Open Conceptual Data Models

Open Conceptual Data Models
Making the Conceptual Layer Real
via
RDF Linked Data

Conceptual Data Models in the Linked Data Web
Linked Data Vision:
The transition of the Web
from a Web of linked documents
to a Web 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 vs logical data models
The Big Question:
To what extent does the 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 RDF Views
ADO.NET Data Services & the Entity Data Model

Conceptual & Logical Data Models
Describe a software system’s target problem space
Typically, in today’s database-driven applications
Three levels of data model
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 applications
Due to pervasiveness of SQL in application code
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 human level interaction

Loss of Semantics During Development
Process:
Develop conceptual model (E-R modelling)
Transform to logical model for implementation
Derive physical model from logical model
Problems:
Each move to a lower level model discards meaning
Higher level model typically not retained
Model semantics fragmented across schema / business rules / application code
Application must know 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 SQL 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 the Semantic Web
Growing recognition in the industry of the benefits of a conceptual, rather than logical, model for data-centric applications
e.g. Microsoft’s Entity Data Model / Entity Framework
Semantic Web technologies provide powerful tools for this paradigm shift

Benefits of Conceptual Models
How the Semantic Web benefits
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 promises better search engines
Much easier heterogeneous data integration
Data on the Web is inherently heterogeneous

Application Areas – Present & Future
Social networking, e-commerce, collaborative working
Require shareable, standards-based, cross-platform conceptual views of data
Data portability
Needed as Web users maintain multiple points of presence – 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 information processing

Semantic Web Technology Benefits
What Semantic Web technologies bring:
Ontologies
Can represent common semantics
Spanning databases, applications, enterprises, on-line communities
Act as a shared conceptual model
Provide common models (FOAF, SIOC etc)
Common Semantics (Ontologies) & Common Data Representation (RDF)
Enable cross data source querying using SPARQL
Content from several sites can be combined / explored
Querying using proprietary APIs unnecessary
Brute force data merging unnecessary
Open Data Formats, Platform Independence, Common Models
Allow data portability and data integration

Realizing Conceptual Models
Ontologies
Provide the building blocks of Semantic Web conceptual models
Define the concepts and their relationships in a domain of interest
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
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

RDF Conceptual Model – Artist / Records / Tracks

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 HTTP-based URLs
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 the Web
A term coined by Tim Berners-Lee that describes HTTP-based Data Access by Reference for the Web
Open Data Access & Connectivity mechanism for the Web
A richer linking mechanism for the Web that takes us from Hypertext Links (Document to Document) to Hyperdata Links (across things that documents are about)

Linked Data – Why Is It Important
It exposes the compound nature of Web Resources
Information resources (Containers) are uniquely identified & referenceable
Entities within Containers are uniquely identified & referencable
It provides an Open Data Access & Connectivity mechanism for the Web
It delivers a powerful mechanism for meshing disparate and heterogeneous data sources

Linked Data Model Changes the focus from linked documents to linked entities The document as a data container becomes less relevant

Hyperdata Links Between Data Objects

Linked Data Benefits – Natural Navigation
Natural Navigation Through Typed Links
RDF entities are identified by dereferencable URIs (URLs)
Navigating from one data item to another is easy
One click to dereference in Semantic Web Browser
e.g. OpenLink Data Explorer
URI of object in an RDF statement is a typed link
Link’s “type” is defined by the statement predicate
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 entity 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 – Data Aggregation

Linked Data Benefits - Self Describing Data
RDF
A technology for creating self-describing Web resources
Entity’s type definition ‘accompanies’ it using rdfs:type
An RDF dataset can be queried using SPARQL without knowing anything beforehand about the data
Provides the basis for powerful 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 RDF subject, predicate or object, it can use the URI to retrieve the 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 entity class
select * where { <http://my.org/resourceTypes/Department> ?property ?hasValue }
Determine all the properties and values of an entity 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 Views
Expose relational data as RDF
Provide the means to move from a logical model view 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 +
Meta-Schema Language
MSL =
A domain specific, declarative language for mapping a logical SQL data model to a conceptual RDF data model

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 . Country Phone Postal Code Address City Fax Contact Title Contact Name Company Name Customer ID

Northwind Demo Database: Customer Table to RDF Entity Mapping Orders Table Germany Country 030 - 0074321 Phone 12209 Postal Code Obere Str. 57 Address Berlin City 030 - 0076545 Sales Represe-ntative Maria Anders Alfreds Futterkiste ALFKI Fax Contact Title Contact Name Company Name Customer ID 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 … ALFKI 10643 … ALFKI 10692 … Customer ID Order ID

LinqToRdf + Virtuoso

LinqToRdf to MusicBrainz - Conceptual Model Veneer

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. Semantic Web & Linked Data
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
Emerging tools for exposing SQL data as RDF
Astoria lacks scalability & scope of Semantic Web technologies

Open Conceptual Data Models

by rumito on Nov 30, 2008

Accessibility

Categories

Tags

Upload Details

Usage Rights

1 Embed 4

Statistics

Open Conceptual Data Models — Presentation Transcript