RDF Knowledge Graph Visualization From a Knowledge Extraction System

In this paper, we present a system to visualize RDF knowledge graphs. These graphs are obtained from a knowledge extraction system designed by GEOLSemantics. This extraction is performed using natural language processing and trigger detection. The user can visualize subgraphs by selecting some ontology features like concepts or individuals. The system is also multilingual, with the use of the annotated ontology in English, French, Arabic and Chinese

DCU 250 Arabic dependency bank: an LFG gold standard resource for the Arabic Penn treebank

This paper describes the construction of a dependency bank gold standard for Arabic, DCU 250 Arabic Dependency Bank (DCU 250), based on the Arabic Penn Treebank Corpus (ATB) (Bies and Maamouri, 2003; Maamouri and Bies, 2004) within the theoretical framework of Lexical Functional Grammar (LFG). For parsing and automatically extracting grammatical and lexical resources from treebanks, it is necessary to evaluate against established gold standard resources. Gold standards for various languages have been developed, but to our knowledge, such a resource has not yet been constructed for Arabic. The construction of the DCU 250 marks the first step towards the creation of an automatic LFG f-structure annotation algorithm for the ATB, and for the extraction of Arabic grammatical and lexical resources

Early texts on Hindu-Arabic calculation

This article describes how the decimal place value system was transmitted from India via the Arabs to the West up to the end of the fifteenth century. The arithmetical work of al-Khw¯arizm¯ı’s, ca. 825, is the oldest Arabic work on Indian arithmetic of which we have detailed knowledge. There is no known Arabic manuscript of this work; our knowledge of it is based on an early reworking of a Latin translation. Until some years ago, only one fragmentary manuscript of this twelfth-century reworking was known (Cambridge, UL, Ii.6.5). Another manuscript that transmits the complete text (New York, Hispanic Society of America, HC 397/726) has made possible a more exact study of al-Khw¯arizm¯ı’s work. This article gives an outline of this manuscript’s contents and discusses some characteristics of its presentation

Unconstrained Scene Text and Video Text Recognition for Arabic Script

Building robust recognizers for Arabic has always been challenging. We demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid architecture in recognizing Arabic text in videos and natural scenes. We outperform previous state-of-the-art on two publicly available video text datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a new Arabic scene text dataset and establish baseline results. For scripts like Arabic, a major challenge in developing robust recognizers is the lack of large quantity of annotated data. We overcome this by synthesising millions of Arabic text images from a large vocabulary of Arabic words and phrases. Our implementation is built on top of the model introduced here [37] which is proven quite effective for English scene text recognition. The model follows a segmentation-free, sequence to sequence transcription approach. The network transcribes a sequence of convolutional features from the input image to a sequence of target labels. This does away with the need for segmenting input image into constituent characters/glyphs, which is often difficult for Arabic script. Further, the ability of RNNs to model contextual dependencies yields superior recognition results.Comment: 5 page