Computer Sciences Commons^™

Querying Formal Contexts With Answer Set Programs, Pascal Hitzler, Markus Krotzsch

Computer Science and Engineering Faculty Publications

Recent studies showed how a seamless integration of formal concept analysis (FCA), logic of domains, and answer set programming (ASP) can be achieved. Based on these results for combining hierarchical knowledge with classical rule-based formalisms, we introduce an expressive common-sense query language for formal contexts. Although this approach is conceptually based on order-theoretic paradigms, we show how it can be implemented on top of standard ASP systems. Advanced features, such as default negation and disjunctive rules, thus become practically available for processing contextual data.

Geospatial Ontology Development And Semantic Analytics, I. Budak Arpinar, Cartic Ramakrishnan, Molly Azami, Amit P. Sheth, E. Lynn Usery, Mei-Po Kwan

Kno.e.sis Publications

Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we …

A Short Tutorial On Reconstructability Analysis, Martin Zwick

Complex Systems Faculty Publications and Presentations

This talk will present the basic ideas of Reconstructability Analysis. RA is a general modeling methodology developed in the systems community since the early 60s (pioneered by Ross Ashby) for exploratory (data mining, machine learning) and confirmatory modeling. RA is based in graph theory, set theory, information theory, and statistics. It is applicable to qualitative (nominal) and, via binning, to quantitative (continuous) data, with both statistical & non-statistical applications. It overlaps and augments more widely known methods, e.g., log-linear modeling and Bayesian networks.

Keywords: Reconstructability Analysis, Log-Linear Modeling, categorical data, multivariate statistics, exploratory modeling, confirmatory modeling

Bi-Level Clustering Of Mixed Categorical And Numerical Biomedical Data, Bill Andreopoulos, Aijun An, Xiaogang Wang

Faculty Publications, Computer Science

Biomedical data sets often have mixed categorical and numerical types, where the former represent semantic information on the objects and the latter represent experimental results. We present the BILCOM algorithm for |Bi-Level Clustering of Mixed categorical and numerical data types|. BILCOM performs a pseudo-Bayesian process, where the prior is categorical clustering. BILCOM partitions biomedical data sets of mixed types, such as hepatitis, thyroid disease and yeast gene expression data with Gene Ontology annotations, more accurately than if using one type alone.

Masquerader Detection Using Oclep: One-Class Classification Using Length Statistics Of Emerging Patterns, Lijun Chen, Guozhu Dong

Kno.e.sis Publications

We introduce a new method for masquerader detection that only uses a user’s own data for training, called Oneclass Classification using Length statistics of Emerging Patterns (OCLEP). Emerging patterns (EPs) are patterns whose support increases from one dataset/class to another with a big ratio, and have been very useful in earlier studies. OCLEP classifies a case T as self or masquerader by using the average length of EPs obtained by contrasting T against sets of samples of a user’s normal data. It is based on the observation that one needs long EPs to differentiate instances from a common class, but …

A Metamodel And Uml Profile For Rule-Extended Owl Dl Ontologies, Saartje Brockmans, Peter Haase, Pascal Hitzler, Rudi Studer

Computer Science and Engineering Faculty Publications

In this paper we present a MOF compliant metamodel and UML profile for the Semantic Web Rule Language (SWRL) that integrates with our previous work on a metamodel and UML profile for OWL DL. Based on this metamodel and profile, UML tools can be used for visual modeling of rule-extended ontologies.

Semantic Empowerment Of Health Care And Life Science Applications, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Semantic Analytics Visualization, Leonidas Deligiannidis, Amit P. Sheth, Boanerges Aleman-Meza

Kno.e.sis Publications

In this paper we present a new tool for semantic analytics through 3D visualization called “Semantic Analytics Visualization” (SAV). It has the capability for visualizing ontologies and meta-data including annotated web-documents, images, and digital media such as audio and video clips in a synthetic three-dimensional semi-immersive environment. More importantly, SAV supports visual semantic analytics, whereby an analyst can interactively investigate complex relationships between heterogeneous information. The tool is built using Virtual Reality technology which makes SAV a highly interactive system. The backend of SAV consists of a Semantic Analytics system that supports query processing and semantic association discovery. Using a …

2nd Annual Undergraduate Research Conference Abstract Book, University Of Missouri--Rolla

Undergraduate Research Conference at Missouri S&T

No abstract provided.

Ivibrate: Interactive Visualization Based Framework For Clustering Large Datasets, Keke Chen, Ling Liu

Kno.e.sis Publications

With continued advances in communication network technology and sensing technology, there is astounding growth in the amount of data produced and made available through cyberspace. Efficient and high-quality clustering of large datasets continues to be one of the most important problems in large-scale data analysis. A commonly used methodology for cluster analysis on large datasets is the three-phase framework of sampling/summarization, iterative cluster analysis, and disk-labeling. There are three known problems with this framework which demand effective solutions. The first problem is how to effectively define and validate irregularly shaped clusters, especially in large datasets. Automated algorithms and statistical methods …

Detecting The Change Of Clustering Structure In Categorical Data Streams, Keke Chen, Ling Liu

Kno.e.sis Publications

Analyzing clustering structures in data streams can provide critical information for making decision in real time. In this paper, we present a framework for detecting the change of critical clustering structure in categorical data streams. The framework consists of the Hierarchical Entropy Tree structure (HE-Tree) and the extended ACE clustering algorithm. HE-Tree can efficiently capture the entropy property of the categorical data streams and allow us to draw precise clustering information from the data stream for high-quality BkPLots with the extended ACE algorithm.

Semantic Web Applications In Financial Industry, Government, Health Care And Life Sciences, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Wsdl-S: Specification, Tools, Use Cases And Applications, Amit P. Sheth, Kunal Verma, Karthik Gomadam

Kno.e.sis Publications

No abstract provided.

Visual Ontology Modeling For Electronic Markets, Saartje Brockmans, Andreas Geyer-Schulz, Pascal Hitzler, Rudi Studer

Computer Science and Engineering Faculty Publications

The research program, Information Management and Market Engineering, focuses on the analysis and the design of electronic markets. Taking a holistic view of the conceptualization and realization of solutions, the research integrates the disciplines business administration, economics, computer science, and law. Topics of interest range from the implementation, quality assurance, and further development of electronic markets to their integration into business processes, innovative business models, and legal frameworks.

Taxaminer: Improving Taxonomy Label Quality Using Latent Semantic Indexing, Cartic Ramakrishnan, Christopher Thomas, Vipul Kashyap, Amit P. Sheth

Kno.e.sis Publications

The development of taxonomies/ontologies is a human intensive process requiring prohibitively large resource commitments in terms of time and cost. In our previous work we have identified an experimentation framework for semi-automatic taxonomy/hierarchy generation from unstructured text. In the preliminary results presented, the taxonomy/hierarchy quality was lower than we had anticipated. In this paper, we present two variations of our experimentation framework, viz. Latent semantic Indexing (LSI) for document indexing and the use of term vectors to prune labels assigned to nodes in the final taxonomy/hierarchy. Using our previous results of taxonomy/hierarchy quality as the baseline we present results that …

Data Processing In Space, Time, And Semantics Dimensions, Farshad Hakimpour, Boanerges Aleman-Meza, Matthew Perry, Amit P. Sheth

Kno.e.sis Publications

This work presents an experimental system for data processing in space, time and semantics dimensions using current Semantic Web technologies. The paper describes how we obtain geographic and event data from Internet sources and also how we integrate them into an RDF store. We briefly introduce a set of functionalities in space, time and semantics dimensions. These functionalities are implemented based on our existing technology for main-memory based RDF data processing developed in the LSDIS Lab. A number of these functionalities are exposed as REST Web services. We present two sample client side applications that are developed using a combination …

Semi-Supervised Conditional Random Fields For Improved Sequence Segmentation And Labeling, Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, Dale Schuurmans

Kno.e.sis Publications

We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled and unlabeled training data. Our approach is based on extending the minimum entropy regularization framework to the structured prediction case, yielding a training objective that combines unlabeled conditional entropy with labeled conditional likelihood. Although the training objective is no longer concave, it can still be used to improve an initial model (e.g. obtained from supervised training) by iterative ascent. We apply our new training algorithm to the problem of identifying gene and protein …

An Investigation Of Codon Usage Bias Including Visualization And Quantification In Organisms Exhibiting Multiple Biases, Douglas W. Raiford, Travis E. Doom, Dan E. Krane, Michael L. Raymer

Kno.e.sis Publications

Prokaryotic genomic sequence data provides a rich resource for bioinformatic analytic algorithms. Information can be extracted in many ways from the sequence data. One often overlooked process involves investigating an organism’s codon usage. Degeneracy in the genetic code leads to multiple codons coding for the same amino acids. Organism’s often preferentially utilize specific codons when coding for an amino acid. This biased codon usage can be a useful trait when predicting a gene’s expressivity or whether the gene originated from horizontal transfer. There can be multiple biases at play in a genome causing errors in the predictive process. For this …

A Semantic Future For Ai, Rudi Studer, Anupriya Ankolekar, Pascal Hitzler

Computer Science and Engineering Faculty Publications

In our modern information society, people need to manage ever-increasing numbers of personal devices and conduct more of their work and activities online, often making use of heterogeneous services. The amount of information to be processed by each individual is constantly growing, making it increasingly difficult to control, channel, share and make constructive use of it. To mitigate this, computing needs to become much more human-centered, e.g. by presenting personalised information to users and by respecting personal preferences in controlling multiple devices or invoking various services. Appropriate representation of the semantics of the information and functionality of devices and services …

Predicting Domain Specific Entities With Limited Background Knowledge, Christopher Thomas, Amit P. Sheth

Kno.e.sis Publications

This paper proposes a framework for automatic recognition of domain-specific entities from text, given limited background knowledge, e.g. in form of an ontology. The algorithm exploits several lightweight natural language processing techniques, such as tokenization and stemming, as well as statistical techniques, such as singular value decomposition (SVD) to suggest domain relatedness of unknown entities.

Using Query-Specific Variance Estimates To Combine Bayesian Classifiers, Chi-Hoon Lee, Russell Greiner, Shaojun Wang

Kno.e.sis Publications

Many of today's best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a novel "query specific" combination rule: After learning a set of simple belief network classifiers, we produce an answer to each query by combining their individual responses, using weights based inversely on their respective variances around their responses. These variances are based on the uncertainty of the network parameters, which in turn depend on the training datasample. In essence, this variance quantifies the base classifier's confidence of its response to this query. …

Clustering Similarity Comparison Using Density Profiles, Eric Bae, James Bailey, Guozhu Dong

Kno.e.sis Publications

The unsupervised nature of cluster analysis means that objects can be clustered in many ways, allowing different clustering algorithms to generate vastly different results. To address this, clustering comparison methods have traditionally been used to quantify the degree of similarity between alternative clusterings. However, existing techniques utilize only the point memberships to calculate the similarity, which can lead to unintuitive results. They also cannot be applied to analyze clusterings which only partially share points, which can be the case in stream clustering. In this paper we introduce a new measure named ADCO, which takes into account density profiles for each …

An Online Discriminative Approach To Background Subtraction, Li Cheng, Shaojun Wang, Terry Caelli

Kno.e.sis Publications

We present a simple, principled approach to detecting foreground objects in video sequences in real-time. Our method is based on an on-line discriminative learning technique that is able to cope with illumination changes due to discontinuous switching, or illumination drifts caused by slower processes such as varying time of the day. Starting from a discriminative learning principle, we derive a training algorithm that, for each pixel, computes a weighted linear combination of selected past observations with time-decay. We present experimental results that show the proposed approach outperforms existing methods on both synthetic sequences and real video data.

Fm-Test: A Fuzzy-Set-Theory-Based Approach To Differential Gene Expression Data Analysis, Lily R. Liang, Shiyong Lu, Xuena Wang, Yi Lu, Vinay Mandal, Dorrelyn Patacsil, Deepak Kumar

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Microarray techniques have revolutionized genomic research by making it possible to monitor the expression of thousands of genes in parallel. As the amount of microarray data being produced is increasing at an exponential rate, there is a great demand for efficient and effective expression data analysis tools. Comparison of gene expression profiles of patients against those of normal counterpart people will enhance our understanding of a disease and identify leads for therapeutic intervention.

Results

In this paper, we propose an innovative approach, fuzzy membership test (FM-test), based on fuzzy set theory to identify disease associated genes from microarray …

Driving Deep Semantics In Middleware And Networks: What, Why And How?, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Knowledge Modeling And Its Application In Life Sciences: A Tale Of Two Ontologies, Satya S. Sahoo, Christopher Thomas, Amit P. Sheth, William S. York, Samir Tartir

Kno.e.sis Publications

High throughput glycoproteomics, similar to genomics and proteomics, involves extremely large volumes of distributed, heterogeneous data as a basis for identification and quantification of a structurally diverse collection of biomolecules. The ability to share, compare, query for and most critically correlate datasets using the native biological relationships are some of the challenges being faced by glycobiology researchers. As a solution for these challenges, we are building a semantic structure, using a suite of ontologies, which supports management of data and information at each step of the experimental lifecycle. This framework will enable researchers to leverage the large scale of glycoproteomics …

Show Me What You Mean! Exploiting Domain Semantics In Ontology Visualization, Ravi Pavagada, Christopher Thomas, Amit P. Sheth, William S. York

Kno.e.sis Publications

Ontologies build the backbone for many life-sciences applications. These ontologies, however, are represented in XML based languages that are meant for machine-consumption and hence are difficult for humans to comprehend. For a meaningful visualization of these ontologies, it is important that the display of entities and relationships captures the cognitive representation of the domain as perceived by the domain experts. In this paper we present OntoVista, an ontology visualization tool that is adaptable to the needs of different domains, especially in the life sciences. While keeping the graph structures as the predominant model, we provide a semantically enhanced graph display …

Automated Photo-Identification Of Cetaceans : An Integrated Software Solution, Daniel Griggs

Theses : Honours

This study investigates current techniques used for automated photo-identification of cetaceans (i.e. dolphins and whales). The primary focus constitutes various techniques that can be applied to identify and extract dorsal fins from digital photographs. A comprehensive analysis of these techniques demonstrates the most effective software solution. To further support this analysis, four prototypes are developed to demonstrate the effectiveness of each technique in a practical environment. The analysis bases its final conclusions on test results generated from these prototype software examples. Final conclusions provide recommendations for an effective, accurate, and practical software solution. This software solution allows dorsal fins to …

Emd: An Ensemble Algorithm For Discovering Regulatory Motifs In Dna Sequences, Jianjun Hu, Yifeng D. Yang, Daisuke Kihara

Department of Biological Sciences Faculty Publications

Background

Understanding gene regulatory networks has become one of the central research problems in bioinformatics. More than thirty algorithms have been proposed to identify DNA regulatory sites during the past thirty years. However, the prediction accuracy of these algorithms is still quite low. Ensemble algorithms have emerged as an effective strategy in bioinformatics for improving the prediction accuracy by exploiting the synergetic prediction capability of multiple algorithms.

Results

We proposed a novel clustering-based ensemble algorithm named EMD for de novo motif discovery by combining multiple predictions from multiple runs of one or more base component algorithms. The ensemble approach is …

Openws-Transaction: Enabling Reliable Web Service Transactions, Ivan Vasquez, John A. Miller, Kunal Verma, Amit P. Sheth

Kno.e.sis Publications

OpenWS-Transaction is an open source middleware that enables Web services to participate in a distributed transaction as prescribed by the WS-Coordination and WS-Transaction set of specifications. Central to the framework are the Coordinator and Participant entities, which can be integrated into existing services by introducing minimal changes to application code. OpenWS-Transaction allows transaction members to recover their original state in case of operational failure by leveraging techniques in logical logging and recovery at the application level. Depending on transaction style, system recovery may involve restoring key application variables and replaying uncommitted database activity. Transactions are assumed to be defined in …