RDF Stream Taxonomy: Systematizing RDF Stream Types in Research and Practice

• URL: http://arxiv.org/abs/2311.14540v3
• Date: Thu, 27 Jun 2024 09:27:54 GMT
• Title: RDF Stream Taxonomy: Systematizing RDF Stream Types in Research and Practice
• Authors: Piotr Sowinski, Pawel Szmeja, Maria Ganzha, Marcin Paprzycki,
• Abstract summary: This work attempts to address this critical research gap, by systematizing RDF stream types present in the literature in a novel taxonomy. Extensive documentation and additional resources are provided, to foster the adoption of the ontology. RDF-STaX is expected to help drive innovation in RDF streaming, by fostering scientific discussion, cooperation, and tool interoperability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the years, RDF streaming was explored in research and practice from many angles, resulting in a wide range of RDF stream definitions. This variety presents a major challenge in discussing and integrating streaming systems, due to the lack of a common language. This work attempts to address this critical research gap, by systematizing RDF stream types present in the literature in a novel taxonomy. The proposed RDF Stream Taxonomy (RDF-STaX) is embodied in an OWL 2 DL ontology that follows the FAIR principles, making it readily applicable in practice. Extensive documentation and additional resources are provided, to foster the adoption of the ontology. Three use cases for the ontology are presented with accompanying competency questions, demonstrating the usefulness of the resource. Additionally, this work introduces a novel nanopublications dataset, which serves as a collaborative, living state-of-the-art review of RDF streaming. The results of a multifaceted evaluation of the resource are presented, testing its logical validity, use case coverage, and adherence to the community's best practices, while also comparing it to other works. RDF-STaX is expected to help drive innovation in RDF streaming, by fostering scientific discussion, cooperation, and tool interoperability.

Related papers

• RWESummary: A Framework and Test for Choosing Large Language Models to Summarize Real-World Evidence (RWE) Studies [0.0]
  Large Language Models (LLMs) have been extensively evaluated for general summarization tasks as well as medical research assistance.<n>We introduce RWESummary, a proposed addition to the MedHELM framework to enable benchmarking of LLMs for this task.<n>RWESummary includes one scenario and three evaluations covering major types of errors observed in summarization of medical research studies.
  arXiv  Detail & Related papers  (2025-06-23T16:28:03Z)
• GeAR: Generation Augmented Retrieval [82.20696567697016]
  Document retrieval techniques form the foundation for the development of large-scale information systems. The prevailing methodology is to construct a bi-encoder and compute the semantic similarity. We propose a new method called $textbfGe$neration that incorporates well-designed fusion and decoding modules.
  arXiv  Detail & Related papers  (2025-01-06T05:29:00Z)
• VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents [66.42579289213941]
  Retrieval-augmented generation (RAG) is an effective technique that enables large language models to utilize external knowledge sources for generation. In this paper, we introduce VisRAG, which tackles this issue by establishing a vision-language model (VLM)-based RAG pipeline. In this pipeline, instead of first parsing the document to obtain text, the document is directly embedded using a VLM as an image and then retrieved to enhance the generation of a VLM.
  arXiv  Detail & Related papers  (2024-10-14T15:04:18Z)
• How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation [7.151108031568037]
  This paper constructs low-resource relation extraction datasets in 10 low-resource languages (LRLs) in three regions (Central Asia, Southeast Asia and Middle East) The corpora are constructed by translating the original publicly available English RE datasets (NYT10, FewRel and CrossRE) using an effective multilingual machine translation. Then, we use the language perplexity (PPL) to filter out the low-quality data from the translated datasets.
  arXiv  Detail & Related papers  (2024-06-17T03:02:04Z)
• Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions [62.0123588983514]
  Large Language Models (LLMs) have demonstrated wide-ranging applications across various fields. We reformulate the peer-review process as a multi-turn, long-context dialogue, incorporating distinct roles for authors, reviewers, and decision makers. We construct a comprehensive dataset containing over 26,841 papers with 92,017 reviews collected from multiple sources.
  arXiv  Detail & Related papers  (2024-06-09T08:24:17Z)
• 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)
• A Survey on Retrieval-Augmented Text Generation for Large Language Models [1.4579344926652844]
  Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements. This paper organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval, and generation. It outlines RAG's evolution and discusses the field's progression through the analysis of significant studies.
  arXiv  Detail & Related papers  (2024-04-17T01:27:42Z)
• Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint [56.74058752955209]
  This paper studies the alignment process of generative models with Reinforcement Learning from Human Feedback (RLHF) We first identify the primary challenges of existing popular methods like offline PPO and offline DPO as lacking in strategical exploration of the environment. We propose efficient algorithms with finite-sample theoretical guarantees.
  arXiv  Detail & Related papers  (2023-12-18T18:58:42Z)
• Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
  InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-12T11:58:15Z)
• DiffusionRet: Generative Text-Video Retrieval with Diffusion Model [56.03464169048182]
  Existing text-video retrieval solutions focus on maximizing the conditional likelihood, i.e., p(candidates|query) We creatively tackle this task from a generative viewpoint and model the correlation between the text and the video as their joint probability p(candidates,query) This is accomplished through a diffusion-based text-video retrieval framework (DiffusionRet), which models the retrieval task as a process of gradually generating joint distribution from noise.
  arXiv  Detail & Related papers  (2023-03-17T10:07:19Z)
• A path in regression Random Forest looking for spatial dependence: a taxonomy and a systematic review [0.0]
  In environmental applications, phenomenon of interest may present spatial and/or temporal dependence that is not taken explicitly into account by Random Forest (RF) We propose a taxonomy to classify strategies according to when (Pre-, In- and/or Post-processing) they try to include the spatial information into regression RF. We provide a systematic review and classify the most recent strategies adopted to "adjust" regression RF to spatially dependent data.
  arXiv  Detail & Related papers  (2023-03-08T16:34:12Z)
• Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection [0.0]
  Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This paper seeks to explore current research being conducted on RNNs in four very important areas, being biometric authentication, expression recognition, anomaly detection, and applications to aircraft.
  arXiv  Detail & Related papers  (2021-09-13T04:37:18Z)
• Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study [1.2345552555178128]
  We construct a new taxonomy and perform a comprehensive review of UDTL-based IFD according to different tasks. To emphasize the importance and importance of UDTL-based IFD, the whole test framework will be released to the research community.
  arXiv  Detail & Related papers  (2019-12-28T21:45:34Z)

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.