KLAUS-Tr: Knowledge & learning-based unit focused arithmetic word problem solver for transfer cases

Semantic web-based diagnosis and treatment of vector-borne diseases using SWRL rules

Clinical practice guideline is an evolving reference document that contains recommendations and knowledge which aims to assist professionals to master a medical domain. In our work, we are interested in using these documents in order to assist doctors to make decisions about appropriate health care for patients who are at risk of cardiovascular disease. More precisely, our paper proposes an automatic approach that parses and transforms text (clinical practice guideline) into OWL DL (Ontology Language Web Description Logic) axioms and SWRL (Semantic Web Rule Language) rules. To parse the text, we have used an existing ontology of cardiovascular domain and natural language processing tools (NLP). Our work is original in that studies the mapping between text and SWRL rules while the related works are focused only on mapping text on OWL lightweight axioms.

This paper presents a novel approach to automatically solving arithmetic word problems. This is the first algorithmic approach that can handle arithmetic problems with multiple steps and operations, without depending on additional annotations or predefined templates. We develop a theory for expression trees that can be used to represent and evaluate the target arithmetic expressions; we use it to uniquely decompose the target arithmetic problem to multiple classification problems; we then compose an expression tree, combining these with world knowledge through a constrained inference framework. Our classifiers gain from the use of quantity schemas that supports better extraction of features. Experimental results show that our method outperforms existing systems, achieving state of the art performance on benchmark datasets of arithmetic word problems.

The internal representation of arithmetical word problem sentences: Frontal and posterior-injured patients compared

Web Ontology Language (OWL), Semantic Web Rule Language (SWRL) and Model-Driven Engineering (MDE) are technologies being developed in parallel, but by different communities. They have common points and issues and can be brought closer together. Many authors have so far stressed this problem and have proposed several solutions. The result of these efforts is the recent OMG’s initiative for defining an ontology development platform. However, the problem of transformation between Semantic Web ontology and rule languages and MDE-based languages has been solved using rather partial and ad hoc solutions, most often by XSLT. In this paper, we relations between the Semantic Web languages and MDE-compliant languages as separate technical spaces. In order to achieve a synergy between these technical spaces, we present ontology and rule languages in terms of MDE standards, recognize relations between the OWL and SWRL langauges and MDE-based ontology languages, and propose mapping techniques. In order to illustrate the approach, we use an MDE-defined architecture that includes the ontology and rule metamodels and ontology UML Profile. We also show how MDE techniques, such as model transformations, can be used to enable sharing rules and ontologies by using REWERSE Rule Markup Language (R2ML), a proposal for a general rule language. The main benefit of this approach is that it keeps the focus on the language concepts (i.e., languages’ abstract syntax - metamodels) rather than on technical issues caused by different concrete syntax. Yet, we also provide transformations that bridge between both languages’ concrete (XML) and abstract (MOF) syntax.

Cognitive precursors of word reading versus arithmetic competencies in young Chinese children

Arithmetic word problems remain one of the most difficult area of teaching mathematics. Learning by problem posing has been suggested as an effective way to improve students’ understanding. However, the practice in usual classroom is difficult due to extra time needed for assessment and giving feedback to students’ posed problems. To address this issue, we have developed a tablet PC software named Monsakun for learning by posing arithmetic word problems based on Triplet Structure Model. It uses the mechanism of sentence-integration, an efficient implementation of problem-posing that enables agent-assessment of posed problems. The learning environment has been used in actual Japanese elementary school classrooms and the effectiveness has been confirmed in previous researches. In this study, ten Indonesian elementary school students living in Japan participated in a learning session of problem posing using Monsakun in Indonesian language. We analyzed their learning activities and show that students were able to interact with the structure of simple word problem using this learning environment. The results of data analysis and questionnaire suggested that the use of Monsakun provides a way of creating an interactive and fun environment for learning by problem posing for Indonesian elementary school students.

In this study, we analyze how 9–10-year-old pupils work with equations, a central aspect of algebraic thinking in early grades and a cornerstone for more formal learning of algebra. Specifically, we seek: (a) to describe the main characteristics of the tasks that support algebraic thinking through a translation process from arithmetic word problems to algebraic language and vice versa, and (b) to identify how pupils refer to indeterminate quantities in these contexts and what meaning they give to them. The analysis focuses on the semantic congruence of the expressions proposed by them and on the dialogue they held during the translation process. We analyzed the oral discussion in the pools and the written responses to the problem that pupils posed. The results show that arithmetic word problems allow the indeterminate to become an object of thought for pupils, who represent it in multiple ways and refer to it when proposing equations that represent the structure of each problem. Another finding highlights that reflection on the interpretation of the equations supports the identification of two meanings associated with indeterminate quantities, namely, unknown and variable.

Purpose This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.Design/methodology/approach The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.Findings Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.Originality/value An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

Current standard models have improved interoperability in Product Lifecycle Management. But the increase of constrains surrounding information management in an industrial context, and notably for power plant design, like the increase of data, actors, systems and the need of automation and collaboration leads to an increasing use of ontology-based models. However, the design of power plants presents a specific issue, which is rules expression and validation. This need of rules is generated by safety, interoperability or collaboration constrains. The Semantic Web Rule Language (SWRL) is an ontological language based on OWL-DL that allows in addition rules expression. This paper presents SWRL rules and their industrial applications but also limits of this language in PLM applications. It proposes in consequence other rules languages.

Solving simple arithmetic word problems is a major ability that children must acquire throughout the primary-grade mathematics curriculum. However, this skill is often challenging for them. For instance, “unknown referent problems” are more difficult to solve than “unknown compare problems.” In unknown compare problems, the relational term (e.g., “more than”) is consistent with the arithmetic operation (e.g., addition) required to find the solution, whereas in unknown referent problems, the relational term (e.g., “more than”) is inconsistent with the arithmetic operation (i.e., subtraction). To determine whether solving unknown referent problems relies on the ability to inhibit the heuristic “add if more, subtract if less” strategy, we devised a negative priming (NP) paradigm for children, adolescents, and adults. For each test trial, an unknown referent problem served as a prime, in which the inhibition of the heuristic strategy was necessary and was immediately followed by an unknown compare problem that served as a probe, in which the heuristic strategy became the appropriate strategy to use. For each control trial, a neutral problem served as a prime and was followed by an unknown compare problem as a probe. All participants performed the unknown compare problems more slowly when they were preceded by unknown referent problems, which reflected typical NP effects. Our results confirm that success in completing unknown referent problems reveals not only participants’ ability to grasp the underlying logic of the problem but also their ability to inhibit a misleading strategy. This study sheds light on the importance of inhibitory control in learning mathematics.

<abstract><p>Solving arithmetic word problems ($ AWPs $) that involve deep implicit relations can be quite challenging. However, the paper proposed two approaches to tackle this issue. The first approach used the modifier-to-matrix ($ MTM $) model to extract noun modification components from the problem text. Specifically, a missing entity recovery ($ MER $) model translated explicit expressions into a node dependency graph ($ NDG $). The nodes on the graph then recursively acquired connections from the knowledge base through the $ MER $ model until the goal was achieved with a known quantity. The solving engine then selected the appropriate knowledge as the prompt. The second approach proposed in the paper was a comprehensive one that combined explicit and implicit knowledge to enhance reasoning abilities. The experimental results of the dataset demonstrate that the proposed algorithm is superior to the baseline algorithms in solving $ AWPs $ that require deep implicit relations.</p></abstract>

Macready & Dayton (1980) showed that state mas tery models are handled optimally within the general latent class framework for data from a single time point. An extension of this idea is presented here for longitudinal data obtained from repeated measure ments across time. The static approach is extended using multiple-indicator Markov chain models. The approach presented here emphasizes the dynamic as pects of the process of change, such as growth, decay, and stability. The general approach is presented, and models with purely categorical and ordered categorical states and several extensions of these models are dis cussed. Problems of estimation, identification, assess ment of model fit, and hypothesis testing associated with these models also are discussed. The applicability of these models is demonstrated using data from a lon gitudinal study on solving arithmetic word problems. The advantages and disadvantages of using the ap proach presented here are discussed. Index terms: arithmetic word problems, dynamic latent class mod els, latent class models, longitudinal categorical data, Markov models, state mastery models.

In the recent past, solving mathematical word problems of varying complexities has gained popularity among researchers, for which Natural Language Processing (NLP) and Understanding (NLU) techniques, Machine Learning, Deep learning and several artificial intelligence (AI) based approaches are widely used. The paper presents a work aiming to solve single-sentence mathematical or arithmetic word problems or queries given in natural language (English). The work involves identification of problem types, simplification of the given word problem to extract the operators and the operands, mapping the operators to appropriate operands to create the mathematical expression and solve the query to generate the final result. We developed a dataset consisting of 430 diverse arithmetic word problems of different complexity levels. Our natural language calculator was evaluated on this dataset and it resulted in 86.28% accuracy.

Purpose As visualisation becomes increasingly important in mathematics education, preservice teachers (PSTs) must develop awareness of the didactical decisions involved in formulating and solving arithmetic word problems (AWPs). This study examines how their use of external visualisations in planning and teaching AWPs supports sustainable problem-solving strategies and shapes their reflective practice. Design/methodology/approach Integrating variation theory and reflection theory, the study conceptualises critical aspects and patterns of variation as tools for making problem structures visible. Twenty-two first-year PSTs worked collaboratively in six groups within simulated teaching scenarios. Data from lesson plans, recorded discussions and enactments, and reflective reports were analysed using a combined deductive–inductive approach. Findings The results show that PSTs used diverse visualisations, such as bar models, ratio schemas, pie charts, and drawings, to make problem structures and relationships visible. These supported reflection-on-, in-, and for-action across planning, enactment, and future-oriented evaluation. The use of visualisations became productive sites for collaborative reflection and informed instructional decisions. Sustainability in problem-solving did not emerge automatically but depended on structured opportunities for critique, comparison, and iterative refinement. Research limitations/implications Small sample and simulated contexts limit generalisability but indicate visualisations support PSTs' reasoning. Practical implications The findings have practical implications for mathematics teacher education, showing how visualisations can support conceptual understanding and teaching skills. The study concludes that using visualisations as a reflective tool links theory and practice, and helps PSTs to develop sustainable problem-solving strategies. Social implications Promotes equitable, concept-focused mathematics teaching. Originality/value This study demonstrates the dual role of visualisations as instructional tools and reflective resources in mathematics teacher education. It introduces a functional-analytic, theory-informed framework that links visualisation, reflection, and the development of sustainable problem-solving strategies.