OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment
- URL: http://arxiv.org/abs/2408.06310v2
- Date: Wed, 23 Oct 2024 09:59:15 GMT
- Title: OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment
- Authors: Sevinj Teymurova, Ernesto Jiménez-Ruiz, Tillman Weyde, Jiaoyan Chen,
- Abstract summary: This paper proposes OWL2Vec4OA, an extension of the embedding system OWL2Vec*.
We present the theoretical foundations, implementation details, and experimental evaluation of our proposed extension.
- Score: 14.955861200588664
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Ontology alignment is integral to achieving semantic interoperability as the number of available ontologies covering intersecting domains is increasing. This paper proposes OWL2Vec4OA, an extension of the ontology embedding system OWL2Vec*. While OWL2Vec* has emerged as a powerful technique for ontology embedding, it currently lacks a mechanism to tailor the embedding to the ontology alignment task. OWL2Vec4OA incorporates edge confidence values from seed mappings to guide the random walk strategy. We present the theoretical foundations, implementation details, and experimental evaluation of our proposed extension, demonstrating its potential effectiveness for ontology alignment tasks.
Related papers
- Edge Classification on Graphs: New Directions in Topological Imbalance [53.42066415249078]
We identify a novel Topological Imbalance Issue', which arises from the skewed distribution of edges across different classes.
We introduce Topological Entropy (TE), a novel topological-based metric that measures the topological imbalance for each edge.
We develop two strategies - Topological Reweighting and TE Wedge-based Mixup - to focus training on (synthetic) edges based on their TEs.
arXiv Detail & Related papers (2024-06-17T16:02:36Z) - Towards Deeply Unified Depth-aware Panoptic Segmentation with
Bi-directional Guidance Learning [63.63516124646916]
We propose a deeply unified framework for depth-aware panoptic segmentation.
We propose a bi-directional guidance learning approach to facilitate cross-task feature learning.
Our method sets the new state of the art for depth-aware panoptic segmentation on both Cityscapes-DVPS and SemKITTI-DVPS datasets.
arXiv Detail & Related papers (2023-07-27T11:28:33Z) - On the non-universality of deep learning: quantifying the cost of
symmetry [24.86176236641865]
We prove computational limitations for learning with neural networks trained by noisy gradient descent (GD)
We characterize functions that fully-connected networks can weak-learn on the binary hypercube and unit sphere.
Our techniques extend to gradient descent (SGD), for which we show nontrivial results for learning with fully-connected networks.
arXiv Detail & Related papers (2022-08-05T11:54:52Z) - Network Topology Optimization via Deep Reinforcement Learning [37.31672024989399]
We propose a novel deep reinforcement learning algorithm, called Advantage Actor Critic-Graph Searching (A2C-GS) for network topology optimization.
A2C-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL actor layer to conduct a topology search.
We conduct a case study based on a real network scenario, and our experimental results demonstrate the superior performance of A2C-GS in terms of both efficiency and performance.
arXiv Detail & Related papers (2022-04-19T07:45:07Z) - Contextual Semantic Embeddings for Ontology Subsumption Prediction [37.61925808225345]
We present a new prediction method for contextual embeddings of classes of Web Ontology (OWL) named BERTSubs.
It exploits the pre-trained language model BERT to compute embeddings of a class, where customized templates are proposed to incorporate the class context and the logical existential restriction.
arXiv Detail & Related papers (2022-02-20T11:14:04Z) - TA-Net: Topology-Aware Network for Gland Segmentation [71.52681611057271]
We propose a novel topology-aware network (TA-Net) to accurately separate densely clustered and severely deformed glands.
TA-Net has a multitask learning architecture and enhances the generalization of gland segmentation.
It achieves state-of-the-art performance on the two datasets.
arXiv Detail & Related papers (2021-10-27T17:10:58Z) - Provable Hierarchy-Based Meta-Reinforcement Learning [50.17896588738377]
We analyze HRL in the meta-RL setting, where learner learns latent hierarchical structure during meta-training for use in a downstream task.
We provide "diversity conditions" which, together with a tractable optimism-based algorithm, guarantee sample-efficient recovery of this natural hierarchy.
Our bounds incorporate common notions in HRL literature such as temporal and state/action abstractions, suggesting that our setting and analysis capture important features of HRL in practice.
arXiv Detail & Related papers (2021-10-18T17:56:02Z) - SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from
Monocular images [94.36401543589523]
We introduce the concept of semantic objectness to exploit the geometric relationship of these two tasks.
We then propose a Semantic Object and Depth Estimation Network (SOSD-Net) based on the objectness assumption.
To the best of our knowledge, SOSD-Net is the first network that exploits the geometry constraint for simultaneous monocular depth estimation and semantic segmentation.
arXiv Detail & Related papers (2021-01-19T02:41:03Z) - OWL2Vec*: Embedding of OWL Ontologies [27.169755467590836]
We propose a random walk and word embedding based embedding method named OWL2Vec*.
OWL2Vec* encodes the semantics of an OWL by taking into account its graph structure, lexical information and logical constructors.
arXiv Detail & Related papers (2020-09-30T13:07:50Z) - A Trainable Optimal Transport Embedding for Feature Aggregation and its
Relationship to Attention [96.77554122595578]
We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference.
Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
arXiv Detail & Related papers (2020-06-22T08:35:58Z)
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.