8 research outputs found
A Survey on Retrieval of Mathematical Knowledge
We present a short survey of the literature on indexing and retrieval of
mathematical knowledge, with pointers to 72 papers and tentative taxonomies of
both retrieval problems and recurring techniques.Comment: CICM 2015, 20 page
Bravo MaRDI: A Wikibase Powered Knowledge Graph on Mathematics
Mathematical world knowledge is a fundamental component of Wikidata. However,
to date, no expertly curated knowledge graph has focused specifically on
contemporary mathematics. Addressing this gap, the Mathematical Research Data
Initiative (MaRDI) has developed a comprehensive knowledge graph that links
multimodal research data in mathematics. This encompasses traditional research
data items like datasets, software, and publications and includes semantically
advanced objects such as mathematical formulas and hypotheses. This paper
details the abilities of the MaRDI knowledge graph, which is based on Wikibase,
leading up to its inaugural public release, codenamed Bravo, available on
https://portal.mardi4nfdi.de.Comment: Accepted at Wikidata'23: Wikidata workshop at ISWC 202
Intelligent Computer Mathematics : International Conference, CICM 2014, Coimbra, Portugal, July 7-11, 2014. Proceedings
Item does not contain fulltext456 p
Deep Understanding of Technical Documents : Automated Generation of Pseudocode from Digital Diagrams & Analysis/Synthesis of Mathematical Formulas
The technical document is an entity that consists of several essential and interconnected parts, often referred to as modalities. Despite the extensive attention that certain parts have already received, per say the textual information, there are several aspects that severely under researched. Two such modalities are the utility of diagram images and the deep automated understanding of mathematical formulas. Inspired by existing holistic approaches to the deep understanding of technical documents, we develop a novel formal scheme for the modelling of digital diagram images. This extends to a generative framework that allows for the creation of artificial images and their annotation. We contribute on the field with the creation of a novel synthetic dataset and its generation mechanism. We propose the conversion of the pseudocode generation problem to an image captioning task and provide a family of techniques based on adaptive image partitioning. We address the mathematical formulas’ semantic understanding by conducting an evaluating survey on the field, published in May 2021. We then propose a formal synthesis framework that utilized formula graphs as metadata, reaching for novel valuable formulas. The synthesis framework is validated by a deep geometric learning mechanism, that outsources formula data to simulate the missing a priori knowledge. We close with the proof of concept, the description of the overall pipeline and our future aims