Using Applied Ontology to Saturate Semantic Relations

Tuchkova

2021

Preprint

The problem of preserving knowledge in the digital space arose in the middle of the last century and is gradually aggravated with the increase in the flow of digital information. The scientific community is faced with new challenges related to the speed of dissemination of information and scientific ideas. Classical scientific schools, which were the basis of university education, have undergone changes in connection with distance learning and the idea of openness of science as part of the common values of society. Against the background of increasing information noise, the search for reliable data is becoming an independent activity. The period of accumulation of digitized information is being replaced by the accumulation of knowledge in an integrated form, in the form of structured data. However, the increase in the number of information resources does not improve the quality of the information provided. One of the tasks of the scientific community is to provide an expert assessment of the accumulated digital knowledge. This assessment should be based on the authority of scientific schools. The paper discusses the problem of supporting information images of traditional scientific schools and their development in the digital space. On the example of the integration of mathematical knowledge, the tasks and ways of their solution in the process of assessing and preserving digitized knowledge are formulated. A variant of specialized author indexes and thesauri of mathematical schools is proposed. The content of the digital library LibMeta and the Russian Mathematical Encyclopedia edited by I.M. Vinogradov.

Section: Examples Of Representing Mathematical Knowledge In a Digital Environmentmentioning

confidence: 99%

“…Naturally, this data analysis cannot be carried out without artificial intelligence methods and machine learning algorithms. Experience with the content of the LibMeta library [3,4], including the digitized version of the encyclopedia in Russian [5], allows using vector and other learning algorithms to solve this problem.…”

mentioning

confidence: 99%

The Role and Challenges of the Digitalization Process in the Preservation and Development of Scientific Schools

Tuchkova

2021

Preprint

On Synonyms Search Model

Scientific Conference “Scientific Services &Amp; Internet”

Tuchkova

2021

The problem of finding the most relevant documents as a result of an extended and refined query is considered. To solve it, a search model and a text preprocessing mechanism are proposed. It is proposed to use a search engine and a model based on an index using word2vec algorithms to generate an extended query with synonyms. To refine the search results, the idea of selecting similar documents in the digital semantic library is used. The paper investigates the construction of a vector representation of documents in relation to the data array of the digital semantic library LibMeta. Each piece of text is labeled. Both the whole document and its separate parts can be marked. Search through the library content, search for new terms and new semantic relationships between terms of the subject area becomes more meaningful and accurate. The task of enriching user queries with synonyms was solved. When building a search model in conjunction with word2vec algorithms, a "indexing first, then learning" approach is used, which allows obtaining more accurate search results. This work can be considered one of the first stages in the formation of a training data array for the subject area of problems of mathematical physics and the formation of a dictionary of synonyms for this subject area. The model was trained on the basis of the library's mathematical content. Examples of training, extended query and search quality assessment using training and synonyms are given.

Search model for similar documents in the semantic library

Scientific Conference “Scientific Services &Amp; Internet”

Tuchkova³

2021

The problem of finding the most relevant documents as a result of an extended and refined query is considered. For this, a search model and a text preprocessing mechanism are proposed, as well as the joint use of a search engine and a neural network model built on the basis of an index using word2vec algorithms to generate an extended query with synonyms and refine search results based on a selection of similar documents in a digital semantic library. The paper investigates the construction of a vector representation of documents based on paragraphs in relation to the data array of the digital semantic library LibMeta. Each piece of text is labeled. Both the whole document and its separate parts can be marked. The problem of enriching user queries with synonyms was solved, then when building a search model together with word2vec algorithms, an approach of "indexing first, then training" was used to cover more information and give more accurate search results.