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Register a new userتطوير قواعد التعلم العلاقاتية لاستخلاص المعلومات
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Aِl-Baath University أطروحة دكتوراه
Publication date
2018
fields
Mathematics
and research's language is
العربية
Created by
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في الآونة الأخيرة حدث تضخم للمعلومات على شكل أخبار ومقالات مختلفة وجزء كبير من هذه البيانات يكون بشكل غير منظم
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References used
الديب, بسام 2017 دراسة فعالية خوارزميات اكتشاف المعرفة على قواعد البيانات غرضية التوجه الطبعة الأولى كلية العلوم
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relation extraction systems have made extensive use of features generated
by linguistic analysis modules. Errors in these features lead to errors of
relation detection and classification. In this work, we depart from these
traditional approaches w
ith complicated feature engineering by introducing
a convolutional neural network for relation extraction that automatically
learns features from sentences and minimizes the dependence on external
toolkits and resources. Our model takes advantages of multiple window
sizes for filters and pre-trained word embeddings as an initializer on a nonstatic
architecture to improve the performance.
The huge growth in the data storing and the wide use of social networks and applications that depend mainly on complicated and interrelated relations between entities which need specific models of databases to store and retriev quickly and effectivel
y, the relational databases are no longer adequate in distributed systems and websites that deal with big data which have to be accessible and operable.
This paper explains briefly the challenges of relational databases and the reasons that led to emergence the non-relational databases (NoSQL), in addition to CAP theorem and some of NoSQL types, specially GraphQL
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.
Linguistic typology is an area of linguistics concerned with analysis of and comparison between natural languages of the world based on their certain linguistic features. For that purpose, historically, the area has relied on manual extraction of lin
guistic feature values from textural descriptions of languages. This makes it a laborious and time expensive task and is also bound by human brain capacity. In this study, we present a deep learning system for the task of automatic extraction of linguistic features from textual descriptions of natural languages. First, textual descriptions are manually annotated with special structures called semantic frames. Those annotations are learned by a recurrent neural network, which is then used to annotate un-annotated text. Finally, the annotations are converted to linguistic feature values using a separate rule based module. Word embeddings, learned from general purpose text, are used as a major source of knowledge by the recurrent neural network. We compare the proposed deep learning system to a previously reported machine learning based system for the same task, and the deep learning system wins in terms of F1 scores with a fair margin. Such a system is expected to be a useful contribution for the automatic curation of typological databases, which otherwise are manually developed.
This study aims to identify the impact of the use of information
technology to develop and improve the performance of human
resources at all the different levels of management in an
organization and the impact on job performance.
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