Matching Items (8)

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GCKEngine - An Algorithm for Automatic Ontology Building

Description

To facilitate the development of the Semantic Web, we propose in this thesis a general automatic ontology-building algorithm which, given a pool of potential terms and a set of relationships

To facilitate the development of the Semantic Web, we propose in this thesis a general automatic ontology-building algorithm which, given a pool of potential terms and a set of relationships to include in the ontology, can utilize information gathered from Google queries to build a full ontology for a certain domain. We utilized this ontology-building algorithm as part of a larger system to tag computer tutorials for three systems with different kinds of metadata, and index the tagged documents into a search engine. Our evaluation of the resultant search engine indicates that our automatic ontology-building algorithm is able to build relatively good-quality ontologies and utilize this ontology to effectively apply metadata to documents.

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Date Created
  • 2013-05

SMART SCHEDULING FOR INSTRUCTIONAL MODULE DEVELOPMENT SYSTEM

Description

Many organizational course design methodologies feature general guidelines for the chronological and time-management aspects of course design development. Proper course structure and instructional strategy pacing has been shown to facilitate

Many organizational course design methodologies feature general guidelines for the chronological and time-management aspects of course design development. Proper course structure and instructional strategy pacing has been shown to facilitate student knowledge acquisition of novel material. These course-scheduling details influencing student learning outcomes implies the need for an effective and tightly coupled component of an instructional module. The Instructional Module Development System, or IMODS, seeks to improve STEM, or ‘science, technology, engineering, and math’, education, by equipping educators with a powerful informational tool that helps guide course design by providing information based on contemporary research about pedagogical methodology and assessment practices. This is particularly salient within the higher-education STEM fields because many instructors come from backgrounds that are more technical and most Ph.Ds. in science fields have traditionally not focused on preparing doctoral candidates to teach. This thesis project aims to apply a multidisciplinary approach, blending educational psychology and computer science, to help improve STEM education. By developing an instructional module-scheduling feature for the Web-based IMODS, Instructional Module Development System, system, we can help instructors plan out and organize their course work inside and outside of the classroom, while providing them with relevant helpful research that will help them improve their courses. This article illustrates the iterative design process to gather background research on pacing of workload and learning activities and their influence on student knowledge acquisition, constructively critique and analyze pre-existing information technology (IT) scheduling tools, synthesize graphical user interface, or GUI, mockups based on the background research, and then implement a functional-working prototype using the IMODs framework.

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Date Created
  • 2016-05

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An ontology-based approach to attribute management in ABAC environment

Description

Attribute Based Access Control (ABAC) mechanisms have been attracting a lot of interest from the research community in recent times. This is especially because of the flexibility and extensibility it

Attribute Based Access Control (ABAC) mechanisms have been attracting a lot of interest from the research community in recent times. This is especially because of the flexibility and extensibility it provides by using attributes assigned to subjects as the basis for access control. ABAC enables an administrator of a server to enforce access policies on the data, services and other such resources fairly easily. It also accommodates new policies and changes to existing policies gracefully, thereby making it a potentially good mechanism for implementing access control in large systems, particularly in today's age of Cloud Computing. However management of the attributes in ABAC environment is an area that has been little touched upon. Having a mechanism to allow multiple ABAC based systems to share data and resources can go a long way in making ABAC scalable. At the same time each system should be able to specify their own attribute sets independently. In the research presented in this document a new mechanism is proposed that would enable users to share resources and data in a cloud environment using ABAC techniques in a distributed manner. The focus is mainly on decentralizing the access policy specifications for the shared data so that each data owner can specify the access policy independent of others. The concept of ontologies and semantic web is introduced in the ABAC paradigm that would help in giving a scalable structure to the attributes and also allow systems having different sets of attributes to communicate and share resources.

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Date Created
  • 2014

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A Quadruple-Based Text Analysis System for History and Philosophy of Science

Description

Computational tools in the digital humanities often either work on the macro-scale, enabling researchers to analyze huge amounts of data, or on the micro-scale, supporting scholars in the interpretation and

Computational tools in the digital humanities often either work on the macro-scale, enabling researchers to analyze huge amounts of data, or on the micro-scale, supporting scholars in the interpretation and analysis of individual documents. The proposed research system that was developed in the context of this dissertation ("Quadriga System") works to bridge these two extremes by offering tools to support close reading and interpretation of texts, while at the same time providing a means for collaboration and data collection that could lead to analyses based on big datasets. In the field of history of science, researchers usually use unstructured data such as texts or images. To computationally analyze such data, it first has to be transformed into a machine-understandable format. The Quadriga System is based on the idea to represent texts as graphs of contextualized triples (or quadruples). Those graphs (or networks) can then be mathematically analyzed and visualized. This dissertation describes two projects that use the Quadriga System for the analysis and exploration of texts and the creation of social networks. Furthermore, a model for digital humanities education is proposed that brings together students from the humanities and computer science in order to develop user-oriented, innovative tools, methods, and infrastructures.

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Date Created
  • 2014

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Template-Based Question Answering over Linked Data using Recursive Neural Networks

Description

The Semantic Web contains large amounts of related information in the form of knowledge graphs such as DBpedia. These knowledge graphs are typically enormous and are not easily accessible for

The Semantic Web contains large amounts of related information in the form of knowledge graphs such as DBpedia. These knowledge graphs are typically enormous and are not easily accessible for users as they need specialized knowledge in query languages (such as SPARQL) as well as deep familiarity of the ontologies used by these knowledge graphs. So, to make these knowledge graphs more accessible (even for non- experts) several question answering (QA) systems have been developed over the last decade. Due to the complexity of the task, several approaches have been undertaken that include techniques from natural language processing (NLP), information retrieval (IR), machine learning (ML) and the Semantic Web (SW). At a higher level, most question answering systems approach the question answering task as a conversion from the natural language question to its corresponding SPARQL query. These systems then utilize the query to retrieve the desired entities or literals. One approach to solve this problem, that is used by most systems today, is to apply deep syntactic and semantic analysis on the input question to derive the SPARQL query. This has resulted in the evolution of natural language processing pipelines that have common characteristics such as answer type detection, segmentation, phrase matching, part-of-speech-tagging, named entity recognition, named entity disambiguation, syntactic or dependency parsing, semantic role labeling, etc.

This has lead to NLP pipeline architectures that integrate components that solve a specific aspect of the problem and pass on the results to subsequent components for further processing eg: DBpedia Spotlight for named entity recognition, RelMatch for relational mapping, etc. A major drawback in this approach is error propagation that is a common problem in NLP. This can occur due to mistakes early on in the pipeline that can adversely affect successive steps further down the pipeline. Another approach is to use query templates either manually generated or extracted from existing benchmark datasets such as Question Answering over Linked Data (QALD) to generate the SPARQL queries that is basically a set of predefined queries with various slots that need to be filled. This approach potentially shifts the question answering problem into a classification task where the system needs to match the input question to the appropriate template (class label).

This thesis proposes a neural network approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. An obvious advantage of using neural networks is the elimination for the need of laborious feature engineering that can be cumbersome and error prone. The input question would be encoded into a vector representation. The model will be trained and evaluated on the LC-QuAD Dataset (Large-scale Complex Question Answering Dataset). The dataset was created explicitly for machine learning based QA approaches for learning complex SPARQL queries. The dataset consists of 5000 questions along with their corresponding SPARQL queries over the DBpedia dataset spanning 5042 entities and 615 predicates. These queries were annotated based on 38 unique templates that the model will attempt to classify. The resulting model will be evaluated against both the LC-QuAD dataset and the Question Answering Over Linked Data (QALD-7) dataset.

The recursive neural network achieves template classification accuracy of 0.828 on the LC-QuAD dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most likely templates were considered the model achieves an accuracy of 0.945 on the LC-QuAD dataset and 0.786 on the QALD-7 dataset.

After slot filling, the overall system achieves a macro F-score 0.419 on the LC- QuAD dataset and a macro F-score of 0.417 on the QALD-7 dataset.

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Date Created
  • 2018

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Distributed SPARQL over big RDF data: a comparative analysis using Presto and MapReduce

Description

The processing of large volumes of RDF data require an efficient storage and query processing engine that can scale well with the volume of data. The initial attempts to

The processing of large volumes of RDF data require an efficient storage and query processing engine that can scale well with the volume of data. The initial attempts to address this issue focused on optimizing native RDF stores as well as conventional relational databases management systems. But as the volume of RDF data grew to exponential proportions, the limitations of these systems became apparent and researchers began to focus on using big data analysis tools, most notably Hadoop, to process RDF data. Various studies and benchmarks that evaluate these tools for RDF data processing have been published. In the past two and half years, however, heavy users of big data systems, like Facebook, noted limitations with the query performance of these big data systems and began to develop new distributed query engines for big data that do not rely on map-reduce. Facebook's Presto is one such example.

This thesis deals with evaluating the performance of Presto in processing big RDF data against Apache Hive. A comparative analysis was also conducted against 4store, a native RDF store. To evaluate the performance Presto for big RDF data processing, a map-reduce program and a compiler, based on Flex and Bison, were implemented. The map-reduce program loads RDF data into HDFS while the compiler translates SPARQL queries into a subset of SQL that Presto (and Hive) can understand. The evaluation was done on four and eight node Linux clusters installed on Microsoft Windows Azure platform with RDF datasets of size 10, 20, and 30 million triples. The results of the experiment show that Presto has a much higher performance than Hive can be used to process big RDF data. The thesis also proposes an architecture based on Presto, Presto-RDF, that can be used to process big RDF data.

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Date Created
  • 2014

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A semantic framework for integrating and publishing linked data on the Web

Description

Semantic web is the web of data that provides a common framework and technologies for sharing and reusing data in various applications. In semantic web terminology, linked data is the

Semantic web is the web of data that provides a common framework and technologies for sharing and reusing data in various applications. In semantic web terminology, linked data is the term used to describe a method of exposing and connecting data on the web from different sources. The purpose of linked data and semantic web is to publish data in an open and standard format and to link this data with existing data on the Linked Open Data Cloud. The goal of this thesis to come up with a semantic framework for integrating and publishing linked data on the web. Traditionally integrating data from multiple sources usually involves an Extract-Transform-Load (ETL) framework to generate datasets for analytics and visualization. The thesis proposes introducing a semantic component in the ETL framework to semi-automate the generation and publishing of linked data. In this thesis, various existing ETL tools and data integration techniques have been analyzed and deficiencies have been identified. This thesis proposes a set of requirements for the semantic ETL framework by conducting a manual process to integrate data from various sources such as weather, holidays, airports, flight arrival, departure and delays. The research questions that are addressed are: (i) to what extent can the integration, generation, and publishing of linked data to the cloud using a semantic ETL framework be automated; (ii) does use of semantic technologies produce a richer data model and integrated data. Details of the methodology, data collection, and application that uses the linked data generated are presented. Evaluation is done by comparing traditional data integration approach with semantic ETL approach in terms of effort involved in integration, data model generated and querying the data generated.

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Date Created
  • 2016

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MOOCLink: linking and maintaining qulity of data provided by various MOOC providers

Description

The concept of Linked Data is gaining widespread popularity and importance. The method of publishing and linking structured data on the web is called Linked Data. Emergence of Linked Data

The concept of Linked Data is gaining widespread popularity and importance. The method of publishing and linking structured data on the web is called Linked Data. Emergence of Linked Data has made it possible to make sense of huge data, which is scattered all over the web, and link multiple heterogeneous sources. This leads to the challenge of maintaining the quality of Linked Data, i.e., ensuring outdated data is removed and new data is included. The focus of this thesis is devising strategies to effectively integrate data from multiple sources, publish it as Linked Data, and maintain the quality of Linked Data. The domain used in the study is online education. With so many online courses offered by Massive Open Online Courses (MOOC), it is becoming increasingly difficult for an end user to gauge which course best fits his/her needs.

Users are spoilt for choices. It would be very helpful for them to make a choice if there is a single place where they can visually compare the offerings of various MOOC providers for the course they are interested in. Previous work has been done in this area through the MOOCLink project that involved integrating data from Coursera, EdX, and Udacity and generation of linked data, i.e. Resource Description Framework (RDF) triples.

The research objective of this thesis is to determine a methodology by which the quality

of data available through the MOOCLink application is maintained, as there are lots of new courses being constantly added and old courses being removed by data providers. This thesis presents the integration of data from various MOOC providers and algorithms for incrementally updating linked data to maintain their quality and compare it against a naïve approach in order to constantly keep the users engaged with up-to-date data. A master threshold value was determined through experiments and analysis that quantifies one algorithm being better than the other in terms of time efficiency. An evaluation of the tool shows the effectiveness of the algorithms presented in this thesis.

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Created

Date Created
  • 2016