Revisiting Indirect Ontology Alignment : New Challenging Issues in
Cross-Lingual Context
- URL: http://arxiv.org/abs/2104.01628v1
- Date: Sun, 4 Apr 2021 15:21:09 GMT
- Title: Revisiting Indirect Ontology Alignment : New Challenging Issues in
Cross-Lingual Context
- Authors: Marouen Kachroudi
- Abstract summary: This article introduces a new method of indirect alignment of in a cross-lingual context.
The proposed method is based on alignment algebra which governs the composition of relationships and confidence values.
The obtained results are very encouraging and highlight many positive aspects about the new proposed method.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Ontology alignment process is overwhelmingly cited in Knowledge Engineering
as a key mechanism aimed at bypassing heterogeneity and reconciling various
data sources, represented by ontologies, i.e., the the Semantic Web
cornerstone. In such infrastructures and environments, it is inconceivable to
assume that all ontologies covering a particular domain of knowledge are
aligned in pairs. Moreover, the high performance of alignment approaches is
closely related to two factors, i.e., time consumption and machine resource
limitations. Thus, good quality alignments are valuable and it would be
appropriate to exploit them. Based on this observation, this article introduces
a new method of indirect alignment of ontologies in a cross-lingual context.
Indeed, the proposed method deals with alignments of multilingual ontologies
and implements an indirect ontology alignment strategy based on a composition
and reuse of effective direct alignments. The trigger of the proposed method
process is based on alignment algebra which governs the semantics composition
of relationships and confidence values. The obtained results, after a thorough
and detailed experiment are very encouraging and highlight many positive
aspects about the new proposed method.
Related papers
- Boosting CNN-based Handwriting Recognition Systems with Learnable Relaxation Labeling [48.78361527873024]
We propose a novel approach to handwriting recognition that integrates the strengths of two distinct methodologies.
We introduce a sparsification technique that accelerates the convergence of the algorithm and enhances the overall system's performance.
arXiv Detail & Related papers (2024-09-09T15:12:28Z) - Mind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role Labeling for Legal Documents [1.2562034805037443]
This study introduces novel techniques to enhance Rhetorical Role Labeling (RRL) performance.
For inference-based methods, we explore techniques that bolster label predictions without re-training.
While in training-based methods, we integrate learning with our novel discourse-aware contrastive method that work directly on embedding spaces.
arXiv Detail & Related papers (2024-03-31T08:10:45Z) - Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via
Optimization Trajectory Distillation [73.83178465971552]
The success of automated medical image analysis depends on large-scale and expert-annotated training sets.
Unsupervised domain adaptation (UDA) has been raised as a promising approach to alleviate the burden of labeled data collection.
We propose optimization trajectory distillation, a unified approach to address the two technical challenges from a new perspective.
arXiv Detail & Related papers (2023-07-27T08:58:05Z) - From Patches to Objects: Exploiting Spatial Reasoning for Better Visual
Representations [2.363388546004777]
We propose a novel auxiliary pretraining method that is based on spatial reasoning.
Our proposed method takes advantage of a more flexible formulation of contrastive learning by introducing spatial reasoning as an auxiliary task for discriminative self-supervised methods.
arXiv Detail & Related papers (2023-05-21T07:46:46Z) - Ontology Matching Through Absolute Orientation of Embedding Spaces [1.5169370091868053]
Ontology is a core task when creating interoperable and linked open datasets.
In this paper, we explore a structure-based mapping approach which is based on knowledge graph embeddings.
We find in experiments with synthetic data, that the approach works very well on similarly structured datasets.
arXiv Detail & Related papers (2022-04-08T12:59:31Z) - A cross-domain recommender system using deep coupled autoencoders [77.86290991564829]
Two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation.
The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains.
The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors.
arXiv Detail & Related papers (2021-12-08T15:14:26Z) - MCDAL: Maximum Classifier Discrepancy for Active Learning [74.73133545019877]
Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition.
We propose in this paper a novel active learning framework that we call Maximum Discrepancy for Active Learning (MCDAL)
In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them.
arXiv Detail & Related papers (2021-07-23T06:57:08Z) - Visualization of Supervised and Self-Supervised Neural Networks via
Attribution Guided Factorization [87.96102461221415]
We develop an algorithm that provides per-class explainability.
In an extensive battery of experiments, we demonstrate the ability of our methods to class-specific visualization.
arXiv Detail & Related papers (2020-12-03T18:48:39Z) - Multifaceted Context Representation using Dual Attention for Ontology
Alignment [6.445605125467574]
Ontology alignment is an important research problem that finds application in various fields such as data integration, data transfer, data preparation etc.
We propose VeeAlign, a Deep Learning based model that uses a dual-attention mechanism to compute the contextualized representation of a concept in order to learn alignments.
We validate our approach on various datasets from different domains and in multilingual settings, and show its superior performance over SOTA methods.
arXiv Detail & Related papers (2020-10-16T18:28:38Z) - Text Recognition in Real Scenarios with a Few Labeled Samples [55.07859517380136]
Scene text recognition (STR) is still a hot research topic in computer vision field.
This paper proposes a few-shot adversarial sequence domain adaptation (FASDA) approach to build sequence adaptation.
Our approach can maximize the character-level confusion between the source domain and the target domain.
arXiv Detail & Related papers (2020-06-22T13:03:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.