A framework to explore results in multiple media information aggregated search

Rashid, Umer; Bhatti, Muhammad Afzal

doi:10.1007/s11042-017-4769-8

Cited by 14 publications

(54 citation statements)

References 68 publications

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“…This diversification may include the integration of verticals from social media, shopping, movies & dramas, maps, songs, etc. However, this integration of the verticals is mostly partial-blended ( Rashid & Bhatti, 2017 ). The relationship between the multimedia artifacts inside each disjoint vertical is often ignored.…”

Section: Related Workmentioning

confidence: 99%

“…The format of the information is now extremely varied. The individual bits of data coming from blogs, articles, web services, picture galleries, etc., are resulting in exponential growth of multimedia data on the web ( Batrinca & Treleaven, 2015 ; Rashid & Bhatti, 2017 ). The web is becoming the most ubiquitous platform ever since its birth and has increased in both quantity and quality ( Taheri et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…It is reported as a highly complex problem bridging the different areas of information seeking, interactive information retrieval, and user interface design ( Gäde et al, 2015 ). Moreover, the increasing amount of heterogeneous content on the web has transformed user needs from simple lookup-based queries to broader exploratory queries, requiring the diverse heterogeneous contents to satisfy the desired information needs ( Rashid & Bhatti, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…The goal is to compose the most appropriate responses to the tasks at hand in the form of multimedia documents ( Kopliku, Pinel-Sauvagnat & Boughanem, 2014 ). A multimedia document is a collection of co-existing heterogeneous multimedia objects sharing the same semantics ( Rashid & Bhatti, 2017 ). Users prefer aggregation of useful multimedia information residing in diverse sources through unified interfaces ( Kopliku, Pinel-Sauvagnat & Boughanem, 2014 ; Rashid & Bhatti, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…A multimedia document is a collection of co-existing heterogeneous multimedia objects sharing the same semantics ( Rashid & Bhatti, 2017 ). Users prefer aggregation of useful multimedia information residing in diverse sources through unified interfaces ( Kopliku, Pinel-Sauvagnat & Boughanem, 2014 ; Rashid & Bhatti, 2017 ). Similarly, the user interface presenting aggregated contents encouraged participants to view more diversified sources from the search results, and 75% of the participants found this blended approach more comfortable to use ( Sushmita, Joho & Lalmas, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%