Quran Intelligent Ontology Construction Approach Using Association Rules Mining

• URL: http://arxiv.org/abs/2008.03232v2
• Date: Mon, 10 Aug 2020 01:30:31 GMT
• Title: Quran Intelligent Ontology Construction Approach Using Association Rules Mining
• Authors: Fouzi Harrag, Abdullah Al-Nasser, Abdullah Al-Musnad, Rayan Al-Shaya
• Abstract summary: This research project is concerned with the use of association rules to extract the Quran ontology. Our system is based on the combination of statistics and methods to extract semantic and conceptual relations from Quran verses. The Quran concepts will offer a new and powerful representation of Quran knowledge, and the association rules will help to represent the relations between all classes of connected concepts in the Quran.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ontology can be seen as a formal representation of knowledge. They have been investigated in many artificial intelligence studies including semantic web, software engineering, and information retrieval. The aim of ontology is to develop knowledge representations that can be shared and reused. This research project is concerned with the use of association rules to extract the Quran ontology. The manual acquisition of ontologies from Quran verses can be very costly; therefore, we need an intelligent system for Quran ontology construction using patternbased schemes and associations rules to discover Quran concepts and semantics relations from Quran verses. Our system is based on the combination of statistics and linguistics methods to extract concepts and conceptual relations from Quran. In particular, a linguistic pattern-based approach is exploited to extract specific concepts from the Quran, while the conceptual relations are found based on association rules technique. The Quran ontology will offer a new and powerful representation of Quran knowledge, and the association rules will help to represent the relations between all classes of connected concepts in the Quran ontology.

Related papers

• Quranic Conversations: Developing a Semantic Search tool for the Quran using Arabic NLP Techniques [0.7673339435080445]
  The Holy Book of Quran is believed to be the literal word of God (Allah) as revealed to the Prophet Muhammad (PBUH) over a period of approximately 23 years. It is challenging for Muslims to get all relevant ayahs (verses) pertaining to a matter or inquiry of interest. We developed a Quran semantic search tool which finds the verses pertaining to the user inquiry or prompt.
  arXiv  Detail & Related papers  (2023-11-09T03:14:54Z)
• Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension [80.99865844249106]
  We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
  arXiv  Detail & Related papers  (2023-06-21T07:34:27Z)
• Universal Language Modelling agent [0.0]
  This research paper draws inspiration from the linguistic concepts found in the Quran, a revealed Holy Arabic scripture dating back 1400 years. By exploring the linguistic structure of the Quran, specifically the components of ism, fil, and harf, we aim to unlock the underlying intentions and meanings embedded within animal conversations.
  arXiv  Detail & Related papers  (2023-06-10T21:09:16Z)
• The Arabic Ontology -- An Arabic Wordnet with Ontologically Clean Content [0.0]
  Ontology consists of about 1,300 well-investigated concepts in addition to 11,000 concepts that are partially validated. Ontology is accessible and searchable through a lexicographic search engine. Ontology is fully mapped with Princeton WordNet, Wikidata, and other resources.
  arXiv  Detail & Related papers  (2022-05-19T16:27:44Z)
• An ontology for the formalization and visualization of scientific knowledge [0.0]
  We present the first version built from ontological sources (ontologies of knowledge objects of certain fields, lexical and higher level ones), specialized knowledge bases and interviews with scientists. The validation consists in using it to formalize knowledge from various sources, which we have begun to do in the field of physics.
  arXiv  Detail & Related papers  (2021-07-09T10:33:45Z)
• pix2rule: End-to-end Neuro-symbolic Rule Learning [84.76439511271711]
  This paper presents a complete neuro-symbolic method for processing images into objects, learning relations and logical rules. The main contribution is a differentiable layer in a deep learning architecture from which symbolic relations and rules can be extracted. We demonstrate that our model scales beyond state-of-the-art symbolic learners and outperforms deep relational neural network architectures.
  arXiv  Detail & Related papers  (2021-06-14T15:19:06Z)
• Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge [77.77684299758494]
  We extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG) First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network (KNN), which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. To handle the diversity and complexity of KG information, we propose a Contextualized Knowledge-aware Attentive Neural Network (CKANN), which improves the knowledge representation learning with structure information via a customized Graph Convolutional Network (GCN) and comprehensively learns context-based and knowledge-based sentence representation via
  arXiv  Detail & Related papers  (2021-04-12T05:52:20Z)
• Formalising Concepts as Grounded Abstractions [68.24080871981869]
  This report shows how representation learning can be used to induce concepts from raw data. The main technical goal of this report is to show how techniques from representation learning can be married with a lattice-theoretic formulation of conceptual spaces.
  arXiv  Detail & Related papers  (2021-01-13T15:22:01Z)
• Smartajweed Automatic Recognition of Arabic Quranic Recitation Rules [0.0]
  Tajweed is a set of rules to read the Quran in a correct Pronunciation of the letters with all its Qualities, while Reciting the Quran. These characteristics include melodic rules, like where to stop and for how long, when to merge two letters in pronunciation or when to stretch some, or even when to put more strength on some letters over other.
  arXiv  Detail & Related papers  (2020-12-26T11:24:03Z)
• Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering [50.72473345911147]
  This paper augments a general commonsense QA framework with a knowledgeable path generator. By extrapolating over existing paths in a KG with a state-of-the-art language model, our generator learns to connect a pair of entities in text with a dynamic, and potentially novel, multi-hop relational path.
  arXiv  Detail & Related papers  (2020-05-02T03:53:21Z)
• Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
  We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches. Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
  arXiv  Detail & Related papers  (2020-03-09T15:04:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.