Citation for published version: Day, C 2005, An ontological approach to song scheduling for an automated radio station. Computer Science Technical Reports, no. CSBU-2005-10, Department of Computer Science, University of Bath. Publication date: 2005 Link to publication ©The Author October 2005 University of Bath Alternative formats If you require this document in an alternative format, please contact: [email protected] General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 26. Sep. 2021 Department of Computer Science Technical Report Undergraduate Dissertation: An ontological approach to song scheduling for an automated radio station Chris Day Technical Report 2005-10 October 2005 ISSN 1740-9497 Copyright ­c October 2005 by the authors. Contact Address: Department of Computer Science University of Bath Bath, BA2 7AY United Kingdom URL: http://www.cs.bath.ac.uk ISSN 1740-9497 An ontological approach to song scheduling for an automated radio station Chris Day BSc (Hons) Computer Science 2005 - 1 - An ontological approach to song scheduling for an automated radio station Submitted by Chris Day COPYRIGHT Attention is drawn to the fact that copyright of this thesis rests with its author. The Intellectual Property Rights of the products produced as part of the project belong to the University of Bath (see http://www.bath.ac.uk/ordinances/#intelprop ). This copy of the dissertation has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with its author and that no quotation from the thesis and no information derived Declaration This dissertation is submitted to the University of Bath in accordance with the requirements of the degree of Batchelor of Science in the Department of Computer Science. No portion of the work in this dissertation has been submitted in support of an application for any other degree or qualification of this or any other university or institution of learning. Except where specifically acknowledged, it is the work of the author. ………………………………………………………….. - 2 - Abstract In the competitive market of radio broadcasting it is imperative that radio stations tailor their content to suit their target audience and this process of targeting starts with the music policy. If a radio station gets the music wrong, then it risks alienating its listeners. This process of developing a music policy is never an easy process and what this project intends to do is implement a fully customisable song recommender system making use of semantic web technology to specify the relationship between songs and to allow the user to develop rules by which these songs are recommended. This customisation must not come at a price, however, and the system must continue to operate even if given bad or conflicting rules by the administrator. Any system that is broadcast-critical must be seen to be reliable whether it is for a national commercial station or a lowly student radio one. - 3 - Acknowledgements Many thanks to my project supervisor Julian Padget for his support and guidance towards the direction of technologies new. Thanks too to the entire committee at URB (University Radio Bath) in particular David Mayo for imparting his intimate knowledge of URB’s existing play-out system and finally a personal thanks to Gareth Gwynn, Lyndsay Fenner and Diet Coke TM . Without them I would have probably left the library in a straight-jacket. - 4 - Contents 1. Introduction........................................................................................................... 7 2. Literature Review.................................................................................................. 8 2.1. Introduction.....................................................................................................8 2.2. Agent Orientated Programming......................................................................8 2.3. Introducing the semantic web .......................................................................11 2.4. Agents and the Semantic Web ......................................................................15 2.5. Introducing Ontologies and OWL.................................................................15 2.6. Related completed work................................................................................16 2.7. Summary.......................................................................................................17 3. Requirements Elicitation..................................................................................... 18 3.1. Methodology.................................................................................................18 3.2. Music Metadata.............................................................................................18 3.3. System structure............................................................................................19 3.4. Song selector algorithm.................................................................................20 3.5. Repetition of songs........................................................................................21 3.6. Category inheritance .....................................................................................22 3.7. Category limiting ..........................................................................................26 3.8. Coping with errors ........................................................................................28 3.9. Scheduling features.......................................................................................28 3.10. Summary .......................................................................................................29 4. Requirements Analysis........................................................................................ 30 4.1. Recommending songs when rules clash........................................................30 4.2. On-the-fly generation and song corruption...................................................31 4.3. Applying the user-rules to listener requests..................................................32 4.4. Ensuring category spread when prioritising the least recently played..........32 4.5. Music metadata versus system efficiency.....................................................32 4.6. Summary.......................................................................................................33 5. Design ................................................................................................................ .34 5.1. Introduction...................................................................................................34 5.2. Primary Design Stages..................................................................................34 5.3. The song selector algorithm..........................................................................35 5.4. Limiting the set of songs for selection by category ......................................36 5.5. Recommending songs over time...................................................................40 5.6. Enforcing the rules and guarding against failure ..........................................43 5.7. Requesting Songs..........................................................................................45 5.8. Scheduling features on time..........................................................................46 6. Implementation ................................................................................................... 48 6.1. Overview.......................................................................................................48 6.2. Metadata storage ...........................................................................................49 6.3. Database Platform Layer...............................................................................51 - 5 - 6.4. Recommender Engine...................................................................................52 6.5. Category inference ........................................................................................54 6.6. Summary.......................................................................................................58 7. Evaluation ........................................................................................................... 59 7.1. Overview.......................................................................................................59 7.2. Enqueue and dequeue test.............................................................................59 7.3. Weighted random category test: ‘How random is random’..........................60 7.4. Enqueue, dequeue, queue monitoring and concurrency test.........................61 7.5. Finding the parents and the children of a category .......................................61 7.6. Full-system test of stability...........................................................................62 8. Conclusion .......................................................................................................... 66 8.1. Appraisal .......................................................................................................66