Constraining ontology mappings using metaphysical choices

• URL: http://arxiv.org/abs/2608.08122v1
• Date: Sat, 08 Aug 2026 13:13:09 GMT
• Title: Constraining ontology mappings using metaphysical choices
• Abstract summary: We provide a test case of mappings between IES and BFO.<n>We focus on providing cardinal constraints on the mappings between the two.<n>In order to demonstrate the method, we showcased how these principles can be operationalized through SPARQL queries.
• Score: 0.09558392439655013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we discuss the foundations behind a novel methodology for the validation of semantic mappings between different data sources based upon different foundation ontologies, where the methodology builds a framework based upon the metaphysical commitments of the ontologies. We provide as example the test case of mappings between IES and BFO, and we especially focused on providing cardinality constraints on the mappings between the two ontologies. In order to demonstrate the applicability of our method, we showcased how these principles can be operationalized through SPARQL queries validating the results of a mapping pipeline.

Related papers

• Surprising Effectiveness of Self-Demonstrations in Enhancing Schema-Ontology Mapping with LLMs [23.62124559072917]
  This paper presents a self-demonstration-driven approach that combines a neuro-symbolic task decomposition with a novel mechanism for automatically generating pattern-guided demonstrations.<n> Experiments on three of the most challenging scenarios from the RODI benchmark show that our approach achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-09-12T07:42:55Z)
• A Concise Survey on Lane Topology Reasoning for HD Mapping [30.73664953504888]
  Lane topology reasoning techniques play a crucial role in high-definition (HD) mapping and autonomous driving applications.<n>Recent years have witnessed significant advances in this field, but there has been limited effort to consolidate these works into a comprehensive overview.<n>This survey systematically reviews the evolution and current state of lane topology reasoning methods.
  arXiv  Detail & Related papers  (2025-03-31T11:30:40Z)
• A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology [0.0]
  PROV-O is a World Wide Web Consortium (W3C) recommended used to structure data about provenance across a variety of domains.<n>To enhance interoperability between these two, their extensions, and data organized by them, a mapping methodology and set of alignments are presented.
  arXiv  Detail & Related papers  (2024-08-02T16:50:17Z)
• Conceptual Engineering Using Large Language Models [0.0]
  We use data from the Wikidata knowledge graph to evaluate stipulative definitions related to two conceptual engineering projects. Our results show that classification procedures built using our approach can exhibit good classification performance. We consider objections to this work for three aspects of theory and practice of conceptual engineering.
  arXiv  Detail & Related papers  (2023-12-01T01:58:16Z)
• Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF [53.55504611255664]
  We propose a novel method, referred to as ASAC, to solve the dilemma caused by the nested phenomenon. The proposed method contains two key modules: the adaptive shared (AS) part and the attentive conditional random field (ACRF) module. Our model could learn better entity representations by capturing the implicit distinctions and relationships between different categories of entities.
  arXiv  Detail & Related papers  (2022-11-09T09:23:56Z)
• Semantic Search for Large Scale Clinical Ontologies [63.71950996116403]
  We present a deep learning approach to build a search system for large clinical vocabularies. We propose a Triplet-BERT model and a method that generates training data based on semantic training data. The model is evaluated using five real benchmark data sets and the results show that our approach achieves high results on both free text to concept and concept to searching concept vocabularies.
  arXiv  Detail & Related papers  (2022-01-01T05:15:42Z)
• Learning Gaussian Graphical Models with Latent Confounders [74.72998362041088]
  We compare and contrast two strategies for inference in graphical models with latent confounders. While these two approaches have similar goals, they are motivated by different assumptions about confounding. We propose a new method, which combines the strengths of these two approaches.
  arXiv  Detail & Related papers  (2021-05-14T00:53:03Z)
• Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding [22.123001954919893]
  We propose an iterative framework named PRASE which is based on probabilistic reasoning and semantic embedding. The PRASE framework is compatible with different embedding-based models, and our experiments on multiple datasets have demonstrated its state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-12T11:27:46Z)
• Transforming Feature Space to Interpret Machine Learning Models [91.62936410696409]
  This contribution proposes a novel approach that interprets machine-learning models through the lens of feature space transformations. It can be used to enhance unconditional as well as conditional post-hoc diagnostic tools. A case study on remote-sensing landcover classification with 46 features is used to demonstrate the potential of the proposed approach.
  arXiv  Detail & Related papers  (2021-04-09T10:48:11Z)
• Mapping Patterns for Virtual Knowledge Graphs [71.61234136161742]
  Virtual Knowledge Graphs (VKG) constitute one of the most promising paradigms for integrating and accessing legacy data sources. We build on well-established methodologies and patterns studied in data management, data analysis, and conceptual modeling. We validate our catalog on the considered VKG scenarios, showing it covers the vast majority of patterns present therein.
  arXiv  Detail & Related papers  (2020-12-03T13:54:52Z)
• There and Back Again: Revisiting Backpropagation Saliency Methods [87.40330595283969]
  Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample. A popular class of such methods is based on backpropagating a signal and analyzing the resulting gradient. We propose a single framework under which several such methods can be unified.
  arXiv  Detail & Related papers  (2020-04-06T17:58:08Z)

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.