PN-OWL: A Two Stage Algorithm to Learn Fuzzy Concept Inclusions from OWL Ontologies

• URL: http://arxiv.org/abs/2303.07192v1
• Date: Wed, 1 Mar 2023 09:08:55 GMT
• Title: PN-OWL: A Two Stage Algorithm to Learn Fuzzy Concept Inclusions from OWL Ontologies
• Authors: Franco Alberto Cardillo and Franca Debole and Umberto Straccia
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: OWL ontologies are 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, with a focus on ontologies with real- and boolean-valued data properties, we address the problem of learning graded fuzzy concept inclusion axioms with the aim of describing enough conditions for being an individual classified as instance of the class T. To do so, we present PN-OWL that is a two-stage learning algorithm made of a P-stage and an N-stage. Roughly, in the P-stage the algorithm tries to cover as many positive examples as possible (increase recall), without compromising too much precision, while in the N-stage, the algorithm tries to rule out as many false positives, covered by the P-stage, as possible. PN-OWL then aggregates the fuzzy inclusion axioms learnt at the P-stage and the N-stage by combining them via aggregation functions to allow for a final decision whether an individual is instance of T or not. We also illustrate its effectiveness by means of an experimentation. An interesting feature is that fuzzy datatypes are built automatically, the learnt fuzzy concept inclusions can be represented directly into Fuzzy OWL 2 and, thus, 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 or not.

Related papers

• Learning Probabilistic Temporal Logic Specifications for Stochastic Systems [24.82640206181621]
  We propose a novel learning algorithm that infers conciseL specifications from a set of Markov chains.<n>We demonstrate the effectiveness of our algorithm in two use cases: learning from policies induced by algorithms and learning from a probabilistic model.
  arXiv  Detail & Related papers  (2025-05-17T18:19:35Z)
• Hyperparameters in Continual Learning: A Reality Check [53.30082523545212]
  Continual learning (CL) aims to train a model on a sequence of tasks while balancing the trade-off between plasticity (learning new tasks) and stability (retaining prior knowledge)
  arXiv  Detail & Related papers  (2024-03-14T03:13:01Z)
• Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern Recognition [49.536368818512116]
  Tactics, Techniques and Procedures (TTPs) represent sophisticated attack patterns in the cybersecurity domain. We formulate the problem in a different learning paradigm, where the assignment of a text to a TTP label is decided by the direct semantic similarity between the two. We propose a neural matching architecture with an effective sampling-based learn-to-compare mechanism.
  arXiv  Detail & Related papers  (2024-01-18T19:02:00Z)
• Generalization on the Unseen, Logic Reasoning and Degree Curriculum [25.7378861650474]
  This paper considers the learning of logical (Boolean) functions with a focus on the generalization on the unseen (GOTU) setting. We study how different network architectures trained by (S)GD perform under GOTU. More specifically, this means an interpolator of the training data that has minimal Fourier mass on the higher degree basis elements.
  arXiv  Detail & Related papers  (2023-01-30T17:44:05Z)
• Machine Learning with Probabilistic Law Discovery: A Concise Introduction [77.34726150561087]
  Probabilistic Law Discovery (PLD) is a logic based Machine Learning method, which implements a variant of probabilistic rule learning. PLD is close to Decision Tree/Random Forest methods, but it differs significantly in how relevant rules are defined. This paper outlines the main principles of PLD, highlight its benefits and limitations and provide some application guidelines.
  arXiv  Detail & Related papers  (2022-12-22T17:40:13Z)
• Learning versus Refutation in Noninteractive Local Differential Privacy [133.80204506727526]
  We study two basic statistical tasks in non-interactive local differential privacy (LDP): learning and refutation. Our main result is a complete characterization of the sample complexity of PAC learning for non-interactive LDP protocols.
  arXiv  Detail & Related papers  (2022-10-26T03:19:24Z)
• PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot Learning [2.236663830879273]
  Logic Networks (LTNs) are neuro-symbolic systems based on a differentiable, first-order logic grounded into a deep neural network. We focus here on the subsumption or textttisOfClass predicate, which is fundamental to encode most semantic image interpretation tasks. We propose a common textttisOfClass predicate, whose level of truth is a function of the distance between an object embedding and the corresponding class prototype.
  arXiv  Detail & Related papers  (2022-06-26T18:34:07Z)
• Semantic Probabilistic Layers for Neuro-Symbolic Learning [83.25785999205932]
  We design a predictive layer for structured-output prediction (SOP) It can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints. Our Semantic Probabilistic Layer (SPL) can model intricate correlations, and hard constraints, over a structured output space.
  arXiv  Detail & Related papers  (2022-06-01T12:02:38Z)
• Automated Imbalanced Classification via Layered Learning [0.734084539365505]
  Applying resampling strategies to balance the class distribution of training instances is a common approach to tackle these problems. Many state-of-the-art methods find instances of interest close to the decision boundary to drive the resampling process. Over-sampling may increase the chance of overfitting by propagating the information contained in instances from the minority class.
  arXiv  Detail & Related papers  (2022-05-05T10:32:24Z)
• Contrastive Conditional Neural Processes [45.70735205041254]
  Conditional Neural Processes(CNPs) bridge neural networks with probabilistic inference to approximate functions of Processes under meta-learning settings. Two auxiliary contrastive branches are set up hierarchically, namely in-instantiation temporal contrastive learning(tt TCL) and cross-instantiation function contrastive learning(tt FCL) We empirically show that tt TCL captures high-level abstraction of observations, whereas tt FCL helps identify underlying functions, which in turn provides more efficient representations.
  arXiv  Detail & Related papers  (2022-03-08T10:08:45Z)
• A Lagrangian Duality Approach to Active Learning [119.36233726867992]
  We consider the batch active learning problem, where only a subset of the training data is labeled. We formulate the learning problem using constrained optimization, where each constraint bounds the performance of the model on labeled samples. We show, via numerical experiments, that our proposed approach performs similarly to or better than state-of-the-art active learning methods.
  arXiv  Detail & Related papers  (2022-02-08T19:18:49Z)
• Aligning Pretraining for Detection via Object-Level Contrastive Learning [57.845286545603415]
  Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning. We argue that this could be sub-optimal and thus advocate a design principle which encourages alignment between the self-supervised pretext task and the downstream task. Our method, called Selective Object COntrastive learning (SoCo), achieves state-of-the-art results for transfer performance on COCO detection.
  arXiv  Detail & Related papers  (2021-06-04T17:59:52Z)
• Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting [0.0]
  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.
  arXiv  Detail & Related papers  (2020-08-03T15:19:31Z)

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.