Chapter 23
Entity-Event Ontology Construction
by Conceptualization of Mentions in Text
Corpus
Michael C. Ridley
Abstract Knowledge-based systems constitute a powerful tool for tackling and
navigating complex domains, but they have the potential to be employed more often
in practical tasks if some obstacles are cleared. Creating and keeping knowledge
bases up-to-date is a challenging problem without automatic extraction of knowledge
from data sources like documents. One of the solutions is ontology learning, which
enables automatic construction and population of ontologies used to store knowledge. This chapter proposes an automatic method for domain ontology construction
based on extracting entities and events from texts. Also, it is stated that upperlevel template ontologies used when analyzing text corpus are suitable for creating
target instance ontologies that describe a specific domain. The task of instance
ontology construction is formulated in the terms of reconstructing real-world events
via analyzing their mentions in a text corpus and structuring them according to the
template ontology. This method allows an automatic analysis of big volumes of
textual data like posts from social networks, news, contracts, specifications, etc., by
utilizing natural language understanding tools used to extract domain knowledge.
We developed a system that collects texts from the Internet, analyzes them, builds an
ontology, and presents it as a knowledge base. One of the current applications is in
optimizing business processes in a domain of civil aviation: document management,
sorting and navigating documents, text summarization, semantic enterprise search,
and exploratory search. Furthermore, it is claimed that extracted knowledge can be
used to construct informative features in machine learning tasks.
23.1 Introduction
One of the dominant applications of information systems is the analysis of numerical,
categorical, and other structured data. As an example, CRM systems often operate on
M. C. Ridley (B)
Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow
125993, Russian Federation
e-mail: mr@kalabi.ru
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
L. C. Jain et al. (eds.), Applied Mathematics and Computational Mechanics for Smart
Applications, Smart Innovation, Systems and Technologies 217,
https://doi.org/10.1007/978-981-33-4826-4_23
341
Entity-Event Ontology Construction
by Conceptualization of Mentions in Text
Corpus
Michael C. Ridley
Abstract Knowledge-based systems constitute a powerful tool for tackling and
navigating complex domains, but they have the potential to be employed more often
in practical tasks if some obstacles are cleared. Creating and keeping knowledge
bases up-to-date is a challenging problem without automatic extraction of knowledge
from data sources like documents. One of the solutions is ontology learning, which
enables automatic construction and population of ontologies used to store knowledge. This chapter proposes an automatic method for domain ontology construction
based on extracting entities and events from texts. Also, it is stated that upperlevel template ontologies used when analyzing text corpus are suitable for creating
target instance ontologies that describe a specific domain. The task of instance
ontology construction is formulated in the terms of reconstructing real-world events
via analyzing their mentions in a text corpus and structuring them according to the
template ontology. This method allows an automatic analysis of big volumes of
textual data like posts from social networks, news, contracts, specifications, etc., by
utilizing natural language understanding tools used to extract domain knowledge.
We developed a system that collects texts from the Internet, analyzes them, builds an
ontology, and presents it as a knowledge base. One of the current applications is in
optimizing business processes in a domain of civil aviation: document management,
sorting and navigating documents, text summarization, semantic enterprise search,
and exploratory search. Furthermore, it is claimed that extracted knowledge can be
used to construct informative features in machine learning tasks.
23.1 Introduction
One of the dominant applications of information systems is the analysis of numerical,
categorical, and other structured data. As an example, CRM systems often operate on
M. C. Ridley (B)
Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow
125993, Russian Federation
e-mail: mr@kalabi.ru
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
L. C. Jain et al. (eds.), Applied Mathematics and Computational Mechanics for Smart
Applications, Smart Innovation, Systems and Technologies 217,
https://doi.org/10.1007/978-981-33-4826-4_23
341