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Register a new userKnowledge Discovery in Semantic Web (Information Retrieval from Knowledge Bases)
استكشاف المعارف في الويب الدلالي (استرجاع المعلومات من قواعد المعرفة)
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يُعتبر الويب الدلالي ثورة جديدة في عالم الويب، حيث تصبح المعلومات و البيانات قابلة للمعالجة منطقياً من قِبل برامج الحاسوب، بحيث تتحول تلك المعلومات و البيانات إلى شبكة معارف ذات معنى مُحدّد. يُشكّل الويب الدلالي مستقبل الويب الحالي مع الإشارة إلى أن البحوث و الدراسات العربية ما زالت قليلة نسبياً في هذا المجال. نعرض في هذه الورقة دراسة مرجعية عن الويب الدلالي و الطرق المختلفة لاستكشاف المعارف و استنباط المعلومات المفيدة من الكم الهائل من البيانات التي يوفرها الويب. كما نُقدّم مثال برمجي قمنا بتطويره كتطبيق لبعض التقانات التي يُقدمها الويب الدلالي و لاسيما طرق استكشاف المعارف فيه. نوفر في هذا المثال البرمجي المبسط بعض الخدمات المتعلقة بالتعليم العالي الحكومي السوري، مثل معلومات عن الجامعات الحكومية السورية كاسم الجامعة (الافتراضية، دمشق، تشرين، حلب و البعث)، موقع الجامعة، عنوان موقعها على شبكة الويب، عدد الطلاب فيها، و ملخص عن الجامعة، مما يُساعد العملاء الأذكياء على إيجاد تلك الخدمات ديناميكيا.
Semantic Web is a new revolution in the world of the Web, where information and data become viable for logical processing by computer programs. Where they are transformed into meaningful data network. Although Semantic Web is considered the future of World Wide Web, the Arabic research and studies are still relatively rare in this field. Therefore, this paper gives a reference study of Semantic Web and the different methods to explore the knowledge and discover useful information from the vast amount of data provided by the web. It gives a programming example like application of some of these techniques provided by the Semantic Web and methods to discover the knowledge of it. This simplified programming example provides services related to higher education Syrian government, such as information about the Syrian public universities like the name of the university (Syrian Virtual University, Tishreen, Aleppo, Damascus, and Al Baath), address of the university, its web site, number of students and a summary of the university, which helps intelligent agents to find those services dynamically.
References used
HEFLIN, J. D. Towards the Semantic Web: Knowledge Representation in a Dynamic Distributed Environment. University of Maryland, USA, 2001, 236
WALTON, C. Agency and the Semantic Web. Oxford University Press, USA, 2007, 249
HITZLER, P; KRöTZSCH, M; PARSIA, B, PATEL-SCHNEIDER, P.F; RUDOLPH, S. OWL 2 Web Ontology Language Primer. World Wide Web Consortium, 2013, 415
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Finally we tried to discuss the details of the various
implementations of these systems by explaining two platforms Gate and OpenCalais and comparing between their information
extraction systems and discuss the results.
Knowledge graphs (KGs) are widely used to store and access information about entities and their relationships. Given a query, the task of entity retrieval from a KG aims at presenting a ranked list of entities relevant to the query. Lately, an increa
sing number of models for entity retrieval have shown a significant improvement over traditional methods. These models, however, were developed for English KGs. In this work, we build on one such system, named KEWER, to propose SERAG (Semantic Entity Retrieval from Arabic knowledge Graphs). Like KEWER, SERAG uses random walks to generate entity embeddings. DBpedia-Entity v2 is considered the standard test collection for entity retrieval. We discuss the challenges of using it for non-English languages in general and Arabic in particular. We provide an Arabic version of this standard collection, and use it to evaluate SERAG. SERAG is shown to significantly outperform the popular BM25 model thanks to its multi-hop reasoning.
Codifying commonsense knowledge in machines is a longstanding goal of artificial intelligence. Recently, much progress toward this goal has been made with automatic knowledge base (KB) construction techniques. However, such techniques focus primarily
on the acquisition of positive (true) KB statements, even though negative (false) statements are often also important for discriminative reasoning over commonsense KBs. As a first step toward the latter, this paper proposes NegatER, a framework that ranks potential negatives in commonsense KBs using a contextual language model (LM). Importantly, as most KBs do not contain negatives, NegatER relies only on the positive knowledge in the LM and does not require ground-truth negative examples. Experiments demonstrate that, compared to multiple contrastive data augmentation approaches, NegatER yields negatives that are more grammatical, coherent, and informative---leading to statistically significant accuracy improvements in a challenging KB completion task and confirming that the positive knowledge in LMs can be re-purposed'' to generate negative knowledge.
هذه المقالة تحوي ترجمة وتلخيص وتوضيح للمذكور في الورقة البحثية المذكور اسمها أعلاه والموجودة في https://annals-csis.org/Volume_8/pliks/221.pdf , والتي تقوم باستخراج المعلومات الدلالية المهمة الموجودة في الوثائق والملفات والأوراق البحثية .
This study aims to identify the concept of Knowledge management as considering it
as one of the new management theory that many organization trying to apply it useful of
his advantage, also the aim was to identify the Knowledge procedures and cause
s of return
of libraries and information system to Knowledge management, in addition to identify
requester of Knowledge management application in the libraries. The research design was
the theory deism as most appropriate for this study. The results of this research show that
there was no significant different between libraries duties, information systems, and
Knowledge management procedures, also that the most important requisites of Knowledge
management application in libraries was existing of three elements which are:
organizational culture, organizational knowledge, knowledge management technology.
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