Objectives/Methods

This study aims to extract relations between entities from Arabic text. RelationExtraction is one of the most important tasks in text mining. Relation extraction is considered as a main step for many applications such as extracting triples from the text, Question Answering and Ontology building. However, extracting relations from the Arabic text is a difficult task compared to English due to lack of annotated Arabic corpora. This paper proposes a method for extracting relations from Arabic text based on ArabicWikipedia articles characteristics. The propose system extracts sentences that contain principle entity, secondary entity and relation from Wikipedia article, then we use WordNet and DBpedia to build the training set. Finally Naive Bayes Classifier is used to train and test the datasets.

Finding

There are few works to extract relations from Arabic text. These works depend on classification, clustering and rule based.

Application/improvement

The experiments show the effectiveness of the proposed approach which achieves high precision with 89% for classifying 19 type of semantic relations.

One of the crucial tasks in the Semantic Web research is extracting information from unstructured text and converting it into semantic form to be machine understandable. This semantic representation is useful for many purposes such as question answering, summarization and information retrieval. This paper provides a system for converting Arabic text into Resource Description Framework (RDF) semantic format. The proposed system includes syntactical parser that used to extract triples (subject-predicate-object) from preprocessed Arabic text. Moreover, name entity recognition is used to extract entities which mapped with DBpedia to get URIs. Finally, the corresponding RDF representation which captures semantics of Arabic text is generated.

Dense multi-view image reconstruction has been a focal point of research for an extended period, with recent surges in interest. The utilization of multi-view images offers solutions to numerous challenges and amplifies the effectiveness of various applications including 3D reconstruction, de-occlusion, depth sensing, saliency detection, and identifying salient objects. This paper introduces an approach to reconstructing high-density light field (LF) images, addressing the inherent challenge of balancing angular and spatial resolution caused by limited sensor resolution. We introduce an innovative approach to reconstructing LF images through a CNN-based network that combines spatial and epipolar features in both initial and deep feature extraction phases. Our network utilizes angular information during upsampling and employs dual feature extraction to effectively analyze horizontal and vertical epipolar data. Weight sharing within the CNN block between horizontal and vertically transposed stacks enhances quality while preserving model compactness. The outcomes of experiments carried out on real-world and synthetic datasets demonstrate the effectiveness of our method, showcasing its superior performance in both inference speed and reconstruction quality when compared to state-of-the-art (SOTA) techniques.

Recent research on dense multi-view image reconstruction has attracted considerable attention, due to its enhancement of applications such as 3D reconstruction, de-occlusion, depth sensing, saliency detection, and prominent object identification. This paper introduces a method for reconstructing high-density light field images, addressing the challenge of balancing angular and spatial resolution within the constraints of sensor resolution. We propose a three-stage network architecture for LF reconstruction that processes dense epipolar, spatial, and angular information efficiently. Our network processes epipolar information in the first stage, spatial information in the second stage, and angular information in the third stage. By extracting quadrilateral epipolar features from multiple directions, our model constructs a robust feature hierarchy for accurate reconstruction. We employ weight sharing in the initial stage to …