RDF Surfaces: Computer Says No

• URL: http://arxiv.org/abs/2305.08476v1
• Date: Mon, 15 May 2023 09:27:46 GMT
• Title: RDF Surfaces: Computer Says No
• Authors: Patrick Hochstenbach, Jos De Roo, Ruben Verborgh
• Abstract summary: This vision paper provides basic principles and compares existing work. We create RDF Surfaces in order to express the full expressivity of FOL including saying explicitly no' RDF Surfaces provide the direct translation of FOL for the Semantic Web.
• Score: 0.22099217573031676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Logic can define how agents are provided or denied access to resources, how to interlink resources using mining processes and provide users with choices for possible next steps in a workflow. These decisions are for the most part hidden, internal to machines processing data. In order to exchange this internal logic a portable Web logic is required which the Semantic Web could provide. Combining logic and data provides insights into the reasoning process and creates a new level of trust on the Semantic Web. Current Web logics carries only a fragment of first-order logic (FOL) to keep exchange languages decidable or easily processable. But, this is at a cost: the portability of logic. Machines require implicit agreements to know which fragment of logic is being exchanged and need a strategy for how to cope with the different fragments. These choices could obscure insights into the reasoning process. We created RDF Surfaces in order to express the full expressivity of FOL including saying explicitly `no'. This vision paper provides basic principles and compares existing work. Even though support for FOL is semi-decidable, we argue these problems are surmountable. RDF Surfaces span many use cases, including describing misuse of information, adding explainability and trust to reasoning, and providing scope for reasoning over streams of data and queries. RDF Surfaces provide the direct translation of FOL for the Semantic Web. We hope this vision paper attracts new implementers and opens the discussion to its formal specification.

Related papers

• DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router [57.28685457991806]
  DeepSieve is an agentic RAG framework that incorporates information sieving via LLM-as-a-knowledge-router.<n>Our design emphasizes modularity, transparency, and adaptability, leveraging recent advances in agentic system design.
  arXiv  Detail & Related papers  (2025-07-29T17:55:23Z)
• RemoteReasoner: Towards Unifying Geospatial Reasoning Workflow [19.502882116487005]
  Remote sensing imagery presents vast, inherently unstructured spatial data.<n>We propose RemoteReasoner, a flexible and robust workflow for remote sensing reasoning tasks.<n>Preliminary experiments demonstrated that RemoteReasoner achieves remarkable performance across multi-granularity reasoning tasks.
  arXiv  Detail & Related papers  (2025-07-25T13:58:11Z)
• Learning to Disentangle Latent Reasoning Rules with Language VAEs: A Systematic Study [13.59688284637146]
  This work investigates how reasoning rules can be explicitly embedded and memorised within language models.<n>We propose a complete pipeline for learning reasoning rules within Transformer-based language VAEs.
  arXiv  Detail & Related papers  (2025-06-24T08:38:03Z)
• WebThinker: Empowering Large Reasoning Models with Deep Research Capability [60.81964498221952]
  WebThinker is a deep research agent that empowers large reasoning models to autonomously search the web, navigate web pages, and draft research reports during the reasoning process. It also employs an textbfAutonomous Think-Search-and-Draft strategy, allowing the model to seamlessly interleave reasoning, information gathering, and report writing in real time. Our approach enhances LRM reliability and applicability in complex scenarios, paving the way for more capable and versatile deep research systems.
  arXiv  Detail & Related papers  (2025-04-30T16:25:25Z)
• NAVER: A Neuro-Symbolic Compositional Automaton for Visual Grounding with Explicit Logic Reasoning [22.60247555240363]
  This paper explores challenges for methods that require reasoning like human cognition. We propose NAVER, a compositional visual grounding method that integrates explicit probabilistic logic reasoning. Our results show that NAVER achieves SoTA performance comparing to recent end-to-end and compositional baselines.
  arXiv  Detail & Related papers  (2025-02-01T09:19:08Z)
• Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning [1.3003982724617653]
  Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning. This research introduces Proof of Thought, a framework that enhances the reliability and transparency of LLM outputs. Key contributions include a robust type system with sort management for enhanced logical integrity, explicit representation of rules for clear distinction between factual and inferential knowledge.
  arXiv  Detail & Related papers  (2024-09-25T18:35:45Z)
• Text-Video Retrieval with Global-Local Semantic Consistent Learning [122.15339128463715]
  We propose a simple yet effective method, Global-Local Semantic Consistent Learning (GLSCL) GLSCL capitalizes on latent shared semantics across modalities for text-video retrieval. Our method achieves comparable performance with SOTA as well as being nearly 220 times faster in terms of computational cost.
  arXiv  Detail & Related papers  (2024-05-21T11:59:36Z)
• Inferentialist Resource Semantics [48.65926948745294]
  This paper shows how inferentialism enables a versatile and expressive framework for resource semantics. How inferentialism seamlessly incorporates the assertion-based approach of the logic of Bunched Implications. This integration enables reasoning about shared and separated resources in intuitive and familiar ways.
  arXiv  Detail & Related papers  (2024-02-14T14:54:36Z)
• Language Models can be Logical Solvers [99.40649402395725]
  We introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers. LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers.
  arXiv  Detail & Related papers  (2023-11-10T16:23:50Z)
• Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension [80.99865844249106]
  We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
  arXiv  Detail & Related papers  (2023-06-21T07:34:27Z)
• Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning [27.224364543134094]
  We introduce a novel logic-driven data augmentation approach, AMR-LDA. AMR-LDA converts the original text into an Abstract Meaning Representation (AMR) graph. The modified AMR graphs are subsequently converted back into text to create augmented data.
  arXiv  Detail & Related papers  (2023-05-21T23:16:26Z)
• APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning [73.3035118224719]
  We propose APOLLO, an adaptively pretrained language model that has improved logical reasoning abilities. APOLLO performs comparably on ReClor and outperforms baselines on LogiQA.
  arXiv  Detail & Related papers  (2022-12-19T07:40:02Z)
• OPERA:Operation-Pivoted Discrete Reasoning over Text [33.36388276371693]
  OPERA is an operation-pivoted discrete reasoning framework for machine reading comprehension. It uses lightweight symbolic operations as neural modules to facilitate the reasoning ability and interpretability. Experiments on both DROP and RACENum datasets show the reasoning ability of OPERA.
  arXiv  Detail & Related papers  (2022-04-29T15:41:47Z)
• Logic Explained Networks [27.800583434727805]
  We show how a mindful design of the networks leads to a family of interpretable deep learning models called Logic Explained Networks (LENs) LENs only require their inputs to be human-understandable predicates, and they provide explanations in terms of simple First-Order Logic (FOL) formulas. LENs may yield better classifications than established white-box models, such as decision trees and Bayesian rule lists.
  arXiv  Detail & Related papers  (2021-08-11T10:55:42Z)
• Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text [65.24325614642223]
  We propose to understand logical symbols and expressions in the text to arrive at the answer. Based on such logical information, we put forward a context extension framework and a data augmentation algorithm. Our method achieves the state-of-the-art performance, and both logic-driven context extension framework and data augmentation algorithm can help improve the accuracy.
  arXiv  Detail & Related papers  (2021-05-08T10:09:36Z)
• Fixed Point Semantics for Stream Reasoning [0.0]
  Stream reasoning has emerged as a research area within the AI-community with many potential applications. The rule-based formalism em LARS for non-monotonic stream reasoning under the answer set semantics has been introduced. We show that our semantics is sound and constructive in the sense that answer sets are derivable bottom-up and free of circular justifications.
  arXiv  Detail & Related papers  (2020-05-17T22:25:24Z)

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.