Improving ontology-based text classification: An occupational health and security application

Sánchez-Pi, Nayat; Martí, Luis; Garcia, Ana Cristina Bicharra

doi:10.1016/j.jal.2015.09.008

Cited by 40 publications

(16 citation statements)

References 11 publications

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“…В работе [8] решается задача классификации текстов по темам при помощи тезауруса. Тезаурус OpenOffice бразильского варианта португальского языка является вспомогательным инструментом для соотнесения текстов и терминов из онтологии.…”

Section: обзор смежных работunclassified

Analysis of Influence of Different Relations Types on the Quality of Thesaurus Application to Text Classification Problems

Lagutina

Shchitov

et al. 2019

Aut. Control Comp. Sci.

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Анализ использования различных типов связей между терминами тезауруса, сгенерированного с помощью гибридных методов, в задачах классификации текстов Лагутина Н. С., Лагутина К. В., Щитов И. А., Парамонов И. В. 1,2 получена 16 октября 2017Аннотация. Цель данной статьи проанализировать, насколько эффективно могут применяться различные типы тезаурусных связей в задачах классификации текстов. Основой исследования является автоматически сгенерированный тезаурус предметной области, содержащий три типа связей: синонимические, иерархические и ассоциативные. Для генерации тезауруса используется гибридный метод, основанный на нескольких лингвистических и статистических алгоритмах выделения семантических связей и позволяющий создать тезаурус с достаточно большим числом терминов и связей между ними. Авторы рассматривают две задачи: тематическая классификация текстов и классификация больших новостных статей по тональности. Для решения каждой из них авторами были использованы два подхода, каждый из которых дополняет стандартные алгоритмы процедурой, применяющей связи тезауруса для определения семантических особенностей текстов. Подход к тематической классификации включает в себя стандартный алгоритм BM25 вида обучение без учителя и процедуру, использующую синонимические и иерархические связи тезауруса предметной области. Подход к классификации по тональности состоит из двух шагов. На первом шаге создается тезаурус, тональные веса терминов которого считаются в зависимости от частоты встречаемости в обучаемой выборке или от веса соседей по тезаурусу. На втором шаге тезаурус применяется для вычисления признаков слов из текстов и классификации текстов методом опорных векторов или наивным байесовским классификатором. В экспериментах с корпусами BBCSport, Reuters, PubMed и корпусом статей об американских иммигрантах авторы варьировали типы связей, которые участвуют в классификации, и степень их использования. Результаты экспериментов позволяют оценить эффективность применения тезаурусных связей для классификации текстов на естественном языке и определить, при каких условиях те или иные связи имеют большую значимость. В частности, наиболее полезными тезаурусными связями оказались синонимические и иерархические, так как они обеспечивает лучшее качество классификации.Ключевые слова: тезаурус, семантические отношения, тезаурусные связи, тематическая классификация, классификация по тональности Для цитирования: Лагутина Н. С., Лагутина К. В., Щитов И. А., Парамонов И. В., "Анализ использования различных типов связей между терминами тезауруса, сгенерированного с помощью гибридных методов, в задачах классификации текстов", Моделирование и анализ информационных систем, 24:6 (2017), 772-787.Об авторах: Лагутина Надежда Станиславовна, orcid.org/0000-0002-6137-8643, канд. физ.-мат. наук, доцент, Ярославский государственный университет им. П.Г. Демидова,

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Section: обзор смежных работunclassified

Analysis of Influence of Different Relations Types on the Quality of Thesaurus Application to Text Classification Problems

Lagutina

Shchitov

et al. 2019

Aut. Control Comp. Sci.

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“…Norway and Sweden are part of Europe. This wide coverage of concepts and relationships provided by ontologies is the most common background knowledge used in literature [15,102,130,131,154] to embed the semantics into documents in order to improve documents representation.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Other ontology-based approaches [131,139,140,141] concern with real time document classification. These approaches do not require a training set or a learning process to train the classifier but they generally rely on the computation of similarity between the terminology information extracted from text documents and the ontology categories.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Concept Learning Approach to Ontology Enrichment

Kastrati

Imran

Yayilgan

2018

Innovations, Developments, and Applications of Semantic Web and Information Systems

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Declaration of AuthorshipI, Zenun Kastrati, hereby declare that this thesis and the work presented in it is entirely my own. Where I have consulted the work of others, this is always clearly stated.Signed:(Zenun Kastrati) Date: SummaryWe are living in the age of internet where massive amount of information is produced from various digital resources on daily basis. The information of these resources is typically stored in unstructured textual format such as reports, news, e-mails, blogs, etc., therefore, a proper classification and organization of this huge amount of information is apparently needed. In this regard, an automatic classification, particularly ontology-based classification, plays an important role in helping people to classify and organize the information accordingly. The ontology-based classification system is an automatic system that utilizes the ontology in order to take advantages of organizing and classifying the knowledge in a more structural and formal way, thus providing better classification accuracy comparing to the traditional keyword-based classification system.The performance of an ontology-based document classification system can be affected by several aspects involved in the entire classification process that generally is constituted of steps such as document collection and preprocessing, document representation, dimensionality reduction, and the classifier. It is almost impossible to address all these research aspects in order to obtain performance improvement in a single dissertation research work, therefore we selected to work on the aspects that we consider are either rarely studied or have a crucial role on the ontology-based classification system. Document representation is one of the main aspects that affects the performance of ontology-based document classification, thus the first research aspect that we investigated is enriching document representation with semantics utilizing the background knowledge exploited by ontologies. The background knowledge derived from an ontology is embedded in a document using a matching technique. The idea behind this technique is mapping of terms that occur in a document with the relevant ontology concepts by searching only the presence of concepts labels in that document. Searching only the presence of concepts labels occurring in a document limits the capabilities of the classification system to capture and exploit the entire conceptualization involved in that document due to the semantic gap issue, the lack of an in depth-coverage of concepts, and the ambiguity problem. In this thesis, the focus is placed on the conceptual document representation, in which, a document is associated with a set of concepts not only by looking for the appearance of concept labels, but also through the acquisition of lexical information integrated (linked) to the ontology to enriching its coverage with new concepts. In this respect, an automatic ontology concept enrichment model is developed to enrich ontologies with new concepts in order to provide a broader co...

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“…Classification can be supervised, unsupervised and semi-supervised and further, based on the number of target classes, classification is called as binary, multi-label and multi-class classification. In this paper, we have taken up multi-class classification where the number of target classes are more than two [2]. The aim is to achieve unsupervised multi class classification in document filtering.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Framework for Ontology Based Classifier using Unstructured Data

Thangaraj*,

Sivakami

2019

IJEAT

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The knowledge contained within the natural language data can be used to build expert systems. Classifying unstructured data using ontology and text classification algorithms to extract information is one way of approaching the problem of building intelligent systems. One major problem with text processing is most data generated is unstructured and ambiguous, as, data with a structure helps to identify meaningful patterns and eventually exhibit the latent knowledge. Ambiguity in natural language affects accuracy of categorization. Also, Natural Language Processing techniques when combined with semantic data modeling through ontological knowledge will also solve the problem of domain knowledge representation thereby enabling improved data classification facilities, particularly in large datasets where number of features scale to unmanageable proportions. In this paper, the domain knowledge is presented as a knowledge graph, derived from the semantic data modeling. Further, to achieve better Multi Class classification, Multinomial Naive Bayes algorithm is applied to categorize items in their respective classes. For the experiments, Data about various news groups were used for testing the accuracy of the model. Experimental results have proved that the proposed classifier performs better compared to existing systems.

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Improving ontology-based text classification: An occupational health and security application

Cited by 40 publications

References 11 publications

Analysis of Influence of Different Relations Types on the Quality of Thesaurus Application to Text Classification Problems

Analysis of Influence of Different Relations Types on the Quality of Thesaurus Application to Text Classification Problems

A Hybrid Concept Learning Approach to Ontology Enrichment

A Comprehensive Framework for Ontology Based Classifier using Unstructured Data

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