An ontology-based deep learning approach for triple classification with out-of-knowledge-base entities

Amador-Domínguez, Elvira; Serrano, Emilio; Manrique, Daniel; Hohenecker, Patrick; Lukasiewicz, Thomas

doi:10.1016/j.ins.2021.02.018

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…• WordNet is the most widely used KG for augmenting both ZSL [2,5,36,62,63,88,98,103,105,183,188,192] and FSL [2,39,85,125,139,174]. As a large lexical database with several different relationships between words, such as synonym, hyponym, hypernym and meronym [122], it is often used to build task-specific class hierarchies, especially for image classification.…”

Section: Sub-kg Extractionmentioning

confidence: 99%

“…• Freebase is a large-scale general purpose KG with relational facts, contributed by multiple sources such as Wikipedia, MusicBrainz (a music database), Notable Names Database (an online database of biographical details of famous people) and volunteers [19]. Its official API has been shut down, but it can still be accessed as a dump or via Google's Knowledge Graph API, and has been widely used for investigating KG techniques including KG augmented ZSL [5,84,114] and FSL [114,218]. Different from WordNet and ConceptNet, Freebase is mainly applied in open information extraction.…”

Section: Sub-kg Extractionmentioning

confidence: 99%

“…It has also been used to augment ZSL, often acting as a complement of relational facts and literals such as entity descriptions [41,62,63]. DBpedia entities are often retrieved via its lookup service which is based on a lexical index built on entity labels and descriptions 5 , while the entity associated facts and literals can be accessed from a DBpedia dump or its online SPARQL endpoint 6 . DBpedia also includes an ontological schema, and its hierarchical classes have been extracted by Amador et al [5] for augmenting zero-shot KG completion with unseen entities.…”

Section: Sub-kg Extractionmentioning

confidence: 99%

“…Text Feature Fusion [5,10,66,109,129,159,180,181,187,208,213,226,230] Multi-modal Feature Fusion [5,128,150,217] Table 1. A summary of KG-aware ZSL paradigms.…”

Section: Mapping-basedmentioning

confidence: 99%

See 3 more Smart Citations

Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey

Chen¹,

Geng²,

Chen³

et al. 2021

Preprint

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Machine learning methods especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for training. In real-world applications, we often need to address sample shortage due to e.g., dynamic contexts with emerging prediction targets and costly sample annotation. Therefore, low-resource learning, which aims to learn robust prediction models with no enough resources (especially training samples), is now being widely investigated. Among all the low-resource learning studies, many prefer to utilize some auxiliary information in the form of Knowledge Graph (KG), which is becoming more and more popular for knowledge representation, to reduce the reliance on labeled samples. In this survey, we very comprehensively reviewed over 90 papers about KG-aware research for two major low-resource learning settings -zero-shot learning (ZSL) where new classes for prediction have never appeared in training, and few-shot learning (FSL) where new classes for prediction have only a small number of labeled samples that are available. We first introduced the KGs used in ZSL and FSL studies as well as the existing and potential KG construction solutions, and then systematically categorized and summarized KG-aware ZSL and FSL methods, dividing them into different paradigms such as the mapping-based, the data augmentation, the propagation-based and the optimization-based. We next presented different applications, including not only KG augmented prediction tasks in Computer Vision and Natural Language Processing (e.g., image classification, visual question answering, text classification and knowledge extraction), but also tasks for KG curation (e.g., inductive KG completion), and some typical evaluation resources for each task. We eventually discussed some challenges and future directions on aspects such as new learning and reasoning paradigms, and the construction of high quality KGs.

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Section: Sub-kg Extractionmentioning

confidence: 99%

Section: Sub-kg Extractionmentioning

confidence: 99%

Section: Sub-kg Extractionmentioning

confidence: 99%

“…Text Feature Fusion [5,10,66,109,129,159,180,181,187,208,213,226,230] Multi-modal Feature Fusion [5,128,150,217] Table 1. A summary of KG-aware ZSL paradigms.…”

Section: Mapping-basedmentioning

confidence: 99%

See 2 more Smart Citations

Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey

Chen¹,

Geng²,

Chen³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…e ontological representation of social networks such as Friend of a Friend (FOAF) is a very popular method in applications, but these ontologies still lack the ability to support the automated integration of social information on a semantic basis and capture established concepts in social network analysis. As a popular knowledge representation form, a knowledge graph can also be used to represent social relationships [53], and the large number of methods and techniques developed in the knowledge graph research area can be adapted to social relationship applications [54,55].…”

Section: Social Relationship Representations In Computer Sciencementioning

confidence: 99%

Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies

Huang

Liu

2021

Discrete Dynamics in Nature and Society

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People worldwide communicate online and create a great amount of data on social media. The understanding of such large-scale data generated on social media and uncovering patterns from social relationship has received much attention from academics and practitioners. However, it still faces challenges to represent and manage the large-scale social relationship data in a formal manner. Therefore, this study proposes a social relationship representation model, which addresses both conceptual graph and domain ontology. Such a formal representation of a social relationship graph can provide a flexible and adaptive way to complete social relationship discovery. Using the term-define capability of ontologies and the graphical structure of the conceptual graph, this paper presents a social relationship description with formal syntax and semantics. The reasoning procedure working on this formal representation can exploit the capability of ontology reasoning and graph homomorphism-based reasoning. A social relationship graph constructed from the Lehigh University Benchmark (LUBM) is used to test the efficiency of the relationship discovery method.

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Interactive Large-Scale Graph Visualization and Analysis for Social Networks

Elhachmi

Cheikhani

2022

Digital Technologies and Applications

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An ontology-based deep learning approach for triple classification with out-of-knowledge-base entities

Cited by 10 publications

References 20 publications

Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey

Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey

Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies

Interactive Large-Scale Graph Visualization and Analysis for Social Networks

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