Reasoning is a fundamental capability for harnessing valuable insight, knowledge and patterns from knowledge graphs. Existing work has primarily been focusing on point-wise reasoning, including search, link predication, entity prediction, subgraph matching and so on. This paper introduces comparative reasoning over knowledge graphs, which aims to infer the commonality and inconsistency with respect to multiple pieces of clues. We envision that the comparative reasoning will complement and expand the existing point-wise reasoning over knowledge graphs. In detail, we develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs. We present both the system architecture and its core algorithms, including knowledge segment extraction, pairwise reasoning and collective reasoning. Empirical evaluations demonstrate the efficacy of the proposed KompaRe.
University of California, Berkeley（加州大学伯克利分校）；Stanford University（斯坦福大学）
Qualitative and quantitative approaches to reasoning about uncertainty can lead to different logical systems for formalizing such reasoning, even when the language for expressing uncertainty is the same. In the case of reasoning about relative likelihood, with statements of the form $φ\succsimψ$ expressing that $φ$ is at least as likely as $ψ$, a standard qualitative approach using preordered preferential structures yields a dramatically different logical system than a quantitative approach using probability measures. In fact, the standard preferential approach validates principles of reasonin…
Language-Specific versus Cross-Lingual Knowledge Graphs for Implicit Aspect Identification in Arabic: A Comparative Study of Reasoning and Adaptation Strategies
Conceptual Graphs (CG) are a graph-based knowledge representation and reasoning formalism; fuzzy Conceptual Graphs (fCG) constitute an extension that enriches their expressiveness, exploiting the fuzzy set theory so as to relax their constraints at various levels. This paper proposes a comparative study of existing approaches over their respective advantages and possible limitations. The discussion revolves around three axes: (a) Critical view of each approach and comparison with previous propositions from the state of the art; (b) Presentation of the many possible interpretations of each defi…
University of Cambridge（剑桥大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
Multihop Question Answering is a complex Natural Language Processing task that requires multiple steps of reasoning to find the correct answer to a given question. Previous research has explored the use of models based on Graph Neural Networks for tackling this task. Various architectures have been proposed, including Relational Graph Convolutional Networks (RGCN). For these many node types and relations between them have been introduced, such as simple entity co-occurrences, modelling coreferences, or "reasoning paths" from questions to answers via intermediary entities. Nevertheless, a thoug…
Evaluating Large Language Models on the Spanish Medical Intern Resident (MIR) Examination 2024/2025:A Comparative Analysis of Clinical Reasoning and Knowledge Application
Reliability of Large Language Model Generated Clinical Reasoning in Assisted Reproductive Technology: Blinded Comparative Evaluation Study
West China Second University Hospital, Sichuan University（四川大学华西第二医院）；University of Michigan（密歇根大学）；Sichuan University（四川大学）；West China Hospital, Sichuan University（四川大学华西医院）；West China School of Medicine, Sichuan University（四川大学华西临床医学院）
Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology
Charnock Hospital（查诺克医院）；Jagannath Gupta Institute of Medical Sciences and Hospital（贾格纳特·古普塔医学科学与医院）
A Comparative Study of Specialized LLMs as Dense Retrievers
Key Laboratory of Network Data Science and Technology（网络数据科学与技术重点实验室）；Institute of Computing Technology（计算技术研究所）；Chinese Academy of Sciences（中国科学院）；State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；University of Chinese Academy of Science（中国科学院大学）
Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches, recent methods in (deep) representation learning has shown promising results for specialized tasks such as knowledge base completion. These approaches abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their resp…
VisioMath: Benchmarking Figure-based Mathematical Reasoning in LMMs
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Beijing Key Laboratory of Artificial Intelligence for Education（北京人工智能教育重点实验室）；Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education（教育部智能技术与教育应用工程研究中心）
Fusing Knowledge and Language: A Comparative Study of Knowledge Graph-Based Question Answering with LLMs
Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）；Maharishi Markandeshwar (Deemed to be University)（马哈里希·马坎德什瓦尔大学）；IMD, Ministry of Earth Sciences（印度气象局，地球科学部）；NePeur（NePeur公司）
