Semantic web service automation with lightweight annotations

Web services, both RESTful and WSDL-based, are an increasingly important part of the Web. With the application of semantic technologies, we can achieve automation of the use of those services. In this paper, we present WSMO-Lite and MicroWSMO, two related lightweight approaches to semantic Web service description, evolved from the WSMO framework. WSMO-Lite uses SAWSDL to annotate WSDL-based services, whereas MicroWSMO uses the hRESTS microformat to annotate RESTful APIs and services. Both frameworks share an ontology for service semantics together with most of automation algorithms

Semantic information systems engineering: A query-based approach for semi-automatic annotation of web services

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.There has been an increasing interest in Semantic Web services (SWS) as a proposed solution to facilitate automatic discovery, composition and deployment of existing syntactic Web services. Successful implementation and wider adoption of SWS by research and industry are, however, profoundly based on the existence of effective and easy to use methods for service semantic description. Unfortunately, Web service semantic annotation is currently performed by manual means. Manual annotation is a difficult, error-prone and time-consuming task and few approaches exist aiming to semi-automate that task. Existing approaches are difficult to use since they require ontology building. Moreover, these approaches employ ineffective matching methods and suffer from the Low Percentage Problem. The latter problem happens when a small number of service elements - in comparison to the total number of elements – are annotated in a given service. This research addresses the Web services annotation problem by developing a semi-automatic annotation approach that allows SWS developers to effectively and easily annotate their syntactic services. The proposed approach does not require application ontologies to model service semantics. Instead, a standard query template is used: This template is filled with data and semantics extracted from WSDL files in order to produce query instances. The input of the annotation approach is the WSDL file of a candidate service and a set of ontologies. The output is an annotated WSDL file. The proposed approach is composed of five phases: (1) Concept extraction; (2) concept filtering and query filling; (3) query execution; (4) results assessment; and (5) SAWSDL annotation. The query execution engine makes use of name-based and structural matching techniques. The name-based matching is carried out by CN-Match which is a novel matching method and tool that is developed and evaluated in this research. The proposed annotation approach is evaluated using a set of existing Web services and ontologies. Precision (P), Recall (R), F-Measure (F) and Percentage of annotated elements are used as evaluation metrics. The evaluation reveals that the proposed approach is effective since - in relation to manual results - accurate and almost complete annotation results are obtained. In addition, high percentage of annotated elements is achieved using the proposed approach because it makes use of effective ontology extension mechanisms

Map Generation from Large Scale Incomplete and Inaccurate Data Labels

Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc.. In this paper we present progress in developing an algorithmic pipeline and distributed compute system that automates the process of map creation using high resolution aerial images. Unlike previous studies, most of which use datasets that are available only in a few cities across the world, we utilizes publicly available imagery and map data, both of which cover the contiguous United States (CONUS). We approach the technical challenge of inaccurate and incomplete training data adopting state-of-the-art convolutional neural network architectures such as the U-Net and the CycleGAN to incrementally generate maps with increasingly more accurate and more complete labels of man-made infrastructure such as roads and houses. Since scaling the mapping task to CONUS calls for parallelization, we then adopted an asynchronous distributed stochastic parallel gradient descent training scheme to distribute the computational workload onto a cluster of GPUs with nearly linear speed-up.Comment: This paper is accepted by KDD 202

A Brief History of Web Crawlers

Web crawlers visit internet applications, collect data, and learn about new web pages from visited pages. Web crawlers have a long and interesting history. Early web crawlers collected statistics about the web. In addition to collecting statistics about the web and indexing the applications for search engines, modern crawlers can be used to perform accessibility and vulnerability checks on the application. Quick expansion of the web, and the complexity added to web applications have made the process of crawling a very challenging one. Throughout the history of web crawling many researchers and industrial groups addressed different issues and challenges that web crawlers face. Different solutions have been proposed to reduce the time and cost of crawling. Performing an exhaustive crawl is a challenging question. Additionally capturing the model of a modern web application and extracting data from it automatically is another open question. What follows is a brief history of different technique and algorithms used from the early days of crawling up to the recent days. We introduce criteria to evaluate the relative performance of web crawlers. Based on these criteria we plot the evolution of web crawlers and compare their performanc

Supporting personalised content management in smart health information portals

Information portals are seen as an appropriate platform for personalised healthcare and wellbeing information provision. Efficient content management is a core capability of a successful smart health information portal (SHIP) and domain expertise is a vital input to content management when it comes to matching user profiles with the appropriate resources. The rate of generation of new health-related content far exceeds the numbers that can be manually examined by domain experts for relevance to a specific topic and audience. In this paper we investigate automated content discovery as a plausible solution to this shortcoming that capitalises on the existing database of expert-endorsed content as an implicit store of knowledge to guide such a solution. We propose a novel content discovery technique based on a text analytics approach that utilises an existing content repository to acquire new and relevant content. We also highlight the contribution of this technique towards realisation of smart content management for SHIPs.<br /

Taxonomy Induction using Hypernym Subsequences

We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimumcost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms stateof- the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, no previous approaches have been empirically proven to manifest noise-robustness in the input vocabulary

Personalized learning paths based on Wikipedia article statistics

We propose a new semi-automated method for generating personalized learning paths from the Wikipediaonline encyclopedia by following inter-article hyperlink chains based on various rankings that are retrieved from the statistics of the articles. Alternative perspectives for learning topics are achieved when the next hyperlink to access is selected based on hierarchy of hyperlinks, repetition of hyperlink terms, article size, viewing rate, editing rate, or user-defined weighted mixture of them all. We have implemented the method in a prototype enabling the learner to build independently concept maps following her needs and consideration. A list of related concepts is shown in a desired type of ranking to label new nodes (titles of target articles for current hyperlinks) accompanied with parsed explanation phrases from the sentences surrounding each hyperlink to label directed arcs connecting nodes. In experiments the alternative ranking schemes well supported various learning needs suggesting new pedagogical networking practices.Peer reviewe