Iswc 2014-hammond-pasin-presentation-final
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The document outlines Macmillan's efforts in utilizing linked data to enhance scientific and scholarly content discovery, focusing on a semantic data model and user-centric design. It details their development of a linked data platform, including an ontology and various datasets, while highlighting internal use cases and system architecture for improved performance. Future goals include expanding the ontology and enhancing query capabilities to better align with product requirements.
Iswc 2014-hammond-pasin-presentation-final
- 1. LINKED DATA EXPERIENCE AT MACMILLAN Building discovery services for scientific and scholarly content on top of a semantic data model 22 October 2014 Tony Hammond Michele Pasin
- 2. Linked Data at Macmillan | 22 October 2014 1 Background About Macmillan and what we are doing
- 3. Macmillan Science and Education Group brands and businesses Linked Data at Macmillan | 22 October 2014
- 4. MS&E Current trends Developing a richer graph of objects Change Drivers ● Digital first workflow – print becomes secondary – support for multiple workflows ● User-centric design – things, not data – focus on user experience ● Deeply integrated datasets – standard naming convention – common metadata model – flexible schema management – rich dataset descriptions Linked Data at Macmillan | 22 October 2014
- 5. NPG Linked Data Platform (2012) data.nature.com Deliverables (2012–2014) ● Prototype for external use ● Two RDF dataset releases in 2012 – April 2012 (22m triples) – July 2012 (270m triples) ● Live updates to query endpoint ● SPARQL query service (decommissioned) Current Work (2014–) ● Focus on internal use-cases ● Publish ontology pages ● Periodic data snapshots Linked Data at Macmillan | 22 October 2014
- 6. NPG Core Ontology (2014) Things: assets, documents, events, types Features ● Classes: ~65 ● Properties: ~200 ● Named graphs (per class) Namespaces ● npg: => http://ns.nature.com/terms/ ● npgg: => http://ns.nature.com/graphs/ Approach ● Incremental formalization (RDF, RDFS, OWL-DL) ● Shared metamodel vs. automatic inference ● Minimal commitment to external vocabs Linked Data at Macmillan | 22 October 2014
- 7. NPG Subject Pages (2014) Topical access to content Features ● Based on SKOS taxonomy – >2500 scientific terms – content inherited via SKOS tree ● Dynamically generated – one webpage per subject term – secondary pages for article types ● Various formats, e.g. e-alerts, feeds – allows people to ‘follow’ a subject ● Customized related content – ads, jobs, events, etc. Linked Data at Macmillan | 22 October 2014
- 8. Linked Data at Macmillan | 22 October 2014 2 Data Storage and Query Achieving speed by means of a hybrid architecture
- 9. Content Hub Managed content warehouse for data discovery Capabilities ● Discovery – Graph ● Storage – Content Repos Features ● Hybrid RDF + XML architecture – MarkLogic for XML, RDF/XML – Triplestore (TDB) for RDF validation ● Repo’s for binary assets Datasets ● Documents (large; >1m) ● Ontologies (small; <10k) Linked Data at Macmillan | 22 October 2014
- 10. System Architecture Hub content Linked Data at Macmillan | 22 October 2014
- 11. Content Discovery – Principles Readying the API for applications Generations ● 1st – Generic linked data API (RDF/*) ● 2nd – Specific page model API (JSON) Concerns ● Speed (20ms single object; 200ms filtered object) ● Simplicity (data construction) ● Stability (backup, clustering, security, transactions) Principles ● Chunky not chatty, all data in a single response ● Data as consumed, rather than as stored ● Support common use cases in simple, obvious ways ● Ensure a guaranteed, consistent speed of response for more complex queries ● Build on foundation of standard, pragmatic REST (collections, items) Linked Data at Macmillan | 22 October 2014
- 12. Content Discovery – Optimization Tuning the API for performance Approaches ● TDB + Fuseki – SPARQL ● MarkLogic Semantics – SPARQL ● MarkLogic – XQuery ● MarkLogic (Optimized) – XQuery Techniques ● Partitioning – RDF/XML objects ● Streaming – serialization ● Hashing – dictionary lookup ● Cacheing – Varnish Linked Data at Macmillan | 22 October 2014
- 13. Content Storage – Layout and Indexing Readying the data for page delivery Challenges ● Sort orders ● RDF Lists ● Facetting, counting Layout ● Semantic RDF/XML includes in XML ● RDF objects serialized in list order ● Application XML for subject hierarchy Indexes ● Indexes over all elements ● Range indexes for datatypes (e.g. datetimes) Linked Data at Macmillan | 22 October 2014
- 14. In Conclusion A few lessons learned Summary ● An RDF metamodel allows for scalable enterprise-level data organization ● It is crucial to adequately distinguish between external and internal use cases ● A hybrid architecture proved to be an efficient internal solution for content delivery Future Work ● Grow the ontology so that it matches product requirements more closely ● Support automated reasoning and richer query options – both RDF and XML based ● Maintain and expand the vision of a shared semantic model as a core enterprise asset Linked Data at Macmillan | 22 October 2014
- 15. For more information please contact TONY HAMMOND Data Architect, Content Data Servicestony.hammond@macmillan.com MICHELE PASIN Information Architect, Product Office michele.pasin@macmillan.com Thank you