OntoMath Digital Ecosystem: Ontologies, Mathematical Knowledge Analytics and Management

Елизаров, А. М.; Kirillovich, Alexander; Lipachev, Evgeny K.; Nevzorova, Olga

doi:10.48550/arxiv.1702.05112

Cited by 1 publication

(2 citation statements)

References 23 publications

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“…Based on the presented results, one can still argue that more settings should be explored (e.g., different parameters and embedding techniques) for the embedding phase, different pre-processing steps (e.g., stemming and lemmatization) should be adopted, and post-processing techniques (e.g., boosting terms of interest based on a knowledge database such as OntoMathPro (Elizarov et al 2017)) should also be investigated. This presumably solves some minor problems, such as removing the inaccurate first hit in Table 8.…”

Section: Visualizing Our Modelmentioning

confidence: 99%

“…As a result, a lexicon containing several meanings for a large set of mathematical symbols is developed. OntoMathPro (Elizarov et al 2017) aims for generating a comprehensive ontology of mathematical knowledge and, therefore, also contain information about the different meanings of mathematical tokens. Such dictionaries might enable the disambiguation approaches in linguistics to be used in mathematical embedding in the near future.…”

Section: Overcoming Issues Of Knowledge Extractionmentioning

confidence: 99%

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Math-word embedding in math search and semantic extraction

et al. 2020

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Word embedding, which represents individual words with semantically fixed-length vectors, has made it possible to successfully apply deep learning to natural language processing tasks such as semantic role-modeling, question answering, and machine translation. As math text consists of natural text, as well as math expressions that similarly exhibit linear correlation and contextual characteristics, word embedding techniques can also be applied to math documents. However, while mathematics is a precise and accurate science, it is usually expressed through imprecise and less accurate descriptions, contributing to the relative dearth of machine learning applications for information retrieval in this domain. Generally, mathematical documents communicate their knowledge with an ambiguous, context-dependent, and non-formal language. Given recent advances in word embedding, it is worthwhile to explore their use and effectiveness in math information retrieval tasks, such as math language processing and semantic knowledge extraction. In this paper, we explore math embedding by testing it on several different scenarios, namely, (1) math-term similarity, (2) analogy, (3) numerical concept-modeling based on the centroid of the keywords that characterize a concept, (4) math search using query expansions, and (5) semantic extraction, i.e., extracting descriptive phrases for math expressions. Due to the lack of benchmarks, our investigations were performed using the arXiv collection of STEM documents and carefully selected illustrations on the Digital Library of Mathematical Functions (DLMF: NIST digital library of mathematical functions. Release 1.0.20 of 2018-09-1, 2018). Our results show that math embedding holds much promise for similarity, analogy, and search tasks. However, we also observed the need for more robust math embedding approaches. Moreover, we explore and discuss fundamental issues that we believe thwart the progress in mathematical information retrieval in the direction of machine learning.

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Section: Visualizing Our Modelmentioning

confidence: 99%

Section: Overcoming Issues Of Knowledge Extractionmentioning

confidence: 99%