Computer Sciences Commons^™

Loqus: Linked Open Data Sparql Querying System, Prateek Jain, Kunal Verma, Peter Z. Yeh, Pascal Hitzler, Amit P. Sheth

Kno.e.sis Publications

The LOD cloud is gathering a lot of momentum, with the number of contributors growing manifold. Many prominent data providers have submitted and linked their data to other dataset with the help of manual mappings. The potential of the LOD cloud is enormous ranging from challenging AI issues such as open domain question answering to automated knowledge discovery. We believe that there is not enough technology support available to effectively query the LOD cloud. To this effect, we present a system called Linked Open Data SPARQL Querying System (LOQUS), which automatically maps users queries written in terms of a conceptual …

Computing For The Human Experience: Semantics-Empowered Sensors, Services, And Social Computing On The Ubiquitous Web, Amit P. Sheth

Kno.e.sis Publications

People are on the verge of an era in which the human experience can be enriched in ways they couldn't have imagined two decades ago. Rather than depending on a single technology, people progressed with several whose semantics-empowered convergence and integration will enable us to capture, understand, and reapply human knowledge and intellect. Such capabilities will consequently elevate our technological ability to deal with the abstractions, concepts, and actions that characterize human experiences. This will herald computing for human experience (CHE). The CHE vision is built on a suite of technologies that serves, assists, and cooperates with humans to nondestructively …

Provenance Context Entity (Pace): Scalable Provenance Tracking For Scientific Rdf Data, Satya S. Sahoo, Olivier Bodenreider, Pascal Hitzler, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The Resource Description Framework (RDF) format is being used by a large number of scientific applications to store and disseminate their datasets. The provenance information, describing the source or lineage of the datasets, is playing an increasingly significant role in ensuring data quality, computing trust value of the datasets, and ranking query results. Current provenance tracking approaches using the RDF reification vocabulary suffer from a number of known issues, including lack of formal semantics, use of blank nodes, and application-dependent interpretation of reified RDF triples. In this paper, we introduce a new approach called Provenance Context Entity (PaCE) that uses …

A Study In Hadoop Streaming With Matlab For Nmr Data Processing, Kalpa Gunaratna, Paul E. Anderson, Ajith Harshana Ranabahu, Amit P. Sheth

Kno.e.sis Publications

Applying Cloud computing techniques for analyzing large data sets has shown promise in many data-driven scientific applications. Our approach presented here is to use Cloud computing for Nuclear Magnetic Resonance (NMR)data analysis which normally consists of large amounts of data. Biologists often use third party or commercial software for ease of use. Enabling the capability to use this kind of software in a Cloud will be highly advantageous in many ways. Scripting languages especially designed for clouds may not have the flexibility biologists need for their purposes. Although this is true, they are familiar with special software packages that allow …

Twitris 2.0 : Semantically Empowered System For Understanding Perceptions From Social Data, Ashutosh Sopan Jadhav, Hemant Purohit, Pavan Kapanipathi, Pramod Anantharam, Ajith H. Ranabahu, Vinh Nguyen, Pablo N. Mendes, Alan Gary Smith, Michael Cooney, Amit P. Sheth

Kno.e.sis Publications

We present Twitris 2.0, a Semantic Web application that facilitates understanding of social perceptions by Semantics-based processing of massive amounts of event-centric data. Twitris 2.0 addresses challenges in large scale processing of social data, preserving spatio-temporal-thematic properties. Twitris 2.0 also covers context based semantic integration of multiple Web resources and expose semantically enriched social data to the public domain. Semantic Web technologies enable the systematic integration and analysis abilities.

Power Of Clouds In Your Pocket: An Efficient Approach For Cloud Mobile Hybrid Application Development, Ashwin Manjunatha, Ajith Harshana Ranabahu, Amit P. Sheth, 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. However, taking full advantage of this mobile and cloud computing landscape, especially for the data intensive domains has been hampered by the many heterogeneities that exist in the mobile space as well as the Cloud space. Our research focuses on exploiting the capabilities of the mobile and cloud landscape by defining a new class of applications called cloud mobile hybrid (CMH) applications and a Domain Specific …

Semantic Web – Interoperability, Usability, Applicability, Pascal Hitzler, Krzysztof Janowicz

Computer Science and Engineering Faculty Publications

No abstract provided.

Approximate Instance Retrieval On Ontologies, Tuvshintur Tserendorj, Stephan Grimm, Pascal Hitzler

Computer Science and Engineering Faculty Publications

With the development of more expressive description logics (DLs) for the Web Ontology Language OWL the question arises how we can properly deal with the high computational complexity for efficient reasoning. In application cases that require scalable reasoning with expressive ontologies, non-standard reasoning solutions such as approximate reasoning are necessary to tackle the intractability of reasoning in expressive DLs. In this paper, we are concerned with the approximation of the reasoning task of instance retrieval on DL knowledge bases, trading correctness of retrieval results for gain of speed. We introduce our notion of an approximate concept extension and we provide …

Semantics Centric Solutions For Application And Data Portability In Cloud Computing, Ajith Harshana Ranabahu, Amit P. Sheth

Kno.e.sis Publications

Cloud computing has become one of the key considerations both in academia and industry. Cheap, seemingly unlimited computing resources that can be allocated almost instantaneously and pay-as-you-go pricing schemes are some of the reasons for the success of Cloud computing. The Cloud computing landscape, however, is plagued by many issues hindering adoption. One such issue is vendor lock-in, forcing the Cloud users to adhere to one service provider in terms of data and application logic. Semantic Web has been an important research area that has seen significant attention from both academic and industrial researchers. One key property of Semantic Web …

Automated Interpretation Of The Tandem Mass Spectra Of Peptides Using Artificial Neural Networks, Timothy Patrick Manning

Theses

The manual interpretation of mass spectra is a complex and time consuming task. The problem of manually interpreting this data is further exacerbated by the large numbers of mass spectra which can potentially be produced in a single proteoniics experiment. This shows the need for high throughput approaches to the interpretation of mass spectra. Existing automated approaches are however error prone due to the complexity of the task. Accordingly, this thesis discusses and evaluates the application of neural networks to improving the sensitivity, specificity and robustness of current approaches to the automated interpretation of such mass spectral data.

Several neural …

A Reasonable Semantic Web, Pascal Hitzler, Frank Van Harmelen

Computer Science and Engineering Faculty Publications

The realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which forces us to question established lines of research and to rethink the underlying approaches. We argue that reasoning for the Semantic Web should be understood as "shared inference," which is not necessarily based on deductive methods. Model-theoretic semantics (and sound and complete reasoning based on it) functions as a gold standard, but applications dealing with large-scale and noisy data usually cannot afford the required runtimes. Approximate methods, including deductive ones, but also approaches based on …

Automated Isolation Of Translational Efficiency Bias That Resists The Confounding Effect Of Gc(At)-Content, Douglas W. Raiford, Dan E. Krane, Travis E. Doom, Michael L. Raymer

Kno.e.sis Publications

Genomic sequencing projects are an abundant source of information for biological studies ranging from the molecular to the ecological in scale; however, much of the information present may yet be hidden from casual analysis. One such information domain, trends in codon usage, can provide a wealth of information about an organism's genes and their expression. Degeneracy in the genetic code allows more than one triplet codon to code for the same amino acid, and usage of these codons is often biased such that one or more of these synonymous codons is preferred. Detection of this bias is an important tool …

Linked Open Social Signals, Pablo N. Mendes, Alexandre Passant, Pavan Kapanipathi, Amit P. Sheth

Kno.e.sis Publications

In this paper we discuss the collection, semantic annotation and analysis of real-time social signals from micro-blogging data. We focus on users interested in analyzing social signals collectively for sensemaking. Our proposal enables flexibility in selecting subsets for analysis, alleviating information overload. We define an architecture that is based on state-of-the-art Semantic Web technologies and a distributed publish subscribe protocol for real time communication. In addition, we discuss our method and application in a scenario related to the health care reform in the United States.

Continuous Semantics To Analyze Real-Time Data, Amit P. Sheth, Christopher Thomas, Pankaj Mehra

Kno.e.sis Publications

Increasingly we are presented with dynamic domains involved in social, mobile, and sensor webs. Such domains are spontaneous (arising suddenly), follow a period of rapid evolution, involving real-time or near real-time data, involve many distributed participants and diverse viewpoints involving topical or contentious subjects, and involve feature context colored by local knowledge and sociocultural backgrounds. This article present continuous semantics can help us model such dynamic domains and analyze the related real-time data. Capabilities include crating dynamic domain model by mining social data, and using dynamic models for semantic analysis of real-time data.

An Intelligent Data-Centric Approach Toward Identification Of Conserved Motifs In Protein Sequences, Kathryn Dempsey Cooper, Benjamin Currall, Richard Hallworth, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

The continued integration of the computational and biological sciences has revolutionized genomic and proteomic studies. However, efficient collaboration between these fields requires the creation of shared standards. A common problem arises when biological input does not properly fit the expectations of the algorithm, which can result in misinterpretation of the output. This potential confounding of input/output is a drawback especially when regarding motif finding software. Here we propose a method for improving output by selecting input based upon evolutionary distance, domain architecture, and known function. This method improved detection of both known and unknown motifs in two separate case studies. …

Detecting Phenotype-Specific Interactions Between Biological Processes From Microarray Data And Annotations, Nadeem Ahmed Ansari

Wayne State University Dissertations

The development of high throughput technologies such as DNA microarrays has enabled researchers to measure expression levels on a genomic scale. Correct and efficient biological interpretation of the voluminous data generated by these technologies, however, remains a challenging problem. A commonly used approach in interpreting the results of such high throughput experiments is to map the list of differentially expressed (DE) genes to gene ontology (GO) terms, which provides a list of biological processes, biochemical functions, and cellular locations associated with the DE genes. A previously unexplored aspect is the identifications of unusual associations between biological processes. Such associations may …

Genbank, Dennis A. Benson, Ilene Karasch-Mizrachi, David J. Lipman, James Ostell, Eric W. Sayers

Harold W. Manter Laboratory of Parasitology: Library Materials

GenBank(R) is a comprehensive database that contains publicly available nucleotide sequences for more than 380,000 organisms named at the genus level or lower, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole genome shotgun (WGS) and environmental sampling projects. Most submissions are made using the web-based BankIt or standalone Sequin programs, and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. GenBank is accessible through the NCBI Entrez retrieval system that integrates data …

A Multidimensional Strategy To Detect Polypharmacological Targets In The Absence Of Structural And Sequence Homology, Jacob D. Durrant, Rommie E. Amaro, Lei Xie, Michael D. Urbaniak, Michael A. J. Ferguson, Antti Haapalainen, Zhijun Chen, Anne Marie Di Guilmi, Frank Wunder, Philip E. Bourne, J. Andrew Mccammon

Publications and Research

Conventional drug design embraces the ‘‘one gene, one drug, one disease’’ philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional …

Improving Predicted Protein Loop Structure Ranking Using A Pareto-Optimality Consensus Method, Yaohang Li, Ionel Rata, See-Wing Chiu, Erik Jakobsson

Computer Science Faculty Publications

Background

Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction.

Results

We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy …

Research In Semantic Web And Information Retrieval: Trust, Sensors, And Search, Krishnaprasad Thirunarayan

Kno.e.sis Publications

No abstract provided.

Biological Sequence Simulation For Testing Complex Evolutionary Hypotheses: Indel-Seq-Gen Version 2.0, Cory L. Strope

School of Computing: Dissertations, Theses, and Student Research

Reconstructing the evolutionary history of biological sequences will provide a better understanding of mechanisms of sequence divergence and functional evolution. Long-term sequence evolution includes not only substitutions of residues but also more dynamic changes such as insertion, deletion, and long-range rearrangements. Such dynamic changes make reconstructing sequence evolution history difficult and affect the accuracy of molecular evolutionary methods, such as multiple sequence alignments (MSAs) and phylogenetic methods. In order to test the accuracy of these methods, benchmark datasets are required. However, currently available benchmark datasets have limitations in their sizes and evolutionary histories of the included sequences are unknown. These …

Sparql Query Re-Writing For Spatial Datasets Using Partonomy Based Transformation Rules, Prateek Jain, Cory Andrew Henson, Amit P. Sheth, Peter Z. Yeh, Kunal Verma

Kno.e.sis Publications

Often the information present in a spatial knowledge base is represented at a different level of granularity and abstraction than the query constraints. For querying ontology’s containing spatial information, the precise relationships between spatial entities has to be specified in the basic graph pattern of SPARQL query which can result in long and complex queries. We present a novel approach to help users intuitively write SPARQL queries to query spatial data, rather than relying on knowledge of the ontology structure. Our framework re-writes queries, using transformation rules to exploit part-whole relations between geographical entities to address the mismatches between query …

A Local Qualitative Approach To Referral And Functional Trust, Krishnaprasad Thirunarayan, Dharan Althuru, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

Trust and confidence are becoming key issues in diverse applications such as ecommerce, social networks, semantic sensor web, semantic web information retrieval systems, etc. Both humans and machines use some form of trust to make informed and reliable decisions before acting. In this work, we briefly review existing work on trust networks, pointing out some of its drawbacks. We then propose a local framework to explore two different kinds of trust among agents called referral trust and functional trust, that are modelled using local partial orders, to enable qualitative trust personalization. The proposed approach formalizes reasoning with trust, distinguishing between …

Protein Secondary Structure Prediction Using Parallelized Rule Induction From Coverings, Leong Lee, Cyriac Kandoth, Jennifer Leopold, Ronald L. Frank

Computer Science Faculty Research & Creative Works

Protein 3D structure prediction has always been an important research area in bioinformatics. In particular, the prediction of secondary structure has been a well-studied research topic. Despite the recent breakthrough of combining multiple sequence alignment information and artificial intelligence algorithms to predict protein secondary structure, the Q3 accuracy of various computational prediction algorithms rarely has exceeded 75%. In a previous paper [1], this research team presented a rule-based method called RT-RICO (Relaxed Threshold Rule Induction from Coverings) to predict protein secondary structure. The average Q3 accuracy on the sample datasets using RT-RICO was 80.3%, an improvement over comparable computational methods. …

Towards Reasoning Pragmatics, Pascal Hitzler

Computer Science and Engineering Faculty Publications

The realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which force us to question established lines of research and to rethink the underlying approaches.

A Contrast Pattern Based Clustering Quality Index For Categorical Data, Qingbao Liu, Guozhu Dong

Kno.e.sis Publications

Since clustering is unsupervised and highly explorative, clustering validation (i.e. assessing the quality of clustering solutions) has been an important and long standing research problem. Existing validity measures have significant shortcomings. This paper proposes a novel contrast pattern based clustering quality index (CPCQ) for categorical data, by utilizing the quality and diversity of the contrast patterns (CPs) which contrast the clusters in clusterings. High quality CPs can characterize clusters and discriminate them against each other. Experiments show that the CPCQ index (1) can recognize that expert-determined classes are the best clusters for many datasets from the UCI repository; (2) does …

Classification, Clustering And Data-Mining Of Biological Data, Thomas Triplet

School of Computing: Dissertations, Theses, and Student Research

The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on biological sciences and transforming the way research is conducted. There are currently over 1,100 molecular biology databases dispersed throughout the Internet. However, very few of them integrate data from multiple sources. To assist in the functional and evolutionary analysis of the abundant number of novel proteins, we introduce the PROFESS (PROtein Function, Evolution, Structure and Sequence) database that integrates data from various biological sources. PROFESS is freely available at http://cse.unl.edu/~profess/. Our database is designed to be versatile and expandable and …

Ontology-Driven Provenance Management In Escience: An Application In Parasite Research, Satya S. Sahoo, D. Brent Weatherly, Raghava Mutharaju, Pramod Anantharam, Amit P. Sheth, Rick L. Tarleton

Kno.e.sis Publications

Provenance, from the French word “provenir”, describes the lineage or history of a data entity. Provenance is critical information in scientific applications to verify experiment process, validate data quality and associate trust values with scientific results. Current industrial scale eScience projects require an end-to-end provenance management infrastructure. This infrastructure needs to be underpinned by formal semantics to enable analysis of large scale provenance information by software applications. Further, effective analysis of provenance information requires well-defined query mechanisms to support complex queries over large datasets. This paper introduces an ontology-driven provenance management infrastructure for biology experiment data, as part …

An Anytime Algorithm For Computing Inconsistency Measurement, Yue Ma, Guilin Qi, Guohui Xiao, Pascal Hitzler, Zuoquan Lin

Computer Science and Engineering Faculty Publications

Measuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first analyze its computational complexity. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximation of the inconsistency degree from above and below. We show that …

A Survey Of The Semantic Specification Of Sensors, Michael Compton, Cory Andrew Henson, Laurent Lefort, Holger Neuhaus, Amit P. Sheth

Kno.e.sis Publications

Semantic sensor networks use declarative descriptions of sensors promote reuse and integration, and to help solve the difficulties of installing, querying and maintaining complex, heterogeneous sensor networks. This paper reviews the state of the art for the semantic specification of sensors, one of the fundamental technologies in the semantic sensor network vision. Twelve sensor ontologies are reviewed and analysed for the range and expressive power of their concepts. The reasoning and search technology developed in conjunction with these ontologies is also reviewed, as is technology for annotating OGC standards with links to ontologies. Sensor concepts that cannot be expressed accurately …