[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123203-en":3,"doc-seo-123203-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123203,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Exploiting Functional Discourse Grammar to Enhance Complex Arabic Relation Extraction Using A Hybrid Semantic Knowledge Base-Machine Learning Approach","Relation extraction from unstructured Arabic text is difficult due to complex morphology and shifting lexical semantics. This paper proposes a hybrid Semantic Knowledge base–Machine Learning approach that applies Functional Discourse Grammar to model semantic and pragmatic properties and identify relation elements. A domain-specific semantic knowledge is encoded to guide an initial FDG-SKML extraction stage, improving accuracy for complex relations. The stage output is then integrated into a machine learning classifier to better handle complex n-ary relations with large variation in presence, order, and correlation, demonstrated in the Economics domain.","Exploiting Functional Discourse Grammar to Enhance Complex Arabic Relation Extraction Using A Hybrid Semantic Knowledge Base-Machine Learning Approach  \nTaha Osman  \nComputer Science, Nottingham Trent University, [taha.osman @ntu.ac.uk](taha.osman @ntu.ac.uk)[ ](taha.osman @ntu.ac.uk)Hussein Khalil  \nComputer Science, Misurata University, [hussein.khalil@misuratau.edu.ly](hussein.khalil@misuratau.edu.ly)[ ](hussein.khalil@misuratau.edu.ly)Mohammed Miltan  \nArabic Department, Faculty of Arts, Misurata University, [mmlitan@gmail.com](mmlitan@gmail.com)[ ](mmlitan@gmail.com)Khaled Shaalan  \nFaculty of Engineering & Information Technology, The British University in Dubai, [Khaled.shaalan@buid.ac.ae](Khaled.shaalan@buid.ac.ae)[ ](Khaled.shaalan@buid.ac.ae)Rowida Alfrjani  \nComputer Science, Nottingham Trent University, [rowida.alfrjani@ntu.ac.uk](rowida.alfrjani@ntu.ac.uk)  \nRelation extraction from unstructured Arabic text is especially challenging due to the Arabic language complex morphology and the variation in word semantics and lexical categories. The research documented in this paper presents a hybrid Semantic Knowledge baseMachine Learning (SKML) approach for extracting complex Arabic relations from unstructured Arabic documents; the proposed approach exploits the principles of Functional Discourse Grammar (FDG) to emphasise the semantic and pragmatic properties of the language and facilitate the identification of relation elements. At the initial phase, the novel FDG-SKML relation extraction approach deploys lexicalbased mechanism that utilises a purposely built domain-specific Semantic Knowledge to encode the semantic association between the identified relations’ elements. The evaluation of the initial stage evidenced improved accuracy for extracting most complex Arabic relations. The initial relation extraction mechanism was further extended by integrating its output into a Machine Learning classifier that facilitated extracting especially complex relations with significant disparity in the relation elements’ presence, order, and correlation. Using Economics as the problem domain, experimental evaluation evidenced the high accuracy of our FDG-SKML approach in complex Arabic relation extraction task and demonstrated its further improvement upon integration with machine learning classifiers.  \nAdditional Keywords and Phrases: Arabic Relation Extraction, Natural Language Processing, Semantic Web Base, Functional Discourse Grammar, Hybrid Knowledge-Based Machine Learning Classification  \n1 INTRODUCTION  \nThe volume of published information on the Web is growing rapidly with the increase number of Internet’s users. According to the Internet World Stats, the number of Internet’s users have exceeded 4.5 Billion at the time of writing this paper1 . As most of the published information is unstructured text, the need for systems that can automate the extraction of useful information from the unstructured documents is becoming ever more desirable, which contributed to the development of Information Extraction as a major research area in computational linguistics. Information Extraction has two essential tasks, Named-Entity Recognition, also known as Entity Extraction or Entity Identification, and Relation Extraction, which is based on recognising the semantic relation between named entities [Martinez-Rodriguez et al. 2020] .  \nRelation Extraction is critical to the identification of the problem domain’s key events where structured knowledge is extracted from unstructured raw text; therefore, it is considered as a key task to the majority of Information Extraction applications such as semantic search, question answering, knowledge harvesting, sentiment analysis and recommender systems [Konstantinova 2014] . Many developed systems have focused on extracting binary relations such as (is-a, part-of) for facts extraction purposes, whereas more recent research efforts have focused on extracting complex relation (n-ary relations) for even","cbCaimHFTc5EIXSm","https://ap.wps.com/l/cbCaimHFTc5EIXSm","pdf",2348822,1,33,"English","en",105,"# Introduction\n## Background: information extraction and relation extraction\n## Motivation for complex (n-ary) relation extraction\n## Arabic language challenges and research gap\n## Paper approach overview","[{\"question\":\"How is machine learning incorporated after the initial extraction stage?\",\"answer\":\"The output from the initial FDG-SKML relation extraction mechanism is fed into a machine learning classifier to improve extraction of especially complex relations with significant variation in element presence, order, and correlation.\"}]","Exploiting Functional Discourse Grammar to Enhance Complex Arabic Relation Extraction Using A Hybrid Semantic Knowledge Base-Machine Learning Approach | PDF",1785815202,83,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"exploiting-functional-discourse-grammar-to-enhance-complex-arabic-relation-extraction-using-a-hybrid-semantic-knowledge-basemachine-learning-approach","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/exploiting-functional-discourse-grammar-to-enhance-complex-arabic-relation-extraction-using-a-hybrid-semantic-knowledge-basemachine-learning-approach/123203/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How is machine learning incorporated after the initial extraction stage?","Question",{"text":75,"@type":76},"The output from the initial FDG-SKML relation extraction mechanism is fed into a machine learning classifier to improve extraction of especially complex relations with significant variation in element presence, order, and correlation.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]