Ontological Reasoning over Shy and Warded Datalog$+/-$ for Streaming-based Architectures (technical report)

• URL: http://arxiv.org/abs/2311.12236v1
• Date: Mon, 20 Nov 2023 23:27:43 GMT
• Title: Ontological Reasoning over Shy and Warded Datalog$+/-$ for Streaming-based Architectures (technical report)
• Authors: Teodoro Baldazzi, Luigi Bellomarini, Marco Favorito, Emanuel Sallinger
• Abstract summary: Datalog-based ontological reasoning systems adopt languages, often shared under the collective name of Datalog$ +/-$. In this paper, we focus on two extremely promising, expressive, and tractable languages, namely, Shy and Warded Datalog$ +/-$. We leverage their theoretical underpinnings to introduce novel reasoning techniques, technically, "chase variants", that are particularly fit for efficient reasoning in streaming-based architectures. We then implement them in Vadalog, our reference streaming-based engine, to efficiently solve ontological reasoning tasks over real-world settings.
• Score: 6.689509223124273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years witnessed a rising interest towards Datalog-based ontological reasoning systems, both in academia and industry. These systems adopt languages, often shared under the collective name of Datalog$+/-$, that extend Datalog with the essential feature of existential quantification, while introducing syntactic limitations to sustain reasoning decidability and achieve a good trade-off between expressive power and computational complexity. From an implementation perspective, modern reasoners borrow the vast experience of the database community in developing streaming-based data processing systems, such as volcano-iterator architectures, that sustain a limited memory footprint and good scalability. In this paper, we focus on two extremely promising, expressive, and tractable languages, namely, Shy and Warded Datalog$+/-$. We leverage their theoretical underpinnings to introduce novel reasoning techniques, technically, "chase variants", that are particularly fit for efficient reasoning in streaming-based architectures. We then implement them in Vadalog, our reference streaming-based engine, to efficiently solve ontological reasoning tasks over real-world settings.

Related papers

• Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic [53.363888563647976]
  We develop a consistent and theoretically grounded approach to annotating decompositional entailment datasets. We find that our resulting dataset, RDTE, has a substantially higher internal consistency (+9%) than prior decompositional entailment datasets. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in a modern neuro-symbolic reasoning engine significantly improves results.
  arXiv  Detail & Related papers  (2024-02-22T18:55:17Z)
• Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching [23.487431014596556]
  Long-range interactions are essential for the correct description of complex systems in many scientific fields. Most deep graph networks cannot really model long-range dependencies due to intrinsic limitations of (synchronous) message passing. This work proposes a general framework that learns to mitigate these limitations.
  arXiv  Detail & Related papers  (2023-12-27T12:49:27Z)
• Log Parsing Evaluation in the Era of Modern Software Systems [47.370291246632114]
  We focus on one integral part of automated log analysis, log parsing, which is the prerequisite to deriving any insights from logs. Our investigation reveals problematic aspects within the log parsing field, particularly its inefficiency in handling heterogeneous real-world logs. We propose a tool, Logchimera, that enables estimating log parsing performance in industry contexts.
  arXiv  Detail & Related papers  (2023-08-17T14:19:22Z)
• ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering [70.6359636116848]
  We propose a new large-scale dataset, ConvFinQA, to study the chain of numerical reasoning in conversational question answering. Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations.
  arXiv  Detail & Related papers  (2022-10-07T23:48:50Z)
• On the Relationship between Shy and Warded Datalog+/- [3.4696964555947694]
  DatalogE is the extension of Datalog with existential quantification. It is suitable for modern applications on knowledge graphs, query answering (QA) over such language is known to be undecidable in general. Different fragments have emerged, introducing syntactic limitations to DatalogE that strike a balance between its expressive power and the computational complexity of QA.
  arXiv  Detail & Related papers  (2022-02-13T11:24:22Z)
• MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators [12.145849273069627]
  We present a novel approach for practical reasoning in DatalogMTL which combines materialisation (a.k.a. forward chaining) with automata-based techniques. MeTeoR is a scalable system which enables reasoning over complex temporal rules and involving datasets of millions of temporal facts.
  arXiv  Detail & Related papers  (2022-01-12T17:46:18Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• iWarded: A System for Benchmarking Datalog+/- Reasoning (technical report) [0.0]
  iWarded is a system that can generate very large, complex, realistic reasoning settings. We present the iWarded system and a set of novel theoretical results adopted to generate effective scenarios.
  arXiv  Detail & Related papers  (2021-03-15T17:56:46Z)
• Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models [59.04636530383049]
  Anomalies or failures in large computer systems, such as the cloud, have an impact on a large number of users. We propose a framework for anomaly detection in log data, as a major troubleshooting source of system information.
  arXiv  Detail & Related papers  (2021-02-23T09:17:05Z)
• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation [51.29486247405601]
  We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP) By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
  arXiv  Detail & Related papers  (2021-01-18T08:37:13Z)

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.