musicSpace mspace.fm/ Principle investigator: dr monica mc schraefel

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

2. Project’s Objectives • To integrate musicology’s heterogeneous data sources so that they can be explored effectively via one interface service; • To deliver an optimal interaction approach to support this exploration; • 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. 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. 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. ‘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 notinclude semantic tags that would make them machine-readable. • So there is a need for semantic tagging or text extraction technologies.

6. Grove Example: ‘Opera buffa’ text

7. Grove Example: ‘Opera buffa’ code

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. 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/ • The musicSpace UI will add: • ‘Audio cues’ of musical works; • Advanced graphical interfaces (e.g. ‘Continuum’, http://mspace.fm/projects/continuum).

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