The Comprehensibility Assessment of Visualization of Semantic Data Representation (VSDR) Reflecting User Capability of Knowledge Exploration and Discovery

Kanjanakuha, Natanun; Janecek, Paul; Techawut, Churee

doi:10.1145/3348445.3348450

Cited by 4 publications

(8 citation statements)

References 6 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, in ( Kanjanakuha, Janecek & Techawut, 2019 ), researchers provided semantic data representation in a hyperbolic tree format. Their framework consists of a 3-layers hyperbolic tree-based modal approach that takes the input in the form of keywords from the user.…”

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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show abstract

“…Recently, increased computational power enabled researchers to deploy complex graph-based dynamic visualizations in more user-friendly SUIs [26]. The result of such interfaces, however, lacks a thorough comparison with existing state-of-the-art approaches [11], [27].…”

Section: Background and Hypothesismentioning

confidence: 99%

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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“…Similarly, in (Kanjanakuha et al, 2019), researchers provided semantic data representation in a hyperbolic tree format. Their framework consists of a 3-layers hyperbolic tree-based modal approach that takes the input in the form of keywords from the user.…”

Section: Renovation In Information Exploration Data-models and Semant...mentioning

confidence: 99%

“…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 et al, 2019), or using some generic similarity measures (Rashid and 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: /19mentioning

confidence: 99%

Peer Review #1 of "An architecture for non-linear discovery of aggregated multimedia document web search results (v0.1)"

Speretta¹

2021

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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 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 allows 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.

show abstract

The Comprehensibility Assessment of Visualization of Semantic Data Representation (VSDR) Reflecting User Capability of Knowledge Exploration and Discovery

Cited by 4 publications

References 6 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

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

Peer Review #1 of "An architecture for non-linear discovery of aggregated multimedia document web search results (v0.1)"

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