Working out a common task: design and evaluation of user-intelligent system collaboration

This paper describes the design and user evaluation of an intelligent user interface intended to mediate between users and an Adaptive Information Extraction (AIE) system. The design goal was to support a synergistic and cooperative work. Laboratory tests showed the approach was efficient and effective; focus groups were run to assess its ease of use. Logs, user satisfaction questionnaires, and interviews were exploited to investigate the interaction experience. We found that user’ attitude is mainly hierarchical with the user wishing to control and check the system’s initiatives. However when confidence in the system capabilities rises, a more cooperative interaction is adopted

Using Recurrent Neural Networks in a Distributed Computing Environment for Predicting Time-Variant Data

By applying the concepts of machine learning, the aim is to create a program that utilizes neural networks to analyze the wait times at various Florida theme parks. These parks include SeaWorld, Busch Gardens, both Universal parks, and all four Walt Disney World parks. The project hinges on a distributed computing architecture that divides the work as assigned by a master, rather than strictly parallelizing the code. The technology used throughout this project is hosted on Amazon Web Services, utilizing their Relational Database Service and Sagemaker platforms. MySQL, Python, and Tensorflow are the core software technologies running on this infrastructure. Each of these programs plays a role in creating a complete solution towards creating a recurrent neural network that delivers a list of wait times synthesized for the following hour that users can benefit from in real time. For our analysis of the network\u27s validity, we will create a statistical distribution for the error present in each ride’s prediction. This will be performed on a testing data set, which is composed of twenty percent of the overall data chosen at random

Using Protege for automatic ontology instantiation

This paper gives an overview on the use of Protégé in the Artequakt system, which integrated Protégé with a set of natural language tools to automatically extract knowledge about artists from web documents and instantiate a given ontology. Protégé was also linked to structured templates that generate documents from the knowledge fragments it maintains

Web based knowledge extraction and consolidation for automatic ontology instantiation

The Web is probably the largest and richest information repository available today. Search engines are the common access routes to this valuable source. However, the role of these search engines is often limited to the retrieval of lists of potentially relevant documents. The burden of analysing the returned documents and identifying the knowledge of interest is therefore left to the user. The Artequakt system aims to deploy natural language tools to automatically ex-tract and consolidate knowledge from web documents and instantiate a given ontology, which dictates the type and form of knowledge to extract. Artequakt focuses on the domain of artists, and uses the harvested knowledge to gen-erate tailored biographies. This paper describes the latest developments of the system and discusses the problem of knowledge consolidation

Automatic extraction of knowledge from web documents

A large amount of digital information available is written as text documents in the form of web pages, reports, papers, emails, etc. Extracting the knowledge of interest from such documents from multiple sources in a timely fashion is therefore crucial. This paper provides an update on the Artequakt system which uses natural language tools to automatically extract knowledge about artists from multiple documents based on a predefined ontology. The ontology represents the type and form of knowledge to extract. This knowledge is then used to generate tailored biographies. The information extraction process of Artequakt is detailed and evaluated in this paper

Generating adaptive hypertext content from the semantic web

Accessing and extracting knowledge from online documents is crucial for therealisation of the Semantic Web and the provision of advanced knowledge services. The Artequakt project is an ongoing investigation tackling these issues to facilitate the creation of tailored biographies from information harvested from the web. In this paper we will present the methods we currently use to model, consolidate and store knowledge extracted from the web so that it can be re-purposed as adaptive content. We look at how Semantic Web technology could be used within this process and also how such techniques might be used to provide content to be published via the Semantic Web

Shear design of HSC beams with combination of links and horizontal web steel

The existing recommendations in Eurocode 2 and the British Code of Practice for the shear design of beams are derived from research conducted essentially on normal-strength concrete (NSC) with cube strengths up to 50 MPa, and it was found that the shear strengths of high-strength concrete (HSC) members made with limestone aggregate are below the characteristic resistances of identical NSC members. Previous experimental tests have also shown that significant differences exist in the angle of crack of shear failure of NSC and HSC. This paper presents data from five beam tests, which demonstrate that HSC with limestone aggregate has a reduced shear strength compared with NSC made with gravel and thus shows a gap in knowledge in the design approach to shear resistance of HSC beams. Previous investigations have suggested that horizontal web steels can contribute to the overall shear resistance of a reinforced concrete member in conjunction with the other constituents, concrete, tension and shear steel. The paper also presents data from tests on 11 beam tests and shows that the shear resistance of HSC beams is highly dependent on dowel action resulting from horizontal web bars positioned at the centre of the depth of the beam. Past attempts to quantify this dowel action are investigated and an improved design rule is proposed

Evaluating the semantic web: a task-based approach

The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape

On the emergent Semantic Web and overlooked issues

The emergent Semantic Web, despite being in its infancy, has already received a lotof attention from academia and industry. This resulted in an abundance of prototype systems and discussion most of which are centred around the underlying infrastructure. However, when we critically review the work done to date we realise that there is little discussion with respect to the vision of the Semantic Web. In particular, there is an observed dearth of discussion on how to deliver knowledge sharing in an environment such as the Semantic Web in effective and efficient manners. There are a lot of overlooked issues, associated with agents and trust to hidden assumptions made with respect to knowledge representation and robust reasoning in a distributed environment. These issues could potentially hinder further development if not considered at the early stages of designing Semantic Web systems. In this perspectives paper, we aim to help engineers and practitioners of the Semantic Web by raising awareness of these issues

Evaluating semantic relations by exploring ontologies on the Semantic Web

We investigate the problem of evaluating the correctness of a semantic relation and propose two methods which explore the increasing number of online ontologies as a source of evidence for predicting correctness. We obtain encouraging results, with some of our measures reaching average precision values of 75%