Using a multimedia semantic graph for web document visualization and summarization
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Using a multimedia semantic graph for web document visualization and summarization Antonio M. Rinaldi1,2
· Cristiano Russo1
Received: 25 January 2020 / Revised: 22 July 2020 / Accepted: 28 August 2020 / © The Author(s) 2020
Abstract The synthesis process of document content and its visualization play a basic role in the context of knowledge representation and retrieval. Existing methods for tag-clouds generations are mostly based on text content of documents, others also consider statistical or semantic information to enrich the document summary, while precious information deriving from multimedia content is often neglected. In this paper we present a document summarization and visualization technique based on both statistical and semantic analysis of textual and visual contents. The result of our framework is a Visual Semantic Tag Cloud based on the highlighting of relevant terms in a document using some features (font size, color, etc.) showing the importance of a term compared to other ones. The semantic information is derived from a knowledge base where concepts are represented through several multimedia items. The Visual Semantic Tag Cloud can be used not only to synthesize a document but also to represent a set of documents grouped by categories using a topic detection technique based on textual and visual analysis of multimedia features. Our work aims at demonstrating that with the help of semantic analysis and the combination of textual and visual features it is possible to improve the user knowledge acquisition by means of a synthesized visualization. The whole strategy has been evaluated by means of a ground truth and compared with similar approaches. Experimental results show the effectiveness of our approach, which outperforms state-of-art algorithms in topic detection combining both visual and semantic information. Keywords Multimedia topic detection · Document classification · Semantic analysis · Ontologies · Big data · Deep neural networks · Knowledge graph
Antonio M. Rinaldi
[email protected] Cristiano Russo [email protected] 1
Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Napoli, Italy
2
IKNOS-LAB Intelligent and Knowledge Systems (LUPT), Napoli, Italy
Multimedia Tools and Applications
1 Introduction The fast growth of user-centered information retrieval applications required new methodologies and techniques to assist users during their searching and browsing processes. In this scenario, people use different tagging services to manage, organize and retrieve useful information. A tagging system does not require much effort by the user and it is a good way to find relevant information. Nowadays a user inserts tags into videos, images and other resources with just a few words to easily retrieve and share them. User-centered approaches need effective methodologies to implement efficient strategies for the cooperation and analysis of data and applications, supporting the use of formal knowledge representations such as
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