Description Logic EL++ Embeddings with Intersectional Closure

• URL: http://arxiv.org/abs/2202.14018v1
• Date: Mon, 28 Feb 2022 18:37:14 GMT
• Title: Description Logic EL++ Embeddings with Intersectional Closure
• Authors: Xi Peng, Zhenwei Tang, Maxat Kulmanov, Kexin Niu, Robert Hoehndorf
• Abstract summary: We develop EL Embedding (ELBE) to learn Description Logic EL++ embeddings using axis-parallel boxes. We report extensive experimental results on three datasets and present a case study to demonstrate the effectiveness of the proposed method.
• Score: 10.570100236658705
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many ontologies, in particular in the biomedical domain, are based on the Description Logic EL++. Several efforts have been made to interpret and exploit EL++ ontologies by distributed representation learning. Specifically, concepts within EL++ theories have been represented as n-balls within an n-dimensional embedding space. However, the intersectional closure is not satisfied when using n-balls to represent concepts because the intersection of two n-balls is not an n-ball. This leads to challenges when measuring the distance between concepts and inferring equivalence between concepts. To this end, we developed EL Box Embedding (ELBE) to learn Description Logic EL++ embeddings using axis-parallel boxes. We generate specially designed box-based geometric constraints from EL++ axioms for model training. Since the intersection of boxes remains as a box, the intersectional closure is satisfied. We report extensive experimental results on three datasets and present a case study to demonstrate the effectiveness of the proposed method.

Related papers

• TransBox: EL++-closed Ontology Embedding [14.850996103983187]
  We develop an effective EL++-closed embedding method that can handle many-to-one, one-to-many and many-to-many relations. Our experiments demonstrate that TransBox achieves state-of-the-art performance across various real-world datasets for predicting complex axioms.
  arXiv  Detail & Related papers  (2024-10-18T16:17:10Z)
• Enhancing Geometric Ontology Embeddings for $\mathcal{EL}^{++}$ with Negative Sampling and Deductive Closure Filtering [45.817946135388176]
  Ontology embeddings map classes, relations, and individuals in into $mathbbn$, and within $bbRn$ similarity between entities can be computed. We develop novel negative losses that account both for the deductive closure and different types of negatives.
  arXiv  Detail & Related papers  (2024-05-08T07:50:21Z)
• Lattice-preserving $\mathcal{ALC}$ ontology embeddings with saturation [50.05281461410368]
  An order-preserving embedding method is proposed to generate embeddings of OWL representations. We show that our method outperforms state-the-art theory-of-the-art embedding methods in several knowledge base completion tasks.
  arXiv  Detail & Related papers  (2023-05-11T22:27:51Z)
• Dual Box Embeddings for the Description Logic EL++ [16.70961576041243]
  Similar to Knowledge Graphs (KGs), Knowledge Graphs are often incomplete, and maintaining and constructing them has proved challenging. Similar to KGs, a promising approach is to learn embeddings in a latent vector space, while additionally ensuring they adhere to the semantics of the underlying DL. We propose a novel ontology embedding method named Box$2$EL for the DL EL++, which represents both concepts and roles as boxes.
  arXiv  Detail & Related papers  (2023-01-26T14:13:37Z)
• Concept Activation Regions: A Generalized Framework For Concept-Based Explanations [95.94432031144716]
  Existing methods assume that the examples illustrating a concept are mapped in a fixed direction of the deep neural network's latent space. In this work, we propose allowing concept examples to be scattered across different clusters in the DNN's latent space. This concept activation region (CAR) formalism yields global concept-based explanations and local concept-based feature importance.
  arXiv  Detail & Related papers  (2022-09-22T17:59:03Z)
• Discrete Langevin Sampler via Wasserstein Gradient Flow [102.94731267405056]
  We show how LB functions give rise to LB dynamics corresponding to Wasserstein gradient flow in a discrete space. We propose a new algorithm, the Locally Balanced Jump (LBJ), by discretizing the LB dynamics with respect to simulation time.
  arXiv  Detail & Related papers  (2022-06-29T20:33:54Z)
• Connection-minimal Abduction in EL via Translation to FOL -- Technical Report [12.90382979353427]
  We show how to compute a class of connection-minimal hypotheses in a sound and complete way. Our technique is based on a translation to first-order logic, and constructs hypotheses based on prime implicates.
  arXiv  Detail & Related papers  (2022-05-17T15:50:27Z)
• Box Embeddings for the Description Logic EL++ [21.89072991669119]
  We present BoxEL, a geometric KB embedding approach that allows for better capturing logical structure. We show theoretical guarantees (soundness) of BoxEL for preserving logical structure. Experimental results on subsumption reasoning and a real-world application-protein-protein prediction show that BoxEL outperforms traditional knowledge graph embedding methods.
  arXiv  Detail & Related papers  (2022-01-24T19:24:22Z)
• ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs [73.86041481470261]
  Cone Embeddings (ConE) is the first geometry-based query embedding model that can handle conjunction, disjunction, and negation. ConE significantly outperforms existing state-of-the-art methods on benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-26T14:04:02Z)
• Abstract Reasoning via Logic-guided Generation [65.92805601327649]
  Abstract reasoning, i.e., inferring complicated patterns from given observations, is a central building block of artificial general intelligence. This paper aims to design a framework for the latter approach and bridge the gap between artificial and human intelligence. We propose logic-guided generation (LoGe), a novel generative DNN framework that reduces abstract reasoning as an optimization problem in propositional logic.
  arXiv  Detail & Related papers  (2021-07-22T07:28:24Z)
• BoxE: A Box Embedding Model for Knowledge Base Completion [53.57588201197374]
  Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB) Existing embedding models are subject to at least one of the following limitations. BoxE embeds entities as points, and relations as a set of hyper-rectangles (or boxes)
  arXiv  Detail & Related papers  (2020-07-13T09:40:49Z)

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.