Semantic Web Service Engineering: Annotation Based Approach

Web services are an emerging paradigm which aims at implementing software components in the Web. They are based on syntactic standards, notably WSDL. Semantic annotation of Web services provides better qualitative and scalable solutions to the areas of service interoperation, service discovery, service composition and process orchestration. Manual annotation is a time-consuming process which requires deep domain knowledge and consistency of interpretation within annotation teams. Therefore, we propose an approach for semi-automatically annotating WSDL Web services descriptions. This is allowed by Semantic Web Service Engineering. The annotation approach consists of two main processes: categorization and matching. Categorization process consists in classifying WSDL service description to its corresponding domain. Matching process consists in mapping WSDL entities to pre-existing domain ontology. Both categorization and matching rely on ontology matching techniques. A tool has been developed and some experiments have been carried out to evaluate the proposed approach

Dynamic Annotation of Search Results from Web Databases

The Internet provides a great extent of beneficial knowledge which is usually formatted for its users, which makes it troublesome to extract relevant data from diverse sources. The World Wide Web plays a major role as all kinds of information repository and has been very success full in disseminating information to users. For the encoded data units to be machine process able, which is essential for many applications such as deep web data collection and internet comparison shopping, they need to be extracted out and allot meaningful labels. This paper deals with the automatic annotation of Search result records from the multiple web databases. Search result presents an automatic annotation approach that first aligns the data units on a result page into different groups such that the data in the same group have the same semantic. Then for each group annotate it from different aspects and aggregate the different annotations to predict a final annotation label for it. Finally wrapper is automatically generated by the automatic tag matching weight method. DOI: 10.17762/ijritcc2321-8169.15070

Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation

Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: (1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and (2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a second opinion for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by 77% w.r.t. learning-based segmentation methods using pixel-wise labels for training

Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation

Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: (1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and (2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a second opinion for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ∼77% w.r.t. learning-based segmentation methods using pixel-wise labels for training

From Wrapping to Knowledge

One the most challenging problems for Enterprise Information Integration is to deal with heterogeneous information sources on the Web. The reason is that they usually provide information that is in human-readable form only, which makes it difficult for a software agent to understand it. Current solutions build on the idea of annotating the information with semantics. If the information is unstructured, proposals such as S-CREAM, MnM, or Armadillo may be effective enough since they rely on using natural language processing techniques; furthermore, their accuracy can be improved by using redundant information on the Web, as C-PANKOW has proved recently. If the information is structured and closely related to a back-end database, Deep Annotation ranges among the most effective proposals, but it requires the information providers to modify their applications; if Deep Annotation is not applicable, the easiest solution consists of using a wrapper and transforming its output into annotations. In this paper, we prove that this transformation can be automated by means of an efficient, domain-independent algorithm. To the best of our knowledge, this is the first attempt to devise and formalize such a systematic, general solution.Comisión Interministerial de Ciencia y Tecnología TIC2003-02737-C02-0