XAI (eXplanable AI) techniques that have the property of explaining the reasons for their conclusions, i.e. explainability or interpretability, are attracting attention. XAI is expected to be used in the development of forensic science and the justice system. In today's forensic and criminal investigation environment, experts face many challenges due to large amounts of data, small pieces of evidence in a chaotic and complex environment, traditional laboratory structures and sometimes inadequate knowledge. All these can lead to failed investigations and miscarriages of justice. In this paper, we describe the application of one logical approach to crime scene investigation. The subject of the application is ``The Adventure of the Speckled Band'' from the Sherlock Holmes short stories. The applied data is the knowledge graph created for the Knowledge Graph Reasoning Challenge. We tried to find the murderer by inferring each person with the motive, opportunity, and method. We created an ontology of motives and methods of murder from dictionaries and dictionaries, added it to the knowledge graph of ``The Adventure of the Speckled Band'', and applied scripts to determine motives, opportunities, and methods.
近年来,具有可解释性(Explainability或可理解性)的XAI技术引起了人们的关注。预计,在法医学和司法系统的开发中,XAI将得到应用。在今天的法医学和刑事调查环境中,专家面临着许多挑战,由于数据量巨大,证据碎片化且复杂的环境,传统实验室结构和有时缺乏的知识,所有这些可能导致调查失败和司法公正的失败。在本文中,我们描述了在犯罪现场调查中应用的一种逻辑方法。该方法的主题是《福尔摩斯短篇故事》中的《斑点带》。所应用的数据是知识图谱推理挑战中的知识图谱。我们试图通过推断每个人的动机、机会和方法来找到凶手。我们从字典和词典中创建了杀人的动机和方法的语义网络,并将其添加到《福尔摩斯短篇故事》的知识图中,然后应用脚本来确定动机、机会和方法。
https://arxiv.org/abs/2402.08284
Metamodeling is a general approach to expressing knowledge about classes and properties in an ontology. It is a desirable modeling feature in multiple applications that simplifies the extension and reuse of ontologies. Nevertheless, allowing metamodeling without restrictions is problematic for several reasons, mainly due to undecidability issues. Practical languages, therefore, forbid classes to occur as instances of other classes or treat such occurrences as semantically different objects. Specifically, meta-querying in SPARQL under the Direct Semantic Entailment Regime (DSER) uses the latter approach, thereby effectively not supporting meta-queries. However, several extensions enabling different metamodeling features have been proposed over the last decade. This paper deals with the Metamodeling Semantics (MS) over OWL 2 QL and the Metamodeling Semantic Entailment Regime (MSER), as proposed in Lenzerini et al. (2015) and Lenzerini et al. (2020); Cima et al. (2017). A reduction from OWL 2 QL to Datalog for meta-querying was proposed in Cima et al. (2017). In this paper, we experiment with various logic programming tools that support Datalog querying to determine their suitability as back-ends to MSER query answering. These tools stem from different logic programming paradigms (Prolog, pure Datalog, Answer Set Programming, Hybrid Knowledge Bases). Our work shows that the Datalog approach to MSER querying is practical also for sizeable ontologies with limited resources (time and memory). This paper significantly extends Qureshi & Faber (2021) by a more detailed experimental analysis and more background. Under consideration in Theory and Practice of Logic Programming (TPLP).
元建模是一种表达关于类和属性的知识的方法,应用于知识图谱。在多个应用中,元建模是一个理想的建模特征,可以简化知识图谱的扩展和重用。然而,无限制地允许元建模会存在问题,主要原因是不可判定性问题。因此,实用的语言禁止类作为其他类的实例出现,或者将这种现象视为语义上不同的对象。具体来说,在SPARQL的元查询 under Direct Semantic Entailment Regime (DSER) 下,元查询采用后者的方法,从而实质上不支持元查询。然而,在过去的十年里,已经提出了许多支持不同元建模功能的扩展。本文处理的是OWL 2 QL和元建模语义规则(MSR)中的元建模语义(MS)以及Cima等人(2017)提出的元建模语义规则。Cima等人(2017)提出了从OWL 2 QL到Datalog的减少方案,用于元查询。本文我们还研究了各种支持Datalog查询的逻辑编程工具,以确定它们作为MSER查询回答后端的可行性。这些工具源于不同的逻辑编程范式(Prolog,纯Datalog,答案集编程,混合知识数据库)。我们的工作表明,即使对于资源有限的大型知识图谱,元建模方法在MSER查询上也具有实用性。本文在《理论与实践逻辑编程》(TPLP)中大大扩展了Qureshi & Faber(2021)的内容,增加了更详细的实验分析和背景。
https://arxiv.org/abs/2402.02978
The use of social network theory and methods of analysis have been applied to different domains in recent years, including public health. The complete procedure for carrying out a social network analysis (SNA) is a time-consuming task that entails a series of steps in which the expert in social network analysis could make mistakes. This research presents a multi-domain knowledge model capable of automatically gathering data and carrying out different social network analyses in different domains, without errors and obtaining the same conclusions that an expert in SNA would obtain. The model is represented in an ontology called OntoSNAQA, which is made up of classes, properties and rules representing the domains of People, Questionnaires and Social Network Analysis. Besides the ontology itself, different rules are represented by SWRL and SPARQL queries. A Knowledge Based System was created using OntoSNAQA and applied to a real case study in order to show the advantages of the approach. Finally, the results of an SNA analysis obtained through the model were compared to those obtained from some of the most widely used SNA applications: UCINET, Pajek, Cytoscape and Gephi, to test and confirm the validity of the model.
近年来,社交网络理论和方法的运用已经应用于许多领域,包括公共卫生。进行社交网络分析(SNA)的完整程序是一个耗时且容易出错的过程,在这个过程中,社交网络分析专家可能会犯错误。这项研究提出了一个多领域知识模型,能够自动收集数据并在不同领域进行不同的社交网络分析,不会出现错误,并获得与SNA专家相同的结论。该模型由OntoSNAQA ontology组成,包含类、属性和规则,表示People、Questionnaires和Social Network Analysis领域。除了OntoSNAQA本身之外,不同的规则由SWRL和SPARQL查询表示。使用OntoSNAQA创建了一个知识基础系统,并将其应用于一个实际案例研究,以展示该方法的优势。最后,将SNA分析的结果与一些最广泛使用的SNA应用程序(UCINET、Pajek、Cytoscape和Gephi)进行比较,以测试并证实模型的有效性。
https://arxiv.org/abs/2402.02181
We build on the theory of ontology logs (ologs) created by Spivak and Kent, and define a notion of wiring diagrams. In this article, a wiring diagram is a finite directed labelled graph. The labels correspond to types in an olog; they can also be interpreted as readings of sensors in an autonomous system. As such, wiring diagrams can be used as a framework for an autonomous system to form abstract concepts. We show that the graphs underlying skeleton wiring diagrams form a category. This allows skeleton wiring diagrams to be compared and manipulated using techniques from both graph theory and category theory. We also extend the usual definition of graph edit distance to the case of wiring diagrams by using operations only available to wiring diagrams, leading to a metric on the set of all skeleton wiring diagrams. In the end, we give an extended example on calculating the distance between two concepts represented by wiring diagrams, and explain how to apply our framework to any application domain.
我们基于Spivak和Kent所创建的语义逻辑理论,定义了一个概念图的定义。在本文中,一个布局图是一个有限的有向无环图。标签对应于语义逻辑中的类型;它们还可以解释为自治系统中的传感器读数。因此,布局图可以作为自主系统形成抽象概念的框架。我们证明了支持骨架布局图的图形成一个类别。这使得骨架布局图可以比对其进行比较和操作,同时使用仅限于布局图的算术操作。我们还扩展了通常的图编辑距离定义,使其适用于布局图,从而定义了一个距离度量集合的所有布局图。最后,我们给出了一个关于计算用布局图表示的两个概念之间距离的扩展示例,并解释了如何将我们的框架应用于任何应用领域。
https://arxiv.org/abs/2402.01020
This paper presents sandra, a neuro-symbolic reasoner combining vectorial representations with deductive reasoning. Sandra builds a vector space constrained by an ontology and performs reasoning over it. The geometric nature of the reasoner allows its combination with neural networks, bridging the gap with symbolic knowledge representations. Sandra is based on the Description and Situation (DnS) ontology design pattern, a formalization of frame semantics. Given a set of facts (a situation) it allows to infer all possible perspectives (descriptions) that can provide a plausible interpretation for it, even in presence of incomplete information. We prove that our method is correct with respect to the DnS model. We experiment with two different tasks and their standard benchmarks, demonstrating that, without increasing complexity, sandra (i) outperforms all the baselines (ii) provides interpretability in the classification process, and (iii) allows control over the vector space, which is designed a priori.
本文介绍了Sandra,一种结合向量表示和演绎推理的神经符号推理器。Sandra在一个约束了语义信息的多维向量空间中进行推理。推理器的几何性质允许其与神经网络相结合,从而跨越符号知识表示的差距。Sandra基于描述和情境(DnS)本体设计模式,这是语义知识表示的一种形式化定义。给定一组事实(情境),它允许推断出所有可能的角度(描述),即使在不完全信息的情况下也能提供合理的解释。我们证明,相对于DnS模型,我们的方法是正确的。我们进行了两个不同任务的实验和它们的标准基准,证明了在没有增加复杂性的情况下,Sandra(i)超过了所有基线(ii),并在分类过程中提供了可解释性,(iii)允许控制向量空间,该向量空间是先验设计的。
https://arxiv.org/abs/2402.00591
The intention of this article is to propose the use of artificial intelligence to detect through analysis by UFO ontology the emergence of verbal and physical aggression related to psychosocial deficiencies and their provoking agents, in an attempt to prevent catastrophic consequences within school environments.
这篇文章的目的是提出使用人工智能通过UFO语义网络分析来检测心理和社会缺陷相关的精神和身体攻击 emergence,试图在学校环境中预防灾难性后果。
https://arxiv.org/abs/2403.08795
Background: Single-Subject Design is used in several areas such as education and biomedicine. However, no suited formal vocabulary exists for annotating the detailed configuration and the results of this type of research studies with the appropriate granularity for looking for information about them. Therefore, the search for those study designs relies heavily on a syntactical search on the abstract, keywords or full text of the publications about the study, which entails some limitations. Objective: To present SSDOnt, a specific purpose ontology for describing and annotating single-subject design studies, so that complex questions can be asked about them afterwards. Methods: The ontology was developed following the NeOn methodology. Once the requirements of the ontology were defined, a formal model was described in a Description Logic and later implemented in the ontology language OWL 2 DL. Results: We show how the ontology provides a reference model with a suitable terminology for the annotation and searching of single-subject design studies and their main components, such as the phases, the intervention types, the outcomes and the results. Some mappings with terms of related ontologies have been established. We show as proof-of-concept that classes in the ontology can be easily extended to annotate more precise information about specific interventions and outcomes such as those related to autism. Moreover, we provide examples of some types of queries that can be posed to the ontology. Conclusions: SSDOnt has achieved the purpose of covering the descriptions of the domain of single-subject research studies.
背景:单样本设计(Single-Subject Design)在教育和生物医学等领域都有应用。然而,尚未有一种适当的术语来描述和注释这种研究设计的详细配置和结果。因此,对于寻找相关信息,它依赖于对研究出版物摘要、关键词或全文的语义搜索,这存在一些限制。目标:为了向用户提供SSDOnt,一种特定目的的语义网,用于描述和注释单样本设计研究,以便稍后可以提出复杂的问题。方法:本研究采用了NeOn方法论来开发该语义网。在定义了需求之后,用描述逻辑和后来在OWL 2 DL中实现的正式模型描述了该语义网。结果:我们证明了该语义网为单样本设计研究提供了参考模型,以及用于注释和搜索的主要组件的合适术语。一些与相关元数据本体的映射已经建立。我们用示例展示了该语义网可以轻松扩展来注释有关特定干预和结果(如与自闭症相关的)更精确的信息。此外,我们还提供了几种查询语法的示例。结论:SSDOnt已经实现了覆盖领域描述的目标。
https://arxiv.org/abs/2401.14933
In this paper an agent-based simulation is developed in order to evaluate an AmI scenario based on agents. Many AmI applications are implemented through agents but they are not compared to any other existing alternative in order to evaluate the relative benefits of using them. The proposal simulation environment developed in Netlogo analyse such benefits using two evaluation criteria: First, measuring agent satisfaction of different types of desires along the execution. Second, measuring time savings obtained through a correct use of context information. So, here, a previously suggested agent architecture, an ontology and a 12-steps protocol to provide AmI services in airports, is evaluated using a NetLogo simulation environment. The present work uses a NetLogo model considering scalability problems of this application domain but using FIPA and BDI extensions to be coherent with our previous works and our previous JADE implementation of them. The NetLogo model presented simulates an airport with agent users passing through several zones located in a specific order in a map: passport controls, check-in counters of airline companies, boarding gates, different types of shopping. Although initial data in simulations are generated randomly, and the model is just an approximation of real-world airports, the definition of this case of use of Ambient Intelligence through NetLogo agents opens an interesting way to evaluate the benefits of using Ambient Intelligence, which is a significant contribution to the final development of them.
本文旨在开发一个基于代理的仿真模型,以评估基于代理的AmI场景。许多AmI应用程序是通过代理实现的,但它们并未与其他现有替代方案进行比较,以评估使用它们的优势。在Netlogo中开发的提议仿真环境使用两个评估标准来分析这些优势:首先,衡量不同类型欲望的代理满意度;其次,通过正确使用上下文信息获得的时间节省。因此,本文使用Netlogo仿真环境评估了一个之前建议的代理架构、本体和12步骤协议,以提供AmI服务在机场。本研究使用Netlogo模型考虑了该应用领域的不可扩展性问题,同时使用FIPA和BDI扩展与之前的工作和之前JADE实现的AmI系统保持一致。Netlogo模型所提出的模拟了一个机场,代理用户通过地图上的多个区域:护照检查区,航空公司 check-in 柜台,登机口,不同类型的购物区。尽管模拟开始的数据显示是随机的,但模型只是一个机场的近似,因此通过Netlogo代理定义此使用案例为AmI打开了有趣评估AmI优势的新途径,这是它们最终发展的关键贡献。
https://arxiv.org/abs/2401.14153
Although the goal of achieving semantic interoperability of electronic health records (EHRs) is pursued by many researchers, it has not been accomplished yet. In this paper, we present a proposal that smoothes out the way toward the achievement of that goal. In particular, our study focuses on medical diagnoses statements. In summary, the main contributions of our ontology-based proposal are the following: first, it includes a canonical ontology whose EHR-related terms focus on semantic aspects. As a result, their descriptions are independent of languages and technology aspects used in different organizations to represent EHRs. Moreover, those terms are related to their corresponding codes in well-known medical terminologies. Second, it deals with modules that allow obtaining rich ontological representations of EHR information managed by proprietary models of health information systems. The features of one specific module are shown as reference. Third, it considers the necessary mapping axioms between ontological terms enhanced with so-called path mappings. This feature smoothes out structural differences between heterogeneous EHR representations, allowing proper alignment of information.
尽管许多研究人员致力于实现电子健康记录(EHR)的语义互操作性,但目前尚无实现这一目标的具体方案。在本文中,我们提出了一个建议,该建议平滑了实现这一目标的道路。特别是,我们的研究关注于医疗诊断陈述。总之,基于语义模型的我们建议的主要贡献包括以下几点:首先,它包括一个规范化的本体论,其中EHR相关术语集中关注语义方面。因此,它们的描述与不同组织为表示EHR采用的各种语言和技术无关。此外,这些术语与知名医学术语中的相应代码相关联。其次,它涉及模块,允许获得由专有健康信息系统模型获得的EHR信息的有用语义表示。某个特定模块的特征被用作参考。第三,它考虑了增强 ontological 术语之间映射轴的必要性。这一功能平滑了异质 EHR 表示之间的结构差异,允许正确对齐信息。
https://arxiv.org/abs/2401.11865
Semantically rich descriptions of manufacturing machines, offered in a machine-interpretable code, can provide interesting benefits in Industry 4.0 scenarios. However, the lack of that type of descriptions is evident. In this paper we present the development effort made to build an ontology, called ExtruOnt, for describing a type of manufacturing machine, more precisely, a type that performs an extrusion process (extruder). Although the scope of the ontology is restricted to a concrete domain, it could be used as a model for the development of other ontologies for describing manufacturing machines in Industry 4.0 scenarios. The terms of the ExtruOnt ontology provide different types of information related with an extruder, which are reflected in distinct modules that constitute the ontology. Thus, it contains classes and properties for expressing descriptions about components of an extruder, spatial connections, features, and 3D representations of those components, and finally the sensors used to capture indicators about the performance of this type of machine. The ontology development process has been carried out in close collaboration with domain experts.
本文介绍了为描述一种制造业机器( extruder)而开发的一个名为ExtruOnt的语义丰富描述。这种描述以机器可读代码的形式提供,可以在工业4.0场景中提供有趣的益处。然而,这种类型的描述是明显的缺乏。在本文中,我们提出了开发一个 ontology(本体)的尝试,该本体用于描述这种类型的制造机器,即执行挤出过程的机器。尽管本体的范围限制在具体的领域内,但它可以作为描述工业4.0场景中其他制造机器本体的模型。ExtruOnt本体的术语提供了与挤出过程相关的不同类型的信息,这些信息在本体中体现在不同的模块中。因此,它包括表示挤出机部件、空间连接、特征以及3D模型等内容的类和属性,以及最后用于捕捉这种机器性能指标的传感器。本体开发过程与领域专家进行了密切的合作。
https://arxiv.org/abs/2401.11848
Predicting next visit diagnosis using Electronic Health Records (EHR) is an essential task in healthcare, critical for devising proactive future plans for both healthcare providers and patients. Nonetheless, many preceding studies have not sufficiently addressed the heterogeneous and hierarchical characteristics inherent in EHR data, inevitably leading to sub-optimal performance. To this end, we propose NECHO, a novel medical code-centric multimodal contrastive EHR learning framework with hierarchical regularisation. First, we integrate multifaceted information encompassing medical codes, demographics, and clinical notes using a tailored network design and a pair of bimodal contrastive losses, all of which pivot around a medical code representation. We also regularise modality-specific encoders using a parental level information in medical ontology to learn hierarchical structure of EHR data. A series of experiments on MIMIC-III data demonstrates effectiveness of our approach.
使用电子病历(EHR)进行预测下一个就诊诊断是医疗保健中一个至关重要的任务,对于医疗保健提供商和患者来说,制定主动的未来计划至关重要。然而,许多先前的研究并没有充分解决EHR数据中固有的异质性和层次结构特征,从而导致性能不足。为此,我们提出了NECHO,一种新型的基于医疗代码的多模态对比EHR学习框架,带有多级正则化。首先,我们通过自适应网络设计和一对双模态对比损失,将多维度信息(包括医疗代码、人口统计学信息和临床记录)集成到EHR数据中,并以医疗代码表示为中心。我们还使用医学本体论中的父母级别信息对模态特定的编码器进行正则化,以学习EHR数据的层次结构。在MIMIC-III数据上进行的一系列实验证明了我们方法的的有效性。
https://arxiv.org/abs/2401.11648
Ontologies contain rich knowledge within domain, which can be divided into two categories, namely extensional knowledge and intensional knowledge. Extensional knowledge provides information about the concrete instances that belong to specific concepts in the ontology, while intensional knowledge details inherent properties, characteristics, and semantic associations among concepts. However, existing ontology embedding approaches fail to take both extensional knowledge and intensional knowledge into fine consideration simultaneously. In this paper, we propose a novel ontology embedding approach named EIKE (Extensional and Intensional Knowledge Embedding) by representing ontologies in two spaces, called extensional space and intensional space. EIKE presents a unified framework for embedding instances, concepts and their relations in an ontology, applying a geometry-based method to model extensional knowledge and a pretrained language model to model intensional knowledge, which can capture both structure information and textual information. Experimental results show that EIKE significantly outperforms state-of-the-art methods in three datasets for both triple classification and link prediction, indicating that EIKE provides a more comprehensive and representative perspective of the domain.
知识图谱包含领域内的丰富知识,可以分为两类:外延知识和内延知识。外延知识提供了关于属于特定概念的具体实例的信息,而内延知识则详细描述了概念之间的内蕴性质、特征和语义联系。然而,现有的知识图谱嵌入方法未能同时考虑外延知识和内延知识。在本文中,我们提出了一个名为 EIKE(外延和内延知识嵌入)的新知识图谱嵌入方法,通过将知识图谱表示为两个空间,即外延空间和内延空间。EIKE 提供了一个统一的方法来嵌入实例、概念及其关系在知识图谱中,应用了一种基于几何的方法来建模外延知识,并使用预训练语言模型来建模内延知识,可以捕捉到结构和文本信息。实验结果表明,EIKE 在三个数据集上的三元分类和链接预测都显著优于最先进的方法,表明 EIKE 提供了一个更全面和代表性的领域视角。
https://arxiv.org/abs/2402.01677
Emotions are a subject of intense debate in various disciplines. Despite the proliferation of theories and definitions, there is still no consensus on what emotions are, and how to model the different concepts involved when we talk about - or categorize - them. In this paper, we propose an OWL frame-based ontology of emotions: the Emotion Frames Ontology (EFO). EFO treats emotions as semantic frames, with a set of semantic roles that capture the different aspects of emotional experience. EFO follows pattern-based ontology design, and is aligned to the DOLCE foundational ontology. EFO is used to model multiple emotion theories, which can be cross-linked as modules in an Emotion Ontology Network. In this paper, we exemplify it by modeling Ekman's Basic Emotions (BE) Theory as an EFO-BE module, and demonstrate how to perform automated inferences on the representation of emotion situations. EFO-BE has been evaluated by lexicalizing the BE emotion frames from within the Framester knowledge graph, and implementing a graph-based emotion detector from text. In addition, an EFO integration of multimodal datasets, including emotional speech and emotional face expressions, has been performed to enable further inquiry into crossmodal emotion semantics.
情感是一个在各种学科中引起激烈争论的话题。尽管有理论化和定义的增多,但在我们谈论或分类情感时,并没有就情感是什么以及如何建模其中涉及的不同概念达成一致意见。在本文中,我们提出了一个基于OWL框架的情绪本体论:情感本体论(EFO)。EFO将情感视为语义框架,具有一组语义角色,捕捉了情感体验的不同方面。EFO遵循模式基于本体论设计,与DOLCE基础本体论保持一致。EFO用于建模多个情绪理论,这些理论可以作为情绪本体论网络中的模块进行跨链接。本文我们通过将埃克曼的基本情感(BE)理论建模为EFO-BE模块,并展示如何对情感情况的表示进行自动推断,来举例说明EFO。BE情感框架已通过从Framester知识图谱内对BE情感框架进行词汇化,并实现基于文本的图形化情感检测来评估。此外,还执行了EFO对多模态数据集的整合,包括情感语音和情感面部表情,以进一步研究跨模态情感语义。
https://arxiv.org/abs/2401.10751
The growing trends in automation, Internet of Things, big data and cloud computing technologies have led to the fourth industrial revolution (Industry 4.0), where it is possible to visualize and identify patterns and insights, which results in a better understanding of the data and can improve the manufacturing process. However, many times, the task of data exploration results difficult for manufacturing experts because they might be interested in analyzing also data that does not appear in pre-designed visualizations and therefore they must be assisted by Information Technology experts. In this paper, we present a proposal materialized in a semantic-based visual query system developed for a real Industry 4.0 scenario that allows domain experts to explore and visualize data in a friendly way. The main novelty of the system is the combined use that it makes of captured data that are semantically annotated first, and a 2D customized digital representation of a machine that is also linked with semantic descriptions. Those descriptions are expressed using terms of an ontology, where, among others, the sensors that are used to capture indicators about the performance of a machine that belongs to a Industry 4.0 scenario have been modeled. Moreover, this semantic description allows to: formulate queries at a higher level of abstraction, provide customized graphical visualizations of the results based on the format and nature of the data, and download enriched data enabling further types of analysis.
自动化、物联网、大数据和云计算等技术的发展导致了工业4.0,在这里可以 visualize 和识别模式和见解,从而更好地了解数据,并改善制造过程。然而,许多时候,数据探索的任务对于制造专家来说也是困难的,因为他们可能感兴趣分析那些不出现在预设可视化中的数据。因此,他们必须得到信息技术的专家的协助。在本文中,我们提出了一个在为工业4.0场景设计的语义基础可视查询系统中的提案,该系统使领域专家以友好方式探索和可视化数据。系统的关键创新点是它同时利用了捕获的数据的语义注释和与语义描述相连的机器的二维定制数字表示。这些描述使用术语表示一个本体论,其中,例如,用于捕获机器性能指标的传感器模型化。此外,这种语义描述允许:以更高抽象级别制定查询,根据数据格式和性质提供自定义图形可视化结果,并下载丰富数据,以便进行进一步类型的分析。
https://arxiv.org/abs/2401.09789
Recent advancements in Large Language Models (LLMs) have showcased striking results on existing logical reasoning benchmarks, with some models even surpassing human performance. However, the true depth of their competencies and robustness, in mathematical reasoning tasks, remains an open question. In response, we develop (i) an ontology of perturbations of maths questions, (ii) a semi-automatic method of perturbation, and (iii) a dataset of perturbed maths questions to probe the limits of LLM capabilities in mathematical reasoning tasks. These controlled perturbations span across multiple fine dimensions of the structural and representational aspects of maths questions. Using GPT-4, we generated the MORE dataset by perturbing randomly selected five seed questions from GSM8K. This process was guided by our ontology and involved a thorough automatic and manual filtering process, yielding a set of 216 maths problems. We conducted comprehensive evaluation of both closed-source and open-source LLMs on MORE. The results show a significant performance drop across all the models against the perturbed questions. This strongly suggests that current LLMs lack robust mathematical skills and deep reasoning abilities. This research not only identifies multiple gaps in the capabilities of current models, but also highlights multiple potential directions for future development. Our dataset will be made publicly available at this https URL.
近年来,在大型语言模型(LLMs)的进步中,已经在现有的逻辑推理基准测试中展示了惊人的结果,有些模型甚至超过了人类的表现。然而,它们在数学推理任务中的真正实力和稳健性仍然是一个未解之谜。为了回答这个问题,我们开发了一个数学问题偏差的 ontology(i)、一个半自动化的偏置方法(ii)和一个用于探究 LLM 在数学推理任务中的能力的数据集。这些受控的偏差跨越了数学问题的结构和表示方面的多个细小维度。使用 GPT-4,我们通过随机选择五个性子问题对 GSM8K 进行偏置,生成了more数据集。这一过程由我们的 ontology 指导,并涉及了彻底的自动和手动筛选过程,最终得到216个数学问题。我们对more数据集以及闭源和开源的 LLM 进行了全面评估。结果表明,所有模型在偏置问题上的性能都明显下降。这强烈表明,当前的 LLM 缺乏稳健的数学技能和深入的推理能力。这项研究不仅 identified了当前模型的多个功能缺陷,还强调了未来发展的多个潜在方向。我们的数据集将公开发布在上述链接处。
https://arxiv.org/abs/2401.09395
Telerehabilitation systems that support physical therapy sessions anywhere can help save healthcare costs while also improving the quality of life of the users that need rehabilitation. The main contribution of this paper is to present, as a whole, all the features supported by the innovative Kinect-based Telerehabilitation System (KiReS). In addition to the functionalities provided by current systems, it handles two new ones that could be incorporated into them, in order to give a step forward towards a new generation of telerehabilitation systems. The knowledge extraction functionality handles knowledge about the physical therapy record of patients and treatment protocols described in an ontology, named TRHONT, to select the adequate exercises for the rehabilitation of patients. The teleimmersion functionality provides a convenient, effective and user-friendly experience when performing the telerehabilitation, through a two-way real-time multimedia communication. The ontology contains about 2300 classes and 100 properties, and the system allows a reliable transmission of Kinect video depth, audio and skeleton data, being able to adapt to various network conditions. Moreover, the system has been tested with patients who suffered from shoulder disorders or total hip replacement.
支持任何地方进行物理治疗会话的远程康复系统可以帮助节省医疗费用,同时改善需要康复的用户的生活质量。本文的主要贡献是整体介绍支持Kinect技术的远程康复系统(KiReS)的所有功能特性。除了当前系统提供的功能外,还处理了两种可能纳入它们的新功能,以迈向新一代远程康复系统。知识提取功能处理患者在元数据中描述的物理治疗记录和治疗方案,以选择适当的康复锻炼。远程沉浸功能通过双向实时多媒体通信提供便利、有效的用户友好体验进行远程康复。元数据包含大约2300个类和100个属性,系统能够可靠地传输Kinect视频深度、音频和骨骼数据,能够适应各种网络条件。此外,该系统还与患有肩痛或全髋置换的患者进行了测试。
https://arxiv.org/abs/2401.08721
Planning and reasoning about actions and processes, in addition to reasoning about propositions, are important issues in recent logical and computer science studies. The widespread use of actions in everyday life such as IoT, semantic web services, etc., and the limitations and issues in the action formalisms are two factors that lead us to study about how actions are represented. Since 2007, there was some ideas to integrate Description Logic (DL) and action formalisms for representing both static and dynamic knowledge. In meanwhile, time is an important factor in dynamic situations, and actions change states over time. In this study, on the one hand, we examined related logical structures such as extensions of description logics (DLs), temporal formalisms, and action formalisms. On the other hand, we analyzed possible tools for designing and developing the Knowledge and Action Base (KAB). For representation and reasoning about actions, we embedded actions into DLs (such as Dynamic-ALC and its extensions). We propose a terminable algorithm for action projection, planning, checking the satisfiability, consistency, realizability, and executability, and also querying from KAB. Actions in this framework were modeled with SPIN and added to state space. This framework has also been implemented as a plugin for the Protégé ontology editor. During the last two decades, various algorithms have been presented, but due to the high computational complexity, we face many problems in implementing dynamic ontologies. In addition, an algorithm to detect the inconsistency of actions' effects was not explicitly stated. In the proposed strategy, the interactions of actions with other parts of modeled knowledge, and a method to check consistency between the effects of actions are presented. With this framework, the ramification problem can be well handled in future works.
近年来,在逻辑和计算机科学领域,关于动作和过程的规划和推理,以及关于命题的推理,是一个重要问题。在日常生活中,例如物联网、语义网络服务等技术,普遍使用动作,而动作形式化在其中的局限性和问题,是导致我们研究动作表示的两个因素。自2007年以来,一些想法是将描述逻辑(DL)和动作形式化集成起来,以表示静态和动态知识。与此同时,在动态情况下,时间是一个重要因素,动作会随着时间的推移而改变状态。在这项研究中,一方面,我们研究了相关的逻辑结构,如描述逻辑(DL)的扩展、时间形式化,以及动作形式化。另一方面,我们分析了用于设计和开发知识库(KAB)的可能工具。为了表示和推理动作,我们将动作嵌入到DL中(如Dynamic-ALC及其扩展)。我们提出了一个可终止的算法进行动作投影、规划、检查满足性、一致性、可实现性和可执行性,以及从KAB进行查询。在框架中,动作用SPIN建模并添加到状态空间。该框架还作为Protégé语义网编辑器的插件实现。在过去的20年里,提出了许多算法,但由于计算复杂度高,我们在实现动态本体时面临着许多问题。此外,没有明确阐述如何检测动作效果的不一致性。在所提出的策略中,我们提出了动作与其他建模知识部分之间的交互,以及检查动作效果之间一致性的方法。利用这项框架,可以在未来工作中很好地处理分叉问题。
https://arxiv.org/abs/2401.07890
Motivated by the desire to explore the process of combining inductive and deductive reasoning, we conducted a systematic literature review of articles that investigate the integration of machine learning and ontologies. The objective was to identify diverse techniques that incorporate both inductive reasoning (performed by machine learning) and deductive reasoning (performed by ontologies) into artificial intelligence systems. Our review, which included the analysis of 128 studies, allowed us to identify three main categories of hybridization between machine learning and ontologies: learning-enhanced ontologies, semantic data mining, and learning and reasoning systems. We provide a comprehensive examination of all these categories, emphasizing the various machine learning algorithms utilized in the studies. Furthermore, we compared our classification with similar recent work in the field of hybrid AI and neuro-symbolic approaches.
出于探索将归纳和演绎推理相结合的愿望,我们对研究机器学习和知识图谱整合的文章进行了系统性的文献综述。 objective 是识别将归纳推理(由机器学习执行)和演绎推理(由知识图谱执行)整合到人工智能系统中的多样技术。我们的审查,其中包括对128项研究的分析,使我们能够确定将机器学习和知识图谱整合到人工智能系统中的三种主要类别:学习增强知识图谱、语义数据挖掘和学习与推理系统。我们全面探讨了所有这些类别,并着重介绍了研究中使用的各种机器学习算法。此外,我们还将我们的分类与该领域类似 recent 研究进行了比较。
https://arxiv.org/abs/2401.07744
Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. Newly desirable wheat variety traits include disease resistance to reduce pesticide use, adaptation to climate change, resistance to heat and drought stresses, or low gluten content of grains. Wheat breeding experiments are documented by a large body of scientific literature and observational data obtained in-field and under controlled conditions. The cross-referencing of complementary information from the literature and observational data is essential to the study of the genotype-phenotype relationship and to the improvement of wheat selection. The scientific literature on genetic marker-assisted selection describes much information about the genotype-phenotype relationship. However, the variety of expressions used to refer to traits and phenotype values in scientific articles is a hinder to finding information and cross-referencing it. When trained adequately by annotated examples, recent text mining methods perform highly in named entity recognition and linking in the scientific domain. While several corpora contain annotations of human and animal phenotypes, currently, no corpus is available for training and evaluating named entity recognition and entity-linking methods in plant phenotype literature. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 540 PubMed references fully annotated for trait, phenotype, and species named entities using the Wheat Trait and Phenotype Ontology and the species taxonomy of the National Center for Biotechnology Information. A study of the performance of tools trained on the Triticum aestivum trait Corpus shows that the corpus is suitable for the training and evaluation of named entity recognition and linking.
小麦品种表现出多样性 traits 和表型。将它们与遗传多样性联系起来对于缩短和更有效的育种计划至关重要。现在受欢迎的小麦品种特性包括抗病性降低农药使用、适应气候变化、抗热和干旱压力或谷物低筋含量等。小麦育种实验通过大量科学文献和现场观测数据进行记录。文献和观测数据的交叉参考对于研究基因型-表型关系和改进小麦选择至关重要。 遗传标记辅助选择科学文献描述了关于基因型-表型关系的大量信息。然而,用于描述科学文章中表型和表型值的变体多样性是一个阻碍,以查找信息和进行交叉参考。经过注释的实例训练后,最近的语言挖掘方法在命名实体识别和科学领域中具有高度的表现。虽然几个数据集包含人类和动物表型的注释,但目前还没有可用于培训和评估命名实体识别和实体链接方法在植物表型文献中的数据集。 Triticum aestivum Trait Corpus 是小麦表型和特性的新金标准。它由 540 个PubMed引用完全注释的表型、表型和物种命名实体以及国家生物技术信息中心物种分类的Triticum aestivum品种组成。对Triticum aestivum Trait Corpus上训练的工具的性能研究显示,该数据集适用于命名实体识别和链接的训练和评估。
https://arxiv.org/abs/2401.07447
Emotions have played an important part in many sectors, including psychology, medicine, mental health, computer science, and so on, and categorizing them has proven extremely useful in separating one emotion from another. Emotions can be classified using the following two methods: (1) The supervised method's efficiency is strongly dependent on the size and domain of the data collected. A categorization established using relevant data from one domain may not work well in another. (2) An unsupervised method that uses either domain expertise or a knowledge base of emotion types already exists. Though this second approach provides a suitable and generic categorization of emotions and is cost-effective, the literature doesn't possess a publicly available knowledge base that can be directly applied to any emotion categorization-related task. This pushes us to create a knowledge base that can be used for emotion classification across domains, and ontology is often used for this purpose. In this study, we provide TONE, an emotion-based ontology that effectively creates an emotional hierarchy based on Dr. Gerrod Parrot's group of emotions. In addition to ontology development, we introduce a semi-automated vocabulary construction process to generate a detailed collection of terms for emotions at each tier of the hierarchy. We also demonstrate automated methods for establishing three sorts of dependencies in order to develop linkages between different emotions. Our human and automatic evaluation results show the ontology's quality. Furthermore, we describe three distinct use cases that demonstrate the applicability of our ontology.
情感在许多领域都扮演着重要的角色,包括心理学、医学、心理健康、计算机科学等等,并且对它们进行分类证明在区分不同情感方面非常实用。情感可以通过以下两种方法进行分类:(1)收集的数据量越大,数据集的效率就越强。在一个领域使用相关数据建立的分类可能在其他领域并不适用。(2)已经存在使用领域专业知识或情绪类型知识库的无需监督的方法。尽管这种第二种方法提供了一种合适和通用的情感分类,并且是经济有效的,但文献中并没有一个可以公开应用到任何情感分类任务的知识库。这推动着我们创造一个可以在各个领域用于情感分类的知识库,而本体论通常用于这个目的。在本研究中,我们提供了TONE,一种基于情感的语义网络,它根据Dr. Gerrod Parrot的一组情感创建了情感层次结构。除了本体论开发之外,我们还引入了一种半自动化的词汇构建过程,用于生成每个层次结构的详细词汇集。我们还展示了用于建立三种不同关系的方法,以开发不同情感之间的联系。我们的人和自动评估结果证明了本体论的质量。此外,我们描述了三种不同的应用案例,展示了本体论的应用价值。
https://arxiv.org/abs/2401.06810