Results 61 to 70 of about 22,607,729 (330)
Rationale-Enhanced Language Models are Better Continual Relation Learners [PDF]
arXiv, 2023 Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that catastrophic forgetting arises from the model's lack of robustness against future analogous relations.
arxiv
Dynamic infinite relational model for time-varying relational data analysis [PDF]
, 2017 We propose a new probabilistic model for analyzing dynamic evolutions of relational data, such as additions, deletions and split & merge, of relation clusters like communities in social networks. Our proposed model abstracts observed timevarying object-
Ishiguro, Katsuhiko +3 more
core
Graph Neural Networks with Generated Parameters for Relation Extraction
, 2019 Recently, progress has been made towards improving relational reasoning in machine learning field. Among existing models, graph neural networks (GNNs) is one of the most effective approaches for multi-hop relational reasoning.
Chua, Tat-seng +5 more
core +1 more source
Relational parametricity for higher kinds [PDF]
, 2012 Reynolds’ notion of relational parametricity has been extremely influential and well studied for polymorphic programming languages and type theories based on System F.
Atkey, Robert
core +2 more sources
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
Annual Meeting of the Association for Computational Linguistics, 2018 The relational facts in sentences are often complicated. Different relational triplets may have overlaps in a sentence. We divided the sentences into three types according to triplet overlap degree, including Normal, EntityPairOverlap and ...
Xiangrong Zeng +4 more
semanticscholar +1 more source
In Situ Graph Reasoning and Knowledge Expansion Using Graph‐PRefLexOR
Advanced Intelligent Discovery, EarlyView.Graph‐PRefLexOR is a novel framework that enhances language models with in situ graph reasoning, symbolic abstraction, and recursive refinement. By integrating graph‐based representations into generative tasks, the approach enables interpretable, multistep reasoning.
Markus J. Buehler
wiley +1 more source
Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction [PDF]
arXiv, 2020 Relation ties, defined as the correlation and mutual exclusion between different relations, are critical for distant supervised relation extraction. Existing approaches model this property by greedily learning local dependencies. However, they are essentially limited by failing to capture the global topology structure of relation ties.
arxiv
NOSQL design for analytical workloads: Variability matters [PDF]
, 2016 Big Data has recently gained popularity and has strongly questioned relational databases as universal storage systems, especially in the presence of analytical workloads.
Abelló Gamazo, Alberto +2 more
core +1 more source
Advanced Intelligent Systems, EarlyView.This paper presents a novel Multi‐Distance Spatial‐Temporal Graph Neural Network for detecting anomalies in blockchain transactions. The model combines multi‐distance graph convolutions with adaptive temporal modeling to capture complex patterns in anonymized cryptocurrency networks.
Shiyang Chen +4 more
wiley +1 more source
CREATING AND ENCRYPTING E-COMMERCE DATABASE FOR SELLING MECHANICAL ELEMENTS
Applied Engineering Letters, 2018 In this paper it is presented creation and encryption of e-commerce database for selling mechanical elements. It is shown a procedure of creation a database and tables with appropriate data, which is the first step in developing stable e-commerce ...
Djordje Dihovični, Vlado Krunić
doaj +1 more source