Simple Method for Ontology Automatic Extraction from Documents

Novelli, A.; Oliveira, José M. Parente de

doi:10.14569/ijacsa.2012.031206

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…Some papers explain the implementation of Enterprise Knowledge Graphs [11,30,33]. Additionally, some tools allow to integrate database contents [1,31] or documents [9,14] into a Knowledge Graph or even directly from texts through Named Entity Resolution [12] and Thematic Scope Resolution [4,12]. A few less researches have been done around the querying side of Enterprise Knowledge Graphs [30].…”

Section: Related Workmentioning

confidence: 99%

Designing a Business View of Enterprise Data

Vidé

Marty²,

Ravat

et al. 2021

25th International Database Engineering &Amp; Applications Symposium

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Nowadays, companies manage a large volume of data usually organised in "silos". Each "data silo" contains data related to a specific Business Unit, or a project. This scattering of data does not facilitate decision-making requiring the use and cross-checking of data coming from different silos. So, a challenge remains: the construction of a Business View of all data in a company. In this paper, we introduce the concepts of Enterprise Knowledge Graph (EKG) and Decentralised EKG (DEKG). Our DEKG aims at generating a Business View corresponding to a synthetic view of data sources. We first define and model a DEKG with an original process to generate a Business View before presenting the possible implementation of a DEKG.

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Section: Related Workmentioning

confidence: 99%

Designing a Business View of Enterprise Data

Vidé

Marty²,

Ravat

et al. 2021

25th International Database Engineering &Amp; Applications Symposium

View full text Add to dashboard Cite

show abstract

“…XONTO [17] proposed an OBIE system for semantic extraction of data from PDF documents with the guide of ontologies. In contrast to XONTO, Dal and Maria [9] suggested an ontology creation method using ML and external knowledge. They extract concepts from documents using latent semantic analysis and clustering techniques.…”

Section: Ontology-based Information Extractionmentioning

confidence: 99%

“…However, no relevant answer was found in the returned list for five classes (group IV), such as Radiation Tolerance and Mechanical Vibration. There is no general concept of "Radiation Tolerance" in Wikidata at the time this evaluation was conducted 9 . This is why we need the domain specific ontologies as well.…”

Section: Ontology Enrichermentioning

confidence: 99%

ConTrOn: Continuously Trained Ontology based on Technical Data Sheets and Wikidata

Opasjumruskit¹,

Peters²,

Schindler³

2019

Preprint

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In engineering projects involving various parts from global suppliers, one common task is to determine which parts are best suited for the project requirements. Information about specific parts' characteristics is published in so called data sheets. However, these data sheets are oftentimes only published in textual form, e.g., as a PDF. Hence, they have to be transformed into a machine-interpretable format. This transformation process still requires a lot of manual intervention and is prone to errors. Automated approaches make use of ontologies to capture the given domain and thus improve automated information extraction from the data sheets. However, ontologies rely solely on experiences and perspectives of their creators at the time of creation and cannot accumulate knowledge over time on their own. This paper presents Con-TrOn -Continuously Trained Ontology -a system that automatically augments ontologies. ConTrOn tackles terminology problems by combining the knowledge extracted from data sheets with an ontology created by domain experts and external knowledge bases such as WordNet and Wikidata. To demonstrate how the enriched ontology can improve the information extraction process, we selected data sheets from spacecraft development as a use case. The evaluation results show that the amount of information extracted from data sheets based on ontologies is significantly increased after the ontology enrichment.

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Ontology extension with NLP-based concept extraction for domain experts in catalytic sciences

2023

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Ontologies store semantic knowledge in a machine-readable way and represent domain knowledge in controlled vocabulary. In this work, a workflow is set up to derive classes from a text dataset using natural language processing (NLP) methods. Furthermore, ontologies and thesauri are browsed for those classes and corresponding existing textual definitions are extracted. A base ontology is selected to be extended with knowledge from catalysis science, while word similarity is used to introduce new classes to the ontology based on the class candidates. Relations are introduced to automatically reference them to already existing classes in the selected ontology. The workflow is conducted for a text dataset related to catalysis research on methanation of CO$$_2$$ 2 and seven semantic artifacts assisting ontology extension by domain experts. Undefined concepts and unstructured relations can be more easily introduced automatically into existing ontologies. Domain experts can then revise the resulting extended ontology by choosing the best fitting definition of a class and specifying suggested relations between concepts of catalyst research. A structured extension of ontologies supported by NLP methods is made possible to facilitate a Findable, Accessible, Interoperable, Reusable (FAIR) data management workflow.

show abstract

Simple Method for Ontology Automatic Extraction from Documents

Cited by 8 publications

References 9 publications

Designing a Business View of Enterprise Data

Designing a Business View of Enterprise Data

ConTrOn: Continuously Trained Ontology based on Technical Data Sheets and Wikidata

Ontology extension with NLP-based concept extraction for domain experts in catalytic sciences

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