The paradigm of context-aware computing allows storing situational and environmental information in such a way that its interpretation can be done easily and more meaningfully. In turn, this understanding is used to anticipate users’ needs, and proactively provide them with situation-aware content and experiences. Whereas context-awareness has been investigated extensively in the computer science and IoT disciplines, it has been largely overlooked by the research community dealing with musical interfaces design. Existing musical instruments are not equipped with the ability to understand the context around them, namely who is the musician playing them, what musical activity is being conducted, as well as where and when. Enhancing musical instruments with context-awareness has the concrete potential to enable novel kinds of interactions between musicians and musical content in a large variety of situations, from playing alone to playing in a group, from music learning to music composition. To accomplish such a vision of intelligence embedded in musical instruments it is necessary to model the context around their users. In this paper, we present an ontology devised to represent the knowledge related to musicians and musical activities, the “Musician’s Context Ontology” (MUSICO) to facilitate the development of context-aware musical applications. There was no previous comprehensive data model for the domain of musicians’ context, nevertheless, the new ontology relates to several existing ontologies, including the Internet of Musical Things Ontology to represent Internet of Musical Things ecosystems and the Music Ontology that deals with the description of the music value-chain from production to consumption. This paper documents the design of the ontology and its evaluation with respect to specific requirements gathered from an extensive literature review and interviews with musicians. The utility of the ontology is demonstrated by a smartphone application that enables to search for musicians based on both textual and content-based musical queries. MUSICO can be accessed at: https://w3id.org/musico# .

After decades of schema-centric research on data management and integration, the evolution of data on the web and the adoption of resource-based models seem to have shifted the focus towards an entity-centric approach. Our thesis is that the missing element to achieve the full potential of this approach is the development of what we call an Entity Name System (ENS), namely a system which provides a collection of general services for managing the lifecycle of globally unique identifiers in an open and decentralized environment. The claim is that this system can indeed play the coordination role that the DNS played for the document-centric development of the current web.

In this article, we present MELIS (Meaning Elicitation and Lexical Integration System), a method and a software tool for enabling an incremental process of automatic annotation of local schemas (e.g. relational database schemas, directory trees) with lexical information. The distinguishing and original feature of MELIS is the incremental process: the higher the number of schemas which are processed, the more background/ domain knowledge is cumulated in the system (a portion of domain ontology is learned at every step), the better the performance of the systems on annotating new schemas. MELIS has been tested as a component of the MOMIS-Ontology Builder, a framework able to create a domain ontology representing a set of selected data sources, described with a standard W3C language wherein concepts and attributes are annotated according to the lexical reference database. We describe the MELIS component within the MOMIS-Ontology Builder framework and provide some experimental results of MELIS as a standalone tool and as a component integrated in MOMIS.

In most web sites, web-based applications (such as web portals, e-marketplaces, search engines), and in the file systems of personal computers, a wide variety of schemas (such as taxonomies, directory trees, thesauri, Entity-Relationship schemas, RDF Schemas) are published which (i) convey a clear meaning to humans (e.g. help in the navigation of large collections of documents), but (ii) convey only a small fraction (if any) of their meaning to machines, as their intended meaning is not formally/explicitly represented. In this paper we present a general methodology for automatically eliciting and representing the intended meaning of these structures, and for making this meaning available in domains like information integration and interoperability, web service discovery and composition, peer-to-peer knowledge management, and semantic browsers. We also present an implementation (called CtxMatch2) of how such a method can be used for semantic interoperability.