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1. KompaRe: A Knowledge Graph Comparative Reasoning System

   https://doi.org/10.1145/3447548.3467128  

   Liu, Lihui ; Du, Boxin ; Fung, Yi Ren ; Ji, Heng ; Xu, Jiejun ; Tong, Hanghang ( July 2021 , KDD)

   null (Ed.)

   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 prediction, 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. 

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2. A concurrent program logic with a future and history

   https://doi.org/10.1145/3563337  

   Meyer, Roland ; Wies, Thomas ; Wolff, Sebastian ( October 2022 , Proceedings of the ACM on Programming Languages)

   Verifying fine-grained optimistic concurrent programs remains an open problem. Modern program logics provide abstraction mechanisms and compositional reasoning principles to deal with the inherent complexity. However, their use is mostly confined to pencil-and-paper or mechanized proofs. We devise a new separation logic geared towards the lacking automation. While local reasoning is known to be crucial for automation, we are the first to show how to retain this locality for (i) reasoning about inductive properties without the need for ghost code, and (ii) reasoning about computation histories in hindsight. We implemented our new logic in a tool and used it to automatically verify challenging concurrent search structures that require inductive properties and hindsight reasoning, such as the Harris set. 

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3. A Conceptual Blend Analysis of Physics Quantitative Literacy Reasoning Inventory Items

   White Brahmia, Suzanne ; Olsho, Alexis ; Boudreaux, Andrew ; Smith, Trevor I ; Zimmerman, Charlotte ( January 2020 , Proceedings of the 23rd Annual Conference on Research in Undergraduate Mathematics Education)

   Karunakaran, S. S. ; Reed, Z. ; Higgins, A. (Ed.)

   Mathematical reasoning flexibility across physics contexts is a desirable learning outcome of introductory physics, where the “math world” and “physical world” meet. Physics Quantitative Literacy (PQL) is a set of interconnected skills and habits of mind that support quantitative reasoning about the physical world. The Physics Inventory of Quantitative Literacy (PIQL), which we are currently refining and validating, assesses students’ proportional reasoning, co-variational reasoning, and reasoning with signed quantities in physics contexts. In this paper, we apply a Conceptual Blending Theory analysis of two exemplar PIQL items to demonstrate how we are using this theory to help develop an instrument that represents the kind of blended reasoning that characterizes expertise in physics. A Conceptual Blending Theory analysis allows for assessment of hierarchical partially correct reasoning patterns, and thereby holds potential to map the emergence of mathematical reasoning flexibility throughout the introductory physics sequence. 

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4. Opportunities for reasoning and proving in mathematical tasks: A discursive perspective.

   Weingarden, M. ; Buchbinder, O. ; Liu, J. ( January 2022 , Proceedings of the 44th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   Lischka, A. E. ; Dyer, E. B. ; Jones, R. S. ; Lovett, J. N. ; Strayer, J. ; Drown, S. (Ed.)

   In this paper, we offer a novel framework for analyzing the Opportunities for Reasoning-and Proving (ORP) in mathematical tasks. By drawing upon some tenets of the commognitive framework, we conceptualize learning and teaching mathematics via reasoning and proving both as enacting reasoning processes (e.g., conjecturing, justifying) in the curricular-based mathematical discourse and as participation in the meta-discourse about proof, which is focused on the aspects of deductive reasoning. By cluster analysis performed on 106 tasks designed by prospective secondary teachers, we identify four types of tasks corresponding to four types of ORP: limited ORP, curricular-based reasoning ORP, logic related ORP, and fully integrated ORP. We discuss these ORP and the contribution of this framework in light of preparing beginning teachers to integrate reasoning and proving in secondary mathematics classrooms. 

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5. The Mental Models Training App: Enhancing verbal reasoning through a cognitive training mobile application

   https://doi.org/10.3389/fpsyg.2023.1150210  

   Cortes, Robert A ; Weinberger, Adam B ; Green, Adam E ( March 2023 , Frontiers in Psychology)

   Reasoning is a complex form of human cognition whose nature has long been debated. While a number of neurocognitive mechanisms for deductive reasoning have been offered, one of the most prominent accounts is Mental Model Theory (MMT). According to MMT, humans are able to manipulate and represent information for reasoning and problem solving by leveraging the brain’s evolved visuospatial resources. Thus, when solving deductive reasoning problems, reasoners build “mental models” of the essential pieces of information conveyed in the premises, with their relations to each other represented spatially—even when the information contained within a reasoning problem is not intrinsically spatial. Crucially, taking a spatially-based approach, such as building mental models, supports higher accuracy on deductive reasoning problems. However, no study has empirically tested whether explicitly training this mental modeling ability leads to improved deductive reasoning performance. 

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