Na palestra do Giancarlo no SBBD 2023 ele comentou que uma das primeiras definições sobre Knowledge Graphs veio da Universidade de Twente (onde ele está lecionando / pesquisando atualmente) Encontrei no Survey sobre Definições de KG o seguinte trecho: In the 1980s , researchers from the University of Groningen and the University of Twente in the Netherlands initially introduced the term knowledge graph to formally describe their knowledge-based system that integrates knowledge from different sources for representing natural language [10, 15]. The authors proposed KGs with a limited set of relations and focus on qualitative modeling including human interaction, which clearly contrasts with the idea of KGs that has been widely discussed in recent years. Survey : Towards a Definition of Knowledge Graphs. Lisa Ehrlinger and Wolfram Wöß. Institute for Application Oriented Knowledge Processing. Johannes Kepler University Linz, Austria [10] P. James. Knowledge Graphs. In Linguistic. Ins...
Postagens
Keynote on Youtube -> https://youtu.be/DZ6NlcW4YV8?si=4Z5zDA1Vx_D10GKz No Intelligence Without Knowledge Katja Hose TU Wien, Austria Abstract. Knowledge graphs and graph data in general are becoming more and more essential components of intelligent systems. This does not only include native graph data, such as social networks or Linked Data on the Web. The flexibility of the graph model and its ability to store data relationships explicitly enables the integration and exploitation of data from very diverse sources. However, to truly exploit their potential, it becomes crucial to provide intelligent systems with verifiable knowledge, reliable facts, patterns, and a deeper understanding of the underlying domains. This talk will therefore chart a number of challenges for exploiting graphs to manage and bring meaning to large amounts of heterogeneous data and discuss opportunities with, without, and for artificial intelligence emerging from research situated at the confluence of data m...
https://knowledge-nlp.github.io/kdd2023/papers/Kumar5.pdf https://github.com/isunitha98selvan/odqa-tail ABSTRACT Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA). While they exhibit high accuracy in answering questions related to common knowledge, LLMs encounter difficulties in learning about uncommon long-tail knowledge (tail entities). [Entidades com poucas informações disponíveis, não tão populares ou comuns no interesse do público em geral] 1 INTRODUCTION However, the impressive achievements of LLMs in QA tasks are primarily observed with regard to common concepts that frequently appear on the internet (referred to as "head entities"), which are thus more likely to be learned effectively by LLMs during pretraining time. Conversely, when it comes to dealing with long-tail knowledge, which encompasses rarely occurring entities (referred to as "tail entities"), LLMs struggle to provide ac...
Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph https://arxiv.org/pdf/2308.13534.pdf Abstract —Conversational AI systems have emerged as key enablers of human-like interactions across diverse sectors. Nevertheless, the balance between linguistic nuance and factual accuracy has proven elusive. In this paper, we first introduce LLMXplorer, a comprehensive tool that provides an in-depth review of over 150 Large Language Models (LLMs), ... we propose a novel functional architecture that seamlessly integrates the structured dynamics of KG with the linguistic capabilities of LLMs. ...This research provides insights into the evolving landscape of conversational AI, emphasizing the imperative for systems that are efficient, transparent, and trustworthy . INTRODUCTION Hallucination: LLMs may generate information that is coherent but factually incorrect or misaligned with the underlying da...