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
Multi-modal knowledge graphs (MMKG) store structured world knowledge containing rich multi-modal descriptive information. To overcome their inherent incompleteness, multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given MMKGs, leveraging both structural information from the triples and multi-modal information of the entities. Existing MMKGC methods usually extract multi-modal features with pre-trained models and employ a fusion module to integrate multi-modal features with triple prediction. However, this often results in a coarse handling of multi-modal data, overlooking the nuanced, fine-grained semantic details and their interactions. To tackle this shortfall, we introduce a novel framework MyGO to process, fuse, and augment the fine-grained modality information from MMKGs. MyGO tokenizes multi-modal raw data as fine-grained discrete tokens and learns entity representations with a cross-modal entity encoder. To further augment the multi-modal representations, MyGO incorporates fine-grained contrastive learning to highlight the specificity of the entity representations. Experiments on standard MMKGC benchmarks reveal that our method surpasses 20 of the latest models, underlining its superior performance. Code and data are available at this https URL
多模态知识图(MMKG)存储了包含丰富多模态描述信息的结构化世界知识。为了克服其固有不完整性,多模态知识图完成(MMKGC)旨在从给定的MMKG中发掘未观察到的知识,并利用三元组结构和实体多模态信息。现有的MMKGC方法通常使用预训练模型提取多模态特征,并采用融合模块将多模态特征与三元组预测集成。然而,这通常导致对多模态数据的粗略处理,忽视了细微、细粒度的语义细节及其相互作用。为了应对这一缺陷,我们引入了一个名为MyGO的新框架来处理、融合和增强MMKG中的细粒度模态信息。MyGO将多模态原始数据划分为细粒度离散标记,并使用跨模态实体编码器学习实体表示。为了进一步增强多模态表示,MyGO引入了细粒度对比学习来突出实体表示的特定性。在标准MMKGC基准测试上进行的实验显示,我们的方法超越了最新的20个模型,强调了其卓越的性能。代码和数据可在此链接https://进行访问
https://arxiv.org/abs/2404.09468
In this paper, we explore the application of cognitive intelligence in legal knowledge, focusing on the development of judicial artificial intelligence. Utilizing natural language processing (NLP) as the core technology, we propose a method for the automatic construction of case knowledge graphs for judicial cases. Our approach centers on two fundamental NLP tasks: entity recognition and relationship extraction. We compare two pre-trained models for entity recognition to establish their efficacy. Additionally, we introduce a multi-task semantic relationship extraction model that incorporates translational embedding, leading to a nuanced contextualized case knowledge representation. Specifically, in a case study involving a "Motor Vehicle Traffic Accident Liability Dispute," our approach significantly outperforms the baseline model. The entity recognition F1 score improved by 0.36, while the relationship extraction F1 score increased by 2.37. Building on these results, we detail the automatic construction process of case knowledge graphs for judicial cases, enabling the assembly of knowledge graphs for hundreds of thousands of judgments. This framework provides robust semantic support for applications of judicial AI, including the precise categorization and recommendation of related cases.
在本文中,我们探讨了在法律知识中的应用认知智能,重点是发展司法人工智能。利用自然语言处理(NLP)作为核心技术,我们提出了一个自动构建案件知识图谱的方法,用于司法案例。我们的方法以两个基本的NLP任务为基础:实体识别和关系提取。我们比较了两个预训练模型,以评估它们的有效性。此外,我们还引入了一个多任务语义关系提取模型,该模型包括翻译嵌入,导致了一种细微的上下文化案例知识表示。具体来说,在涉及"机动车交通事故责任纠纷"的案件研究中,我们的方法显著优于基线模型。实体识别的F1得分提高了0.36,关系提取的F1得分增加了2.37。基于这些结果,我们详细描述了为司法案例自动构建知识图谱的过程,使得知识图谱可以组装成数百万个判决。这个框架为司法人工智能的应用提供了稳健的语义支持,包括对相关案例的准确分类和推荐。
https://arxiv.org/abs/2404.09416
In today's rapidly evolving landscape of Artificial Intelligence, large language models (LLMs) have emerged as a vibrant research topic. LLMs find applications in various fields and contribute significantly. Despite their powerful language capabilities, similar to pre-trained language models (PLMs), LLMs still face challenges in remembering events, incorporating new information, and addressing domain-specific issues or hallucinations. To overcome these limitations, researchers have proposed Retrieval-Augmented Generation (RAG) techniques, some others have proposed the integration of LLMs with Knowledge Graphs (KGs) to provide factual context, thereby improving performance and delivering more accurate feedback to user queries. Education plays a crucial role in human development and progress. With the technology transformation, traditional education is being replaced by digital or blended education. Therefore, educational data in the digital environment is increasing day by day. Data in higher education institutions are diverse, comprising various sources such as unstructured/structured text, relational databases, web/app-based API access, etc. Constructing a Knowledge Graph from these cross-data sources is not a simple task. This article proposes a method for automatically constructing a Knowledge Graph from multiple data sources and discusses some initial applications (experimental trials) of KG in conjunction with LLMs for question-answering tasks.
在今天快速发展的人工智能环境中,大型语言模型(LLMs)已成为一个充满活力的研究课题。LLMs在各种领域得到了应用,并发挥了重要作用。尽管它们具有强大的语言能力,与预训练语言模型(PLMs)相似,但LLMs仍然面临着记忆事件、融入新信息和解决领域特定问题或幻觉等挑战。为了克服这些限制,研究人员提出了 Retrieval-Augmented Generation(RAG)技术,其他一些人则提出了将LLM与知识图(KG)集成以提供事实背景的方法,从而提高性能并给用户提供更准确的反馈。教育在人类发展和进步中扮演着关键角色。随着技术的发展,传统教育正在被数字或混合教育所取代。因此,数字环境中的教育数据每天都在不断增加。高等教育机构的数据是多样化的,包括各种来源,如无结构/结构化文本、关系数据库、网页/应用API访问等。构建这些跨数据来源的知识图并非易事。本文提出了一种自动从多个数据源构建知识图的方法,并讨论了LLM与KG在问答任务中的初始应用(实验性试验)。
https://arxiv.org/abs/2404.09296
Knowledge Graph (KG) is a graph based data structure to represent facts of the world where nodes represent real world entities or abstract concept and edges represent relation between the entities. Graph as representation for knowledge has several drawbacks like data sparsity, computational complexity and manual feature engineering. Knowledge Graph embedding tackles the drawback by representing entities and relation in low dimensional vector space by capturing the semantic relation between them. There are different KG embedding models. Here, we discuss translation based and neural network based embedding models which differ based on semantic property, scoring function and architecture they use. Further, we discuss application of KG in some domains that use deep learning models and leverage social media data.
知识图(KG)是一种基于图的数据结构,用于表示现实世界中的事实,节点表示现实世界实体或抽象概念,边表示实体之间的关系。作为一种知识表示方式,图形存在一些缺点,如数据稀疏性、计算复杂度和手动特征工程等。知识图嵌入通过将实体和关系表示为低维向量空间中的语义关系来解决这些缺点。知识图嵌入模型根据语义特征、评分函数和架构的不同,可以分为不同的模型。在这里,我们讨论了基于翻译和基于神经网络的嵌入模型,这些模型基于它们的语义特征、评分函数和架构的不同而有所不同。此外,我们讨论了将知识图应用于使用深度学习模型的一些领域,并利用社交媒体数据。
https://arxiv.org/abs/2404.09167
Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task. However, we found that inherent heterogeneity among factor tensors in tensor decomposition significantly hinders the tensor fusion process and further limits the performance of link prediction. To overcome this limitation, we introduce a novel method that maps factor tensors onto a unified smooth Lie group manifold to make the distribution of factor tensors approximating homogeneous in tensor decomposition. We provide the theoretical proof of our motivation that homogeneous tensors are more effective than heterogeneous tensors in tensor fusion and approximating the target for tensor decomposition based TKGE methods. The proposed method can be directly integrated into existing tensor decomposition based TKGE methods without introducing extra parameters. Extensive experiments demonstrate the effectiveness of our method in mitigating the heterogeneity and in enhancing the tensor decomposition based TKGE models.
近年来,研究表明在 Temporal Knowledge Graph Embedding(TKGE)任务中,张量分解方法具有显著的有效性。然而,我们发现,在张量分解中,因素张量的固有异质性严重阻碍了张量融合过程,并进一步限制了链路预测的性能。为了克服这一局限,我们引入了一种新的方法,将因素张量映射到统一的平滑李群流形上,使因素张量的分布在张量分解中近似均匀。我们提供了基于 TKGE 方法的动机,即均匀张量比异质张量在张量融合和基于 TKGE 的目标建模中更有效。所提出的方法可以直接集成到现有的基于 TKGE 的张量分解方法中,而无需引入额外的参数。大量实验证明,我们的方法在减轻异质性和增强基于 TKGE 的张量分解模型方面具有有效性。
https://arxiv.org/abs/2404.09155
In the field of Question Answering (QA), unifying large language models (LLMs) with external databases has shown great success. However, these methods often fall short in providing the advanced reasoning needed for complex QA tasks. To address these issues, we improve over a novel approach called Knowledge Graph Prompting (KGP), which combines knowledge graphs with a LLM-based agent to improve reasoning and search accuracy. Nevertheless, the original KGP framework necessitates costly fine-tuning with large datasets yet still suffers from LLM hallucination. Therefore, we propose a reasoning-infused LLM agent to enhance this framework. This agent mimics human curiosity to ask follow-up questions to more efficiently navigate the search. This simple modification significantly boosts the LLM performance in QA tasks without the high costs and latency associated with the initial KGP framework. Our ultimate goal is to further develop this approach, leading to more accurate, faster, and cost-effective solutions in the QA domain.
在问题回答(QA)领域,将大型语言模型(LLMs)与外部数据库统一的做法取得了巨大的成功。然而,这些方法在提供复杂QA任务所需的高级推理方面常常不足。为解决这些问题,我们改进了一种名为知识图谱提示(KGP)的新方法,该方法将知识图谱与基于LLM的代理相结合以提高推理和搜索精度。然而,原始KGP框架需要对大量数据进行昂贵的微调,但仍存在LLM幻觉的问题。因此,我们提出了一个基于推理的LLM代理以增强这一框架。这个代理模仿人类的好奇心,以更有效地引导搜索。这样的简单修改在不需要初始KGP框架的高昂成本和延迟的情况下显著提高了LLM在QA任务中的性能。我们的最终目标是进一步发展这种方法,为QA领域提供更准确、更快、更经济有效的解决方案。
https://arxiv.org/abs/2404.09077
The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Recent works only utilize the \textit{\textbf{structural knowledge}} in the local neighborhood of entities, disregarding \textit{\textbf{semantic knowledge}} in the textual representations of entities, relations, and types that are also crucial for type inference. Additionally, we observe that the interaction between semantic and structural knowledge can be utilized to address the false-negative problem. In this paper, we propose a novel \textbf{\underline{S}}emantic and \textbf{\underline{S}}tructure-aware KG \textbf{\underline{E}}ntity \textbf{\underline{T}}yping~{(SSET)} framework, which is composed of three modules. First, the \textit{Semantic Knowledge Encoding} module encodes factual knowledge in the KG with a Masked Entity Typing task. Then, the \textit{Structural Knowledge Aggregation} module aggregates knowledge from the multi-hop neighborhood of entities to infer missing types. Finally, the \textit{Unsupervised Type Re-ranking} module utilizes the inference results from the two models above to generate type predictions that are robust to false-negative samples. Extensive experiments show that SSET significantly outperforms existing state-of-the-art methods.
知识图实体类型标注(KGET)任务的目的是预测知识图中实体的缺失类型标注。最近的工作仅利用实体局部邻域中的结构化知识,而忽略了文本表示中实体、关系和类型也至关重要用于类型推理的语义知识。此外,我们还观察到语义和结构化知识的相互作用可以用于解决假阴性问题。在本文中,我们提出了一个新颖的语义和结构感知的知识图实体类型标注(SSET)框架,它由三个模块组成。首先,\textit{语义知识编码}模块通过遮罩实体类型标注任务对知识图进行语义化知识编码。然后,\textit{结构化知识聚合}模块将来自实体多级邻域的知识进行聚合,以推断缺失类型。最后,\textit{无监督类型重新排序}模块利用上述两个模型的推理结果生成对假阴性样本鲁棒的类型预测。大量实验证明,SSET显著优于现有最先进的 methods。
https://arxiv.org/abs/2404.08313
The integration of Large Language Models (LLMs) and knowledge graphs (KGs) has achieved remarkable success in various natural language processing tasks. However, existing methodologies that integrate LLMs and KGs often navigate the task-solving process solely based on the LLM's analysis of the question, overlooking the rich cognitive potential inherent in the vast knowledge encapsulated in KGs. To address this, we introduce Observation-Driven Agent (ODA), a novel AI agent framework tailored for tasks involving KGs. ODA incorporates KG reasoning abilities via global observation that enhances reasoning capabilities through a cyclical paradigm of observation, action, and reflection. Confronting the exponential explosion of knowledge during observation, we innovatively design a recursive observation mechanism. Subsequently, we integrate the observed knowledge into the action and reflection modules. Through extensive experiments, ODA demonstrates state-of-the-art performance on several datasets, notably achieving accuracy improvements of 12.87% and 8.9%.
大规模语言模型(LLMs)与知识图(KGs)的集成在各种自然语言处理任务中取得了显著的成功。然而,现有的将LLMs与KGs集成的方法往往仅基于LLM对问题的分析来解决问题,而忽略了KGs中蕴含的丰富认知潜力。为解决这个问题,我们引入了观察驱动的智能体(ODA),一种专门针对涉及KGs的任务的AI框架。ODA通过通过全局观察来增强推理能力,采用观察、动作和反思的循环模式来解决观察膨胀的问题。在观察爆炸的过程中,我们创新地设计了一个递归的观察机制。然后,我们将观察到的知识集成到动作和反思模块中。通过大量实验,ODA在多个数据集上表现出与最先进方法相当的表现,尤其是在准确性方面,提高了12.87%和8.9%。
https://arxiv.org/abs/2404.07677
Knowledge graphs are useful tools to organize, recommend and sort data. Hierarchies in knowledge graphs provide significant benefit in improving understanding and compartmentalization of the data within a knowledge graph. This work leverages large language models to generate and augment hierarchies in an existing knowledge graph. For small (<100,000 node) domain-specific KGs, we find that a combination of few-shot prompting with one-shot generation works well, while larger KG may require cyclical generation. We present techniques for augmenting hierarchies, which led to coverage increase by 98% for intents and 99% for colors in our knowledge graph.
知识图谱是有用的一些工具来组织、推荐和排序数据。知识图谱中的层次结构在提高数据在知识图谱中的理解和划分方面具有显著的优势。这项工作利用了大型语言模型生成和增强现有知识图谱中的层次结构。对于小(<100,000个节点)领域特定的KG,我们发现少数shot提示与一次生成相结合效果很好,而较大的KG可能需要循环生成。我们提出了增强层次结构的技术,在知识图中使意图和颜色的覆盖率分别增加了98%和99%。
https://arxiv.org/abs/2404.08020
Recently, large language models (LLMs) have demonstrated remarkable potential as an intelligent agent. However, existing researches mainly focus on enhancing the agent's reasoning or decision-making abilities through well-designed prompt engineering or task-specific fine-tuning, ignoring the procedure of exploration and exploitation. When addressing complex tasks within open-world interactive environments, these methods exhibit limitations. Firstly, the lack of global information of environments leads to greedy decisions, resulting in sub-optimal solutions. On the other hand, irrelevant information acquired from the environment not only adversely introduces noise, but also incurs additional cost. This paper proposes a novel approach, Weak Exploration to Strong Exploitation (WESE), to enhance LLM agents in solving open-world interactive tasks. Concretely, WESE involves decoupling the exploration and exploitation process, employing a cost-effective weak agent to perform exploration tasks for global knowledge. A knowledge graph-based strategy is then introduced to store the acquired knowledge and extract task-relevant knowledge, enhancing the stronger agent in success rate and efficiency for the exploitation task. Our approach is flexible enough to incorporate diverse tasks, and obtains significant improvements in both success rates and efficiency across four interactive benchmarks.
近年来,大型语言模型(LLMs)已经在智能代理领域取得了显著的潜力。然而,现有的研究主要关注通过精心设计的问题工程或任务特定微调来增强代理的推理或决策能力,而忽略了探索和利用的过程。当处理开放世界交互环境中的复杂任务时,这些方法表现出局限性。首先,环境的全局信息缺乏导致贪心决策,导致最优解。另一方面,从环境中获得的无关信息不仅带来了噪声,而且还会造成额外的代价。本文提出了一种新颖的方法,即弱探索强利用(WESE),以提高LLM代理在解决开放世界交互任务中的性能。具体来说,WESE包括解耦探索和利用过程,使用一种成本效益的弱代理执行全局知识探索任务。然后引入一个知识图,用于存储获得的知识并提取任务相关的知识,从而增强成功率和效率较强的代理在探索任务中的表现。我们的方法足够灵活,可以涵盖各种任务,并且在四个交互基准测试中都取得了显著的改进。
https://arxiv.org/abs/2404.07456
Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and logical operations. Existing CLQA methods that learn parameters bound to certain entity or relation vocabularies can only be applied to the graph they are trained on which requires substantial training time before being deployed on a new graph. Here we present UltraQuery, an inductive reasoning model that can zero-shot answer logical queries on any KG. The core idea of UltraQuery is to derive both projections and logical operations as vocabulary-independent functions which generalize to new entities and relations in any KG. With the projection operation initialized from a pre-trained inductive KG reasoning model, UltraQuery can solve CLQA on any KG even if it is only finetuned on a single dataset. Experimenting on 23 datasets, UltraQuery in the zero-shot inference mode shows competitive or better query answering performance than best available baselines and sets a new state of the art on 14 of them.
知识图(KG)中的复杂逻辑查询(CLQA)超越了简单的KG完成,旨在回答由多个投影和逻辑操作组成的复合查询。现有的CLQA方法只能应用于它们所训练的图,这需要在新图上进行大量训练时间才能部署。在这里,我们提出了UltraQuery,一种归纳推理模型,可以在任何KG上零 shots地回答逻辑查询。UltraQuery的核心思想是将投影和逻辑操作作为独立于词汇的函数,扩展到任何KG中的新实体和关系。通过从预训练的归纳KG推理模型中初始化投影操作,UltraQuery可以在仅针对单个数据集微调的情况下解决CLQA。在23个数据集上的实验表明,UltraQuery在零 shot推理模式下具有与最佳现有基线竞争或更好的查询回答性能,其中14个数据集的性能达到了当前最先进的水平。
https://arxiv.org/abs/2404.07198
Concept-based explainable AI is promising as a tool to improve the understanding of complex models at the premises of a given user, viz.\ as a tool for personalized explainability. An important class of concept-based explainability methods is constructed with empirically defined concepts, indirectly defined through a set of positive and negative examples, as in the TCAV approach (Kim et al., 2018). While it is appealing to the user to avoid formal definitions of concepts and their operationalization, it can be challenging to establish relevant concept datasets. Here, we address this challenge using general knowledge graphs (such as, e.g., Wikidata or WordNet) for comprehensive concept definition and present a workflow for user-driven data collection in both text and image domains. The concepts derived from knowledge graphs are defined interactively, providing an opportunity for personalization and ensuring that the concepts reflect the user's intentions. We test the retrieved concept datasets on two concept-based explainability methods, namely concept activation vectors (CAVs) and concept activation regions (CARs) (Crabbe and van der Schaar, 2022). We show that CAVs and CARs based on these empirical concept datasets provide robust and accurate explanations. Importantly, we also find good alignment between the models' representations of concepts and the structure of knowledge graphs, i.e., human representations. This supports our conclusion that knowledge graph-based concepts are relevant for XAI.
基于概念的合理解释AI作为提高特定用户基于模型的理解的有前途的工具,例如作为个性化的合理解释工具。一类基于概念的合理解释方法是通过经验定义的概念,通过一系列正面和负面例子间接定义,如TCAV方法(Kim et al., 2018)构建的。虽然用户希望避免概念及其操作的正式定义,但建立相关概念数据集仍然具有挑战性。在这里,我们通过综合知识图(如Wikidata或WordNet)进行全面的 concepts 定义,并呈现了在文本和图像领域中用户驱动数据收集的工作流程。从知识图中获得的 concepts 是交互式定义的,为个性化提供了机会,并确保概念反映了用户的意图。我们在两个概念基于 explainability 方法上测试检索到的概念数据集:概念激活矢量(CAVs)和概念激活区域(CARs)(Crabbe 和 van der Schaar, 2022)。我们证明了基于这些经验概念数据的 CAVs 和 CARs 提供了一种可靠且准确的解释。重要的是,我们还发现模型对概念的表示与知识图的结构之间存在良好的对应关系,即人机表示。这支持了我们关于知识图概念对于 XAI 的结论。
https://arxiv.org/abs/2404.07008
The purpose of emotion-cause pair extraction is to extract the pair of emotion clauses and cause clauses. On the one hand, the existing methods do not take fully into account the relationship between the emotion extraction of two auxiliary tasks. On the other hand, the existing two-stage model has the problem of error propagation. In addition, existing models do not adequately address the emotion and cause-induced locational imbalance of samples. To solve these problems, an end-to-end multitasking model (MM-ECPE) based on shared interaction between GRU, knowledge graph and transformer modules is proposed. Furthermore, based on MM-ECPE, in order to use the encoder layer to better solve the problem of imbalanced distribution of clause distances between clauses and emotion clauses, we propose a novel encoding based on BERT, sentiment lexicon, and position-aware interaction module layer of emotion motif pair retrieval model (MM-ECPE(BERT)). The model first fully models the interaction between different tasks through the multi-level sharing module, and mines the shared information between emotion-cause pair extraction and the emotion extraction and cause extraction. Second, to solve the imbalanced distribution of emotion clauses and cause clauses problem, suitable labels are screened out according to the knowledge graph path length and task-specific features are constructed so that the model can focus on extracting pairs with corresponding emotion-cause relationships. Experimental results on the ECPE benchmark dataset show that the proposed model achieves good performance, especially on position-imbalanced samples.
情感词对提取的目的是提取情感短语和原因短语。一方面,现有的方法没有充分考虑两个自辅助任务之间的情感提取关系。另一方面,现有的两阶段模型存在错误传播问题。此外,现有的模型没有充分解决样本情感和原因诱导的局部不平衡问题。为解决这些问题,我们提出了一个基于GRU、知识图和Transformer模块的端到端多任务模型(MM-ECPE)。 此外,基于MM-ECPE,为了更好地利用编码器层解决词汇表征层之间短语距离的不平衡问题,我们提出了一个基于BERT、情感词汇和位置感知交互模块的情感短语对检索模型(MM-ECPE(BERT))的新编码器层。 模型首先通过多级共享模块全面建模不同任务之间的交互,并挖掘情感词对提取和情感提取及原因提取之间的共享信息。然后,为解决情感短语和原因短语的不平衡分布问题,根据知识图路径长度和任务特定特征筛选出适当的标签,以便模型集中精力提取相应情感词对之间的关系。在ECPE基准数据集的实验结果中,与现有模型相比,所提出的模型在位置不平衡样本上的表现良好。
https://arxiv.org/abs/2404.06812
Sourcing and identification of new manufacturing partners is crucial for manufacturing system integrators to enhance agility and reduce risk through supply chain diversification in the global economy. The advent of advanced large language models has captured significant interest, due to their ability to generate comprehensive and articulate responses across a wide range of knowledge domains. However, the system often falls short in accuracy and completeness when responding to domain-specific inquiries, particularly in areas like manufacturing service discovery. This research explores the potential of leveraging Knowledge Graphs in conjunction with ChatGPT to streamline the process for prospective clients in identifying small manufacturing enterprises. In this study, we propose a method that integrates bottom-up ontology with advanced machine learning models to develop a Manufacturing Service Knowledge Graph from an array of structured and unstructured data sources, including the digital footprints of small-scale manufacturers throughout North America. The Knowledge Graph and the learned graph embedding vectors are leveraged to tackle intricate queries within the digital supply chain network, responding with enhanced reliability and greater interpretability. The approach highlighted is scalable to millions of entities that can be distributed to form a global Manufacturing Service Knowledge Network Graph that can potentially interconnect multiple types of Knowledge Graphs that span industry sectors, geopolitical boundaries, and business domains. The dataset developed for this study, now publicly accessible, encompasses more than 13,000 manufacturers' weblinks, manufacturing services, certifications, and location entity types.
采购和识别新制造商合作伙伴对全球经济中的供应链多元化至关重要,这可以提高制造系统集成商的敏捷性,并通过供应链多元化提高风险降低。先进的大型语言模型的出现引起了广泛关注,因为它们能够生成全面且明确的回答,涵盖广泛的领域知识。然而,当回答领域特定问题时,系统往往存在准确性和完整性不足的情况,特别是在制造业服务发现领域。这项研究探讨了在知识图谱与 ChatGPT 的结合下,简化潜在客户在识别小制造企业过程中的可能性。 在本研究中,我们提出了一种方法,将自下而上的本体与先进机器学习模型相结合,从包括北美地区小型制造商的数字足迹在内的一系列结构和非结构化数据源中开发出制造业服务知识图。知识图和学到的图嵌入向量被用来处理数字供应链网络中的复杂查询,并回应提高可靠性和增强可解释性的答案。 所提出的方法具有可扩展性,可以将数百万实体分配到形成一个全球制造业服务知识网络图,这个网络图可能连接多个跨越行业部门、地理政治边界和企业领域的知识图。 为这项研究创建的数据集,现已成为公开可访问的数据库,包括13,000多个制造商网站、制造业服务、认证和位置实体类型。
https://arxiv.org/abs/2404.06571
Adverse drug reactions considerably impact patient outcomes and healthcare costs in cancer therapy. Using artificial intelligence to predict adverse drug reactions in real time could revolutionize oncology treatment. This study aims to assess the performance of artificial intelligence models in predicting adverse drug reactions in patients with cancer. This is the first systematic review and meta-analysis. Scopus, PubMed, IEEE Xplore, and ACM Digital Library databases were searched for studies in English, French, and Arabic from January 1, 2018, to August 20, 2023. The inclusion criteria were: (1) peer-reviewed research articles; (2) use of artificial intelligence algorithms (machine learning, deep learning, knowledge graphs); (3) study aimed to predict adverse drug reactions (cardiotoxicity, neutropenia, nephrotoxicity, hepatotoxicity); (4) study was on cancer patients. The data were extracted and evaluated by three reviewers for study quality. Of the 332 screened articles, 17 studies (5%) involving 93,248 oncology patients from 17 countries were included in the systematic review, of which ten studies synthesized the meta-analysis. A random-effects model was created to pool the sensitivity, specificity, and AUC of the included studies. The pooled results were 0.82 (95% CI:0.69, 0.9), 0.84 (95% CI:0.75, 0.9), and 0.83 (95% CI:0.77, 0.87) for sensitivity, specificity, and AUC, respectively, of ADR predictive models. Biomarkers proved their effectiveness in predicting ADRs, yet they were adopted by only half of the reviewed studies. The use of AI in cancer treatment shows great potential, with models demonstrating high specificity and sensitivity in predicting ADRs. However, standardized research and multicenter studies are needed to improve the quality of evidence. AI can enhance cancer patient care by bridging the gap between data-driven insights and clinical expertise.
翻译:不良药物反应对癌症治疗的患者结局和医疗费用产生严重影响。利用人工智能在实时预测患者癌症中的不良反应可能彻底颠覆癌症治疗。这项研究旨在评估人工智能模型预测癌症患者中不良反应的性能。这是第一篇系统综述和meta分析。在2018年1月至2023年8月期间,用英语、法语和阿拉伯语从PubMed、IEEE Xplore和ACM Digital Library数据库中搜索研究。纳入标准包括: (1)同行评审的研究文章; (2)应用人工智能算法(机器学习、深度学习、知识图谱); (3)旨在预测不良反应(心血管毒性、中性粒减少、肾毒性、肝脏毒性); (4)研究对象为癌症患者。 数据由三位审稿人评估研究质量。在332篇筛选出的文章中,有17篇(5%)研究(93,248名癌症患者来自17个国家的)纳入系统综述,其中10篇研究进行了元分析。使用随机效应模型对纳入研究的敏感性、特异性、AUC进行了加权平均。加权平均结果分别为: - 敏感性:0.82(95%CI:0.69,0.9); - 特异性:0.84(95%CI:0.75,0.9); - AUC:0.83(95%CI:0.77,0.87)。 生物标志物在预测ADR方面证明了自己的有效性,然而只有半数被回顾的研究采用了这些生物标志物。人工智能在癌症治疗中显示出巨大的潜力,模型在预测ADR方面的特异性和敏感性均很高。然而,需要标准化研究和多中心研究来提高证据的质量。人工智能可以通过缩小数据驱动见解和临床专业知识之间的差距来提高癌症患者的护理。
https://arxiv.org/abs/2404.05762
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web. Although knowledge graph embedding (KGE) models have demonstrated superior predictive performance on KGC tasks, these models infer missing links in a black-box manner that lacks transparency and accountability, preventing researchers from developing accountable models. Existing KGE-based explanation methods focus on exploring key paths or isolated edges as explanations, which is information-less to reason target prediction. Additionally, the missing ground truth leads to these explanation methods being ineffective in quantitatively evaluating explored explanations. To overcome these limitations, we propose KGExplainer, a model-agnostic method that identifies connected subgraph explanations and distills an evaluator to assess them quantitatively. KGExplainer employs a perturbation-based greedy search algorithm to find key connected subgraphs as explanations within the local structure of target predictions. To evaluate the quality of the explored explanations, KGExplainer distills an evaluator from the target KGE model. By forwarding the explanations to the evaluator, our method can examine the fidelity of them. Extensive experiments on benchmark datasets demonstrate that KGExplainer yields promising improvement and achieves an optimal ratio of 83.3% in human evaluation.
知识图完成(KGC)旨在解决知识图(KG)固有的不完整性,这对于各种应用(如在线推荐)至关重要。尽管基于知识图嵌入(KGE)的模型在KGC任务中表现出了卓越的预测性能,但这些模型以黑盒方式推断缺失链接,缺乏透明度和责任,阻碍了研究人员开发可负责任的模型。现有的KGE基于解释方法集中于探索关键路径或离散的边缘作为解释,这是对目标预测信息不足的推理。此外,缺失的标注真相导致这些解释方法在定量评估探索的解释方面变得无效。为了克服这些限制,我们提出了KGExplainer,一种模型无关的方法,它识别出目标预测的连接子图解释,并将其评估为质量。KGExplainer采用基于扰动的贪心搜索算法在目标预测的局部结构中查找关键连接子图作为解释。为了评估探索的解释的质量,KGExplainer从目标KGE模型中提取评估者。通过将解释向前传递给评估者,我们的方法可以检查它们的可靠性。在基准数据集上进行的大量实验证明,KGExplainer取得了改进,并实现了人类评估的83.3%的最优比例。
https://arxiv.org/abs/2404.03893
In this work, we are interested in automated methods for knowledge graph creation (KGC) from input text. Progress on large language models (LLMs) has prompted a series of recent works applying them to KGC, e.g., via zero/few-shot prompting. Despite successes on small domain-specific datasets, these models face difficulties scaling up to text common in many real-world applications. A principal issue is that in prior methods, the KG schema has to be included in the LLM prompt to generate valid triplets; larger and more complex schema easily exceed the LLMs' context window length. To address this problem, we propose a three-phase framework named Extract-Define-Canonicalize (EDC): open information extraction followed by schema definition and post-hoc canonicalization. EDC is flexible in that it can be applied to settings where a pre-defined target schema is available and when it is not; in the latter case, it constructs a schema automatically and applies self-canonicalization. To further improve performance, we introduce a trained component that retrieves schema elements relevant to the input text; this improves the LLMs' extraction performance in a retrieval-augmented generation-like manner. We demonstrate on three KGC benchmarks that EDC is able to extract high-quality triplets without any parameter tuning and with significantly larger schemas compared to prior works.
在这项工作中,我们感兴趣的是从输入文本中自动生成知识图(KGC)的方法。大型语言模型(LLMs)的进步导致了一系列将它们应用于KGC的最近工作,例如通过零/几帧提示。尽管在小型领域特定数据集上取得了成功,但这些模型在许多现实世界应用中扩展到文本common遇到困难。一个主要问题是,在先前的方法中,KG模式必须在LLM提示中包括才能生成有效的三元组;大型的和更复杂模式很容易超过LLMs的上下文窗口长度。为了解决这个问题,我们提出了一个名为Extract-Define-Canonicalize(EDC)的三阶段框架:开放信息提取 followed by 模式定义和后置正则化。EDC具有灵活性,因为它可以应用于具有预定义目标模式和不需要预定义模式的情况;在后一种情况下,它自动构建模式并应用自正则化。为了进一步提高性能,我们引入了一个训练过的组件,它检索与输入文本相关的模式元素;这使得LLM在检索增强生成方式下的提取性能得到提高。我们在三个KGC基准测试中证明了EDC能够在不进行参数调整的情况下提取高质量的三元组,并且与先前的作品相比,具有明显更大的模式。
https://arxiv.org/abs/2404.03868