Our world is shaped by events of various complexity. This includes both small-scale local events like local farmer markets and large complex events like political and military conflicts. The latter are typically not observed directly but through the lenses of intermediaries like newspapers or social media. In other words, we do not witness the unfolding of such events directly but are confronted with narratives surrounding them. Such narratives capture different aspects of a complex event and may also differ with respect to the narrator. Thus, they provide a rich semantics concerning real-world events. In this paper, we show how narratives concerning complex events can be constructed and utilized. We provide a formal representation of narratives based on recursive nodes to represent multiple levels of detail and discuss how narratives can be bound to event-centric knowledge graphs. Additionally, we provide an algorithm based on incremental prompting techniques that mines such narratives from texts to account for different perspectives on complex events. Finally, we show the effectiveness and future research directions in a proof of concept.
我们的世界是由各种复杂事件塑造的。这包括小型的地方事件,如当地农民市场,以及大型复杂事件,如政治和军事冲突。后者的通常不是直接观察到的,而是通过中介机构,如报纸或社交媒体,透过他们的镜头观察到的。换句话说,我们不是直接见证了这些事件的展开,而是面对着围绕这些事件的故事。这些故事捕捉了复杂事件的不同方面,而且也可能与叙述者的观点有所不同。因此,它们提供了关于现实世界事件的丰富语义。在本文中,我们展示了如何构建和利用关于复杂事件的叙述。我们根据递归节点的形式给出了叙述的正式表示,并讨论了叙述如何与事件中心化的知识图谱相联系。此外,我们还基于增量提示技术提供了算法,用于从文本中挖掘这类叙述,以反映复杂事件的不同观点。最后,我们在概念证明中展示了这种方法的有效性和未来的研究方向。
https://arxiv.org/abs/2404.16405
Knowledge Graphs (KGs) are widely employed in artificial intelligence applications, such as question-answering and recommendation systems. However, KGs are frequently found to be incomplete. While much of the existing literature focuses on predicting missing nodes for given incomplete KG triples, there remains an opportunity to complete KGs by exploring relations between existing nodes, a task known as relation prediction. In this study, we propose a relations prediction model that harnesses both textual and structural information within KGs. Our approach integrates walks-based embeddings with language model embeddings to effectively represent nodes. We demonstrate that our model achieves competitive results in the relation prediction task when evaluated on a widely used dataset.
知识图(KGs)广泛应用于人工智能领域,如问答和推荐系统。然而,KGs经常被发现不完整。虽然现有文献主要关注预测给定不完整的KG三元组中的缺失节点,但在关系预测领域仍有机会通过探索现有节点之间的关系来完成KGs,实现名为关系预测的任务。在这项研究中,我们提出了一个利用KGs中文本和结构信息的关联预测模型。我们的方法结合了走行嵌入和语言模型嵌入,有效地表示节点。我们证明了,当在我们的广泛使用数据集上评估时,我们的模型在关系预测任务上实现了具有竞争力的结果。
https://arxiv.org/abs/2404.16206
The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over private and/or previously unseen document collections. However, RAG fails on global questions directed at an entire text corpus, such as "What are the main themes in the dataset?", since this is inherently a query-focused summarization (QFS) task, rather than an explicit retrieval task. Prior QFS methods, meanwhile, fail to scale to the quantities of text indexed by typical RAG systems. To combine the strengths of these contrasting methods, we propose a Graph RAG approach to question answering over private text corpora that scales with both the generality of user questions and the quantity of source text to be indexed. Our approach uses an LLM to build a graph-based text index in two stages: first to derive an entity knowledge graph from the source documents, then to pregenerate community summaries for all groups of closely-related entities. Given a question, each community summary is used to generate a partial response, before all partial responses are again summarized in a final response to the user. For a class of global sensemaking questions over datasets in the 1 million token range, we show that Graph RAG leads to substantial improvements over a naïve RAG baseline for both the comprehensiveness and diversity of generated answers. An open-source, Python-based implementation of both global and local Graph RAG approaches is forthcoming at this https URL.
利用检索增强生成(RAG)从外部知识源中检索相关信息,使得大型语言模型(LLMs)能够回答从私有和/或之前未见过的文档集合中提出的问题。然而,RAG在指向整个文本语料库的全身问题时失败,例如“数据集的主要主题是什么?”,这是因为这是一个本质上的查询关注度总结(QFS)任务,而不是一个明确的检索任务。同时,之前的前QFS方法也无法扩展到典型RAG系统所索引的文本数量。为了结合这些相互矛盾的方法的优势,我们提出了一个基于图的RAG方法来在私有文本语料库上进行问题回答,该方法能够随着用户问题和要索引的源文本的数量而扩展。我们的方法使用LLM在两个阶段构建一个基于图的文本索引:首先从源文档中提取实体知识图,然后为所有密切相关的实体群组预生成社区摘要。对于一个问题,每个社区摘要用于生成部分回答,然后将所有部分回答再次汇总为对用户的最终回答。在100万词范围内的一类全局理解问题,我们证明了图RAG对于完整性和多样性生成的答案比 naive RAG 基线有了显著的改进。要在该https URL上找到一个开源的、基于全局和局部 Graph RAG方法的Python实现。
https://arxiv.org/abs/2404.16130
This study explores the use of Large Language Models (LLMs) for automatic evaluation of knowledge graph (KG) completion models. Historically, validating information in KGs has been a challenging task, requiring large-scale human annotation at prohibitive cost. With the emergence of general-purpose generative AI and LLMs, it is now plausible that human-in-the-loop validation could be replaced by a generative agent. We introduce a framework for consistency and validation when using generative models to validate knowledge graphs. Our framework is based upon recent open-source developments for structural and semantic validation of LLM outputs, and upon flexible approaches to fact checking and verification, supported by the capacity to reference external knowledge sources of any kind. The design is easy to adapt and extend, and can be used to verify any kind of graph-structured data through a combination of model-intrinsic knowledge, user-supplied context, and agents capable of external knowledge retrieval.
本研究探讨了使用大型语言模型(LLMs)自动评估知识图(KG)完成模型的应用。历史上,验证知识图中的有效信息是一个具有挑战性的任务,需要大规模的人类标注,代价高昂。随着通用生成式人工智能(GSA)和LLM的出现,现在可能用生成代理来代替人机交互验证。我们提出了一个使用生成模型验证知识图的一致性和验证框架。该框架基于LLM输出结构和语义验证的最近开源发展,以及支持外部知识来源访问的能力。该设计易于调整和扩展,可以通过模型固有知识、用户提供的上下文和支持外部知识检索的代理来验证任何类型的图状数据。
https://arxiv.org/abs/2404.15923
Knowledge Graph Completion (KGC) has garnered massive research interest recently, and most existing methods are designed following a transductive setting where all entities are observed during training. Despite the great progress on the transductive KGC, these methods struggle to conduct reasoning on emerging KGs involving unseen entities. Thus, inductive KGC, which aims to deduce missing links among unseen entities, has become a new trend. Many existing studies transform inductive KGC as a graph classification problem by extracting enclosing subgraphs surrounding each candidate triple. Unfortunately, they still face certain challenges, such as the expensive time consumption caused by the repeat extraction of enclosing subgraphs, and the deficiency of entity-independent feature learning. To address these issues, we propose a global-local anchor representation (GLAR) learning method for inductive KGC. Unlike previous methods that utilize enclosing subgraphs, we extract a shared opening subgraph for all candidates and perform reasoning on it, enabling the model to perform reasoning more efficiently. Moreover, we design some transferable global and local anchors to learn rich entity-independent features for emerging entities. Finally, a global-local graph reasoning model is applied on the opening subgraph to rank all candidates. Extensive experiments show that our GLAR outperforms most existing state-of-the-art methods.
知识图谱完成(KGC)最近吸引了大量研究兴趣,而且大多数现有方法都是在训练过程中采用转换设置的,其中所有实体都在观察过程中。尽管在转换式KGC方面取得了很大进展,但这些方法在处理涉及未见实体的 emergence KG 时仍然存在挑战。因此,归纳式KGC,旨在从未见实体中推断缺失链接,已成为一个新的趋势。许多现有研究将归纳式KGC转换为一个图分类问题,通过提取围绕每个候选三元组的内包容子图来完成。然而,他们仍然面临着某些挑战,例如由于重复提取内包容子图而产生的高时间消耗,以及实体独立特征学习不足的问题。为了解决这些问题,我们提出了一个全局-局部锚表示(GLAR)学习方法来解决归纳式KGC。与之前的方法不同,我们为所有候选者提取共享的内包容子图,并在其上进行推理,使模型能够更有效地进行推理。此外,我们还设计了一些可转移的全局和局部锚来学习新兴实体的丰富实体独立特征。最后,在打开子图上应用全局-局部图推理模型对所有候选者进行排名。大量实验证明,我们的GLAR超越了大多数现有最先进的方法。
https://arxiv.org/abs/2404.15807
Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep learning, Graph Neural Networks (GNNs) have emerged as a cornerstone in Graph Machine Learning (Graph ML), facilitating the representation and processing of graph structures. Recently, LLMs have demonstrated unprecedented capabilities in language tasks and are widely adopted in a variety of applications such as computer vision and recommender systems. This remarkable success has also attracted interest in applying LLMs to the graph domain. Increasing efforts have been made to explore the potential of LLMs in advancing Graph ML's generalization, transferability, and few-shot learning ability. Meanwhile, graphs, especially knowledge graphs, are rich in reliable factual knowledge, which can be utilized to enhance the reasoning capabilities of LLMs and potentially alleviate their limitations such as hallucinations and the lack of explainability. Given the rapid progress of this research direction, a systematic review summarizing the latest advancements for Graph ML in the era of LLMs is necessary to provide an in-depth understanding to researchers and practitioners. Therefore, in this survey, we first review the recent developments in Graph ML. We then explore how LLMs can be utilized to enhance the quality of graph features, alleviate the reliance on labeled data, and address challenges such as graph heterogeneity and out-of-distribution (OOD) generalization. Afterward, we delve into how graphs can enhance LLMs, highlighting their abilities to enhance LLM pre-training and inference. Furthermore, we investigate various applications and discuss the potential future directions in this promising field.
图在表示复杂关系方面在社交网络、知识图谱和分子发现等领域中发挥着重要作用。随着深度学习的出现,图神经网络(GNNs)成为图机器学习(Graph ML)的一个支柱,推动了图结构的表示和处理。近年来,LLM在语言任务上的表现已经达到了史无前例的水平,并在各种应用领域(如计算机视觉和推荐系统)得到了广泛应用。这一显著的成功也引起了将LLM应用于图形领域的兴趣。越来越多的努力致力于探索LLM在推动图机器学习的一般化、可迁移性和少样本学习能力方面的潜力。同时,特别是知识图谱,图形在可靠的事实知识方面非常丰富,可以利用来增强LLM的推理能力,并可能减轻其局限性,如幻觉和缺乏可解释性。鉴于这一研究领域的快速进步,对于LLM时代图机器学习的系统综述总结最新的进展是必要的,以提供研究人员和实践者对这一领域的深入理解。因此,在本次调查中,我们首先回顾了图机器学习领域的最新发展。然后,我们探讨了LLM如何用于提高图形特征的质量、减轻对标注数据的依赖以及解决诸如图形异质性和离散(OOD)泛化等问题。接着,我们深入研究了图形如何增强LLM,强调了它们在提高LLM预训练和推理能力方面的能力。最后,我们调查了各种应用,并讨论了这一充满前景的领域未来的潜在方向。
https://arxiv.org/abs/2404.14928
A graph is a fundamental data model to represent various entities and their complex relationships in society and nature, such as social networks, transportation networks, financial networks, and biomedical systems. Recently, large language models (LLMs) have showcased a strong generalization ability to handle various NLP and multi-mode tasks to answer users' arbitrary questions and specific-domain content generation. Compared with graph learning models, LLMs enjoy superior advantages in addressing the challenges of generalizing graph tasks by eliminating the need for training graph learning models and reducing the cost of manual annotation. In this survey, we conduct a comprehensive investigation of existing LLM studies on graph data, which summarizes the relevant graph analytics tasks solved by advanced LLM models and points out the existing remaining challenges and future directions. Specifically, we study the key problems of LLM-based generative graph analytics (LLM-GGA) with three categories: LLM-based graph query processing (LLM-GQP), LLM-based graph inference and learning (LLM-GIL), and graph-LLM-based applications. LLM-GQP focuses on an integration of graph analytics techniques and LLM prompts, including graph understanding and knowledge graph (KG) based augmented retrieval, while LLM-GIL focuses on learning and reasoning over graphs, including graph learning, graph-formed reasoning and graph representation. We summarize the useful prompts incorporated into LLM to handle different graph downstream tasks. Moreover, we give a summary of LLM model evaluation, benchmark datasets/tasks, and a deep pro and cons analysis of LLM models. We also explore open problems and future directions in this exciting interdisciplinary research area of LLMs and graph analytics.
图形是一种基本的数据模型,用于表示社会和自然中各种实体及其复杂的关系,如社交网络、交通网络、金融网络和生物医学系统。近年来,大型语言模型(LLMs)在处理各种自然语言处理(NLP)和多模态任务方面表现出强大的泛化能力,从而回答用户的任意问题和特定领域内容生成。与图形学习模型相比,LLMs在解决图形任务的挑战方面具有优越的优势,通过消除训练图形学习模型的需求并降低手动注释的成本。在本次调查中,我们对LLM关于图形数据的现有研究进行全面调查,概述了高级LLM模型解决的相关图形分析任务,并指出了现有的剩余挑战和未来发展方向。具体来说,我们研究了基于LLM的生成图数据分析(LLM-GGA)的三个主要问题:LLM-基于图查询处理(LLM-GQP)、LLM-基于图推理和学习(LLM-GIL)和基于图形-LLM的应用。LLM-GQP关注将图形数据分析技术和LLM提示进行集成,包括基于图理解和知识图(KG)的增强检索,而LLM-GIL关注在图形上进行学习和推理,包括图形学习、图形形成推理和图形表示。我们总结了LLM中纳入不同图形下游任务的有用提示。此外,我们还对LLM模型评估、基准数据集/任务以及LLM模型的优缺点进行了总结。此外,我们在LLM和图数据分析这一激动人心的跨学科研究领域中进行了探索。
https://arxiv.org/abs/2404.14809
To address the issue of insufficient knowledge and the tendency to generate hallucination in Large Language Models (LLMs), numerous studies have endeavored to integrate LLMs with Knowledge Graphs (KGs). However, all these methods are evaluated on conventional Knowledge Graph Question Answering (KGQA) with complete KGs, where the factual triples involved in each question are entirely covered by the given KG. In this situation, LLM mainly acts as an agent to find answer entities by exploring the KG, rather than effectively integrating internal and external knowledge sources. However, in real-world scenarios, KGs are often incomplete to cover all the knowledge required to answer questions. To simulate real-world scenarios and evaluate the ability of LLMs to integrate internal and external knowledge, in this paper, we propose leveraging LLMs for QA under Incomplete Knowledge Graph (IKGQA), where the given KG doesn't include all the factual triples involved in each question. To handle IKGQA, we propose a training-free method called Generate-on-Graph (GoG) that can generate new factual triples while exploring on KGs. Specifically, we propose a selecting-generating-answering framework, which not only treat the LLM as an agent to explore on KGs, but also treat it as a KG to generate new facts based on the explored subgraph and its inherent knowledge. Experimental results on two datasets demonstrate that our GoG can solve IKGQA to a certain extent, while almost all previous methods cannot perform well on IKGQA.
为解决大型语言模型(LLMs)中知识不足和产生虚构现象的问题,许多研究努力将LLMs与知识图谱(KGs)集成。然而,这些方法都在传统的知识图谱问题回答(KGQA)上进行评估,其中每个问题涉及的事实三元组完全由给定的KG覆盖。在这种情况下,LLM主要作为探索KG的代理来查找答案实体,而不是有效地将内部和外部知识来源集成。然而,在现实世界的场景中,知识图谱通常不完整,无法覆盖所有回答问题的知识。为了模拟现实世界的场景,评估LLM将内部和外部知识整合的能力,本文提出了一种利用LLM进行基于不完整知识图谱(IKGQA)的QA的方法,其中给定的KG不包含每个问题涉及的所有事实三元组。为了处理IKGQA,我们提出了一种无需训练的方法,称为生成-在图(GoG)方法,可以在探索KGs的同时生成新的事实三元组。具体来说,我们提出了一种选择-生成-回答框架,不仅将LLM视为在KGs中探索的代理,还将它视为一个生成新事实的KG,基于探索的子图及其固有知识。在两个数据集上的实验结果表明,我们的GoG可以在一定程度上解决IKGQA,而几乎所有以前的方法在IKGQA上表现不佳。
https://arxiv.org/abs/2404.14741
Citation text plays a pivotal role in elucidating the connection between scientific documents, demanding an in-depth comprehension of the cited paper. Constructing citations is often time-consuming, requiring researchers to delve into extensive literature and grapple with articulating relevant content. To address this challenge, the field of citation text generation (CTG) has emerged. However, while earlier methods have primarily centered on creating single-sentence citations, practical scenarios frequently necessitate citing multiple papers within a single paragraph. To bridge this gap, we propose a method that leverages Large Language Models (LLMs) to generate multi-citation sentences. Our approach involves a single source paper and a collection of target papers, culminating in a coherent paragraph containing multi-sentence citation text. Furthermore, we introduce a curated dataset named MCG-S2ORC, composed of English-language academic research papers in Computer Science, showcasing multiple citation instances. In our experiments, we evaluate three LLMs LLaMA, Alpaca, and Vicuna to ascertain the most effective model for this endeavor. Additionally, we exhibit enhanced performance by integrating knowledge graphs from target papers into the prompts for generating citation text. This research underscores the potential of harnessing LLMs for citation generation, opening a compelling avenue for exploring the intricate connections between scientific documents.
引用文本在阐明科学文献之间的联系方面扮演着关键角色,要求对引用的论文进行深入的理解。构建引用通常需要花费时间,需要研究人员深入挖掘广泛的文献,并处理相关内容。为应对这一挑战,文献文本生成领域(CTG)应运而生。然而,虽然早期方法主要集中在创建单句引用,但实际场景常常需要在一段文字中引用多个论文。为了弥合这一空白,我们提出了一种利用大型语言模型(LLMs)生成多句引用文本的方法。我们的方法包括一个单一来源论文和一个目标论文集合,最终形成一个包含多句引用文本的连贯段落。此外,我们还引入了一个名为MCG-S2ORC的精心挑选的数据集,由计算机科学领域的英语学术论文构成,展示了多个引用实例。在我们的实验中,我们评估了三种LLM LLaMA、Alpaca和Vicuna,以确定这项任务中最具效性的模型。此外,通过将目标论文的知识图谱整合到生成引用文本的提示中,我们展示了增强的表现。这项研究突出了利用LLMs进行引用生成的潜力,为探索科学文献之间的复杂联系开启了一个引人入胜的途径。
https://arxiv.org/abs/2404.13865
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). However, existing approaches for cIE suffer from two limitations: (i) they are often pipelines which makes them prone to error propagation, and/or (ii) they are restricted to sentence level which prevents them from capturing long-range dependencies and results in expensive inference time. We address these limitations by proposing REXEL, a highly efficient and accurate model for the joint task of document level cIE (DocIE). REXEL performs mention detection, entity typing, entity disambiguation, coreference resolution and document-level relation classification in a single forward pass to yield facts fully linked to a reference knowledge graph. It is on average 11 times faster than competitive existing approaches in a similar setting and performs competitively both when optimised for any of the individual subtasks and a variety of combinations of different joint tasks, surpassing the baselines by an average of more than 6 F1 points. The combination of speed and accuracy makes REXEL an accurate cost-efficient system for extracting structured information at web-scale. We also release an extension of the DocRED dataset to enable benchmarking of future work on DocIE, which is available at this https URL.
从无结构文本中提取结构化信息对于许多下游自然语言处理(NLP)应用至关重要,而且通常通过关闭信息提取(cIE)来实现。然而,现有的cIE方法存在两个局限:(i)它们通常是流水线,容易传播错误,(ii)它们仅限于句子级别,无法捕捉长距离依赖关系,导致推理时间昂贵。为了克服这些局限,我们提出了REXEL,一种高效且准确的文档级别cIE(DocIE)模型。REXEL在单向传递过程中实现提举检测、实体类型、实体歧义、关系分类和文档级别关系,以产生完全链接到参考知识图谱的事实。在类似设置中,REXEL的平均速度是现有方法的11倍,而且在优化任何单个子任务或各种组合任务时,表现出色,超过了基线平均6个F1分。速度和准确性的结合使REXEL成为在网页规模上提取结构化信息的准确且高效系统。我们还发布了DocRED数据集的扩展,以便于未来在DocIE上进行基准测试,该扩展可通过此链接获得。
https://arxiv.org/abs/2404.12788
The widespread use of knowledge graphs in various fields has brought about a challenge in effectively integrating and updating information within them. When it comes to incorporating contexts, conventional methods often rely on rules or basic machine learning models, which may not fully grasp the complexity and fluidity of context information. This research suggests an approach based on reinforcement learning (RL), specifically utilizing Deep Q Networks (DQN) to enhance the process of integrating contexts into knowledge graphs. By considering the state of the knowledge graph as environment states defining actions as operations for integrating contexts and using a reward function to gauge the improvement in knowledge graph quality post-integration, this method aims to automatically develop strategies for optimal context integration. Our DQN model utilizes networks as function approximators, continually updating Q values to estimate the action value function, thus enabling effective integration of intricate and dynamic context information. Initial experimental findings show that our RL method outperforms techniques in achieving precise context integration across various standard knowledge graph datasets, highlighting the potential and effectiveness of reinforcement learning in enhancing and managing knowledge graphs.
在各个领域的知识图谱的广泛应用给有效地整合和更新知识带来了挑战。当涉及到纳入上下文时,传统的做法通常依赖于规则或基本的机器学习模型,这些模型可能无法完全理解上下文信息的复杂性和流动性。这项研究建议了一种基于强化学习(RL)的方法,具体利用深度 Q 网络(DQN)增强将上下文融入知识图谱的过程。通过将知识图谱的状态定义为操作,将知识图谱的状态作为上下文的整合操作,并使用奖励函数衡量知识图谱质量的改善,这种方法旨在自动开发最优上下文整合策略。我们的 DQN 模型使用网络作为函数近似的参数,不断更新 Q 值来估计动作价值函数,从而实现对复杂和动态上下文信息的有效整合。初步实验结果表明,我们的 RL 方法在各种标准知识图谱数据集上实现了精确的上下文整合,突出了强化学习在增强和管理知识图谱中的潜力和有效性。
https://arxiv.org/abs/2404.12587
Joint entity and relation extraction plays a pivotal role in various applications, notably in the construction of knowledge graphs. Despite recent progress, existing approaches often fall short in two key aspects: richness of representation and coherence in output structure. These models often rely on handcrafted heuristics for computing entity and relation representations, potentially leading to loss of crucial information. Furthermore, they disregard task and/or dataset-specific constraints, resulting in output structures that lack coherence. In our work, we introduce EnriCo, which mitigates these shortcomings. Firstly, to foster rich and expressive representation, our model leverage attention mechanisms that allow both entities and relations to dynamically determine the pertinent information required for accurate extraction. Secondly, we introduce a series of decoding algorithms designed to infer the highest scoring solutions while adhering to task and dataset-specific constraints, thus promoting structured and coherent outputs. Our model demonstrates competitive performance compared to baselines when evaluated on Joint IE datasets.
联合实体和关系提取在各种应用中发挥着重要作用,特别是在知识图谱的构建中。尽管最近取得了进展,但现有的方法在两个关键方面往往存在不足:表示的丰富性和输出结构的连贯性。这些方法通常依赖于手工构建的启发式规则计算实体和关系表示,可能导致关键信息的丢失。此外,它们忽视了任务和/或数据集特定的约束,导致输出结构缺乏连贯性。在我们的工作中,我们引入了EnriCo模型,从而缓解了这些不足。首先,为了促进丰富和表现性的表示,我们的模型利用了注意机制,允许实体和关系动态确定所需的相关信息。其次,我们引入了一系列解码算法,旨在在遵守任务和数据集特定约束的情况下推断最高得分解决方案,从而促进结构和连贯的输出。与基线相比,我们的模型在Joint IE数据集上的评估表现具有竞争力。
https://arxiv.org/abs/2404.12493
Entity alignment (EA) aims to find equivalent entities between two Knowledge Graphs. Existing embedding-based EA methods usually encode entities as embeddings, triples as embeddings' constraint and learn to align the embeddings. The structural and side information are usually utilized via embedding propagation, aggregation or interaction. However, the details of the underlying logical inference steps among the alignment process are usually omitted, resulting in inadequate inference process. In this paper, we introduce P-NAL, an entity alignment method that captures two types of logical inference paths with Non-Axiomatic Logic (NAL). Type 1 is the bridge-like inference path between to-be-aligned entity pairs, consisting of two relation/attribute triples and a similarity sentence between the other two entities. Type 2 links the entity pair by their embeddings. P-NAL iteratively aligns entities and relations by integrating the conclusions of the inference paths. Moreover, our method is logically interpretable and extensible due to the expressiveness of NAL. Our proposed method is suitable for various EA settings. Experimental results show that our method outperforms state-of-the-art methods in terms of Hits@1, achieving 0.98+ on all three datasets of DBP15K with both supervised and unsupervised settings. To our knowledge, we present the first in-depth analysis of entity alignment's basic principles from a unified logical perspective.
实体对齐(EA)旨在在两个知识图之间找到等价的实体。现有的基于嵌入的EA方法通常将实体编码为嵌入,关系/属性为嵌入约束,并学会对齐嵌入。通常,结构性和侧信息通过嵌入传播、聚合或交互来利用。然而,在对齐过程中,通常会忽略对逻辑推理步骤的详细说明,导致推理过程不充分。在本文中,我们介绍了P-NAL,一种名为非直观逻辑(NAL)的实体对齐方法,可以捕捉两种逻辑推理路径。类型1是一种桥式推理路径,由两个关系/属性三元组和另外两个实体之间的相似句子组成。类型2通过实体之间的嵌入将实体对链接起来。P-NAL通过整合推理路径的结论来逐步对实体和关系进行对齐。此外,由于NAL的表述力,我们的方法具有逻辑可解释性和可扩展性。我们提出的方法适用于各种知识图对齐设置。实验结果表明,我们的方法在Hits@1方面优于最先进的现有方法,在所有三个人工标注数据集的监督和无监督设置下均实现了0.98+。据我们所知,这是从统一逻辑角度对实体对齐基本原则的第一次深入分析。
https://arxiv.org/abs/2404.11968
A Knowledge Graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse Natural Language Processing (NLP) tasks where knowledge is required. The need to scale up and complete KG automatically yields Knowledge Graph Embedding (KGE), a shallow machine learning model that is suffering from memory and training time consumption issues. To mitigate the computational load, we propose a parameter-sharing method, i.e., using conjugate parameters for complex numbers employed in KGE models. Our method improves memory efficiency by 2x in relation embedding while achieving comparable performance to the state-of-the-art non-conjugate models, with faster, or at least comparable, training time. We demonstrated the generalizability of our method on two best-performing KGE models $5^{\bigstar}\mathrm{E}$ and $\mathrm{ComplEx}$ on five benchmark datasets.
知识图(KG)是现实世界中实体和关系的有向图表示。KG 可以在各种自然语言处理(NLP)任务中应用,其中需要知识。对 KG 进行扩展和完成导致知识图嵌入(KGE),一种浅层机器学习模型,存在内存和训练时间消耗问题。为了减轻计算负担,我们提出了一个参数共享方法,即使用共轭参数来处理 KGE 模型中使用的复杂数。我们的方法通过在关系嵌入中实现 2x 的内存效率,同时实现与最先进的非共轭模型的性能相似,具有更快的训练时间。我们在五个基准数据集上证明了我们方法的泛化能力。 我们展示了在两个最佳表现的知识图嵌入模型 $5^{\bigstar}\mathrm{E}$ 和 $\mathrm{ComplEx}$ 上,我们方法的泛化能力。
https://arxiv.org/abs/2404.11809
Large language models (LLMs), such as GPT3.5, GPT4 and LLAMA2 perform surprisingly well and outperform human experts on many tasks. However, in many domain-specific evaluations, these LLMs often suffer from hallucination problems due to insufficient training of relevant corpus. Furthermore, fine-tuning large models may face problems such as the LLMs are not open source or the construction of high-quality domain instruction is difficult. Therefore, structured knowledge databases such as knowledge graph can better provide domain back- ground knowledge for LLMs and make full use of the reasoning and analysis capabilities of LLMs. In some previous works, LLM was called multiple times to determine whether the current triplet was suitable for inclusion in the subgraph when retrieving subgraphs through a question. Especially for the question that require a multi-hop reasoning path, frequent calls to LLM will consume a lot of computing power. Moreover, when choosing the reasoning path, LLM will be called once for each step, and if one of the steps is selected incorrectly, it will lead to the accumulation of errors in the following steps. In this paper, we integrated and optimized a pipeline for selecting reasoning paths from KG based on LLM, which can reduce the dependency on LLM. In addition, we propose a simple and effective subgraph retrieval method based on chain of thought (CoT) and page rank which can returns the paths most likely to contain the answer. We conduct experiments on three datasets: GenMedGPT-5k [14], WebQuestions [2], and CMCQA [21]. Finally, RoK can demonstrate that using fewer LLM calls can achieve the same results as previous SOTAs models.
大语言模型(LLMs),如GPT3.5、GPT4和LLAMA2在许多任务上表现出色,甚至超过了人类专家。然而,在许多领域特定评估中,由于相关语料库训练不足,这些LLMs经常受到虚构问题(halo effect)的困扰。此外,对于需要多级推理路径的问题,频繁调用LLM会消耗大量计算资源。在选择推理路径时,LLM将被调用一次每个步骤,如果步骤选择错误,将会导致后续步骤中错误积累。在本文中,我们基于LLM优化和选择了推理路径的管道,从而减少了LLM的依赖。此外,我们提出了一个基于连锁思维(CoT)和页面排名的简单而有效的子图检索方法,可以返回最可能包含答案的路径。我们对三个数据集:GenMedGPT-5k [14]、WebQuestions [2]和CMCQA [21]进行了实验。最后,RoK证明了使用更少的LLM调用可以实现与之前的最优SOTA模型相同的结果。
https://arxiv.org/abs/2404.10384
The automatic recognition of tabular data in document images presents a significant challenge due to the diverse range of table styles and complex structures. Tables offer valuable content representation, enhancing the predictive capabilities of various systems such as search engines and Knowledge Graphs. Addressing the two main problems, namely table detection (TD) and table structure recognition (TSR), has traditionally been approached independently. In this research, we propose an end-to-end pipeline that integrates deep learning models, including DETR, CascadeTabNet, and PP OCR v2, to achieve comprehensive image-based table recognition. This integrated approach effectively handles diverse table styles, complex structures, and image distortions, resulting in improved accuracy and efficiency compared to existing methods like Table Transformers. Our system achieves simultaneous table detection (TD), table structure recognition (TSR), and table content recognition (TCR), preserving table structures and accurately extracting tabular data from document images. The integration of multiple models addresses the intricacies of table recognition, making our approach a promising solution for image-based table understanding, data extraction, and information retrieval applications. Our proposed approach achieves an IOU of 0.96 and an OCR Accuracy of 78%, showcasing a remarkable improvement of approximately 25% in the OCR Accuracy compared to the previous Table Transformer approach.
表格数据在文档图像中的自动识别是一个具有重大挑战性的任务,因为表格样式和复杂结构具有多样性。表格提供了有价值的内容表示,增强了搜索引擎和知识图谱等系统预测能力。传统上,解决表格检测(TD)和表格结构识别(TSR)问题通常是独立处理。在本次研究中,我们提出了一种端到端的管道,整合了包括DETR、CascadeTabNet和PP OCR v2在内的人工智能模型,以实现全面的基于图像的表格识别。这种集成方法有效地处理了多样性的表格样式、复杂结构和图像畸变,使得与现有方法(如Table Transformers)相比,准确性和效率都得到了提高。我们的系统同时实现了表格检测(TD)、表格结构识别(TSR)和表格内容识别(TCR),保留了表格结构并准确从文档图像中提取表格数据。整合多个模型解决了表格识别的复杂性,使我们的方法成为图像为基础的表格理解、数据提取和信息检索应用的的有前景的解决方案。与前Table Transformer方法相比,我们提出的 approach 的 OCR Accuracy 提高了近25%。
https://arxiv.org/abs/2404.10305
Knowledge Graph Completion (KGC) has emerged as a promising solution to address the issue of incompleteness within Knowledge Graphs (KGs). Traditional KGC research primarily centers on triple classification and link prediction. Nevertheless, we contend that these tasks do not align well with real-world scenarios and merely serve as surrogate benchmarks. In this paper, we investigate three crucial processes relevant to real-world construction scenarios: (a) the verification process, which arises from the necessity and limitations of human verifiers; (b) the mining process, which identifies the most promising candidates for verification; and (c) the training process, which harnesses verified data for subsequent utilization; in order to achieve a transition toward more realistic challenges. By integrating these three processes, we introduce the Progressive Knowledge Graph Completion (PKGC) task, which simulates the gradual completion of KGs in real-world scenarios. Furthermore, to expedite PKGC processing, we propose two acceleration modules: Optimized Top-$k$ algorithm and Semantic Validity Filter. These modules significantly enhance the efficiency of the mining procedure. Our experiments demonstrate that performance in link prediction does not accurately reflect performance in PKGC. A more in-depth analysis reveals the key factors influencing the results and provides potential directions for future research.
知识图谱完成(KGC)作为一种解决知识图谱(KG)中不完整性问题的有益解决方案,已经引起了研究人员的关注。传统的KGC研究主要集中在三元分类和链接预测。然而,我们认为这些任务并不符合现实世界的场景,仅仅作为替代指标。在本文中,我们研究了与现实世界构建场景相关的三个关键过程:(a)验证过程,这是由于人类验证者必要性和限制而产生的;(b)挖掘过程,它确定了最有前途的验证候选者;(c)训练过程,它利用验证数据进行后续利用。为了实现更真实的挑战,我们将这三个过程整合起来,引入了渐进式知识图谱完成(PKGC)任务,该任务在现实世界的场景中模拟KG的逐步完成。此外,为了加速PKGC处理,我们提出了两个加速模块:优化前k个算法和语义有效性过滤器。这些模块显著提高了挖掘过程的效率。我们的实验结果表明,链接预测的性能并不能准确反映PKGC的性能。更详细的分析揭示了影响结果的关键因素,并为未来的研究提供了潜在方向。
https://arxiv.org/abs/2404.09897
In a hyper-relational knowledge graph (HKG), each fact is composed of a main triple associated with attribute-value qualifiers, which express additional factual knowledge. The hyper-relational knowledge graph completion (HKGC) task aims at inferring plausible missing links in a HKG. Most existing approaches to HKGC focus on enhancing the communication between qualifier pairs and main triples, while overlooking two important properties that emerge from the monotonicity of the hyper-relational graphs representation regime. Stage Reasoning allows for a two-step reasoning process, facilitating the integration of coarse-grained inference results derived solely from main triples and fine-grained inference results obtained from hyper-relational facts with qualifiers. In the initial stage, coarse-grained results provide an upper bound for correct predictions, which are subsequently refined in the fine-grained step. More generally, Qualifier Monotonicity implies that by attaching more qualifier pairs to a main triple, we may only narrow down the answer set, but never enlarge it. This paper proposes the HyperMono model for hyper-relational knowledge graph completion, which realizes stage reasoning and qualifier monotonicity. To implement qualifier monotonicity HyperMono resorts to cone embeddings. Experiments on three real-world datasets with three different scenario conditions demonstrate the strong performance of HyperMono when compared to the SoTA.
在超关系知识图(HKG)中,每个事实由与属性值定语相关的主要三元组组成,这些定语表示附加事实知识。超关系知识图完成(HKGC)任务的目的是推断HKG中的可能缺失链接。几乎所有现有的HKGC方法都关注于增强定语对之间以及主要三元组之间的通信,而忽略了从超关系图表示范式的单调性产生的两个重要属性。阶段推理允许进行两次推理过程,促进仅从主要三元组获得粗粒度推理结果以及仅从具有定语的知识图获得细粒度推理结果的整合。在初始阶段,粗粒度结果提供正确预测的上限,然后在细粒度阶段进行进一步的优化。更一般地说,定语单调性意味着,将更多的定语与主要三元组相关联,我们只能缩小答案集,但永远不会扩大它。本文提出了超Mono模型,用于超关系知识图完成,实现了阶段推理和定语单调性。为了实现定语单调性,超Mono求助于锥体嵌入。在三个真实世界数据集上进行三个不同情景条件的实验,证明了超Mono与SoTA之间的强烈性能。
https://arxiv.org/abs/2404.09848
Citation Text Generation (CTG) is a task in natural language processing (NLP) that aims to produce text that accurately cites or references a cited document within a source document. In CTG, the generated text draws upon contextual cues from both the source document and the cited paper, ensuring accurate and relevant citation information is provided. Previous work in the field of citation generation is mainly based on the text summarization of documents. Following this, this paper presents a framework, and a comparative study to demonstrate the use of Large Language Models (LLMs) for the task of citation generation. Also, we have shown the improvement in the results of citation generation by incorporating the knowledge graph relations of the papers in the prompt for the LLM to better learn the relationship between the papers. To assess how well our model is performing, we have used a subset of standard S2ORC dataset, which only consists of computer science academic research papers in the English Language. Vicuna performs best for this task with 14.15 Meteor, 12.88 Rouge-1, 1.52 Rouge-2, and 10.94 Rouge-L. Also, Alpaca performs best, and improves the performance by 36.98% in Rouge-1, and 33.14% in Meteor by including knowledge graphs.
参考文献文本生成(CTG)是自然语言处理(NLP)领域的一个任务,旨在生成在源文档中准确引用或参考引用文档的文本。在CTG中,生成的文本从源文档和引用的论文的上下文上下文信息中获取上下文提示,确保提供准确和相关的引用信息。该领域之前的工作主要基于文档的文本摘要。本文提出了一个框架和比较研究,以证明大型语言模型(LLMs)在引用生成任务中的应用。我们还通过将LLM的纸张知识图谱关系融入提示中,展示了引用生成结果的改善。为了评估我们的模型表现,我们使用了一个仅包含英语语言计算机科学学术研究论文的标准化S2ORC数据集。Vicuna在任务中表现最佳,达到14.15 Meteor,12.88 Rouge-1,1.52 Rouge-2和10.94 Rouge-L。此外,Alpaca也表现最佳,通过包括知识图谱提高了Rouge-1和Meteor的性能。
https://arxiv.org/abs/2404.09763
Tourism is one of the most critical sectors of the global economy. Due to its heterogeneous and fragmented nature, it provides one of the most suitable use cases for knowledge graphs. In this poster, we introduce the German Tourism Knowledge Graph that integrates tourism-related data from 16 federal states of Germany and various other sources to provide a curated knowledge source for various applications. It is publicly available through GUIs and an API.
旅游是全球经济中最关键的领域之一。由于其异质和分散的特点,它为知识图谱提供了一个最合适的应用场景。在这张海报上,我们介绍了德国旅游知识图谱,该知识图谱整合了德国16个联邦州以及各种其他来源的旅游相关数据,为各种应用提供了一个经过编选的知识资源。该知识图谱可通过GUIs和API公开访问。
https://arxiv.org/abs/2404.09587