A Statistical Approach for Knowledge Discovery: Bootstrapped Analysis of Language Models for Knowledge base Population from Unstructured Text

Momtazi, Saeedeh; Moradiannasab, Omid

doi:10.24200/sci.2018.20198

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…These models are trained on large datasets of annotated texts to identify patterns corresponding to different types of knowledge. Statistical approaches can be effective in extracting knowledge from large volumes of unstructured data, but they can be limited by the quality of the training data (Momtazi and Moradiannasab, 2019).…”

Section: State Of the Artmentioning

confidence: 99%

T2KG: Transforming Multimodal Document to Knowledge Graph

Galiano,

Muñoz,

Gutiérrez

et al. 2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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The large amount of textual information, in digital format available today, makes the knowledge extraction task unfeasible by manual means. It is therefore necessary to develop automatic tools that allow us to integrate this knowledge into a structure that is easy to use by both machines and humans. This paper presents T2KG, a framework that can incorporate the relevant information from several structured or unstructured documents into a semantic network. Structured documents are processed based on their annotation scheme. For unstructured documents, T2KG uses a set of Natural Language Processing sensors that identify relevant information to enrich the semantic network created by linking all the knowledge from different documents.

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Section: State Of the Artmentioning

confidence: 99%

T2KG: Transforming Multimodal Document to Knowledge Graph

Galiano,

Muñoz,

Gutiérrez

et al. 2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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“…It extracts lexical and ontological knowledge from Persian raw text and uses logical, template-driven, and semantic analysis methods. Momtazi and Moradiannasab [50] also proposed a statistical n-gram method which extracts knowledge from unstructured Persian language texts.…”

Section: Literature In the Persian Languagementioning

confidence: 99%

FarsBase-KBP: A Knowledge Base Population System for the Persian Knowledge Graph

Asgari-Bidhendi¹,

Janfada²,

Minaei-Bidgoli³

2020

Preprint

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While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information. Unlike English knowledge bases such as Wikidata, which have tremendous community support, the population of a knowledge base like FarsBase must rely on automatically extracted knowledge. Knowledge base population can let FarsBase keep growing in size, as the system continues working. In this paper, we present a knowledge base population system for the Persian language, which extracts knowledge from unlabeled raw text, crawled from the Web. The proposed system consists of a

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“…While there are many approaches to knowledge discovery [10,11,12,13] in the context of control mechanism, especially in the realization of a rather straightforward control mechanism like the observer/controller pattern, expert knowledge must be employed in order to specify which data the observer collects and realize the decisions the controller makes. This results in introducing additional cost for modeling the system by the experts.…”

Section: Introductionmentioning

confidence: 99%

Macrophage+: a Game With a Purpose for Applying Human Intelligence in Control Mechanisms

et al. 2020

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Originally, control mechanisms were proposed to replace the need for human intervention in operational environments, and thus, enhance the precision and reaction time. Nowadays, new requirements in computer systems such as adaptation have made the design of control mechanisms more challenging. The Observer/Controller pattern is one of the control mechanisms proposed to control many interacting independent elements by making intelligent decisions. An important challenge in designing these mechanisms is that the knowledge needed for decision making is provided by experts; therefore, the process becomes time consuming and costly, depending on the availability of experts and their costs. In this paper, we hypothesize that employing a Game With A Purpose can help to improve the process of providing knowledge in such control mechanisms by using crowd-sourcing and involving non-expert humans in an enjoyable manner. This hypothesis has been investigated by Macro-phage+, a Game With A Purpose implemented for this goal. We conducted experiments evaluating Macrophage+, focusing on both its applicability and effectiveness in the context of the observer/controller pattern as well as its enjoyability for the players. The results show that Macrophage+ is a successful Game With A Purpose that involves non-expert humans in the application of the observer/controller pattern.

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A Statistical Approach for Knowledge Discovery: Bootstrapped Analysis of Language Models for Knowledge base Population from Unstructured Text

Cited by 5 publications

References 25 publications

T2KG: Transforming Multimodal Document to Knowledge Graph

T2KG: Transforming Multimodal Document to Knowledge Graph

FarsBase-KBP: A Knowledge Base Population System for the Persian Knowledge Graph

Macrophage+: a Game With a Purpose for Applying Human Intelligence in Control Mechanisms

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