Modeling the Complexity of Music Metadata in Semantic Graphs for Exploration and Discovery

Lisena, Pasquale; Troncy, Raphaël; Todorov, Konstantin; Achichi, Manel

doi:10.1145/3144749.3144754

Cited by 9 publications

(10 citation statements)

References 9 publications

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“…Information exploration and discovery is a long, non-trivial, and non-linear journey. To foster non-linear navigation of the search results, existing literature mostly instantiated a graph data-modal using either existing domain knowledge, such as ontologies ( Khalili et al, 2017 ; Lisena et al, 2017 ; Kanjanakuha, Janecek & Techawut, 2019 ), or using some generic similarity measures ( Rashid & Bhatti, 2017 ). Our approach uses domain-independent semantics and similarity measures to construct a non-linear graph to provide non-linear means of search results exploration and discovery.…”

Section: Comparison and Discussionmentioning

confidence: 99%

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An architecture for non-linear discovery of aggregated multimedia document web search results

Khan¹,

Rashid²,

Saleem³

et al. 2021

PeerJ Computer Science

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The recent proliferation of multimedia information on the web enhances user information need from simple textual lookup to multi-modal exploration activities. The current search engines act as major gateways to access the immense amount of multimedia data. However, access to the multimedia content is provided by aggregating disjoint multimedia search verticals. The aggregation of the multimedia search results cannot consider relationships in them and are partially blended. Additionally, the search results’ presentation is via linear lists, which cannot support the users’ non-linear navigation patterns to explore the multimedia search results. Contrarily, users’ are demanding more services from search engines. It includes adequate access to navigate, explore, and discover multimedia information. Our discovery approach allow users to explore and discover multimedia information by semantically aggregating disjoint verticals using sentence embeddings and transforming snippets into conceptually similar multimedia document groups. The proposed aggregation approach retains the relationship in the retrieved multimedia search results. A non-linear graph is instantiated to augment the users’ non-linear information navigation and exploration patterns, which leads to discovering new and interesting search results at various aggregated granularity levels. Our method’s empirical evaluation results achieve 99% accuracy in the aggregation of disjoint search results at different aggregated search granularity levels. Our approach provides a standard baseline for the exploration of multimedia aggregation search results.

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Section: Comparison and Discussionmentioning

confidence: 99%

“…Similarly, Lisena et al (2017) developed a modern web application for music exploration and discovery using semantic RDF graphs to establish links between entities and relationships among them. Khalili et al (2017) used inference techniques on the semantic linked open data to produce notably unique information fostering discovery.…”

Section: Related Workmentioning

confidence: 99%

An architecture for non-linear discovery of aggregated multimedia document web search results

Khan¹,

Rashid²,

Saleem³

et al. 2021

PeerJ Computer Science

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“…Up until now, a large number of music-related datasets have been published as Linked Open Data, where there is a strong emphasis on making music metadata explicit. Linked Data can be applied to describe cataloguing metadata, as exemplied by LinkedBrainz [7] and DoReMus [14]. Emerging elds such as semantic audio combine (audio) analysis techniques and Semantic Web technologies in order to associate provenance metadata with content-based analyses [1,2].…”

Section: Related Work 21 Integration and Interlinkingmentioning

confidence: 99%

“…The returned entities are then associated with the Linked Data URI of the MIDI piece using the dc:subject predicate. 14 The process is entirely automatic, and although a large quantity of entities have been correctly identied, we are aware of inaccuracies in the data (for example, many les have been associated to the entity Life_Model_Decoy, or to Electronic_Dance_Music). Overall, the quality of data could be improved by ltering out entities that are not of specic safe types (Genre, Band, etc.…”

Section: Dataset Additionsmentioning

confidence: 99%

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The Semantic Web MIDI Tape

Meroño‐Peñuela

Valk²,

Daga

et al. 2018

Proceedings of the 1st International Workshop on Semantic Applications for Audio and Music

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The Linked Data paradigm has been used to publish a large number of musical datasets and ontologies on the Semantic Web, such as MusicBrainz, AcousticBrainz, and the Music Ontology. Recently, the MIDI Linked Data Cloud has been added to these datasets, representing more than 300,000 pieces in MIDI format as Linked Data, opening up the possibility for linking ne-grained symbolic music representations to existing music metadata databases. Despite the dataset making MIDI resources available in Web data standard formats such as RDF and SPARQL, the important issue of nding meaningful links between these MIDI resources and relevant contextual metadata in other datasets remains. A fundamental barrier for the provision and generation of such links is the diculty that users have at adding new MIDI performance data and metadata to the platform. In this paper, we propose the Semantic Web MIDI Tape, a set of tools and associated interface for interacting with the MIDI Linked Data Cloud by enabling users to record, enrich, and retrieve MIDI performance data and related metadata in native Web data standards. The goal of such interactions is to nd meaningful links between published MIDI resources and their relevant contextual metadata. We evaluate the Semantic Web MIDI Tape in various use cases involving user-contributed content, MIDI similarity querying, and entity recognition methods, and discuss their potential for nding links between MIDI resources and metadata.

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An Explanatory Study on User Behavior in Discovering Aggregated Multimedia Web Content

2022

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The recent advancements in the web allow users to generate multimedia content. It may cause an immense multimedia information proliferation and demands innovative tools to discover multimedia content. The researchers have devised numerous state-of-the-art approaches; however, analysis to confirm the efficacy has little emphasis. This study investigates users' complex multimedia informationseeking behavior over state-of-the-art web search systems to unveil the user's information-seeking issues. Our research employs between-subjects study and post hoc analysis strategies to analyze participants' seeking characteristics. The study design adopted statistical hypothesis testing to consolidate previous user behavioral studies, confirm existing studies, and present recommended practices for future general-purpose web search engines. The participants were assigned Google and an advanced discovery search system using the same multimedia dataset to ensure the obtained results' credibility. The primary behavioral parameters include search efforts, multimedia content exploration, search user interface (SUI), information management and presentation, and user cognition. This study uncovers several inadequacies of the search engines in meeting users' complex discovery needs, including 29.6% less user engagement, 43% system and searching dissatisfaction, and 32% less knowledge acquisition with 63.9% increased clicking effort on traditional search engines. The results confirmed previous user studies and suggest novel research recommendations statistically significant in multimedia information exploration-related endeavors.

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Modeling the Complexity of Music Metadata in Semantic Graphs for Exploration and Discovery

Cited by 9 publications

References 9 publications

An architecture for non-linear discovery of aggregated multimedia document web search results

An architecture for non-linear discovery of aggregated multimedia document web search results

The Semantic Web MIDI Tape

An Explanatory Study on User Behavior in Discovering Aggregated Multimedia Web Content

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