A unified knowledge compiler to provide support the scientific community

Fernández-Isabel, Alberto; Prieto, Juan Carlos; Ortega, Felipe; Diego, Isaac Martín de; Moguerza, Javier M.; Mena, José; Galindo, Sara; Napalkova, Liana

doi:10.1016/j.knosys.2018.07.044

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…The set of these edges is considered as the relationship between two nodes. The edges store the Digital Object Identifier (DOI) of the research article and its estimated reputation using the Unified Knowledge Compiler (UNIKO) [53] approach as attributes. This reputation, among others, is used to estimate the reliability of new articles that contain this same relationship between concepts.…”

Section: B Learners Modulementioning

confidence: 99%

Detecting Low-Credibility Medical Websites Through Semi-Supervised Learning Techniques

Fernández,

De Diego,

Fernández-Isabel

et al. 2023

IEEE Access

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The rapid spread of biased information and disinformation has intensified in recent times due to the increased reliance on the Internet and social media platforms as primary sources of information. This issue is of particular concern in the fields of medicine and healthcare, given the critical nature of decisions and understandings in these areas. While medical experts can mitigate the repercussions of misinformation by evaluating questionable websites, this approach is time-consuming. Consequently, there is a pressing need to develop software solutions that can automate the detection of misleading information. This paper presents CO-training and Active Learning-based framework for Finding Low-credibility web Addresses in the MEdical field (COAL4FLAME), a novel system designed to analyze health-related websites and identify misinformation. The system integrates results from multiple estimators to reach a comprehensive conclusion. COAL4FLAME uses semi-supervised learning strategies, such as multi-view learning, cotraining, and active learning, to address the challenge of limited labeled data, a major strength of the presented proposal. Medical experts have rigorously tested and evaluated the system using a selected set of websites.

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Section: B Learners Modulementioning

confidence: 99%

Detecting Low-Credibility Medical Websites Through Semi-Supervised Learning Techniques

Fernández,

De Diego,

Fernández-Isabel

et al. 2023

IEEE Access

Self Cite

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“…These systems usually comprehend a storage component (e.g., a database) to ease the knowledge retrieval in response to specific queries, along with learning and justification, or to transfer knowledge from one domain of knowledge to another. They are formed by different modules to address the needs of the users or to optimize the system [6]. Such systems are capable of cooperating with human users and are being used for problem solving, training and assisting users and experts of the domain for which the systems are developed.…”

Section: A Knowledge-based Systemsmentioning

confidence: 99%

Knowledge-Based Approach to Detect Potentially Risky Websites

et al. 2021

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Nowadays, fraudulent and malicious websites are emerging as a harmful and very common problem on the Internet. It causes huge money losses and irreparable damage for both companies and particulars. To face this situation, governments have approved multiple law projects. This way, the legality on the Internet is being enforced and sanctions to those offenders who develop illegal or malicious activities are being imposed. However, governments still need a way to simplify the classification of websites into risky or non-risky, since most of this work is manual. This paper presents the DOmains Classifier based on RIsky Websites (DOCRIW ) framework to detect domains that contain possible fraud or malicious content. It is based on two main components. The first component is a previously built knowledge base containing information from risky websites. The second one complements the system with a binary classifier able to label a website (as risky or not) considering just its domain. The system makes use of web information sources and includes host-based variables. It also applies similarity measures, supervised learning algorithms and optimization methods to enhance its performance. The presented work is experimental, rendering promising outcomes.INDEX TERMS Risky website detection, malware alerts, knowledge-based systems, similarity metrics, combination of information.

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“…This is because the combination of different features plays a crucial role in the classification. The classifier is then trained by a machine learning algorithm to evaluate the degree of polarity and assign a corresponding label to the tested instance [25][26][27].…”

Section: Machine Learning-based Approachmentioning

confidence: 99%

“…Sci. 2018, 8, x 4 of 18 learning algorithm to evaluate the degree of polarity and assign a corresponding label to the tested instance [25][26][27]. Various learning algorithms have been adopted for the machine learning-based approach, including Support Vector Machine (SVM), Decision Tree (DT), Naïve-Bayes (NB), and NN, etc.…”

Section: Machine Learning-based Approachmentioning

confidence: 99%

Feature Weighting Based on Inter-Category and Intra-Category Strength for Twitter Sentiment Analysis

Wang

Youn

2018

Applied Sciences

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The rapid growth in social networking services has led to the generation of a massivevolume of opinionated information in the form of electronic text. As a result, the research on textsentiment analysis has drawn a great deal of interest. In this paper a novel feature weighting approachis proposed for the sentiment analysis of Twitter data. It properly measures the relative significanceof each feature regarding both intra-category and intra-category distribution. A new statistical modelcalled Category Discriminative Strength is introduced to characterize the discriminability of thefeatures among various categories, and a modified Chi-square (2)-based measure is employed tomeasure the intra-category dependency of the features. Moreover, a fine-grained feature clusteringstrategy is proposed to maximize the accuracy of the analysis. Extensive experiments demonstrate thatthe proposed approach significantly outperforms four state-of-the-art sentiment analysis techniquesin terms of accuracy, precision, recall, and F1 measure with various sizes and patterns of training andtest datasets.

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A unified knowledge compiler to provide support the scientific community

Cited by 11 publications

References 27 publications

Detecting Low-Credibility Medical Websites Through Semi-Supervised Learning Techniques

Detecting Low-Credibility Medical Websites Through Semi-Supervised Learning Techniques

Knowledge-Based Approach to Detect Potentially Risky Websites

Feature Weighting Based on Inter-Category and Intra-Category Strength for Twitter Sentiment Analysis

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