Ontology Population from Raw Text Corpus for Open-Source Intelligence

Ganino, Giulio; Lembo, Domenico; Scafoglieri, Federico

doi:10.1007/978-3-319-74433-9_16

Cited by 2 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ontology population is the methodologies for extracting the instances of concepts from the Natural Language text in the external source, then adding them to the existing domain ontology [16][17][18]. There are several approaches to perform ontology population, i.e.…”

Section: Ontology Population Techniquesmentioning

confidence: 99%

New instances classification framework on Quran ontology applied to question answering system

2019

View full text Add to dashboard Cite

Instances classification with the small dataset for Quran ontology is the current research problem which appears in Quran ontology development. The existing classification approach used machine learning: Backpropagation Neural Network. However, this method has a drawback; if the training set amount is small, then the classifier accuracy could decline. Unfortunately, Holy Quran has a small corpus. Based on this problem, our study aims to formulate new instances classification framework for small training corpus applied to semantic question answering system. As a result, the instances classification framework consists of several essential components: pre-processing, morphology analysis, semantic analysis, feature extraction, instances classification with Radial Basis Function Networks algorithm, and the transformation module. This algorithm is chosen since it robustness to noisy data and has an excellent achievement to handle small dataset. Furthermore, document processing module on question answering system is used to access instances classification result in Quran ontology.

show abstract

Section: Ontology Population Techniquesmentioning

confidence: 99%

New instances classification framework on Quran ontology applied to question answering system

2019

View full text Add to dashboard Cite

show abstract

“…Text mining can be used to discover links between different studies, for example, between different diseases 15‐17 . Applications of text mining include: (i) improving text understanding, 8 (ii) extracting the opinion of a group of people on a certain topic, 18 (iii) categorizing documents in large catalogues, 19 (iv) supporting virtual assistants and chatter‐bots, 20‐22 and (v) automatically populating ontologies 23‐25 …”

Section: Introductionmentioning

confidence: 99%

NLPHub: An e‐Infrastructure‐based text mining hub

Coro

Panichi

Pagano

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

Summary Text mining involves a set of processes that analyze text to extract high‐quality information. Among its large number of applications, there are experiments that tackle big data challenges using complex system architectures. However, text mining approaches are neither easy to discover and use nor easily combinable by end‐users. Furthermore, they should be contextualized within new approaches to science (eg, Open Science) that ensure longevity and reuse of methods and results. This article presents NLPHub, a distributed system that orchestrates and combines several state‐of‐the‐art text mining services that recognize spatiotemporal events, keywords, and a large set of named entities. NLPHub adopts an Open Science approach, which fosters the reproducibility, repeatability, and reusability of methods and results, by using an e‐Infrastructure supporting data‐intensive Science. NLPHub adds Open Science‐compliance to the connected services through the use of representational standards for services and computations. It also manages heterogeneous service access policies and enables collaboration and sharing facilities. This article reports a performance assessment based on an annotated corpus of named entities, which demonstrates that NLPHub can improve the performance of the single‐integrated processes by cleverly combining their output.

show abstract

Ontology Population from Raw Text Corpus for Open-Source Intelligence

Cited by 2 publications

References 3 publications

New instances classification framework on Quran ontology applied to question answering system

New instances classification framework on Quran ontology applied to question answering system

NLPHub: An e‐Infrastructure‐based text mining hub

Contact Info

Product

Resources

About