Physical Sciences and Mathematics Commons^™

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

Department of Computer Science and Engineering: 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 …

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.

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 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 …

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 …

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

Department of Computer Science and Engineering: 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 1100 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 athttp://cse.unl.edu/~profess/. Our database is designed to be versatile and expandable and will not …

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 …

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 …

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 …

Provenir Ontology: Towards A Framework For Escience Provenance Management, Satya S. Sahoo, Amit P. Sheth

Kno.e.sis Publications

Provenance metadata describes the 'lineage' or history of an entity and necessary information to verify the quality of data, validate experiment protocols, and associate trust value with scientific results. eScience projects generate data and the associated provenance metadata in a distributed environment (such as myGrid) and on a very large scale that often precludes manual analysis. Given this scenario, provenance information should be, (a) interoperable across projects, research groups, and application domains, and (b) support analysis over large datasets using reasoning to discover implicit information. In this paper, we introduce an ontology-driven framework for eScience provenance management underpinned by an …

Suggestions For Owl 3, Pascal Hitzler

Computer Science and Engineering Faculty Publications

With OWL 2 about to be completed, it is the right time to start discussions on possible future modifications of OWL. We present here a number of suggestions in order to discuss them with the OWL user community. They encompass expressive extensions on polynomial OWL 2 profiles, a suggestion for an OWL Rules language, and expressive extensions for OWL DL.

Paraconsistent Reasoning For Owl 2, Yue Ma, Pascal Hitzler

Computer Science and Engineering Faculty Publications

A four-valued description logic has been proposed to reason with description logic based inconsistent knowledge bases. This approach has a distinct advantage that it can be implemented by invoking classical reasoners to keep the same complexity as under the classical semantics. However, this approach has so far only been studied for the basid description logic ALC. In this paper, we further study how to extend the four-valued semantics to the more expressive description logic SROIQ which underlies the forthcoming revision of the Web Ontology Language, OWL 2, and also investigate how it fares when adapated to tractable description logics including …

A Preferential Tableaux Calculus For Circumscriptive Alco, Stephan Grimm, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Nonmonotonic extensions of description logics (DLs) allow for default and local closed-world reasoning and are an acknowledged desired feature for applications, e.g. in the Semantic Web. A recent approach to such an extension is based on McCarthy's circumscription, which rests on the principle of minimising the extension of selected predicates to close off dedicated parts of a domain model. While decidability and complexity results have been established in the literature, no practical algorithmisation for circumscriptive DLs has been proposed so far. In this paper, we present a tableaux calculus that can be used as a decision procedure for concept satisfiability …

A Best Practice Model For Cloud Middleware Systems, Ajith Harshana Ranabahu, E. Michael Maximilien

Kno.e.sis Publications

Cloud computing is the latest trend in computing where the intention is to facilitate cheap, utility type computing resources in a service-oriented manner. However, the cloud landscape is still maturing and there are heterogeneities between the clouds, ranging from the application development paradigms to their service interfaces,and scaling approaches. These differences hinder the adoption of cloud by major enterprises. We believe that a cloud middleware can solve most of these issues to allow cross-cloud inter-operation. Our proposed system is Altocumulus, a cloud middleware that homogenizes the clouds. In order to provide the best use of the cloud resources and make …

Context And Domain Knowledge Enhanced Entity Spotting In Informal Text, Daniel Gruhl, Meena Nagarajan, Jan Pieper, Christine Robson, Amit P. Sheth

Kno.e.sis Publications

This paper explores the application of restricted relationship graphs (RDF) and statistical NLP techniques to improve named entity annotation in challenging Informal English domains. We validate our approach using on-line forums discussing popular music. Named entity annotation is particularly difficult in this domain because it is characterized by a large number of ambiguous entities, such as the Madonna album “Music” or Lilly Allen’s pop hit “Smile”.

We evaluate improvements in annotation accuracy that can be obtained by restricting the set of possible entities using real-world constraints. We find that constrained domain entity extraction raises the annotation accuracy significantly, making an …

Ibm Altocumulus: A Cross-Cloud Middleware And Platform, E. Michael Maximilien, Ajith Harshana Ranabahu, Roy Engehausen, Laura Anderson

Kno.e.sis Publications

Cloud computing has become the new face of computing and promises to offer virtually unlimited, cheap, readily available, "utility type" computing resources. Many vendors have entered this market with different offerings ranging from infrastructure-as-a-service such as Amazon, to fully functional platform services such as Google App Engine. However, as a result of this heterogeneity, deploying applications to a cloud and managing them needs to be done using vendor specific methods. This "lock in" is seen as a major hurdle in adopting cloud technologies to the enterprise. IBM Altocumulus, the cloud middleware platform from IBM Almaden Services Research, aims to solve …

Context Is Highly Contextual!, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Tableau Algorithm For Concept Satisfiability In Description Logic Alch, Satya S. Sahoo, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The provenir ontology is an upper-level ontology to facilitate interoperability of provenance information in scientific applications. The description logic (DL) expressivity of provenir ontology is ALCH, that is, it models role hierarchies (H) (without transitive roles and inverse roles). Even though the complexity results for concept satisfiability for numerous variants of DL such as ALC with transitively closed roles (ALC_R+ also called S), inverse roles SI, and role hierarchy SHI have been well-established, similar results for ALCH has been surprisingly missing from the literature. Here, we show that the complexity of the concept satisfiability problem for the ALCH variant …

“Best K”: Critical Clustering Structures In Categorical Datasets, Keke Chen, Ling Liu

Kno.e.sis Publications

The demand on cluster analysis for categorical data continues to grow over the last decade. A well-known problem in categorical clustering is to determine the best K number of clusters. Although several categorical clustering algorithms have been developed, surprisingly, none has satisfactorily addressed the problem of best K for categorical clustering. Since categorical data does not have an inherent distance function as the similarity measure, traditional cluster validation techniques based on geometric shapes and density distributions are not appropriate for categorical data. In this paper, we study the entropy property between the clustering results of categorical data with different K …

Triangle Network Motifs Predict Complexes By Complementing High-Error Interactomes With Structural Information, Bill Andreopoulos, Christof Winter, Dirk Labudde, Michael Schroeder

Faculty Publications, Computer Science

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from …

Ontology Supported Knowledge Discovery In The Field Of Human Performance And Cognition, Christopher Thomas, Pablo N. Mendes, Delroy H. Cameron, Amit P. Sheth, Krishnaprasad Thirunarayan, Cartic Ramakrishnan

Kno.e.sis Publications

No abstract provided.

Analysis And Monetization Of Social Data, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Extending Sparql To Support Spatially And Temporally Related Information, Prateek Jain, Amit P. Sheth, Peter Z. Yeh, Kunal Verma

Kno.e.sis Publications

No abstract provided.

An Ontological Representation Of Time Series Observations On The Semantic Sensor Web, Cory Andrew Henson, Holger Neuhaus, Amit P. Sheth, Krishnaprasad Thirunarayan, Rajkumar Buyya

Kno.e.sis Publications

Time series observations are a common method of collecting sensor data. The Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) provides a standard representation for time series observations within the Observations and Measurements language, and therefore is in heavy use on the Sensor Web. By providing a common model, Observations and Measurements (O&M) facilitates syntax-level integration, but lacks the ability to facilitate semantic-level integration. This inability can cause problems with interoperability between disparate sensor networks that may have subtle variations in their sensing methods. An ontological representation of time series observations could provide a more expressive model and resolve problems …

Trykipedia: Collaborative Bio-Ontology Development Using Wiki Environment, Pramod Anantharam, Satya S. Sahoo, Brent Weatherly, Flora Logan, Raghava Mutharaju, Amit P. Sheth, Rick L. Tarleton

Kno.e.sis Publications

Biomedical ontology development is an intensely collaborative process between biology experts and computer scientists. With the proliferation of ontology based approach to solve informatics problems in biological domain, there is a need for collaborative environment that is intuitive and widely accepted for modeling the ontology.

Situation Awareness Via Abductive Reasoning For Semantic Sensor Data: A Preliminary Report, Krishnaprasad Thirunarayan, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

Semantic sensor Web enhances raw sensor data with spatial, temporal, and thematic annotations to enable high-level reasoning. In this paper, we explore how abductive reasoning framework can benefit formalization and interpretation of sensor data to garner situation awareness. Specifically, we show how abductive logic programming techniques, in conjunction with symbolic knowledge rules, can be used to detect inconsistent sensor data and to generate human accessible description of the state of the world from consistent subset of the sensor data. We also show how trust/belief information can be incorporated into the interpreter to enhance reliability. For concreteness, we formalize weather domain …

Semsos: Semantic Sensor Observation Service, Cory Andrew Henson, Josh Pschorr, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

Sensor observation service (SOS) is a Web service specification defined by the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) group in order to standardize the way sensors and sensor data are discovered and accessed on the Web. This standard goes a long way in providing interoperability between repositories of heterogeneous sensor data and applications that use this data. Many of these applications, however, are ill equipped at handling raw sensor data as provided by SOS and require actionable knowledge of the environment in order to be practically useful. There are two approaches to deal with this obstacle, make the …

Resampling-Based Multiple Hypothesis Testing With Applications To Genomics: New Developments In The R/Bioconductor Package Multtest, Houston N. Gilbert, Katherine S. Pollard, Mark J. Van Der Laan, Sandrine Dudoit

U.C. Berkeley Division of Biostatistics Working Paper Series

The multtest package is a standard Bioconductor package containing a suite of functions useful for executing, summarizing, and displaying the results from a wide variety of multiple testing procedures (MTPs). In addition to many popular MTPs, the central methodological focus of the multtest package is the implementation of powerful joint multiple testing procedures. Joint MTPs are able to account for the dependencies between test statistics by effectively making use of (estimates of) the test statistics joint null distribution. To this end, two additional bootstrap-based estimates of the test statistics joint null distribution have been developed for use in the …

Joint Multiple Testing Procedures For Graphical Model Selection With Applications To Biological Networks, Houston N. Gilbert, Mark J. Van Der Laan, Sandrine Dudoit

U.C. Berkeley Division of Biostatistics Working Paper Series

Gaussian graphical models have become popular tools for identifying relationships between genes when analyzing microarray expression data. In the classical undirected Gaussian graphical model setting, conditional independence relationships can be inferred from partial correlations obtained from the concentration matrix (= inverse covariance matrix) when the sample size n exceeds the number of parameters p which need to estimated. In situations where n < p, another approach to graphical model estimation may rely on calculating unconditional (zero-order) and first-order partial correlations. In these settings, the goal is to identify a lower-order conditional independence graph, sometimes referred to as a ‘0-1 graphs’. For either choice of graph, model selection may involve a multiple testing problem, in which edges in a graph are drawn only after rejecting hypotheses involving (saturated or lower-order) partial correlation parameters. Most multiple testing procedures applied in previously proposed graphical model selection algorithms rely on standard, marginal testing methods which do not take into account the joint distribution of the test statistics derived from (partial) correlations. We propose and implement a multiple testing framework useful when testing for edge inclusion during graphical model selection. Two features of our methodology include (i) a computationally efficient and asymptotically valid test statistics joint null distribution derived from influence curves for correlation-based parameters, and (ii) the application of empirical Bayes joint multiple testing procedures which can effectively control a variety of popular Type I error rates by incorpo- rating joint null distributions such as those described here (Dudoit and van der Laan, 2008). Using a dataset from Arabidopsis thaliana, we observe that the use of more sophisticated, modular approaches to multiple testing allows one to identify greater numbers of edges when approximating an undirected graphical model using a 0-1 graph. Our framework may also be extended to edge testing algorithms for other types of graphical models (e.g., for classical undirected, bidirected, and directed acyclic graphs).