LODE : A Legal Ontology DevelopmentEnvironment Using Natural Language and Case Ontologies

this paper focuses on a construction of a domain ontology,i

A Legal Ontology DevelopmentEnvironment using a General Ontology

This paper discusses how to develop a legal ontology using a general ontology that has already been developed. In the development process, we must solvetwo hard issues. The one is to localize legal contexts in a general ontology in order to match a legal ontology with a general ontology. The other is to identify bugs in a developed legal ontology and refine them. Here is presented a method to match a legal concept with the most similar corresponding concept in a general ontology and also two strategies to refine a legal ontology, a static analysis based on the comparison between twoontologies and a dynamic analysis based on Inductive Logic Programming. Wehave just been implementing a software environmentt

Constructing a Legal Ontology from a General Ontology

This paper discusses how to construct a legal ontology from a general ontology that has already been developed. In the construction process, we must solve two hard issues. The one is to localize legal contexts in a general ontology in order to match a legal ontology with a general ontology. The other is to identify bugs in constructed legal ontology and refine them. Here is presented a new method to match a legal concept with the most similar concept in a general ontology and also two strategies to refine a legal ontology, a static analysis based on the comparison between two ontologies and a dynamic analysis based on Inductive Logic Programming. We have just been implementing a computer environment to help a user construct a legal ontology from a general ontology. The experimental results of matching and a static analysis are described and also a scenario for a dynamic analysis is presented. 1 Introduction In developing large scale of expert systems, we must build several kinds of kn..

A Legal Ontology Refinement Environmet using a General Ontology

This paper discusses how to construct a legal ontology using a general ontology that has already been developed. In the construction process, we must solvetwo hard issues. The one is to localize legal contexts in a general ontology in order to extract a legal ontology with a general ontology. The other is to identify bugs in a constructed legal ontology and refine them. Here is presented a method to match a legal concept with the most similar corresponding concept in a general ontology and also two strategies to refine a legal ontology, a static analysis based on the comparison between two ontologies and a dynamic analysis based on Inductive Logic Programming. We have just been implementing a software environment to help a user construct a legal ontology using a general ontology. The experimental results of matching and a static analysis are presented and also a scenario for a dynamic analysis is considered. 1 Introduction In developing large scale of expert systems, we must build sev..

Multi-Task Learning for Scene Text Image Super-Resolution with Multiple Transformers

Scene text image super-resolution aims to improve readability by recovering text shapes from low-resolution degraded text images. Although recent developments in deep learning have greatly improved super-resolution (SR) techniques, recovering text images with irregular shapes, heavy noise, and blurriness is still challenging. This is because networks with Convolutional Neural Network (CNN)-based backbones cannot sufficiently capture the global long-range correlations of text images or detailed sequential information about the text structure. In order to address this issue, this paper proposes a Multi-task learning-based Text Super-resolution (MTSR) Network to approach this problem. MTSR is a multi-task architecture for image reconstruction and SR. It uses transformer-based modules to transfer complementary features of the reconstruction model, such as noise removal capability and text structure information, to the SR model. In addition, another transformer-based module using 2D positional encoding is used to handle irregular deformations of the text. The feature maps generated from these two transformer-based modules are fused to attempt improvement of the visual quality of images with heavy noise, blurriness, and irregular deformations. Experimental results on the TextZoom dataset and several scene text recognition benchmarks show that our MTSR significantly improves the accuracy of existing text recognizers