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استخدام المصادر المفتوحة في بناء انطولوجيا باللغة العربية
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أصبحت القدرة على البحث ضمن مواقع الانترنت ضرورة للكثير من الناس و تعاني كثير من مواقع الانترنت من صعوبة وصول المستخدم للمعلومة المطلوبة بسبب عدم الأخذ بالحسبان معاني الكلمات و مدلولاتها Semantics عند البحث تدعم التقنيات الحالية معظم اللغات الطبيعية مع بقاء ذلك ضعيفاً بالنسبة للغة العربية. تشكل الأنطولوجيا العنصر المحوري في التطبيقات التي تدعم علم دلالات الألفاظ, و على الرغم من وجود الكثير من أدوات بناء الانطولوجيات باللغات الأجنبية، فإن الأداة Arabic WordNet (AWN)، التي تعدّ من المصادر المفتوحة قد تكون الوحيدة التي تسمح لنا بتطوير انطولوجيا باللغة العربية. في هذا البحث سنقوم بتحديد الخطوات اللازمة لتطوير انطولوجيا عربية و بناء انطولوجيا اعتماداً على هذه الخطوات تستخدم في مواقع الجامعات مستخدمين الأداة Arabic WordNet التأكد من نظافة الانطولوجيا المصممة، و تقييم أداء عملية البحث في الموقع بعد استخدام الانطولوجيا المصممة.
The ability to search the Web sites has become essential for many people. However many sites have problems in giving the user the needed information. Search operations are typically limited to keyword searches and do not take into consideration the underlying semantics of the content.The present technologies support most languages; Though Arabic is still not well supported. One of the main application areas of Ontology technology is semantics. Although there are many tools for developing Ontology’s in many languages, Arabic WordNet seems to be the only one that supports Arabic language. In this paper we will define the necessary steps to develop Arabic Ontology for university sites using Arabic WordNet, and check that the developed Ontology is clean.
References used
Taye, M. Ontology Alignment Mechanisms for Improving Web-basedSearching.- United Kingdom,England: De Montfort University, 2009.271 pages
Wilson, R. The Role of Ontologies in Teaching and Learning.-n.p: Ruth Wilson, 2004.16 pages
Kim, H. et al. Implementing an Ontology-Based Knowledge Management System in Korean Financial Firm Environment.-Seoul,Korea:MyongjiUniversity, 2006.10 pages
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There are many methods and suggestions proposed to improve the efficiency of
search in order to catch up with the increasing speed of information boom on the web.
Most of these proposals are concentrated on term frequency and page rank algorithms a
nd
yet very few of them focus on semantic relationship of content.
The objective of this research project is to provide a semantic relationship model that
can be used for semi-structured or unstructured information on the web to help improve the
accuracy and efficiency of search engine.
This research proposes a new way to improve the
search outcome of Arabic semantics by abstractly summarizing the
Arabic texts (Abstractive Summary) using natural language
processing algorithms(NLP),Word Sense Disambiguation (WSD)
and techniques o
f measuring Semantic Similarity in Arabic WordNet
Ontology.
معالجة اللغات الطبيعية
Semantic analysis
استرجاع المعلومات
التلخيص التجريدي
الأنتولوجيا العربية ووردنت
العلاقة الدلالية المفاهيمية
التشابهية الدلالية
التحليل الدلالي
حل غموض معاني الكلمات
(Natural Language Processing (NLP
(Information Retrieval (IR
Abstractive Summarization
(Arabic WordNet (AWN
Conceptual Semantic Relation
Semantic Similarity
(Word Sense Disambiguation (WSD
المزيد..
With the increasing growth in popularity of Web services and SOA, discovering
relevant Web services becomes a significant challenge. The introduction of intentional
services is necessary to bridge the gap between low level, technical software-servi
ce
descriptions and high level, strategic expressions of business needs for services.
Current Web Services technology based on UDDI and WSDL does not make use of
this “intention” and therefore fails to address the problem of matching between capabilities
of services and business user needs.
This work addresses the problem of intentional semantic web service search in
Arabic, where a novel approach is proposed for partitioning user goals based on Arabic
verb ontology, in addition to showing a practical example about the effect of applying verb
ontology in intentional web service search.
Natural language modelling has gained a lot of interest recently. The current state-of-the-art results are achieved by first training a very large language model and then fine-tuning it on multiple tasks. However, there is little work on smaller more
compact language models for resource-limited devices or applications. Not to mention, how to efficiently train such models for a low-resource language like Arabic. In this paper, we investigate how such models can be trained in a compact way for Arabic. We also show how distillation and quantization can be applied to create even smaller models. Our experiments show that our largest model which is 2x smaller than the baseline can achieve better results on multiple tasks with 2x less data for pretraining.
This paper presents ArOntoLearn, a Framework for Arabic Ontology learning from textual resources.
Supporting Arabic language and using domain knowledge in the learning process are the main features of
our framework. Besides it represents the learne
d ontology in Probabilistic Ontology Model (POM), which
can be translated into any knowledge representation formalism, and implements data-driven change
discovery. Therefore it updates the POM according to the corpus changes only, and allows user to trace
the evolution of the ontology with respect to the changes in the underlying corpus. Our framework
analyses Arabic textual resources, and matches them to Arabic Lexico-syntactic patterns in order to learn
new Concepts and Relations.
Supporting Arabic language is not that easy task, because current linguistic analysis tools are not efficient
enough to process unvocalized Arabic corpuses that rarely contain appropriate punctuation. So we tried
to build a flexible and freely configured framework whereas any linguistic analysis tool can be replaced by
more sophisticated one whenever it is available.
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