Capturing the Content of Physics: Systems, Observables, and Experiments

Hilf, Eberhard R.; Kohlhase, Michael; Stamerjohanns, Heinrich

doi:10.1007/11812289_14

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Given markup on object, statement, and theory level, the soundness of mathematical systems can be assessed Scharpf et al [2018]. In addition, the PhysML variant accounts for the special characteristics of physics: observables, physical systems, and experiments Hilf et al [2006]. Moreover, Mathematical Question Answering (MathQA) systems have been built , Scharpf et al [2022a] to provide quick and concise formula answers to mathematical questions in natural language which are commonly asked on the web Scharpf et al [2020b].…”

Section: Related Workmentioning

confidence: 99%

Discovery and Recognition of Formula Concepts using Machine Learning

Scharpf¹,

Schubotz²,

Cohl³

et al. 2023

Preprint

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Citation-based Information Retrieval (IR) methods for scientific documents have proven effective for IR applications, such as Plagiarism Detection or Literature Recommender Systems in academic disciplines that use many references. In science, technology, engineering, and mathematics, researchers often employ mathematical concepts through formula notation to refer to prior knowledge. Our long-term goal is to generalize citation-based IR methods and apply this generalized method to both classical references and mathematical concepts. In this paper, we suggest how mathematical formulas could be cited and define a Formula Concept Retrieval task with two subtasks: Formula Concept Discovery (FCD) and Formula Concept Recognition (FCR). While FCD aims at the definition and exploration of a 'Formula Concept' that names bundled equivalent representations of a formula, FCR is designed to match a given formula to a prior assigned unique mathematical concept identifier. We present machine learning-based approaches to address the FCD and FCR tasks. We then evaluate these approaches on a standardized test collection (NTCIR arXiv dataset). Our FCD approach yields a precision of 68% for retrieving equivalent representations of frequent formulas and a recall of 72% for extracting the formula name from the surrounding text. FCD and FCR enable the citation of formulas within mathematical documents and facilitate semantic search and question answering as well as document similarity assessments for plagiarism detection or recommender systems.

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Section: Related Workmentioning

confidence: 99%

Discovery and Recognition of Formula Concepts using Machine Learning

Scharpf¹,

Schubotz²,

Cohl³

et al. 2023

Preprint

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show abstract

“…The usefulness of migrating content to a structured format that is standardized is proved by the efforts to convert lecture notes from mathematics [18], [19], [20] and physics [21] into open formats like OMDoc or OpenMath. The aforementioned researches focus on converting L A T E X documents into XML vocabularies so that tasks like query the repository, browse related content, reuse and restructure content etc.…”

Section: A Requirements Analysismentioning

confidence: 99%

On Supporting Creative Interaction in Collaborative Systems: A Content Oriented Approach

Podean

Benţa

Costin

2011

2011 IEEE 13th Conference on Commerce and Enterprise Computing

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The term collaboration is often used to refer to cooperation, resulting thus in a large number of implementations that handle partially the requirements for such a system. This paper will review the components that are a must and define a model for a collaborative system. Instruments like project and risk management are either partially or at all taken in consideration in todays collaborative systems. On the other hand, content management is viewed in term of webcontent management or at most enterprise content management when a great emphasis should be on more intelligent content and its ways to be managed. This study evaluates the design of a collaborative system in a very adaptive and evolution enabled manner and focuses also on restructuring how content is regarded and managed by providing a model that emulates our view.

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“…Papers in the natural sciences need an infrastructure for dealing with experimental and measurement data. We have already dealt with foundational aspects of experiments in [32]. For dealing with data aspects at the semantic level, we plan to build on the SACHS system [33,34], our semantic user assistance system for spreadsheets, which understands spreadsheets as active documents with respect to a background ontology in the content commons.…”

Section: Integration Of Scientific Data and Provenancementioning

confidence: 99%