Digital Enterprise Research Institute www.deri.ie
Implementing Semantic Web applications:
reference architecture and challenges
Benjamin Heitmann, Sheila Kinsella,
Conor Hayes, and Stefan Decker
Workshop on Semantic Web Enabled Software Engineering 2009
♥ Copyright 2009 Digital Enterprise Research Institute. All rights reserved.
Chapter

Introduction
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Focus of Semantic Web research until now:
benefits of Semantic Web technology
Less research on:
costs, effort, challenges of Semantic Web technology
Result:
estimating cost/benefit offset for Semantic Web technologies is
difficult
obstacle for uptake of Semantic Web technologies by real-world
projects
Our contributions:
identify main challenges and outline Software Engineering
solutions
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Overview
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Empirical Analysis of 98 Semantic Web applications
architectural analysis + app functionality questionnaire
Reference Architecture for Semantic Web
applications
Main challenges of implementing Semantic Web
technologies
and their effect on an example application
Approaches for mitigating the challenges
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Empirical analysis - Architectural
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Goal: identify common functionality
Result: components, allow comparison between apps
98 papers about apps from SemWeb challenge 2003-2008
& Scripting for SemWeb challenge 2006-2008
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Reference Architecture for
Semantic Web applications
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Empirical basis: architectural analysis
provides standard decomposition criteria
allows comparing of functionality
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Empirical analysis - Functionality
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Goal: characterise capabilities of components
Result: statistics about the range of variations for
each component
Results for 37 apps validated by authors
Survey covers 27 properties in 7 areas of
functionality
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Empirical analysis - Functionality
Functionality Variations(examples)
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Data Interface: data sources used
(external/decentralised/evolving ?)
Persistent Storage: Semantic Web standards
supported (e.g. RDF, OWL, SPARQL ?)
User Interface: generic/domain specific
Data Integration: manual/automatic
Search Service: structured/unstructured data
Authoring: read-only/edit/create new data
Crawling: one-time/continuous
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Implementation challenges (1)
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1. Integrating noisy and heterogeneous data
integration service is very common (72%)
expensive: 80% require manual intervention
76% allow updating data after initial integration
Reasons:
use of non-standard terms
incorrect usage of vocabularies
multiple URIs for the same objects and incorrect
merging
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Implementation challenges (2)
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2. Missing or belated conventions and standards
70% allow access or importing of external data
60% can export data or are reusable as source
only 1/3 allow creation of new data
Reason: standards are just emerging:
Linked Data principles: 2006, ~8 years after RDF (1999)
RDFa for embedding RDF in HTML: finalised 2008
GRDDL for converting (X)HTML to RDF: finalised 2007
SPARQL update: not finalised
RDF forms and RDF pushback: not finalised
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Implementation challenges (3)
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3. Mismatch of data models and APIs between
components:
components have different data models (majority)
object oriented (92%), relational database, graph based
slow, non-native APIs between components
4. Distribution of application logic across multiple
components
Logic included not just in code but queries, rules,
formal vocabularies
58% using inferencing, 24% using queries
Result of 3+4: higher maintenance costs,
performance loss due to non-native API overhead
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Example Application: SIOC explorer
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1 - Integration: all data
is RDF+SIOC, still 2
integration steps
required
2 - Unclear best
practices: every SIOC
exporter requires
different crawling
3 - Mismatched data models: graph/relational/OO
Mismatched APIs: ruby<->java, SPARQL (slow)
4 - distributed app logic: crawler, integration, primary app logic
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Mitigating the challenges (1)
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1. Delegating generic functionality to external providers
72% implement integration, 3 components
required
Delegating generic integration simplifies architecture
Drawback: application specific integration may still be
necessary
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Mitigating the challenges (2)
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2. Assembling applications from components:
most apps in survey created on case-by-case basis:
multiple libraries
multiple programming languages
mismatch of native APIs
distributed application logic
provide frameworks / software factories to assemble
and customise complete applications
provide generic data integration
implement best practices and guidelines
centralise application logic
allow app specific customisation
inspiration: Ruby on Rails, PHPCake, Django
(Python), Struts (Java)
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Summary
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main challenges of implementing SemWeb tech
cost of integrating noisy or heterogeneous data
(non-RDF and RDF data)
missing or belated standards and conventions
mismatch of data models and APIs between components
distribution of application logic across components
approaches to mitigate the challenges:
delegate generic functionality to external services
support assembly of complete applications with
frameworks
empirical foundation: analysis of 98 Semantic Web
applications
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