Zobrazeno 1 - 10
of 3 395
pro vyhledávání: '"knowledge graph embeddings"'
Understanding the complex dynamics of high-dimensional, contingent, and strongly nonlinear economic data, often shaped by multiplicative processes, poses significant challenges for traditional regression methods as such methods offer limited capacity
Externí odkaz:
http://arxiv.org/abs/2410.19835
In this paper we present an approach to reduce hallucinations in Large Language Models (LLMs) by incorporating Knowledge Graphs (KGs) as an additional modality. Our method involves transforming input text into a set of KG embeddings and using an adap
Externí odkaz:
http://arxiv.org/abs/2411.11531
Autor:
Li, Lucian, Silva, Eryclis
Introduction: Tracing the spread of ideas and the presence of influence is a question of special importance across a wide range of disciplines, ranging from intellectual history to cultural analytics, computational social science, and the science of
Externí odkaz:
http://arxiv.org/abs/2410.24021
In recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation systems, amongst others. Large-scale knowledge graphs to this end have
Externí odkaz:
http://arxiv.org/abs/2410.21163
Autor:
Niu, Guanglin
Knowledge Graph Embedding (KGE) techniques play a pivotal role in transforming symbolic Knowledge Graphs (KGs) into numerical representations, thereby enhancing various deep learning models for knowledge-augmented applications. Unlike entities, relat
Externí odkaz:
http://arxiv.org/abs/2410.14733
Autor:
Zhu, Yuqicheng, Potyka, Nico, Xiong, Bo, Tran, Trung-Kien, Nayyeri, Mojtaba, Kharlamov, Evgeny, Staab, Steffen
Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard. We explain how knowledge graph embeddings can be used to approximate probabilistic inference efficiently using the example of Statistical EL (SEL), a s
Externí odkaz:
http://arxiv.org/abs/2407.11821
Publikováno v:
Studies in health technology and informatics vol. 316 (2024): 575-579
Developing novel predictive models with complex biomedical information is challenging due to various idiosyncrasies related to heterogeneity, standardization or sparseness of the data. We previously introduced a person-centric ontology to organize in
Externí odkaz:
http://arxiv.org/abs/2408.15294
Autor:
Zhu, Yuqicheng, Potyka, Nico, Nayyeri, Mojtaba, Xiong, Bo, He, Yunjie, Kharlamov, Evgeny, Staab, Steffen
Knowledge graph embedding (KGE) models are often used to predict missing links for knowledge graphs (KGs). However, multiple KG embeddings can perform almost equally well for link prediction yet give conflicting predictions for unseen queries. This p
Externí odkaz:
http://arxiv.org/abs/2408.08226
Ontology alignment is integral to achieving semantic interoperability as the number of available ontologies covering intersecting domains is increasing. This paper proposes OWL2Vec4OA, an extension of the ontology embedding system OWL2Vec*. While OWL
Externí odkaz:
http://arxiv.org/abs/2408.06310
This paper introduces a post-hoc explainable AI method tailored for Knowledge Graph Embedding models. These models are essential to Knowledge Graph Completion yet criticized for their opaque, black-box nature. Despite their significant success in cap
Externí odkaz:
http://arxiv.org/abs/2406.01759