A semantic approach for document classification using deep neural networks and multimedia knowledge graph

Rinaldi, Antonio M.; Russo, Cristiano; Tommasino, Cristian

doi:10.1016/j.eswa.2020.114320

Cited by 22 publications

(11 citation statements)

References 32 publications

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“…The papers delve into advanced techniques such as neural architecture search for multilingual text classification (Yan et al, 2023), disaster-related multilingual text classification using graph neural networks , and the utilization of graph neural networks in natural language processing (Liu & Wu, 2022). Some contributions focus on semantic parsing via multilingual translation (Procopio et al, 2021), generic frameworks for multilingual short text categorization (Enamoto et al, 2021), and semantic approaches for document classification using deep neural networks and multimedia knowledge graphs (Rinaldi et al, 2021). Additionally, there are studies addressing cross-lingual word sense disambiguation (Janu et al, 2022), semantic graph-based topic modeling for multilingual fake news detection (Mohawesh et al, 2023), and automatic Bangla knowledge graph construction with semantic neural graph filtering (Wasi et al, 2024).…”

Section: Related Workmentioning

confidence: 99%

Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation

Hao Yang

2024

jes

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Multilingual information retrieval using graph neural networks offers practical applications in English translation by leveraging advanced computational models to enhance the efficiency and accuracy of cross-lingual search and translation tasks. By representing textual data as graphs and utilizing graph neural networks (GNNs), this approach captures intricate relationships between words and phrases across different languages, enabling more effective language understanding and translation. GNNs can learn complex linguistic structures and semantic similarities from multilingual corpora, facilitating the development of more robust translation systems that are capable of handling diverse language pairs and domains. The paper introduces a novel approach termed the Multilingual Ant Bee Optimization Graph Neural Network (MABO-GNN) for addressing optimization, classification, and multilingual translation tasks. MABO-GNN integrates ant bee optimization algorithms with graph neural networks to provide a versatile framework capable of optimizing objective functions, improving classification accuracy iteratively, and facilitating high-quality translations across multiple languages. Through comprehensive experimentation, the efficacy of MABO-GNN is demonstrated across various tasks, languages, and datasets. in optimization experiments, MABO-GNN achieves objective function values of 0.012, 0.015, 0.011, and 0.013 in Experiment 1, Experiment 2, Experiment 3, and Experiment 4, respectively, with convergence times ranging from 90 to 150 seconds. In classification tasks, the model exhibits notable performance improvements over iterations, with BLEU scores reaching 0.84 and METEOR scores reaching 0.78 in the fifth iteration. The translation results showcase BLEU scores of 0.85 for English, 0.82 for French, 0.79 for German, 0.81 for Spanish, and 0.75 for Chinese, indicating the model's proficiency in generating high-quality translations across diverse languages.

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Section: Related Workmentioning

confidence: 99%

Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation

Hao Yang

2024

jes

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“…For each entity, the crawler found a description using Wikidata. The text obtained with the combination of entities and descriptions is analyzed with a semantic-based metric [35] to have the best sense of each term. In the image classification task a feature extraction is used on each image of a given webpage using convolutional neural networks (CNN) [38], removing the last layer of the network and Fig.…”

Section: The Knowledge Graphmentioning

confidence: 99%

“…The proper sense of a given term is the sense that reaches the best score. In this work, we use the WSD algorithm proposed in [35] using an innovative technique that differs from others present in literature [17,25,25,27,46]. Such WSD algorithm is based on ontology and in particular it uses WordNet 3.0, and compute the sense disambiguation using a Dynamic Semantic Network (DSN), which is built to extract all hypernyms of a pre-selected input term and for each hyponym extracted from this term, all other semantic relationships of WordNet are explored and, eventually, a score of similarity is computed.…”

Section: The Storytelling Processmentioning

confidence: 99%

A storytelling framework based on multimedia knowledge graph using linked open data and deep neural networks

Renzi

Rinaldi

Russo

et al. 2023

Multimed Tools Appl

Self Cite

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Automatic storytelling is a broad challenge in research contexts such as Natural Language Processing and Contend Based Image Analysis. Despite the considerable achievements of machine learning techniques in these research fields, combining different approaches to fill the gap between an automatic generated story and human handwriting is hard. This work proposes a novel storytelling framework in the Cultural Heritage domain. We developed our framework based on a Multimedia Knowledge Graph (MKG), a crucial point of our work. Furthermore, we populated our Multimedia Knowledge Graph with a focused crawler that employs deep learning techniques to recognise a multimedia object from web resources. Furthermore, we used a combined approach of deep learning techniques and Linked Open Data (LOD) to retrieve information about images and depicted figures using Instance Segmentation. The system has a dynamic, user-friendly interface that guides the user during the storytelling process. Finally, we evaluated the system from a qualitative and quantitative point of view.

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“…Feature selection aims at reducing the number of variables to keep only the most relevant without changing the initial variables (Gomathi and Karlekar, 2019;Mendez et al, 2019). On the contrary, Feature extraction change the initial variables using the prior knowledge of the ontology for get relevant features (Kumar et al, 2020;Radovanovic et al, 2019;Evert et al, 2019;Agarwal et al, 2015;Radinsky et al, 2012;Greenbaum et al, 2019;Liu et al, 2021;Rinaldi et al, 2021;Castillo et al, 2008;Yilmaz, 2017;Hsieh et al, 2013;Rajput and Haider, 2011;Manuja and Garg, 2015;Ahani et al, 2021;Akila et al, 2021;Deepak et al, 2022;Messaoudi et al, 2021;Nayak et al, 2021;Pérez-Pérez et al, 2021;Zhao et al, 2021;. In semantic embedding, always in training data step, raw data are both refined by semantic knowledge and transformed into vectors to be exploited by neural networks (Chen et al, 2021;Ren et al, 2020;Qiu et al, 2019;Ali et al, 2019;Zhang et al, 2019;Makni and Hendler, 2019;Benarab et al, 2019;Moussallem et al, 2019;Gaur et al, 2019;Jang et al, 2018;Hassanzadeh et al, 2020;Ali et al, 2021;Amador-Domínguez et al, 2021;Alexandridis et al, 2021;Niu et al, 2022), SVM…”

Section: Informed Machine Learningmentioning

confidence: 99%

“…Table 8 presents machine learning algorithms of each informed machine learning category. Articles were mainly published after 2017, and a great part of them concern neural networks (Hassanzadeh et al, 2020;Gaur et al, 2019;Ali et al, 2019;Jang et al, 2018;Zhang et al, 2019;Ali et al, 2021;Amador-Domínguez et al, 2021;Benarab et al, 2019;Chen et al, 2021;Wang et al, 2021bWang et al, , 2010Sabra et al, 2020;Pancerz and Lewicki, 2014;Yilmaz, 2017;Kumar et al, 2020;Rinaldi et al, 2021;Gomathi and Karlekar, 2019;Serafini et al, 2017;Kuang et al, 2021;Chung et al, 2020;Fu et al, 2015;Huang et al, 2019;Abdollahi et al, 2021;Ahani et al, 2021;Akila et al, 2021;Deepak et al, 2022;Messaoudi et al, 2021;Nayak et al, 2021;Zhao et al, 2021), especially Recurrent Neural Networks (Makni and Hendler, 2019;Ren et al, 2020;Moussallem et al, 2019;Zhang et al, 2019;Jang et al, 2018;Ali et al, 2021;Liu et al, 2021;Huang et al, 2019;Alexandridis et al, 2021;Niu...…”

Section: Informed Machine Learningmentioning

confidence: 99%

Mixed artificial reasoning, closer to human?

Ghidalia

Narsis

Bertaux

et al. 2022

Preprint

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AI is now used in many areas of our daily life, however we still have difficulties to make machines interact with our world as we perceive it. Humans mainly use two types of reasoning to grasp reality: inductive and deductive reasoning. In AI, inductive reasoning is possible thanks to the use of machine learning models, while deductive reasoning can be provided by the use of ontologies. Based on these statements, we have conducted a review of articles combining the use of machine learning and ontologies in order to identify the most recent techniques that combine induction and deduction within an AI. This systematic literature review allowed us, after reading 128 studies, to identify three main groups of hybridization between machine learning and ontologies: learning-enhanced ontologies, semantic data mining and learning and reasoning system. The combination of these two types of reasoning allows machines to be better informed about the specificity of our world.

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A semantic approach for document classification using deep neural networks and multimedia knowledge graph

Cited by 22 publications

References 32 publications

Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation

Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation

A storytelling framework based on multimedia knowledge graph using linked open data and deep neural networks

Mixed artificial reasoning, closer to human?

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