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تحليل وثوقية المعلومات
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Syrian Virtual University رسالة ماجستير
Publication date
2015
fields
Informatics Engineering
and research's language is
العربية
Created by
Shamra Editor
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إن معظم محركات البحث المعروفة مثل جوجل هي محركات بحث نصية تطابق المفردات المدخلة فقط من قبل المستخدم وتسترجع قائمة من النتائج، يحقق نموذج محرك البحث المقترح في هذه الاطروحة حلا أفضل بشكل لا يطابق المفردات المدخلة من قبل المستخدم فقط، بل ويطابق المفردات الأخرى التي لها علاقة مع المفردات المعطات في الأنطولوجيات الداعمة له.
Most of the well-known search engines such as Google are text search engines that match the vocabulary entered only by the user and retrieve a list of results. The search engine model proposed in this thesis achieves a better solution that does not match the vocabulary entered by the user only, but rather matches other words that are related. With the vocabulary statements in the ontologies supporting it.
References used
M. Horridge, H. Knublauch, A. Recto, R. Stevens, and C. Wroe, "A practical guideto building owl ontologies using the protege-owl plugin and co-ode tools," vol. 27, pp. 0-117, 2004.
Bing - Search API : متوفر على http://www.bing.com/developers/s/APIBasics.html
WordNet Search - 3.1 - Princeton University – : متوافر على http://wordnetweb.princeton.edu/perl/webwn
A. Hogan, A. Harth, J. Umrich, S. Kinsella, A. Polleres, and S. Decker, "Searching and browsing linked data with swse: the semantic web search engine," Web Semantics: Science, Services and Agents on the World Wide Web, vol. 9, no. 4, 2012. [Online]. Available: http://www.websemanticsjournal.org/index.php/ps/article/view/240
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تحليل الشبكات في أنظمة المعلومات الجغرافية ...
تمثل الشبكة مجموعة من الخطوط والنقاط التي تمثل الكائنات الجغرافية المتصلة مع بعضها البعض والتي من خلالها تتحرك الموارد (سيارات , مياه , كهرباء , غاز....الخ)
1- أنظمة تدفق موجهة
يتحرك التدفق من المصا
در إلى المصارف
الموارد لا تملك القدرة على اتخاذ قرارات المسير
(على سبيل المثال، نظام النهر)
2- أنظمة تدفق غير موجهة
النظام لا يتحكم بشكل كامل بعملية التدفق
الموارد تملك القدرة على اتخاذ قرارات المسير الخاصة بها
(على سبيل المثال، نظام السير)
Free and Open Source software (FOSS) is one of computer software, which source code can be accessed, freely used, modify, and distribute by anyone. It is produced by many of people or organizations, and distributed under licenses that comply with the
open source definition. This software has recently begun to play an important role in the academic and scientific research field, as in the professional field. In the past few decades, Geographic Information Systems (GIS) has seen very high growth rate, and this development included each of commercial and open source GIS software.
This research aims to show the great potential of Free and Open Source Geographic Information Systems (FOSS_GIS), and motivating to adopt it in developing countries, as a means to reduce licensing costs, promote local technological development through access to the source code and developing these systems. A case study is taken, in which we have tried to highlight the most important advantages of this software (i.e. FOSS_GIS), such as ease of implementation and good use, the ability to analyze and display of spatial data, professional maps production, and functionality emulator to commercial GIS software. The case study included the methodology of spatial suitability analysis, which is one of the main tasks of GIS; this methodology has been applied to choose the optimal site of urban project in Sheikh Badr area (Tartous Governorate) by proposing a set of general conditions. Using optimal site selection analysis of urban project helps to avoid the indiscriminate expansion and the irregular land-use. The free and open source software QGIS was used in this research, as well as the algorithms and tools of GRASS and SAGA softwares.
Key Words: FOSS_GIS, spatial analysis, QGIS, optimal site selection, Sheikh Bader area
Inspired by mutual information (MI) based feature selection in SVMs and logistic regression, in this paper, we propose MI-based layer-wise pruning: for each layer of a multi-layer neural network, neurons with higher values of MI with respect to prese
rved neurons in the upper layer are preserved. Starting from the top softmax layer, layer-wise pruning proceeds in a top-down fashion until reaching the bottom word embedding layer. The proposed pruning strategy offers merits over weight-based pruning techniques: (1) it avoids irregular memory access since representations and matrices can be squeezed into their smaller but dense counterparts, leading to greater speedup; (2) in a manner of top-down pruning, the proposed method operates from a more global perspective based on training signals in the top layer, and prunes each layer by propagating the effect of global signals through layers, leading to better performances at the same sparsity level. Extensive experiments show that at the same sparsity level, the proposed strategy offers both greater speedup and higher performances than weight-based pruning methods (e.g., magnitude pruning, movement pruning).
Sentiment analysis aims to detect the overall sentiment, i.e., the polarity of a sentence, paragraph, or text span, without considering the entities mentioned and their aspects. Aspect-based sentiment analysis aims to extract the aspects of the given
target entities and their respective sentiments. Prior works formulate this as a sequence tagging problem or solve this task using a span-based extract-then-classify framework where first all the opinion targets are extracted from the sentence, and then with the help of span representations, the targets are classified as positive, negative, or neutral. The sequence tagging problem suffers from issues like sentiment inconsistency and colossal search space. Whereas, Span-based extract-then-classify framework suffers from issues such as half-word coverage and overlapping spans. To overcome this, we propose a similar span-based extract-then-classify framework with a novel and improved heuristic. Experiments on the three benchmark datasets (Restaurant14, Laptop14, Restaurant15) show our model consistently outperforms the current state-of-the-art. Moreover, we also present a novel supervised movie reviews dataset (Movie20) and a pseudo-labeled movie reviews dataset (moviesLarge) made explicitly for this task and report the results on the novel Movie20 dataset as well.
Continual learning has become increasingly important as it enables NLP models to constantly learn and gain knowledge over time. Previous continual learning methods are mainly designed to preserve knowledge from previous tasks, without much emphasis o
n how to well generalize models to new tasks. In this work, we propose an information disentanglement based regularization method for continual learning on text classification. Our proposed method first disentangles text hidden spaces into representations that are generic to all tasks and representations specific to each individual task, and further regularizes these representations differently to better constrain the knowledge required to generalize. We also introduce two simple auxiliary tasks: next sentence prediction and task-id prediction, for learning better generic and specific representation spaces. Experiments conducted on large-scale benchmarks demonstrate the effectiveness of our method in continual text classification tasks with various sequences and lengths over state-of-the-art baselines. We have publicly released our code at https://github.com/GT-SALT/IDBR.
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