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Multimedia context interpretation: a semantics-based cooperative indexing approach
New Review of Hypermedia and Multimedia ( IF 1.2 ) Pub Date : 2020-03-31 , DOI: 10.1080/13614568.2020.1745904
Mohammed Maree 1
Affiliation  

ABSTRACT The relative ineffectiveness of semantics-based multimedia indexing systems on the Web is caused by the semantic knowledge incompleteness and semantic heterogeneity problems. Nevertheless, the need to search multimedia documents with precision on the Web is persistently growing; pressing the demand for effective and efficient indexing strategies. In this article, we present an ontology-based multimedia indexing approach that cooperatively identifies the semantic and taxonomic relations that exist between annotation words that surround multimedia documents on Webpages. In this context, multiple ontologies are jointly employed for indexing each document. We construct inverted indexes in the form of semantic networks where nodes of each network are identified and added based on a majority-voting technique, while edges represent the semantic and taxonomic relations that hold between those nodes. To alleviate the heterogeneity between the resulting networks, we employ ontology merging algorithms to integrate them into consistent networks. We also utilise concept relatedness measures to enrich the networks with semantically-relevant entities that are not recognised by the used ontologies. To validate our proposal, we have developed a prototype system based on the proposed techniques. The produced results using real-world datasets demonstrate an improvement of the effectiveness against state-of-the-art baseline metrics.

中文翻译:

多媒体上下文解释:一种基于语义的合作索引方法

摘要 Web 上基于语义的多媒体索引系统的相对低效是由语义知识不完整和语义异构问题引起的。尽管如此,在 Web 上精确搜索多媒体文档的需求持续增长。迫切需要有效和高效的索引策略。在本文中,我们提出了一种基于本体的多媒体索引方法,该方法可协同识别网页上多媒体文档周围的注释词之间存在的语义和分类关系。在这种情况下,联合使用多个本体来索引每个文档。我们以语义网络的形式构建倒排索引,其中基于多数投票技术识别和添加每个网络的节点,而边表示这些节点之间的语义和分类关系。为了减轻结果网络之间的异构性,我们采用本体合并算法将它们集成到一致的网络中。我们还利用概念相关性度量来丰富网络,其中包含未被使用的本体识别的语义相关实体。为了验证我们的提议,我们开发了一个基于提议技术的原型系统。使用真实世界数据集产生的结果表明,相对于最先进的基线指标的有效性有所提高。我们还利用概念相关性度量来丰富网络,其中包含未被使用的本体识别的语义相关实体。为了验证我们的提议,我们开发了一个基于提议技术的原型系统。使用真实世界数据集产生的结果表明,相对于最先进的基线指标的有效性有所提高。我们还利用概念相关性度量来丰富网络,其中包含未被使用的本体识别的语义相关实体。为了验证我们的提议,我们开发了一个基于提议技术的原型系统。使用真实世界数据集产生的结果表明,相对于最先进的基线指标的有效性有所提高。
更新日期:2020-03-31
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