WiseOWL: A Methodology for Evaluating Ontological Descriptiveness and Semantic Correctness for Ontology Reuse and Ontology Recommendations

• URL: http://arxiv.org/abs/2604.12025v1
• Date: Mon, 13 Apr 2026 20:09:16 GMT
• Title: WiseOWL: A Methodology for Evaluating Ontological Descriptiveness and Semantic Correctness for Ontology Reuse and Ontology Recommendations
• Abstract summary: WiseLOW is proposed, a methodology with scoring and guidance to select for reuse.<n>It scores four metrics: (i) Well-Described, measuring documentation coverage; (ii) Well-Defined, using state-of-the-art embeddings to assess label-definition alignment.<n>WiseOW outputs normalized 0-10 scores with actionable feedback.
• Score: 0.10554048699217668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Semantic Web standardizes concept meaning for humans and machines, enabling machine-operable content and consistent interpretation that improves advanced analytics. Reusing ontologies speeds development and enforces consistency, yet selecting the optimal choice is challenging because authors lack systematic selection criteria and often rely on intuition that is difficult to justify, limiting reuse. To solve this, WiseOWL is proposed, a methodology with scoring and guidance to select ontologies for reuse. It scores four metrics: (i) Well-Described, measuring documentation coverage; (ii) Well-Defined, using state-of-the-art embeddings to assess label-definition alignment; (iii) Connection, capturing structural interconnectedness; and (iv) Hierarchical Breadth, reflecting hierarchical balance. WiseOWL outputs normalized 0-10 scores with actionable feedback. Implemented as a Streamlit app, it ingests OWL format, converts to RDF Turtle, and provides interactive visualizations. Evaluation across six ontologies, including the Plant Ontology (PO), Gene Ontology (GO), Semanticscience Integrated Ontology (SIO), Food Ontology (FoodON), Dublin Core (DC), and GoodRelations, demonstrates promising effectiveness.

Related papers

• Unlocking Biological Workflows for Robust Protein-Text Question Answering: A Dual-Dimensional RAG Framework [60.82334952881798]
  Protein-Text Question Answering (QA) is crucial for interpreting biological sequences through natural language.<n>We propose 2D-ProteinRAG, a novel framework that empowers Large Language Models (LLMs) to operate within the gold-standard biological research workflow.<n>We show that 2D-Protein consistently achieves state-of-the-art performance, outperforming fine-tuned baselines and other RAG methods.
  arXiv  Detail & Related papers  (2026-05-17T05:03:24Z)
• UniDial-EvalKit: A Unified Toolkit for Evaluating Multi-Faceted Conversational Abilities [70.79422099851506]
  We present UniDial-EvalKit (UDE), a unified evaluation toolkit for assessing interactive AI systems.<n>UDE standardizes heterogeneous data formats into a universal schema, streamlines complex evaluation pipelines through a modular architecture, and aligns metric calculations under a consistent scoring interface.
  arXiv  Detail & Related papers  (2026-03-24T13:01:31Z)
• FeClustRE: Hierarchical Clustering and Semantic Tagging of App Features from User Reviews [0.0]
  FeClustRE is a framework integrating hybrid feature extraction, hierarchical clustering with auto-tuning and semantic labelling.<n>We evaluate FeClustRE on public benchmarks for extraction correctness and on a sample study of generative AI assistant app reviews for clustering quality, semantic coherence, and interpretability.
  arXiv  Detail & Related papers  (2025-10-21T16:54:21Z)
• Language Models as Ontology Encoders [32.148744398729896]
  Ontology embeddings can infer plausible new knowledge and approximate complex reasoning.<n>OnT tunes a Pretrained Model Language (PLM) via incorporating hyperbolic modeling in a geometric space.<n>OnT consistently outperforms the baselines in both tasks of prediction and inference.
  arXiv  Detail & Related papers  (2025-07-18T19:26:16Z)
• Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition [63.55828203989405]
  We introduce a novel Topology-Aware Modeling (TAM) framework for Sim2Real UDA on object point clouds.<n>Our approach mitigates the domain gap by leveraging global spatial topology, characterized by low-level, high-frequency 3D structures.<n>We propose an advanced self-training strategy that combines cross-domain contrastive learning with self-training.
  arXiv  Detail & Related papers  (2025-06-26T11:53:59Z)
• OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models [1.2941187430993801]
  We create OntologyRAG, a retrieval-augmented generation (RAG) method for in-context-learning representations.<n>Our solution grounds LLMs to knowledge graphs with mappings between and processes questions.<n>Our solution doesn't require re-training LMs, as all ontology updates could be reflected by updating the knowledge graphs with a standard process.
  arXiv  Detail & Related papers  (2025-02-26T09:56:10Z)
• 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)
• Understanding Token-level Topological Structures in Transformer-based Time Series Forecasting [52.364260925700485]
  Transformer-based methods have achieved state-of-the-art performance in time series forecasting (TSF)<n>It remains unclear whether existing Transformers fully leverage the intrinsic topological structure among tokens throughout intermediate layers.<n>We propose the Topology Enhancement Method (TEM), a novel Transformer-based TSF method that explicitly and adaptively preserves token-level topology.
  arXiv  Detail & Related papers  (2024-04-16T07:21:39Z)
• Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillation [73.83178465971552]
  The success of automated medical image analysis depends on large-scale and expert-annotated training sets. Unsupervised domain adaptation (UDA) has been raised as a promising approach to alleviate the burden of labeled data collection. We propose optimization trajectory distillation, a unified approach to address the two technical challenges from a new perspective.
  arXiv  Detail & Related papers  (2023-07-27T08:58:05Z)
• 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)
• Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction [63.70885228396077]
  We propose a novel model to transfer opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. Our model achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge.
  arXiv  Detail & Related papers  (2020-01-07T11:50:54Z)

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.