1. musicSpace http://mspace.fm/ Principle investigator: dr monica mc schraefel http://mspace.fm/ David Bretherton Research Fellow (musicSpace) D.Bretherton@soton.ac.uk Music, School of Humanities

2. 2 Project’s Objectives 1.To integrate musicology’s heterogeneous data sources so that they can be explored effectively via one interface service; 2.To deliver an optimal interaction approach to support this exploration; 3.To develop a better understanding of how musicologists use musicSpace, so that it can be optimized to support the process of discovery and the development of new knowledge.

3. 3 Heterogeneous Data Sources  Data is catalogued/stored in numerous discrete databases according to: – Media type – Historical period (Hence there are numerous differences in the record fields and database formats that need to be resolved for integration.)  Therefore musicologists have to contend with widely dispersed data when conducting even basic research.

4. 4  For example, researching Monteverdi’s Masses online would involve consulting numerous sources: – Modern scholarly literature (RILM) – Older scholarly literature (BL, Grove) – Modern sound recordings (Naxos) – Older sound recording (BL Sound Archive) – Modern editions of scores (Grove) – Historical manuscript scores (RISM) Inefficiency: Same search is performed 6 times (or rather, 6 different searches).

5. 5 ‘Non-database’ Sources  Grove Music Online contains lots of data that would be really useful to harvest  But the raw XML of the articles does not include semantic tags that would make them machine-readable.  So there is a need for semantic tagging or text extraction technologies.

6. 6 Grove Example: ‘Opera buffa’ text

7. 7 Grove Example: ‘Opera buffa’ code

8. 8 Exploring Data  Increase in quantity of data necessitates and allows for better ways of exploring the data.  Current search interfaces are uninspiring: – Text entry to define searches; complex search queries can be complex and/or time consuming to construct. – Produce a list of ‘hits’ (e.g. Google).  The more hits, the more work needs to be done to assess the relevance of search results, and the more nonsense. – Follow-up searches often have to be formulated anew.

9. 9  The musicSpace UI (based on ‘mSpace’) facilitates searching and encourages exploration by: – Using multiple panes; – Hierarchically displaying search results and search parameters; – Allowing paradigmatic shifts in focus without having to restart the search; – Including a ‘scratch pad’ for recording items of interest.  mSpace demonstration: http://demo.mspace.fm/http://demo.mspace.fm/  The musicSpace UI will add: – ‘Audio cues’ of musical works; – Advanced graphical interfaces (e.g. ‘Continuum’, http://mspace.fm/projects/continuum). http://mspace.fm/projects/continuum

10. 10 Ontologies for Intelligent Searching  Construct a detailed subject ontology for intelligent searching and semi-automatic construction of complex searches. – Searches can include (by default or choice) hypernyms, hyponyms and synonyms. – RDF exported from a MediaWiki site with ‘Halo’ extension installed. – http://beeswax.ecs.soton.ac.uk/musicwiki/ index.php/Opera_buffa http://beeswax.ecs.soton.ac.uk/musicwiki/ index.php/Opera_buffa