Knowledge Extraction from Source Code Based on Hidden Markov Model: Application to EPICAM

Azanzi, Fidel Jiomekong; Camara, Gaoussou

doi:10.1109/aiccsa.2017.99

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The sensibility of this approach is based on the value of 𝜀. Since authors [21][22][23][24][25][26][27][28][29][30][31][32] based on populating ontologies through HMM and mixed HMM and ontologies, in this work, a strictly relationship is outlined between ontology and HMM. As precise in Section 3.2, in the case of multiple ontologies, a single HMM can represent them using this approach.…”

Section: Discussionmentioning

confidence: 99%

“…They first identified and extracted part names from unstructured data and second, they developed TCBR (Textual Case-Based Reasoning) systems for service technicians and engineers. According to this goal, Azanzi and Camara [24] proposed an approach for knowledge extraction from source code based on HMM. It was applied to EPICAM, a tuberculosis surveillance system.…”

Section: Related Workmentioning

confidence: 99%

“…These works are summarized in Table 1. Camara [24] 2017 Extract knowledge from ontology (Java source) using HMM Rani et al [29] 2017 Use ontology to personalize system of E-learning based on FPN and HMM Karmegam [30] 2019 Combine HMM and ontology to build answering system for agricultural domain Monika and Raju [25] 2019 Build ontology using algorithms based on HMMs Elangovan [31] 2021 Train HMM based on ontologies to compute sequence evaluation Muthukumar et al [32] 2021…”

Section: Related Workmentioning

confidence: 99%

“…The HMM is therefore designed to learn ontology concepts and all of their attributes. This concept was backed by two sets of tasks: (1) using HMMs to populate ontologies [21][22][23][24][25]; and (2) combining HMMs and ontologies to develop systems [26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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Integration of Ontology Transformation into Hidden Markov Model

Warda¹,

Kaladzavi²,

Samdalle³

et al. 2022

IDA

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The goal of this study is to suggest a method for turning an ontology into a hidden Markov model (HMM). Ontology properties (relationships between classes) and ontology classes are taken as HMM symbols and states, respectively. Knowledge is represented in many different fields using the central element of the Semantic Web dubbed ontology. The authors employed machine learning technologies like HMM to add knowledge to these ontologies or to extract knowledge from within them. The meaning obtained from ontologies is not described during this task. The ontology triples that were extracted using SPARQL queries are used in this paper to transform the ontology into an HMM in order to handle this semantic. The Pizza ontology has been used to implement this method, which is based on lightweight ontologies.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integration of Ontology Transformation into Hidden Markov Model

Warda¹,

Kaladzavi²,

Samdalle³

et al. 2022

IDA

View full text Add to dashboard Cite

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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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