An ontology-driven multimedia focused crawler based on linked open data and deep learning techniques

Capuano, Alfredo; Rinaldi, Antonio M.; Russo, Cristiano

doi:10.1007/s11042-019-08252-2

Cited by 24 publications

(7 citation statements)

References 38 publications

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“…In this work, we used a crawler [4] proposing a new visual feature extraction approach to populate our KG. The role of the focused crawler is to recognize domain-related documents (i.e.…”

Section: The Knowledge Graphmentioning

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

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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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“…In this work, we used a crawler [4] proposing a new visual feature extraction approach to populate our KG. The role of the focused crawler is to recognize domain-related documents (i.e.…”

Section: The Knowledge Graphmentioning

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

View full text Add to dashboard Cite

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“…(3) Heuristic search strategies are usually used to evaluate URL links. e focused crawler adopts a heuristic search algorithm to calculate the relevancy of the links contained in the captured web pages according to the subject-oriented words or theme 2 Mobile Information Systems representation model set by the user, discards the links with low relevancy value, and selects the links with high relevancy to be added to the link sequence to be crawled [11].…”

Section: Collection Of College English Teaching Information Resourcesmentioning

confidence: 99%

Construction of College English Teaching Information Resource Integration Model Based on Fuzzy Clustering Algorithm

Hua

2022

Mobile Information Systems

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The existing college English teaching information resource integration methods have problems of low recall rate, low accuracy rate, and long integration time. Therefore, this paper constructs a college English teaching information resource integration model based on fuzzy clustering algorithm. The ant colony algorithm is used to optimize the topic crawler, the optimized topic crawler is used to collect the college English teaching information resources in the mixed teaching mode, and the weight and naive Bayes algorithm are combined to classify the collected resources. According to the results of resource classification, fuzzy clustering algorithm is used to construct the integration model of college English teaching information resources. The experimental results show that the recall rate of the proposed model is more than 95%, the accuracy rate is more than 94%, and the average resource integration time is only 0.71 s, indicating that the model has relatively reliable performance.

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“…Disambiguation Term Extraction 0.36 0.61 0.35 0.86 Fig. 4 An example of the disambiguation term extraction Figure 3 gives an example of the ambiguation term identification. In Fig.…”

Section: Ambiguity Resolutionmentioning

confidence: 99%

“…This topic-oriented web crawler only downloads web pages related to a given topic, and determines the priority of unvisited URLs based on the topic similarities of the retrieved web pages. The topic-oriented web crawler called as focused crawler greatly reduces resources such as storage space and indexing time, and improves the accuracy of retrieval results [3,4].…”

Section: Introductionmentioning

confidence: 99%

A focused crawler based on semantic disambiguation vector space model

Liu

et al. 2022

Complex Intell. Syst.

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The focused crawler grabs continuously web pages related to the given topic according to priorities of unvisited hyperlinks. In many previous studies, the focused crawlers predict priorities of unvisited hyperlinks based on the text similarity models. However, the representation terms of the web page ignore the phenomenon of polysemy, and the topic similarity of the text cannot combine the cosine similarity and the semantic similarity effectively. To address these problems, this paper proposes a focused crawler based on semantic disambiguation vector space model (SDVSM). The SDVSM method combines the semantic disambiguation graph (SDG) and the semantic vector space model (SVSM). The SDG is used to remove the ambiguation terms irrelevant to the given topic from representation terms of retrieved web pages. The SVSM is used to calculate the topic similarity of the text by constructing text and topic semantic vectors based on TF × IDF weights of terms and semantic similarities between terms. The experiment results indicate that the SDVSM method can improve the performance of the focused crawler by comparing different evaluation indicators for four focused crawlers. In conclusion, the proposed method can make the focused crawler grab the higher quality and more quantity web pages related to the given topic from the Internet.

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An ontology-driven multimedia focused crawler based on linked open data and deep learning techniques

Cited by 24 publications

References 38 publications

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

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

Construction of College English Teaching Information Resource Integration Model Based on Fuzzy Clustering Algorithm

A focused crawler based on semantic disambiguation vector space model

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