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

David BrethertonResearch Fellow (musicSpace)D.Bretherton@soton.ac.uk Music, School of Humanities

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

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 not include 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