Background Knowledge in Schema Matching: Strategy vs. Data

• URL: http://arxiv.org/abs/2107.00001v1
• Date: Tue, 29 Jun 2021 19:16:33 GMT
• Title: Background Knowledge in Schema Matching: Strategy vs. Data
• Abstract summary: We exploit six general-purpose graphs as sources of background knowledge for the matching task. We find that explicit strategies still outperform latent ones and that the choice of the strategy has a greater impact on the alignment than the actual background dataset. Our best matcher configuration with BabelNet performs very competitively when compared to other matching systems.
• Score: 1.2891210250935146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The use of external background knowledge can be beneficial for the task of matching schemas or ontologies automatically. In this paper, we exploit six general-purpose knowledge graphs as sources of background knowledge for the matching task. The background sources are evaluated by applying three different exploitation strategies. We find that explicit strategies still outperform latent ones and that the choice of the strategy has a greater impact on the final alignment than the actual background dataset on which the strategy is applied. While we could not identify a universally superior resource, BabelNet achieved consistently good results. Our best matcher configuration with BabelNet performs very competitively when compared to other matching systems even though no dataset-specific optimizations were made.

Related papers

• CAAL: Contextual Bandits based Online Hand-Craft Active Learning Strategy Selection [1.3187061540651615]
  We introduce Contextual Adaptive Active Learning (CAAL)<n>In CAAL, each "arm" represents a hand-crafted strategy.<n>We experimentally show that CAAL outperforms the existing baseline adaptive strategy on public datasets.
  arXiv  Detail & Related papers  (2026-06-06T00:20:11Z)
• When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach [80.24372450818323]
  Tabular foundation models based on pretrained prior-data fitted networks(PFNs)<n>We show that strategic manipulation creates a mismatch between the non-strategic prior learned during pretraining and the post-manipulation strategic prior.<n>We propose textbfStrategic Prior-data Fitted Networktextit(SPN), an inference-time strategy-aware framework.
  arXiv  Detail & Related papers  (2026-05-19T10:55:35Z)
• Agentic Planning with Reasoning for Image Styling via Offline RL [66.10749901925941]
  Direct prompt-based editing fails on complex transformations because vague and subjective prompts often require nuanced understanding of what should be changed in the image.<n>We present a tool-based agentic RL post-training framework that addresses this through structured planning with chain-of-thought reasoning.
  arXiv  Detail & Related papers  (2026-03-07T11:14:37Z)
• Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance [86.46794021499511]
  We show a previously underexplored gap between strategy usage and strategy executability.<n>We propose Selective Strategy Retrieval (SSR), a test-time framework that explicitly models executability.<n> SSR yields reliable and consistent improvements over direct solving, in-context learning, and single-source guidance.
  arXiv  Detail & Related papers  (2026-02-26T03:34:23Z)
• Regularisation in neural networks: a survey and empirical analysis of approaches [0.5097809301149341]
  We provide a broad review of regularisation techniques, including modern theories such as double descent.<n>We compare the various regularisation techniques on classification tasks for ten numerical and image datasets.<n>Results show that the efficacy of regularisation is dataset-dependent.
  arXiv  Detail & Related papers  (2026-01-30T16:22:11Z)
• Plan before Solving: Problem-Aware Strategy Routing for Mathematical Reasoning with LLMs [49.995906301946]
  Existing methods usually leverage a fixed strategy to guide Large Language Models (LLMs) to perform mathematical reasoning.<n>Our analysis reveals that the single strategy cannot adapt to problem-specific requirements and thus overlooks the trade-off between effectiveness and efficiency.<n>We propose Planning and Routing through Instance-Specific Modeling (PRISM), a novel framework that decouples mathematical reasoning into two stages: strategy planning and targeted execution.
  arXiv  Detail & Related papers  (2025-09-29T07:22:41Z)
• Graph Neural Bandits [49.85090929163639]
  We propose a framework named Graph Neural Bandits (GNB) to leverage the collaborative nature among users empowered by graph neural networks (GNNs) To refine the recommendation strategy, we utilize separate GNN-based models on estimated user graphs for exploitation and adaptive exploration.
  arXiv  Detail & Related papers  (2023-08-21T15:57:57Z)
• Online Network Source Optimization with Graph-Kernel MAB [62.6067511147939]
  We propose Grab-UCB, a graph- kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks. We describe the network processes with an adaptive graph dictionary model, which typically leads to sparse spectral representations. We derive the performance guarantees that depend on network parameters, which further influence the learning curve of the sequential decision strategy.
  arXiv  Detail & Related papers  (2023-07-07T15:03:42Z)
• Joint Language Semantic and Structure Embedding for Knowledge Graph Completion [66.15933600765835]
  We propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information. Our method embeds knowledge graphs for the completion task via fine-tuning pre-trained language models. Our experiments on a variety of knowledge graph benchmarks have demonstrated the state-of-the-art performance of our method.
  arXiv  Detail & Related papers  (2022-09-19T02:41:02Z)
• ImitAL: Learned Active Learning Strategy on Synthetic Data [30.595138995552748]
  We propose ImitAL, a domain-independent novel query strategy, which encodes AL as a learning-to-rank problem. We train ImitAL on large-scale simulated AL runs on purely synthetic datasets. To show that ImitAL was successfully trained, we perform an extensive evaluation comparing our strategy on 13 different datasets.
  arXiv  Detail & Related papers  (2022-08-24T16:17:53Z)
• Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch [13.024115985194932]
  We propose a new approach for solving the data labeling and inference issues in optimization based on the use of the reinforcement learning (RL) paradigm. We use imitation learning to bootstrap an RL agent and then use Proximal Policy (PPO) to further explore global optimal actions.
  arXiv  Detail & Related papers  (2022-06-14T16:35:58Z)
• Context-based Deep Learning Architecture with Optimal Integration Layer for Image Parsing [0.0]
  The proposed three-layer context-based deep architecture is capable of integrating context explicitly with visual information. The experimental outcomes when evaluated on benchmark datasets are promising.
  arXiv  Detail & Related papers  (2022-04-13T07:35:39Z)
• Understanding Robust Generalization in Learning Regular Languages [85.95124524975202]
  We study robust generalization in the context of using recurrent neural networks to learn regular languages. We propose a compositional strategy to address this. We theoretically prove that the compositional strategy generalizes significantly better than the end-to-end strategy.
  arXiv  Detail & Related papers  (2022-02-20T02:50:09Z)
• Budget-aware Few-shot Learning via Graph Convolutional Network [56.41899553037247]
  This paper tackles the problem of few-shot learning, which aims to learn new visual concepts from a few examples. A common problem setting in few-shot classification assumes random sampling strategy in acquiring data labels. We introduce a new budget-aware few-shot learning problem that aims to learn novel object categories.
  arXiv  Detail & Related papers  (2022-01-07T02:46:35Z)
• ImitAL: Learning Active Learning Strategies from Synthetic Data [14.758287202278918]
  Active Learning is a well-known standard method for efficiently obtaining labeled data. We propose ImitAL, a novel query strategy, which encodes AL as a learning-to-rank problem. We show that our approach is more runtime performant than most other strategies, especially on very large datasets.
  arXiv  Detail & Related papers  (2021-08-17T15:03:31Z)
• Learning active learning at the crossroads? evaluation and discussion [0.03807314298073299]
  Active learning aims to reduce annotation cost by predicting which samples are useful for a human expert to label. There is no best active learning strategy that consistently outperforms all others in all applications. We present the results of a benchmark performed on 20 datasets that compares a strategy learned using a recent meta-learning algorithm with margin sampling.
  arXiv  Detail & Related papers  (2020-12-16T10:35:43Z)
• Unsupervised Learning for Asynchronous Resource Allocation in Ad-hoc Wireless Networks [122.42812336946756]
  We design an unsupervised learning method based on Aggregation Graph Neural Networks (Agg-GNNs) We capture the asynchrony by modeling the activation pattern as a characteristic of each node and train a policy-based resource allocation method.
  arXiv  Detail & Related papers  (2020-11-05T03:38:36Z)

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.