A semi-automatic approach for building ontologies from acollection of structured web documents

Kamel, Mouna; Aussenac-Gilles, Nathalie; Buscaldi, Davide; Comparot, Catherine

doi:10.1145/2479832.2479856

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…DBpedia maintains a Simple Knowledge Organization System (SKOS) 15 [48] representation of the Wikipedia category system. The advantage of using the YAGO hierarchy is its depth and that it encodes detailed information in the class name (e.g.…”

Section: Taxonomy Generationmentioning

confidence: 99%

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A survey on automatically constructed universal knowledge bases

Hossain

Salam

Schwitter

2020

Journal of Information Science

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A universal knowledge base can be defined as a domain-independent ontology containing instances. Ontologies define the concepts and relations among these concepts and are used to represent a domain of interest. These universal knowledge bases are the elementary units for automated reasoning on the Semantic Web. The Semantic Web is an extension of the World Wide Web which facilitates software agents to share content beyond the limitations of applications and websites. This survey focuses on the most prominent automatically constructed universal knowledge bases including KnowItAll, DBpedia, YAGO, NELL, Probase, BabelNet and Knowledge Vault. We take a closer look at how these knowledge bases are built, in particular at the information extraction and taxonomy generation process and investigate how they are used in practical applications. Due to quality concerns, the most successful and widely employed knowledge bases are manually constructed to maintain high quality, but they suffer from low coverage, high assembly and quality assurance cost. On the contrary, automatic approaches for building knowledge bases try to overcome these drawbacks. Although it is strenuous to achieve the same level of quality as for manual knowledge bases, we found that the surveyed automatically constructed knowledge bases have shown promising results and are useful for many real-world applications.

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Section: Taxonomy Generationmentioning

confidence: 99%

“…While manual approaches for building ontologies suffer from low coverage, high assembly and quality assurance cost, automatic approaches suffer from lack of accuracy. Semi-automatic approaches [15][16][17] were introduced to get the benefits from both manual and automatic approaches.…”

Section: Introductionmentioning

confidence: 99%

A survey on automatically constructed universal knowledge bases

Hossain

Salam

Schwitter

2020

Journal of Information Science

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“…In the wider ontology development field, there are tools for semiautomatic (e.g. [39]) and automatic (e.g. [13]) ontology building, however, these tools were designed for IT experts, not educators [32].…”

Section: Introductionmentioning

confidence: 99%

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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