An Approach for Ontology Building from Text Supported by NLP Techniques

Valencia‐García, Rafael; Castellanos–Nieves, Dagoberto; Vicente, P.; Fernández-Breis, Jesualdo Tomás; Martı́nez-Béjar, Rodrigo; Sánchez, Francisco

doi:10.1007/978-3-540-25945-9_13

Cited by 4 publications

(3 citation statements)

References 8 publications

(10 reference statements)

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“…Taxonomies play an important role in developing successful application for the underlying domain [40,49]. In the ontology learning field, a number of research projects used syntactic and semantic techniques to extract hierarchical relationships among the concepts of the underlying domain [13], [61], [40]. However, recently there is a growing trend toward using machine learning techniques to determine relationships among concepts.…”

Section: Concept Hierarchy Construction (Taxonomy Learning)mentioning

confidence: 99%

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A Heuristically Modified FP-Tree for Ontology Learning with Applications in Education

Shatnawi¹,

Gaber²,

Cocea³

2019

Preprint

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We propose a heuristically modified FP-Tree for ontology learning from text. Unlike previous research, for concept extraction, we use a regular expression parser approach widely adopted in compiler construction, i.e., deterministic finite automata (DFA). Thus, the concepts are extracted from unstructured documents. For ontology learning, we use a frequent pattern mining approach and employ a rule mining heuristic function to enhance its quality. This process does not rely on predefined lexico-syntactic patterns, thus, it is applicable for different subjects. We employ the ontology in a question-answering system for students' content-related questions. For validation, we used textbook questions/answers and questions from online course forums. Subject experts rated the quality of the system's answers on a subset of questions and their ratings were used to identify the most appropriate automatic semantic text similarity metric to use as a validation metric for all answers. The Latent Semantic Analysis was identified as the closest to the experts' ratings. We compared the use of our ontology with the use of Text2Onto for the question-answering system and found that with our ontology 80% of the questions were answered, while with Text2Onto only 28.4% were answered, thanks to the finer grained hierarchy our approach is able to produce.

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Section: Concept Hierarchy Construction (Taxonomy Learning)mentioning

confidence: 99%

“…A number of ontology learning researchers explored Natural Language Processing (NLP) techniques to discover domain concepts and relationships among concepts from unstructured text documents [61], [48], [63], [21]. The Text2Onto [13], OntoGain [21], and OntoLearn [63] systems used NLP tools to extract key concepts from text.…”

Section: Introductionmentioning

confidence: 99%

A Heuristically Modified FP-Tree for Ontology Learning with Applications in Education

Shatnawi¹,

Gaber²,

Cocea³

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…For this purpose, an algorithm for semi-automatic detection of ontological elements using Natural Language Processing Technologies (NLP) has been used. This algorithm is based on the work presented in [55], and it is an incremental knowledge acquisition algorithm, consisting of three sequential phases: Preparation; Search; and Set in a context (see Fig. 1).…”

Section: Processing the Students' Answersmentioning

confidence: 99%