A supervised learning approach to detect subsumption relations between tags in folksonomies

Rêgo, Alex Sandro C.; Marinho, Leandro Balby; Pires, Carlos Eduardo Santos

doi:10.1145/2695664.2695904

Cited by 8 publications

(15 citation statements)

References 16 publications

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“…The former downscales non-event data to match the number of safety threats recorded, while the latter upscales the number of safety events to match the remainder of the data set. Undersampling is achieved through the use of cluster centroids [31], random sampling, and Tomek links [32], whereas oversampling is instituted via Adaptive Synthetic (ADASYN) method [33], Synthetic Minority Oversampling TEchnique (SMOTE) [34], and Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) methods. These methods are all applied on feature vector matrices and the best performers are chosen for further hyperparameter tuning and cross-validation.…”

Section: Classificationmentioning

confidence: 99%

Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning

et al. 2020

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In recent years, there has been a rapid growth in the application of data science techniques that leverage aviation data collected from commercial airline operations to improve safety. This paper presents the application of machine learning to improve the understanding of risk factors during flight and their causal chains. With increasing complexity and volume of operations, rapid accumulation and analysis of this safety-related data has the potential to maintain and even lower the low global accident rates in aviation. This paper presents the development of an analytical methodology called Safety Analysis of Flight Events (SAFE) that synthesizes data cleaning, correlation analysis, classification-based supervised learning, and data visualization schema to streamline the isolation of critical parameters and the elimination of tangential factors for safety events in aviation. The SAFE methodology outlines a robust and repeatable framework that is applicable across heterogeneous data sets containing multiple aircraft, airport of operations, and phases of flight. It is demonstrated on Flight Operations Quality Assurance (FOQA) data from a commercial airline through use cases related to three safety events, namely Tire Speed Event, Roll Event, and Landing Distance Event. The application of the SAFE methodology yields a ranked list of critical parameters in line with subject-matter expert conceptions of these events for all three use cases. The work concludes by raising important issues about the compatibility levels of machine learning and human conceptualization of incidents and their precursors, and provides initial guidance for their reconciliation.

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Section: Classificationmentioning

confidence: 99%

Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning

et al. 2020

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“…However, the features may not be fine-grained enough to represent the topic information in social tags. [40] combined several popular co-occurrence based feature extraction mechanisms to develop a binary classifier. The mechanisms considered included support and confidence [41], cosine similarity, set inclusion and generalisation degree [35], mutual overlapping [9] and graph-based taxonomy search adapted from [26].…”

Section: Knowledge Discovery From Social Tagging Datamentioning

confidence: 99%

“…Another class of method employs machine learning techniques, especially supervised learning, to predict relations. Research in this line also leverages co-occurrence features [40] and usually relies on specific contents from the tagged resources [49].…”

Section: Introductionmentioning

confidence: 99%

Knowledge base enrichment by relation learning from social tagging data

Dong

Wang

Coenen

et al. 2020

Information Sciences

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There has been considerable interest in transforming unstructured social tagging data into structured knowledge for semantic-based retrieval and recommendation. Research in this line mostly exploits data co-occurrence and often overlooks the complex and ambiguous meanings of tags. Furthermore, there have been few comprehensive evaluation studies regarding the quality of the discovered knowledge. We propose a supervised learning method to discover subsumption relations from tags. The key to this method is quantifying the probabilistic association among tags to better characterise their relations. We further develop an algorithm to organise tags into hierarchies based on the learned relations. Experiments were conducted using a large, publicly available dataset, Bibsonomy, and three popular, human-engineered or data-driven knowledge bases: DBpedia, Microsoft Concept Graph, and ACM Computing Classification System. We performed a comprehensive evaluation using different strategies: relation-level, ontology-level, and knowledge base enrichment based evaluation. The results clearly show that the proposed method can extract knowledge of better quality than the existing methods against the gold standard knowledge bases. The proposed approach can also enrich knowledge bases with new subsumption relations, having the potential to significantly reduce time and human effort for knowledge base maintenance and ontology evolution.

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“…It is demonstrated that mutual overlapping performs well on the dataset collected from the e-business website Taobao 10 . The two metrics, inclusion-generalization metric and mutual overlapping, are later used as two of the features for the supervised learning approach in [41].…”

Section: ) Set Theorymentioning

confidence: 99%

“…A binary classification approach has been proposed in [41]. Features are extracted based on association rule mining, similarity measures, inclusion and generalization measures [40], mutual overlapping [41] and taxonomy search [34]. The positive and negative classes are labeled using WordNet and ConceptNet 11 .…”

Section: ) Machine Learningmentioning

confidence: 99%

Learning Structured Knowledge from Social Tagging Data: A Critical Review of Methods and Techniques

Dong

Wang

Liang

2015

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

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For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and the Semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data.

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A supervised learning approach to detect subsumption relations between tags in folksonomies

Cited by 8 publications

References 16 publications

Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning

Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning

Knowledge base enrichment by relation learning from social tagging data

Learning Structured Knowledge from Social Tagging Data: A Critical Review of Methods and Techniques

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