyName contactName contactTitle address city PostalCode country phone fax Alfreds Futterkiste Maria Anders Sales Representative Obere Str. 57 Berlin 12209 Germany 030-0074321 030-0076545 … Order/10643#this has_order Order/10692#this … has_order Customer/ALFKI#this prefix <http://demo.openlinksw.com/Northwind/> has_customer has_customer Order ID Customer ID … 10643 ALFKI … 10692 ALFKI …

ADO.NET Data Services & Entity Data Model
A framework for exposing ‘pure data’ service over HTTP
No support for RDF
Fails to imbibe any of RDF’s inherent benefits
Lack of platform independence & standards compliance
Supports REST-style interfaces
Supports Atom, JSON and XML payloads
But
Server-side: Windows only
Consuming Astoria services at a higher level requires Windows .NET client or Silverlight-supported browser

Server-side only conceptual model
Powerful URL addressing to query/navigate/sort/filter etc
Customers collection: http://myserver/data.svc/Customers
Customer ALFKI: http://myserver/data.svc/Customers('ALFKI')
Customer ALFKI's orders: http://myserver/data.svc/Customers('ALFKI')/Orders
But
Client must know conceptual schema
e.g. to construct above URIs
Lack of Deferencable Entity IDs
Ability to discover entities and dereference their descriptions (attributes/relations) is confined to the facilities offered by .NET
c.f. SPARQL’s ability to handle unknown data sources

No Support for Non-SQL Data Sources
Astoria is aimed exclusively at making relational data Web accessible
c.f. Linked Data Realm
Recognize that vast amounts of data resides in unstructured and semi-structured data sources
Support for embedding RDF into existing (X)HTML
RDFa, GRDDL, eRDF
Emerging tools for converting non-RDF data to RDF model data
Emerging tools for exposing Relational data as RDF Graph Model data
Astoria lacks scalability & scope of Semantic Web technologies

Making the Conceptual Layer Real via HTTP based Linked Data

by Kingsley Idehen on Jan 23, 2010

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Making the Conceptual Layer Real via HTTP based Linked Data — Presentation Transcript