Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting

• URL: http://arxiv.org/abs/2008.05297v2
• Date: Fri, 26 Mar 2021 07:10:04 GMT
• Title: Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting
• Authors: Franco Alberto Cardillo and Umberto Straccia
• Abstract summary: We address the problem of fuzzy learning concept axioms that describe sufficient conditions for being an individual instance of T. We present Fuzzy OWL-BOOST that relies on the RealBoost boosting algorithm adapted to the (fuzzy) case.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given a target class T of an OWL ontology, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T. To do so, we present Fuzzy OWL-BOOST that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation. An interesting feature is that the learned rules can be represented directly into Fuzzy OWL 2. As a consequence, any Fuzzy OWL 2 reasoner can then be used to automatically determine/classify (and to which degree) whether an individual belongs to the target class T.

Related papers

• Ontolearn-A Framework for Large-scale OWL Class Expression Learning in Python [39.28980157397703]
  Ontolearn is a framework for learning OWL class expressions over large knowledge graphs.<n>A learned OWL class expression can be used to classify instances in the knowledge graph.<n>Ontolearn can be easily used to operate over a remote triplestore.
  arXiv  Detail & Related papers  (2025-10-13T16:04:06Z)
• Hierarchical Representation Matching for CLIP-based Class-Incremental Learning [80.2317078787969]
  Class-Incremental Learning (CIL) aims to endow models with the ability to continuously adapt to evolving data streams.<n>Recent advances in pre-trained vision-language models (e.g., CLIP) provide a powerful foundation for this task.<n>We introduce HiErarchical Representation MAtchiNg (HERMAN) for CLIP-based CIL.
  arXiv  Detail & Related papers  (2025-09-26T17:59:51Z)
• Weakly-Supervised Contrastive Learning for Imprecise Class Labels [50.57424331797865]
  We introduce the concept of continuous semantic similarity'' to define positive and negative pairs.<n>We propose a graph-theoretic framework for weakly-supervised contrastive learning.<n>Our framework is highly versatile and can be applied to many weakly-supervised learning scenarios.
  arXiv  Detail & Related papers  (2025-05-28T06:50:40Z)
• 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)
• OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment [14.955861200588664]
  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.
  arXiv  Detail & Related papers  (2024-08-12T17:24:19Z)
• Multiclass Boosting: Simple and Intuitive Weak Learning Criteria [72.71096438538254]
  We give a simple and efficient boosting algorithm, that does not require realizability assumptions. We present a new result on boosting for list learners, as well as provide a novel proof for the characterization of multiclass PAC learning.
  arXiv  Detail & Related papers  (2023-07-02T19:26:58Z)
• Interpretability at Scale: Identifying Causal Mechanisms in Alpaca [62.65877150123775]
  We use Boundless DAS to efficiently search for interpretable causal structure in large language models while they follow instructions. Our findings mark a first step toward faithfully understanding the inner-workings of our ever-growing and most widely deployed language models.
  arXiv  Detail & Related papers  (2023-05-15T17:15:40Z)
• PN-OWL: A Two Stage Algorithm to Learn Fuzzy Concept Inclusions from OWL Ontologies [0.0]
  We present PN-OWL that is a two-stage learning algorithm made of a P-stage and an N-stage. PN-OWL aggregates the fuzzy axioms learnt at the P-stage and the N-stage by combining them via aggregation functions. An interesting feature is fuzzy datatypes are built, learnt fuzzy concept can be represented directly into Fuzzy OWL.
  arXiv  Detail & Related papers  (2023-03-01T09:08:55Z)
• When one Logic is Not Enough: Integrating First-order Annotations in OWL Ontologies [0.0]
  We present a tool that supports the development of an 'FOWL' set of axioms that extend OWL with FOL annotations. We show that for the OWL domain OBI, the stronger integration with its FOL top-level BFOWL enables us to detect several inconsistencies.
  arXiv  Detail & Related papers  (2022-10-07T12:33:35Z)
• Prior Knowledge Guided Unsupervised Domain Adaptation [82.9977759320565]
  We propose a Knowledge-guided Unsupervised Domain Adaptation (KUDA) setting where prior knowledge about the target class distribution is available. In particular, we consider two specific types of prior knowledge about the class distribution in the target domain: Unary Bound and Binary Relationship. We propose a rectification module that uses such prior knowledge to refine model generated pseudo labels.
  arXiv  Detail & Related papers  (2022-07-18T18:41:36Z)
• Transductive Learning for Unsupervised Text Style Transfer [60.65782243927698]
  Unsupervised style transfer models are mainly based on an inductive learning approach. We propose a novel transductive learning approach based on a retrieval-based context-aware style representation.
  arXiv  Detail & Related papers  (2021-09-16T08:57:20Z)
• Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition [6.187780920448871]
  We show that the organization of overlapping classes into hierarchies considerably improves classification performances. This is particularly true in the case of activity recognition tasks featured in the SHL dataset. We propose an approach based on transfer affinity among the classes to determine an optimal hierarchy for the learning process.
  arXiv  Detail & Related papers  (2021-04-11T01:08:48Z)
• Intersection Regularization for Extracting Semantic Attributes [72.53481390411173]
  We consider the problem of supervised classification, such that the features that the network extracts match an unseen set of semantic attributes. For example, when learning to classify images of birds into species, we would like to observe the emergence of features that zoologists use to classify birds. We propose training a neural network with discrete top-level activations, which is followed by a multi-layered perceptron (MLP) and a parallel decision tree.
  arXiv  Detail & Related papers  (2021-03-22T14:32:44Z)
• Learning Adaptive Embedding Considering Incremental Class [55.21855842960139]
  Class-Incremental Learning (CIL) aims to train a reliable model with the streaming data, which emerges unknown classes sequentially. Different from traditional closed set learning, CIL has two main challenges: 1) Novel class detection. After the novel classes are detected, the model needs to be updated without re-training using entire previous data.
  arXiv  Detail & Related papers  (2020-08-31T04:11:24Z)

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.