Abstract This paper presents an attempt to show the efficiency of some search engines in dealing with Arabic keywords. This can be achieved by comparing the number of retrieved pages, retrieving time, and stability (in both the number of retrieved pages and the order for each retrieved page) for each one of the selected 20 Arabic keywords (with its roots) that... This paper presents an attempt to show the efficiency of some search engines in dealing with Arabic keywords. This can be achieved by comparing the number of retrieved pages, retrieving time, and stability (in both the number of retrieved pages and the order for each retrieved page) for each one of the selected 20 Arabic keywords (with its roots) that were entered to the selected four search engines at the same time. Google, Yahoo, Al-hoodhood and Ayna were selected as a test bed for the experiment. The obtained results showed that Google was the best search engine among the four selected search engines, the experiments for the results of the stability of the selected search engines took 10 weeks to be obtained. Read More Read Less

Abstract Medical social media data offers an additional source of information on medical issues. Web 2.0 or Medicine 2.0, respectively, open new ways in providing and accessing this information. The medical content available is highly diverse: It can deal with diseases, medical treatments, medications and the like to which in turn different aspects can be considered. Presented content can rather deal... Medical social media data offers an additional source of information on medical issues. Web 2.0 or Medicine 2.0, respectively, open new ways in providing and accessing this information. The medical content available is highly diverse: It can deal with diseases, medical treatments, medications and the like to which in turn different aspects can be considered. Presented content can rather deal with experiences or can provide informative insights into a topic. Making this diversity visible to a user could among others help to recognise unknown facets of a topic or help to get an overview on the information content of specific data sources. The objective of this work is to introduce the problem of diversity of medical Web content and to present a variety of methods for identifying and analysing content diversity in this data. The approaches base on information extraction technologies and involve domain knowledge. They are applied to a set of medical social media data for which the content diversity is studied. Furthermore, it will be shown how diversity information related to the subjectivity can be used for ranking to improve user satisfaction. Read More Read Less

Abstract Text based pictures called text art or ASCII art are often used in Web pages, email text and so on. They enrich expression in text data, but they can be noise for text processing and display of text. For example, they can be obstacle for text-to-speech software and natural language processing, and some of them lose their shape in small... Text based pictures called text art or ASCII art are often used in Web pages, email text and so on. They enrich expression in text data, but they can be noise for text processing and display of text. For example, they can be obstacle for text-to-speech software and natural language processing, and some of them lose their shape in small display devices. With Text art extraction methods, which detects the area of text art in a given text data, we can ignore text arts in text data or replace them with other strings. Because a text data may include one or more natural languages, it is desirable that text art extraction methods are language-independent. In this paper, we propose a decision tree-based text art extraction method without any language-dependent text attribute. Our method uses attributes of a given text data which represent how the text data looks like text art while previously proposed methods use attributes of a given text data which represent how the text data looks like a specific language text. We tested 63 combinations of 7 text attributes including language-dependent attributes and language-independent attributes for text art recognition. The results shows that a combination of language-independent attributes is the best for text art recognition. The attributes are an attribute based on data compression ratio by Run Length Encoding and two text attributes based on text size. We also evaluated the performance of our text art extraction method with the language-independent attributes by an experiment. Read More Read Less

Abstract This paper presents an attempt to show the efficiency of some search engines in handling Arabic keywords. To achieve this, a comparison was made among the number of retrieved pages, retrieving time, and stability (in both the number of retrieved pages and the order for each retrieved page) for each of the 20 Arabic keywords selected (with its roots) after... This paper presents an attempt to show the efficiency of some search engines in handling Arabic keywords. To achieve this, a comparison was made among the number of retrieved pages, retrieving time, and stability (in both the number of retrieved pages and the order for each retrieved page) for each of the 20 Arabic keywords selected (with its roots) after being simultaneously entered to the selected four search engines. Search engines tested in this experiment were Google, Yahoo, Al-hoodhood and Ayna. Google was the best search engine among the four selected search engines according to the results obtained over 10 weeks of experimenting. Read More Read Less

Abstract Character recognition is an important task in pattern analysis that aims to give significance to handwritten data without users’ intervention. Although, an intensive research has been devoted to this problem, it remains a challenging task as humans need to interact with computer in the easiest way. This work attempts to incorporate some meta-heuristics as guidelines searching for the best solution... Character recognition is an important task in pattern analysis that aims to give significance to handwritten data without users’ intervention. Although, an intensive research has been devoted to this problem, it remains a challenging task as humans need to interact with computer in the easiest way. This work attempts to incorporate some meta-heuristics as guidelines searching for the best solution of handwritten digits recognition problem namely the particle swarm optimizer and variations of the bees’ algorithm. The bees’ algorithm is a variant of evolutionary optimization that take inspiration from the foraging behavior of honey bees where individuals called bees are used to perform a neighborhood search in joint with a random search as an attempt to achieve a good balance between exploration and exploitation abilities. We show that this method can be adapted to handwritten characters recognition and can be effectively combined with a neural network classifier which results in a good quality on a wide range of real data against that of the k-nearest neighbour classifier and the back-propagation training algorithm. Read More Read Less

Abstract Discovering matches and mappings between heteroge neous, independently designed data sources, is a challenging issue in data exchange and data integration. To deal with the problem we enrich XML schemas with seman tic information from a domain ontology by annotating the schema. In this paper we discuss how the annotations can be used to establish matching and mapping formulas be... Discovering matches and mappings between heteroge neous, independently designed data sources, is a challenging issue in data exchange and data integration. To deal with the problem we enrich XML schemas with seman tic information from a domain ontology by annotating the schema. In this paper we discuss how the annotations can be used to establish matching and mapping formulas be tween XML schemas automatically and propose a set of rules to infer these formulas. Read More Read Less

Abstract Natural language quantifiers can be classified according to their semantic type in addition to their syntactic expression. Quantification in Natural language (NL) has two types, ambiguous quantification and Unambiguous quantification. Unambiguous quantification is very simple and also called exact quantification, but ambiguous quantification is complex and also called inexact quantification. Inexact quantifiers include “many, much, a lot of, several, some,... Natural language quantifiers can be classified according to their semantic type in addition to their syntactic expression. Quantification in Natural language (NL) has two types, ambiguous quantification and Unambiguous quantification. Unambiguous quantification is very simple and also called exact quantification, but ambiguous quantification is complex and also called inexact quantification. Inexact quantifiers include “many, much, a lot of, several, some, any, a few, little, fewer, fewest, Less, greater, at least, at most, more, exactly”. To identify the problems of Natural language Quantification, convert these Natural Language sentences into First order logic by attaching weights and classify these complex sentences by using Markov Logic. Read More Read Less