Implementing Semantic Web applications: reference architecture and challenges

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Best paper award at the workshop for Semantic Web enabled software engineering 2009, at the International Semantic Web Conference 2009. …

Best paper award at the workshop for Semantic Web enabled software engineering 2009, at the International Semantic Web Conference 2009.

Full paper at: http://ceur-ws.org/Vol-524/swese2009_2.pdf

Summary of the slides and the paper:

* an empirical analysis of 98 Semantic Web applications based on an architectural analysis and an application functionality questionnaire
* a reference architecture for Semantic Web applications
* the main challenges of implementing Semantic Web technologies and their effect on an example application
* approaches for mitigating the challenges

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  • 1. 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
  • 2. Introduction Digital Enterprise Research Institute www.deri.ie 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 Benjamin.Heitmann slide 2 of 14 @deri.org
  • 3. Overview Digital Enterprise Research Institute www.deri.ie 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 Benjamin.Heitmann slide 3 of 14 @deri.org
  • 4. Empirical analysis - Architectural Digital Enterprise Research Institute www.deri.ie 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 Benjamin.Heitmann slide 4 of 14 @deri.org
  • 5. Reference Architecture for Semantic Web applications Digital Enterprise Research Institute www.deri.ie Empirical basis: architectural analysis provides standard decomposition criteria allows comparing of functionality Benjamin.Heitmann slide 5 of 14 @deri.org
  • 6. Empirical analysis - Functionality Digital Enterprise Research Institute www.deri.ie 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 Benjamin.Heitmann slide 6 of 14 @deri.org
  • 7. Empirical analysis - Functionality Functionality Variations(examples) Digital Enterprise Research Institute www.deri.ie 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 Benjamin.Heitmann slide 7 of 14 @deri.org
  • 8. Implementation challenges (1) Digital Enterprise Research Institute www.deri.ie 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 Benjamin.Heitmann slide 8 of 14 @deri.org
  • 9. Implementation challenges (2) Digital Enterprise Research Institute www.deri.ie 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 Benjamin.Heitmann slide 9 of 14 @deri.org
  • 10. Implementation challenges (3) Digital Enterprise Research Institute www.deri.ie 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 Benjamin.Heitmann slide 10of 14 @deri.org
  • 11. Example Application: SIOC explorer Digital Enterprise Research Institute www.deri.ie 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 Benjamin.Heitmann slide 11of 14 @deri.org
  • 12. Mitigating the challenges (1) Digital Enterprise Research Institute www.deri.ie 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 Benjamin.Heitmann slide 12of 14 @deri.org
  • 13. Mitigating the challenges (2) Digital Enterprise Research Institute www.deri.ie 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) Benjamin.Heitmann slide 13of 14 @deri.org
  • 14. Summary Digital Enterprise Research Institute www.deri.ie 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 Benjamin.Heitmann slide 14of 14 @deri.org