Computer Sciences Commons^™

Prediction Of Topic Volume On Twitter, Yiye Ruan, Hemant Purohit, David Fuhry, Srinivasan Parthasarathy, Amit P. Sheth

Kno.e.sis Publications

We discuss an approach for predicting microscopic (individual) and macroscopic (collective) user behavioral patterns with respect to specific trending topics on Twitter. Going beyond previous efforts that have analyzed driving factors in whether and when a user will publish topic-relevant tweets, here we seek to predict the strength of content generation which allows more accurate understanding of Twitter users' behavior and more effective utilization of the online social network for diffusing information. Unlike traditional approaches, we consider multiple dimensions into one regression-based prediction framework covering network structure, user interaction, content characteristics and past activity. Experimental results on three large Twitter …

A Web-Based Study Of Self-Treatment Of Opioid Withdrawal Symptoms With Loperamide, Raminta Daniulaityte, Robert G. Carlson, Russel S. Falck, Delroy H. Cameron, Sujan Udayanaga, Lu Chen, Amit P. Sheth

Kno.e.sis Publications

Aims: Many websites provide a medium for individuals to freely share their experiences and knowledge about different drugs. Such user-generated content can be used as a rich data source to study emerging drug use practices and trends. The study aims to examine web-based reports of loperamide use practices among non-medical opioid users. Loperamide, a piperidine derivative, is an opioid agonist approved for the control of diarrhea symptoms. Because of its general inability to cross the blood-brain barrier, it is considered to have no abuse potential and is available without a prescription. Methods: A website that allows free discussion of illicit …

Phenotype Prediction And Feature Selection In Genome-Wide Association Studies, Andrew Roberts

Theses

Genome wide association studies (GWAS) search for correlations between single nucleotide polymorphisms (SNPs) in a subject genome and an observed phenotype. GWAS can be used to generate models for predicting phenotype based on genotype, as well as aiding in identification of specific genes affecting the biological mechanism underlying the phenotype.

In this investigation, phenotype prediction models are constructed from GWAS training data and are evaluated for performance on test data. Three methods are used to rank SNPs by their correlation with the phenotype: the univariate Wald test, a multivariate, support vector machine (SVM) based technique, and a hybrid method where …

A Comparative Analysis Of Machine Learning Algorithms For Genome Wide Association Studies, Neha Singh

Theses

Variations present in human genome play a vital role in the emergence of genetic disorders and abnormal traits. Single Nucleotide Polymorphism (SNP) is considered as the most common source of genetic variations. Genome Wide Association Studies (GWAS) probe these variations present in human population and find their association with complex genetic disorders. Now these days, recent advances in technology and drastic reduction in costs of Genome Wide Association Studies provide the opportunity to have a plethora of genomic data that delivers huge information of these variations to analyze. In fact, there is significant difference in pace of data generation and …

Data Mining Of Tetraloop-Tetraloop Receptors In Rna Xml Files, Sinan Ramazanoglu

Theses

RNA (Ribonucleic acid) Motifs are tertiary structures that play an important role in the folding mechanism of the RNA molecule. The overall function of a RNA Motif depends on its specific bp (base pairs) sequence that constitutes the secondary structure. Data mining is a novel method in both discovering potential tertiary structures within DNA (Deoxyribonucleic acid), RNA, and protein molecules and storing the information in databases. The RNA Motif of interest is the tetraloop-tetraloop receptor, which is composed of a highly conserved 11 nt (nucleotide) sequence and a tetraloop with the generic form of GNRA (where N = any base …

Integrating Owl And Rules: A Syntax Proposal For Nominal Schemas, David Carral Martinez, Adila Krisnadhi, Pascal Hitzler

Computer Science and Engineering Faculty Publications

This paper proposes an addition to OWL 2 syntax to incorporate nominal schemas, which is a new description-logic style extension of OWL 2 which was recently proposed, and which makes is possible to express "variable nominal classes" within axioms in an OWL 2 ontology. Nominal schemas make it possible to express DL-safe rules of arbitrary arity within the extended OWL paradigm, hence covering the well-known DL-safe SWRL language. To express this feature, we extend OWL 2 syntax to include necessary and minimal modifications to both Functional and Manchester syntax grammars and mappings from these two syntaxes to Turtle/RDF. We also …

Semantic Aspects Of Earthcube, Pascal Hitzler, Krzysztof Janowicz, Gary Berg-Cross, Leo Obrst, Amit P. Sheth, Timothy Finin, Isabel F. Cruz

Kno.e.sis Publications

In this document, we give a high-level overview of selected Semantic (Web) technologies, methods, and other important considerations, that are relevant for the success of EarthCube. The goal of this initial document is to provide entry points and references for discussions between the Semantic Technologies experts and the domain experts within EarthCube. The selected topics are intended to ground the EarthCube roadmap in the state of the art in semantics research and ontology engineering.

We anticipate that this document will evolve as EarthCube progresses. Indeed, all EarthCube parties are asked to provide topics of importance that should be treated in …

Trust Networks, Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

Trust relationships occur naturally in many diverse contexts such as collaborative systems, e-commerce, interpersonal interactions, social networks, semantic sensor web, etc. As collaborating agents providing content and services become increasingly removed from the agents that consume them, the issue of robust trust inference and update become critical. There is a need to find online substitutes for traditional (direct or face-to-face) cues to derive measures of trust, and create efficient and secure system for managing trust, to support decision-making. Unfortunately, there is neither a universal notion of trust that is applicable to all domains nor a clear explication of its semantics …

Extending Description Logic Rules, David Carral Martinez, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Description Logics – the logics underpinning the Web Ontology Language OWL – and rules are currently the most prominent paradigms used for modeling knowledge for the Semantic Web. While both of these approaches are based on classical logic, the paradigms also differ significantly, so that naive combinations result in undesirable properties such as undecidability. Recent work has shown that many rules can in fact be expressed in OWL. In this paper we extend this work to include some types of rules previously excluded. We formally define a set of first order logic rules, C-Rules, which can be expressed within OWL …

Binding Efficacy Of Different Polyphenolic Phytochemicals With Β-Lactoglobulin And Human Serum Albumin: Implication For Therapeutics Against Neurodegenerative Diseases, Rene Duran^, Andres Ortiz^, Mahesh Narayan*, Vladik Kreinovich*

COURI Symposium Abstracts, Spring 2012

No abstract provided.

Entropy And The Complexity Of Graphs Revisited, Abbe Mowshowitz, Matthias Dehmer

Publications and Research

This paper presents a taxonomy and overview of approaches to the measurement of graph and network complexity. The taxonomy distinguishes between deterministic (e.g., Kolmogorov complexity) and probabilistic approaches with a view to placing entropy-based probabilistic measurement in context. Entropy-based measurement is the main focus of the paper. Relationships between the different entropy functions used to measure complexity are examined; and intrinsic (e.g., classical measures) and extrinsic (e.g., Körner entropy) variants of entropy-based models are discussed in some detail.

Localized Deconvolution: Characterizing Nmr-Based Metabolomics Spectroscopic Data Using Localized High-Throughput Deconvolution, Paul E. Anderson, Ajith H. Ranabahu, Deirdre A. Mahle, Nicholas V. Reo, Michael L. Raymer, Amit P. Sheth, Nicholas J. Delraso

Kno.e.sis Publications

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. Standard quantification techniques attempt to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition. These techniques fail to account for adjacent signals which can lead to drastic quantification errors. Attempts at full spectrum deconvolution have been limited in adoption and development due to the computational resources required. Herein, we develop a novel localized deconvolution algorithm for general purpose quantification of NMR-based metabolomics studies. Localized deconvolution decreases average absolute quantification error by 97% and …

Framework For The Analysis Of Coordination In Crisis Response, Hemant Purohit, Andrew Hampton, Valerie L. Shalin, Amit P. Sheth, John M. Flach

Kno.e.sis Publications

Social Media play a critical role during crisis events, revealing a natural coordination dynamic. We propose a computational framework guided by social science principles to measure, analyze, and understand coordination among the different types of organizations and actors in crisis response. The analysis informs both the scientific account of cooperative behavior and the design of applications and protocols to support crisis management.

Discovering Fine-Grained Sentiment In Suicide Notes, Wenbo Wang, Lu Chen, Ming Tan, Shaojun Wang, Amit P. Sheth

Kno.e.sis Publications

This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system …

A Semantic Problem Solving Environment For Integrative Parasite Research: Identification Of Intervention Targets For Trypanosoma Cruzi, Priti Parikh, Todd Minning, Vinh Nguyen, Sarasi Lalithsena, Amir H. Asiaee, Satya S. Sahoo, Prashant Doshi, Rick L. Tarleton, Amit P. Sheth

Kno.e.sis Publications

Background: Research on the biology of parasites requires a sophisticated and integrated computational platform to query and analyze large volumes of data, representing both unpublished (internal) and public (external) data sources. Effective analysis of an integrated data resource using knowledge discovery tools would significantly aid biologists in conducting their research, for example, through identifying various intervention targets in parasites, and in deciding the future direction of ongoing as well as planned projects. A key challenge in achieving this objective is the heterogeneity between the internal lab data, usually stored as flat files, Excel spreadsheets or custom-built databases, and the external …

Cognitive Approaches For The Semantic Web, Dedre Gentner, Frank Van Harmelen, Pascal Hitzler, Krzysztof Janowicz, Kai-Uwe Kuhnberger

Computer Science and Engineering Faculty Publications

A major focus in the design of Semantic Web ontology languages used to be on finding a suitable balance between the expressivity of the language and the tractability of reasoning services defined over this language. This focus mirrors the original vision of a Web composed of machine readable and understandable data. Similarly to the classical Web a few years ago, the attention is recently shifting towards a user-centric vision of the Semantic Web. Essentially, the information stored on the Web is from and for humans. This new focus is not only reflected in the fast growing Linked Data Web but …

Semantics Of Perception: Towards A Semantic Web Approach To Machine Perception, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

The acts of observation and perception provide the building blocks for all human knowledge (Locke, 1690); they are the processes from which all ideas are born; and the sole bond connecting ourselves to the world around us. Now, with the advent of sensor networks capable of observation, this world may be directly accessible to machines. Missing from this vision, however, is the ability of machines to glean semantics from observation; to apprehend entities from detected qualities; to perceive. The systematic automation of this ability is the focus of machine perception -- the ability of computing machines to sense and interpret …

A Scalable Distributed Syntactic, Semantic And Lexical Language Model, Ming Tan, Wenli Zhou, Lei Zheng, Shaojun Wang

Kno.e.sis Publications

This paper presents an attempt at building a large scale distributed composite language model that is formed by seamlessly integrating an n-gram model, a structured language model, and probabilistic latent semantic analysis under a directed Markov random field paradigm to simultaneously account for local word lexical information, mid-range sentence syntactic structure, and long-span document semantic content. The composite language model has been trained by performing a convergent N-best list approximate EM algorithm and a follow-up EM algorithm to improve word prediction power on corpora with up to a billion tokens and stored on a supercomputer. The large scale distributed composite …

Alignment-Based Querying Of Linked Open Data, Amit Krishna Joshi, Prateek Jain, Pascal Hitzler, Peter Z. Yeh, Kunal Verma, Amit P. Sheth, Mariana Damova

Kno.e.sis Publications

The Linked Open Data (LOD) cloud is rapidly becoming the largest interconnected source of structured data on diverse domains. The potential of the LOD cloud is enormous, ranging from solving challenging AI issues such as open domain question answering to automated knowledge discovery. However, due to an inherent distributed nature of LOD and a growing number of ontologies and vocabularies used in LOD datasets, querying over multiple datasets and retrieving LOD data remains a challenging task. In this paper, we propose a novel approach to querying linked data by using alignments for processing queries whose constituent data come from heterogeneous …

On The Design Of Advanced Filters For Biological Networks Using Graph Theoretic Properties, Kathryn Dempsey Cooper, Tzu-Yi Chen, Sanjukta Bhowmick, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Network modeling of biological systems is a powerful tool for analysis of high-throughput datasets by computational systems biologists. Integration of networks to form a heterogeneous model requires that each network be as noise-free as possible while still containing relevant biological information. In earlier work, we have shown that the graph theoretic properties of gene correlation networks can be used to highlight and maintain important structures such as high degree nodes, clusters, and critical links between sparse network branches while reducing noise. In this paper, we propose the design of advanced network filters using structurally related graph theoretic properties. While spanning …

Extracting Diverse Sentiment Expressions With Target-Dependent Polarity From Twitter, Lu Chen, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang, Amit P. Sheth

Kno.e.sis Publications

This study focuses on automatic extraction of sentiment expressions associated with given targets from Twitter. It addresses one of the key challenges in this work: Wide diversity and informal nature of sentiment expressions that cannot be trivially enumerated or captured using predefined lexical patterns.

On The Role Of Social Identity And Cohesion In Characterizing Online Social Communities, Hemant Purohit, Yiye Ruan, David Fuhry, Srinivasan Parthasarathy, Amit P. Sheth

Kno.e.sis Publications

Two prevailing theories for explaining social group or community structure are cohesion and identity. The social cohesion approach posits that social groups arise out of an aggregation of individuals that have mutual interpersonal attraction as they share common characteristics. These characteristics can range from common interests to kinship ties and from social values to ethnic backgrounds. In contrast, the social identity approach posits that an individual is likely to join a group based on an intrinsic self-evaluation at a cognitive or perceptual level. In other words group members typically share an awareness of a common category membership. In this work …

Role Of Semantic Web In Health Informatics, Satya S. Sahoo, Guo-Qiang Zhang, Amit P. Sheth

Kno.e.sis Publications

This tutorial weaves together three themes and the associated topics: [1] The role of biomedical ontologies [2] Key Semantic Web technologies with focus on Semantic provenance and integration [3] In-practice tools and real world use cases built to serve the needs of sleep medicine researchers, cardiologists involved in clinical practice, and work on vaccine development for human pathogens.

Semantics And Ontologies For Earthcube, Gary Berg-Cross, Isabel F. Cruz, Michael Dean, Timothy Finin, Mark Gahegan, Pascal Hitzler, Hook Hau, Krzysztof Janowicz, Naicong Li, Philip Murphy, Bryce Nordgren, Leo Obrst, Mark Schildhauer, Amit P. Sheth, Krishna Sinha, Anne Thessen, Nancy Wiegand, Ilya Zaslavasky

Kno.e.sis Publications

Semantic technologies and ontologies play an increasing role in scientific workflow systems and knowledge infrastructures. While ontologies are mostly used for the semantic annotation of metadata, semantic technologies enable searching metadata catalogs beyond simple keywords, with some early evidence of semantics used for data translation. However, the next generation of distributed and interdisciplinary knowledge infrastructures will require capabilities beyond simple subsumption reasoning over subclass relations. In this work, we report from the EarthCube Semantics Community by highlighting which role semantics and ontologies should play in the EarthCube knowledge infrastructure. We target the interested domain scientist and, thus, introduce the value …

Key Ingredients For Your Next Semantics Elevator Talk, Krzysztof Janowicz, Pascal Hitzler

Computer Science and Engineering Faculty Publications

2012 brought a major change to the semantics research community. Discussions on the use and benefits of semantic technologies are shifting away from the why to the how. Surprisingly this more in stakeholder interest is not accompanied by a more detailed understanding of what semantics research is about. Instead of blaming others for their (wrong) expectations, we need to learn how to emphasize the paradigm shift proposed by semantics research while abstracting from technical details and advocate the added value in a way that relates to the immediate needs of individual stakeholders without overselling. This paper highlights some of …

Hierarchical Multi-Label Classification For Protein Function Prediction Going Beyond Traditional Approaches, Noor Al Aydie

Wayne State University Dissertations

Hierarchical multi-label classification is a variant of traditional classification in which the

instances can belong to several labels, that are in turn organized in a hierarchy. Functional classification of genes is a challenging problem in functional genomics due to several reasons. First, each gene participates in multiple biological activities. Hence, prediction models should support multi-label classification. Second, the genes are organized and classified according to a hierarchical classification scheme that represents the relationships between the functions of the genes. These relationships should be maintained by the prediction models. In addition, various bimolecular data sources, such as gene expression data and …

Open And Transparent: The Review Process Of The Semantic Web Journal, Krzysztof Janowicz, Pascal Hitzler

Computer Science and Engineering Faculty Publications

While open access is established in the world of academic publishing, open reviews are rare. The Semantic Web journal goes further than just open review by implementing an open and transparent review process in which reviews are publicly available, and the assigned editors and reviewers are known by name, and are published together with accepted manuscripts. In this article we introduce the steps to realize such a process from the conceptual design, over the implementation, a overview of the results so far, and up to lessons learned.

The Ontology For Parasite Lifecycle (Opl): Towards A Consistent Vocabulary Of Lifecycle Stages In Parasitic Organisms, Priti Parikh, Jie Zheng, Flora J. Logan-Klumpler, Christian J. Stoeckert, Pantelis Topalis, Anna Protasio, Amit P. Sheth, Mark Carrington, Matthew Berriman, Satya S. Sahoo

Kno.e.sis Publications

Background

Genome sequencing of many eukaryotic pathogens and the volume of data available on public resources have created a clear requirement for a consistent vocabulary to describe the range of developmental forms of parasites. Consistent labeling of experimental data and external data, in databases and the literature, is essential for integration, cross database comparison, and knowledge discovery. The primary objective of this work was to develop a dynamic and controlled vocabulary that can be used for various parasites. The paper describes the Ontology for Parasite Lifecycle (OPL) and discusses its application in parasite research.

Results

The OPL is based on …

Towards Cloud Mobile Hybrid Application Generation Using Semantically Enriched Domain Specific Languages, Ajith Harshana Ranabahu, Amit P. Sheth, Ashwin Manjunatha, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The advancements in computing have resulted in a boom of cheap, ubiquitous, connected mobile devices as well as seemingly unlimited, utility style, pay as you go computing resources, commonly referred to as Cloud computing. Taking advantage of this computing landscape, however, has been hampered by the many heterogeneities that exist in the mobile space as well as the Cloud space.

This research attempts to introduce a disciplined methodology to develop Cloud-mobile hybrid applications by using a Domain Specific Language (DSL) centric approach to generate applications. A Cloud-mobile hybrid is an application that is split between a Cloud based back-end and …

Resident Identification Using Kinect Depth Image Data And Fuzzy Clustering Techniques, Tanvi Banerjee, James M. Keller, Marjorie Skubic

Kno.e.sis Publications

As a part of our passive fall risk assessment research in home environments, we present a method to identify older residents using features extracted from their gait information from a single depth camera. Depth images have been collected continuously for about eight months from several apartments at a senior housing facility. Shape descriptors such as bounding box information and image moments were extracted from silhouettes of the depth images. The features were then clustered using Possibilistic C Means for resident identification. This technology will allow researchers and health professionals to gather more information on the individual residents by filtering out …