An Approach for Knowledge Extraction from Source Code (KNESC) of Typed Programming Languages

Jiomekong, Azanzi; Camara, Gaoussou

doi:10.1007/978-3-319-77703-0_12

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…The majority of previous studies have focused on general news due to the abundance of datasets [23], [112], [125], and a few of them focus on news for specific domains such as IT [20] and sports [126]. Besides news, there is research dedicated to domains such as software and source code [127]- [129], cyber security [130], and reviews [23]. Despite the abundance of training data available (albeit mainly for English), the datasets are restricted to certain domains.…”

Section: B Domainsmentioning

confidence: 99%

Named Entity Extraction for Knowledge Graphs: A Literature Overview

et al. 2020

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An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other's context. The current culmination of these trends are the deep-learning approaches that have recently reported promising results.

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Section: B Domainsmentioning

confidence: 99%

Named Entity Extraction for Knowledge Graphs: A Literature Overview

et al. 2020

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Extracting ontological knowledge from Java source code using Hidden Markov Models

Jiomekong

Camara

Tchuenté

2019

Open Computer Science

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Ontologies have become a key element since many decades in information systems such as in epidemiological surveillance domain. Building domain ontologies requires the access to domain knowledge owned by domain experts or contained in knowledge sources. However, domain experts are not always available for interviews. Therefore, there is a lot of value in using ontology learning which consists in automatic or semi-automatic extraction of ontological knowledge from structured or unstructured knowledge sources such as texts, databases, etc. Many techniques have been used but they all are limited in concepts, properties and terminology extraction leaving behind axioms and rules. Source code which naturally embed domain knowledge is rarely used. In this paper, we propose an approach based on Hidden Markov Models (HMMs) for concepts, properties, axioms and rules learning from Java source code. This approach is experimented with the source code of EPICAM, an epidemiological platform developed in Java and used in Cameroon for tuberculosis surveillance. Domain experts involved in the evaluation estimated that knowledge extracted was relevant to the domain. In addition, we performed an automatic evaluation of the relevance of the terms extracted to the medical domain by aligning them with ontologies hosted on Bioportal platform through the Ontology Recommender tool. The results were interesting since the terms extracted were covered at 82.9% by many biomedical ontologies such as NCIT, SNOWMEDCT and ONTOPARON.

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An Approach for Knowledge Extraction from Source Code (KNESC) of Typed Programming Languages

Cited by 2 publications

References 6 publications

Named Entity Extraction for Knowledge Graphs: A Literature Overview

Named Entity Extraction for Knowledge Graphs: A Literature Overview

Extracting ontological knowledge from Java source code using Hidden Markov Models

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