Multimedia context interpretation: a semantics-based cooperative indexing approach

Maree, Mohammed

doi:10.1080/13614568.2020.1745904

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…Mostly, web documents and traditional database systems are used to store crime news, and then keyword-based search systems are used to retrieve the information. Crime news on the Internet is abundantly available in unstructured formats, so using traditional keyword-based information retrieval systems to organize large amounts of data can lead to problems in obtaining relevant information [1,2].…”

Section: Introductionmentioning

confidence: 99%

Ontology-Based Crime News Semantic Retrieval System

Majeed,

Ahmad,

Hassan

et al. 2023

CMC

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Every day, the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis. Crime news exists on the Internet in unstructured formats such as books, websites, documents, and journals. From such homogeneous data, it is very challenging to extract relevant information which is a timeconsuming and critical task for the public and law enforcement agencies. Keyword-based Information Retrieval (IR) systems rely on statistics to retrieve results, making it difficult to obtain relevant results. They are unable to understand the user's query and thus face word mismatches due to context changes and the inevitable semantics of a given word. Therefore, such datasets need to be organized in a structured configuration, with the goal of efficiently manipulating the data while respecting the semantics of the data. An ontological semantic IR system is needed that can find the right investigative information and find important clues to solve criminal cases. The semantic system retrieves information in view of the similarity of the semantics among indexed data and user queries. In this paper, we develop an ontology-based semantic IR system that leverages the latest semantic technologies including resource description framework (RDF), semantic protocol and RDF query language (SPARQL), semantic web rule language (SWRL), and web ontology language (OWL). We have conducted two experiments. In the first experiment, we implemented a keyword-based textual IR system using Apache Lucene. In the second experiment, we implemented a semantic system that uses ontology to store the data and retrieve precise results with high accuracy using SPARQL queries. The keyword-based system has filtered results with 51% accuracy, while the semantic system has filtered results with 95% accuracy, leading to significant improvements in the field and opening up new horizons for researchers.

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Section: Introductionmentioning

confidence: 99%

Ontology-Based Crime News Semantic Retrieval System

Majeed,

Ahmad,

Hassan

et al. 2023

CMC

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“…Despite these advances, the broader scope of content retrieval faces challenges in interpreting the features of heterogeneous contents and bridging semantic gaps between contents and their representations [17]. In this regard, several studies have noted that semantic web technologies such as linked data and ontologies hold immense potentials to enhance content sharing [18], [19]. Linked data is a set of best practices for publishing, connecting, and querying structured data on the Web through the World Wide Web Consortium's (W3C) standardized protocols [20], while ontologies serve as a foundation for linked data by providing formal, explicit specifications of shared conceptualizations within a specific domain [21].…”

Section: Introductionmentioning

confidence: 99%

A Linked Data and Ontology-Based Framework for Enhanced Sharing of Safety Training Materials in the Construction Industry

Pedro,

Baik,

et al. 2023

IEEE Access

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The provision of suitable training materials is crucial in preparing construction workers for the safety risks inherent in their work environments. However, retrieving relevant training resources for specific work tasks and worksites poses challenges due to the dispersed, unstructured, and multifarious nature of available content. This paper proposes a linked data and ontology-based system framework for effectively sharing and accessing educational safety contents from heterogeneous sources in a standardized way. The main technical solutions in the framework include an ontology that leverages the Resource Description Framework and Ontology Web Language to structure and publish educational safety training contents based on the linked data principles, and a content retrieval method which leverages the SPARQL protocol and RDF query language. Experimental results confirm that the proposed approach outperforms existing repositories in terms of search time, search result precision, and accuracy. It is envisaged that the framework would aid safety trainers in sharing, searching, and retrieving didactically appropriate materials to prepare workers for the unique safety challenges of their jobsite environments.

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“…Topic extraction is a common method used in text analysis to helps us understand lengthy content or high numbers of documents (Maree, 2020). Text topic extraction is a clustering algorithm that divides semantically similar documents into a cluster, maximizes the distance between clusters, and minimizes the distance between the nodes in the cluster (Curiskis et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

What Do Scholars Propose for Future COVID-19 Research in Academic Publications? A Topic Analysis Based on Autoencoder

Guo

Wang

2023

Sage Open

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To analyze the directions for future research suggested and to project future research plans, we extract relevant text from these publications with respect to COVID-19-related research based on 54,136 relevant academic journals published from the initial outbreak of COVID-19 in January 2020 until December 2020. First, we extract and preprocess the corpus and then determine that, according to the Elbow method, the optimal number of clusters is 7. Then, we construct a text clustering model based on an autoencoder, with the support of an artificial neural network. Distance measurements, such as correlation, cosine, Braycurtis, and Jaccard are compared, and the clustering results are evaluated with normal mutual information. The results show that cosine similarity has the best effect on clustering of COVID-19-related documents. A topic model analysis shows that the directions of future research can mainly be grouped into the following seven categories: infectivity testing, genome analysis, vaccine testing, diagnosis and infection characteristics, pandemic management, nursing care, and clinical testing. Among them, the topics of pandemic management, diagnosis and infection characteristics, and clinical testing trended upward in proportion to future directions. The topic of vaccine testing remains steady over the observation window, whereas other topics (infectivity testing, genome analysis, and nursing care) slowly trended downward. Among all the topics, medical research comprises 80%, and about 20% of the topics are related to public management, government functions, and economic development. This study enriches our scientific understanding of COVID-19 and helps us to effectively predict future scientific research output on COVID-19.

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Multimedia context interpretation: a semantics-based cooperative indexing approach

Cited by 4 publications

References 38 publications

Ontology-Based Crime News Semantic Retrieval System

Ontology-Based Crime News Semantic Retrieval System

A Linked Data and Ontology-Based Framework for Enhanced Sharing of Safety Training Materials in the Construction Industry

What Do Scholars Propose for Future COVID-19 Research in Academic Publications? A Topic Analysis Based on Autoencoder

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