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- RDF (4)
- SSW (4)
- Semantic Sensor Web (4)
- Semantic Web (4)
- Information Integration (2)
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- Mashups (2)
- Nicotine Dependence (2)
- Ontologies (2)
- P2P networks (2)
- Reinforcement learning (2)
- SA-REST (2)
- SAWSDL (2)
- SOA (2)
- SPARQL (2)
- Semantic Mashup (2)
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- Smashups (2)
- Social Networks (2)
- Web 2.0 (2)
- 1.2 COMPUTER AND INFORMATION SCIENCE (1)
- 2.2 ELECTRICAL, ELECTRONIC, INFORMATION ENGINEERING (1)
- 4 x 4 Semantic Model (1)
- ADCR (1)
- API Classification (1)
- Academic – UNF – Computing; School of Computing (1)
- Academic – UNF – Master of Science in Computer and Information Sciences; Dissertations (1)
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- Algorithms (1)
- Application of XML and XSLT (1)
- Binary Associations (1)
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Articles 1 - 30 of 74
Full-Text Articles in Physical Sciences and Mathematics
The 4 X 4 Semantic Model: Exploiting Data, Functional, Non-Functional And Execution Semantics Across Business Process, Workflow, Partner Services And Middleware Services Tiers, Amit P. Sheth, Karthik Gomadam
The 4 X 4 Semantic Model: Exploiting Data, Functional, Non-Functional And Execution Semantics Across Business Process, Workflow, Partner Services And Middleware Services Tiers, Amit P. Sheth, Karthik Gomadam
Kno.e.sis Publications
Business processes in the global environment increasingly encompass multiple partners and complex, rapidly changing requirements. In this context it is critical that strategic business objectives align with and map accurately to systems that support flexible and dynamic business processes. To support the demanding requirements of global business processes, we propose a comprehensive, unifying 4 X 4 Semantic Model that uses Semantic Templates to link four tiers of implementation with four types of semantics. The four tiers are the Business Process Tier, the Workflow Enactment Tier, the Partner Services Tier, and the Middleware Services Tier. The four types of semantics are …
Semantic Sensor Web, Amit P. Sheth, Cory Henson, Krishnaprasad Thirunarayan
Semantic Sensor Web, Amit P. Sheth, Cory Henson, Krishnaprasad Thirunarayan
Kno.e.sis Publications
No abstract provided.
Capturing Workflow Event Data For Monitoring, Performance Analysis, And Management Of Scientific Workflows, Matthew Valerio, Satya S. Sahoo, Roger Barga, Jared Jackson
Capturing Workflow Event Data For Monitoring, Performance Analysis, And Management Of Scientific Workflows, Matthew Valerio, Satya S. Sahoo, Roger Barga, Jared Jackson
Kno.e.sis Publications
To effectively support real-time monitoring and performance analysis of scientific workflow execution, varying levels of event data must be captured and made available to interested parties. This paper discusses the creation of an ontology-aware workflow monitoring system for use in the Trident system which utilizes a distributed publish/subscribe event model. The implementation of the publish/subscribe system is discussed and performance results are presented.
On Static And Dynamic Partitioning Behavior Of Large-Scale Networks, Zhongmei Yao, Derek Leonard, Xiaoming Wang, Dmitri Loguinov
On Static And Dynamic Partitioning Behavior Of Large-Scale Networks, Zhongmei Yao, Derek Leonard, Xiaoming Wang, Dmitri Loguinov
Computer Science Faculty Publications
In this paper, we analyze the problem of network disconnection in the context of large-scale P2P networks and understand how both static and dynamic patterns of node failure affect the resilience of such graphs. We start by applying classical results from random graph theory to show that a large variety of deterministic and random P2P graphs almost surely (i.e., with probability 1 − o(1)) remain connected under random failure if and only if they have no isolated nodes. This simple, yet powerful, result subsequently allows us to derive in closed-form the probability that a P2P network develops isolated nodes, and …
Growing Fields Of Interest: Using An Expand And Reduce Strategy For Domain Model Extraction, Christopher Thomas, Pankaj Mehra, Roger Brooks, Amit P. Sheth
Growing Fields Of Interest: Using An Expand And Reduce Strategy For Domain Model Extraction, Christopher Thomas, Pankaj Mehra, Roger Brooks, Amit P. Sheth
Kno.e.sis Publications
Domain hierarchies are widely used as models underlying information retrieval tasks. Formal ontologies and taxonomies enrich such hierarchies further with properties and relationships associated with concepts and categories but require manual effort; therefore they are costly to maintain, and often stale. Folksonomies and vocabularies lack rich category structure and are almost entirely devoid of properties and relationships. Classification and extraction require the coverage of vocabularies and the alterability of folksonomies and can largely benefit from category relationships and other properties. With Doozer, a program for building conceptual models of information domains, we want to bridge the gap between the vocabularies …
Planning With Ifalcon: Towards A Neural-Network-Based Bdi Agent Architecture, Budhitama Subagdja, Ah-Hwee Tan
Planning With Ifalcon: Towards A Neural-Network-Based Bdi Agent Architecture, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper presents iFALCON, a model of BDI (beliefdesire-intention) agents that is fully realized as a selforganizing neural network architecture. Based on multichannel network model called fusion ART, iFALCON is developed to bridge the gap between a self-organizing neural network that autonomously adapts its knowledge and the BDI agent model that follows explicit descriptions. Novel techniques called gradient encoding are introduced for representing sequences and hierarchical structures to realize plans and the intention structure. This paper shows that a simplified plan representation can be encoded as weighted connections in the neural network through a process of supervised learning. A case …
A Neural Network Model For A Hierarchical Spatio-Temporal Memory, Kiruthika Ramanathan, Luping Shi, Jianming Li, Kian Guan Lim, Zhi Ping Ang, Chong Chong Tow
A Neural Network Model For A Hierarchical Spatio-Temporal Memory, Kiruthika Ramanathan, Luping Shi, Jianming Li, Kian Guan Lim, Zhi Ping Ang, Chong Chong Tow
Research Collection School Of Computing and Information Systems
The architecture of the human cortex is uniform and hierarchical in nature. In this paper, we build upon works on hierarchical classification systems that model the cortex to develop a neural network representation for a hierarchical spatio-temporal memory (HST-M) system. The system implements spatial and temporal processing using neural network architectures. We have tested the algorithms developed against both the MLP and the Hierarchical Temporal Memory algorithms. Our results show definite improvement over MLP and are comparable to the performance of HTM.
Relationship Web: Trailblazing, Analytics And Computing For Human Experience, Amit P. Sheth
Relationship Web: Trailblazing, Analytics And Computing For Human Experience, Amit P. Sheth
Kno.e.sis Publications
This panel presentation was give at the 27th International Conference on Conceptual Modeling (ER 2008), Barcelona, Spain, October 20-23, 2008.
Adapting Ranking Functions To User Preference, Keke Chen, Ya Zhang, Zhaohui Zheng, Hongyuan Zha, Gordon Sun
Adapting Ranking Functions To User Preference, Keke Chen, Ya Zhang, Zhaohui Zheng, Hongyuan Zha, Gordon Sun
Kno.e.sis Publications
Learning to rank has become a popular method for web search ranking. Traditionally, expert-judged examples are the major training resource for machine learned web ranking, which is expensive to get for training a satisfactory ranking function. The demands for generating specific web search ranking functions tailored for different domains, such as ranking functions for different regions, have aggravated this problem. Recently, a few methods have been proposed to extract training examples from user clickthrough log. Due to the low cost of getting user preference data, it is attractive to combine these examples in training ranking functions. However, because of the …
An Ontology-Driven Semantic Mash-Up Of Gene And Biological Pathway Information: Application To The Domain Of Nicotine Dependence, Satya S. Sahoo, Olivier Bodenreider, Joni L. Rutter, Karen J. Skinner, Amit P. Sheth
An Ontology-Driven Semantic Mash-Up Of Gene And Biological Pathway Information: Application To The Domain Of Nicotine Dependence, Satya S. Sahoo, Olivier Bodenreider, Joni L. Rutter, Karen J. Skinner, Amit P. Sheth
Kno.e.sis Publications
Objectives: This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base.
Methods: We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL …
Description Logic Reasoning With Decision Diagrams: Compiling Shiq To Disjunctive Datalog, Sebastian Rudolph
Description Logic Reasoning With Decision Diagrams: Compiling Shiq To Disjunctive Datalog, Sebastian Rudolph
Kno.e.sis Publications
We propose a novel method for reasoning in the description logic SHIQ. After a satisfiability preserving transformation from SHIQ to the description logic ALCIb, the obtained ALCIb Tbox T is converted into an ordered binary decision diagram (OBDD) which represents a canonical model for T. This OBDD is turned into a disjunctive datalog program that can be used for Abox reasoning. The algorithm is worst-case optimal w.r.t. data complexity, and admits easy extensions with DL-safe rules and ground conjunctive queries.
Segmenting Brain Tumors Using Pseudo-Conditional Random Fields, Chi-Hoon Lee, Shaojun Wang, Albert Murtha, Matthew R.G. Brown, Russell Greiner
Segmenting Brain Tumors Using Pseudo-Conditional Random Fields, Chi-Hoon Lee, Shaojun Wang, Albert Murtha, Matthew R.G. Brown, Russell Greiner
Kno.e.sis Publications
Locating Brain tumor segmentation within MR (magnetic resonance) images is integral to the treatment of brain cancer. This segmentation task requires classifying each voxel as either tumor or non-tumor, based on a description of that voxel. Unfortunately, standard classifiers, such as Logistic Regression (LR) and Support Vector Machines (SVM), typically have limited accuracy as they treat voxels as independent and identically distributed (iid). Approaches based on random fields, which are able to incorporate spatial constraints, have recently been applied to brain tumor segmentation with notable performance improvement over iid classifiers. However, previous random field systems involved computationally intractable …
A Faceted Classification Based Approach To Search And Rank Web Apis, Karthik Gomadam, Ajith Harshana Ranabahu, Meenakshi Nagarajan, Amit P. Sheth, Kunal Verma
A Faceted Classification Based Approach To Search And Rank Web Apis, Karthik Gomadam, Ajith Harshana Ranabahu, Meenakshi Nagarajan, Amit P. Sheth, Kunal Verma
Kno.e.sis Publications
Web application hybrids, popularly known as mashups, are created by integrating services on the Web using their APIs. Support for finding an API is currently provided by generic search engines or domain specific solutions such as Google and ProgrammableWeb. Shortcomings of both these solutions in terms of and reliance on user tags make the task of identifying an API challenging. Since these APIs are described in HTML documents, it is essential to look beyond the boundaries of current approaches to Web service discovery that rely on formal descriptions. In this work, we present a faceted approach to searching and ranking …
Semantics Enhanced Services: Meteor-S, Sawsdl And Sa-Rest, Amit P. Sheth, Karthik Gomadam, Ajith Harshana Ranabahu
Semantics Enhanced Services: Meteor-S, Sawsdl And Sa-Rest, Amit P. Sheth, Karthik Gomadam, Ajith Harshana Ranabahu
Kno.e.sis Publications
Services Research Lab at the Knoesis center and the LSDIS lab at University of Georgia have played a significant role in advancing the state of research in the areas of workflow management, semantic Web services and service oriented computing. Starting with the METEOR workflow management system in the 90's, researchers have addressed key issues in the area of semantic Web services and more recently, in the domain of RESTful services and Web 2.0. In this article, we present a brief discussion on the various contributions of METEOR-S including SAWSDL, publication and discovery of semantic Web services, data mediation, dynamic configuration …
Cascade Rsvm In Peer-To-Peer Network, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng
Cascade Rsvm In Peer-To-Peer Network, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng
Research Collection School Of Computing and Information Systems
The goal of distributed learning in P2P networks is to achieve results as close as possible to those from centralized approaches. Learning models of classification in a P2P network faces several challenges like scalability, peer dynamism, asynchronism and data privacy preservation. In this paper, we study the feasibility of building SVM classifiers in a P2P network. We show how cascading SVM can be mapped to a P2P network of data propagation. Our proposed P2P SVM provides a method for constructing classifiers in P2P networks with classification accuracy comparable to centralized classifiers and better than other distributed classifiers. The proposed algorithm …
Challenges Of Creating A Knowledge-Based Society: Education & Research For India & Gujarat, Amit P. Sheth
Challenges Of Creating A Knowledge-Based Society: Education & Research For India & Gujarat, Amit P. Sheth
Kno.e.sis Publications
No abstract provided.
Connectionist Model Generation: A First-Order Approach, Sebastian Bader, Pascal Hitzler, Steffen Holldobler
Connectionist Model Generation: A First-Order Approach, Sebastian Bader, Pascal Hitzler, Steffen Holldobler
Computer Science and Engineering Faculty Publications
Knowledge-based artificial neural networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes as expressed e.g., by means of first-order predicate logic, it is not obvious at all what neural-symbolic systems would look like such that they are truly connectionist, are able to learn, and allow for a declarative reading and logical reasoning at the same time. The core method aims at such an integration. It is a method for connectionist model generation using recurrent networks with feed-forward core. We show in this …
Applications Of Voting Theory To Information Mashups, Alfredo Alba, Varun Bhagwan, Julia Grace, Daniel Gruhl, Kevin Haas, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Nachiketa Sahoo
Applications Of Voting Theory To Information Mashups, Alfredo Alba, Varun Bhagwan, Julia Grace, Daniel Gruhl, Kevin Haas, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Nachiketa Sahoo
Kno.e.sis Publications
Blogs, discussion forums and social networking sites are an excellent source for people's opinions on a wide range of topics. We examine the application of voting theory to "information mashups" - the combining and summarizing of data from the multitude of often-conflicting sources. This paper presents an information mashup in the music domain: a Top 10 artist chart based on user comments and listening behavior from several Web communities. We consider different voting systems as algorithms to combine opinions from multiple sources and evaluate their effectiveness using social welfare functions. Different voting schemes are found to work better in some …
Text Analytics For Semantic Computing - The Good, The Bad And The Ugly, Meenakshi Nagarajan, Cartic Ramakrishnan, Amit P. Sheth
Text Analytics For Semantic Computing - The Good, The Bad And The Ugly, Meenakshi Nagarajan, Cartic Ramakrishnan, Amit P. Sheth
Kno.e.sis Publications
This tutorial was give at the Second IEEE International Conference on Semantic Computing Santa Clara, CA, USA - August 4-7, 2008.
Tcruzikb: Enabling Complex Queries For Genomic Data Exploration, Pablo N. Mendes, Bobby Mcknight, Amit P. Sheth, Jessica C. Kissinger
Tcruzikb: Enabling Complex Queries For Genomic Data Exploration, Pablo N. Mendes, Bobby Mcknight, Amit P. Sheth, Jessica C. Kissinger
Kno.e.sis Publications
We developed a novel analytical environment to aid in the examination of the extensive amount of interconnected data available for genome projects. Our focus is to enable flexibility and abstraction from implementation details, while retaining the expressivity required for post-genomic research. To achieve this goal, we associated genomics data to ontologies and implemented a query formulation and execution environment with added visualization capabilities. We use ontology schemas to guide the user through the process of building complex queries in a flexible Web interface. Queries are serialized in SPARQL and sent to servers via Ajax. A component for visualization of the …
Mediatability: Estimating The Degree Of Human Involvement In Xml Schema Mediation, Karthik Gomadam, Ajith Harshana Ranabahu, Lakshmish Ramaswamy, Amit P. Sheth, Kunal Verma
Mediatability: Estimating The Degree Of Human Involvement In Xml Schema Mediation, Karthik Gomadam, Ajith Harshana Ranabahu, Lakshmish Ramaswamy, Amit P. Sheth, Kunal Verma
Kno.e.sis Publications
Mediation and integration of data are significant challenges because the number of services on the Web, and heterogeneities in their data representation, continue to increase rapidly. To address these challenges we introduce a new measure, mediatability, which is a quantifiable and computable metric for the degree of human involvement in XML schema mediation. We present an efficient algorithm to compute mediatability and an experimental study to analyze how semantic annotations affect the ease of mediating between two schemas. We validate our approach by comparing mediatability scores generated by our system with user-perceived difficulty. We also evaluate the scalability of our …
An Xml-Based Approach To Handling Tables In Documents, Krishnaprasad Thirunarayan, Trivikram Immaneni
An Xml-Based Approach To Handling Tables In Documents, Krishnaprasad Thirunarayan, Trivikram Immaneni
Kno.e.sis Publications
We explore application of XML technology for handling tables in legacy semi-structured documents. Specifically, we analyze annotating heterogeneous documents containing tables to obtain a formalized XML Master document that improves traceability (hence easing verification and update) and enables manipulation using XSLT stylesheets. This approach is useful when table instances far outnumber distinct table types because the effort required to annotate a table instance is relatively less compared to formalizing table processing that respects table’s semantics. This work is also relevant for authoring new documents with tables that should be accessible to both humans and machines.
Description Logic Rules, Markus Krotzsch, Sebastian Rudolph, Pascal Hitzler
Description Logic Rules, Markus Krotzsch, Sebastian Rudolph, Pascal Hitzler
Computer Science and Engineering Faculty Publications
We introduce description logic (DL) rules as a new rule-based formalism for knowledge representation in DLs. As a fragment of the Semantic Web Rule Language SWRL, DL rules allow for a tight integration with DL knowledge bases. In contrast to SWRL, however, the combination of DL rules with expressive description logics remains decidable, and we show that the DL SROIQ – the basis for the ongoing standardisation of OWL 2 – can completely internalise DL rules. On the other hand, DL rules capture many expressive features of SROIQ that are not available in simpler DLs yet. While reasoning in SROIQ …
Boosting With Incomplete Information, Gholamreza Haffari, Yang Wang, Shaojun Wang, Greg Mori, Feng Jiao
Boosting With Incomplete Information, Gholamreza Haffari, Yang Wang, Shaojun Wang, Greg Mori, Feng Jiao
Kno.e.sis Publications
In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we present a boosting approach that integrates features with incomplete information and those with complete information to form a strong classifier. By introducing hidden variables to model missing information, we form loss functions that combine fully labeled data with partially labeled data to effectively learn normalized and unnormalized models. The primal problems of the proposed optimization problems with these loss functions are provided to show their close relationship and the motivations behind them. …
Semantic Web: Promising Technologies, Current Applications & Future Directions, Amit P. Sheth
Semantic Web: Promising Technologies, Current Applications & Future Directions, Amit P. Sheth
Kno.e.sis Publications
No abstract provided.
Dependence Of Binary Associations On Co-Occurrence Granularity In News Documents, Krishnaprasad Thirunarayan, Trivikram Immaneni, Mastan Vali Shaik
Dependence Of Binary Associations On Co-Occurrence Granularity In News Documents, Krishnaprasad Thirunarayan, Trivikram Immaneni, Mastan Vali Shaik
Kno.e.sis Publications
We describe and formalize an approach to correlate binary associations (such as between entities and events, between persons and events, etc.) implied by News documents on the co-occurrence granularity (such as document-level, paragraph-level, sentence-level, etc.) of the corresponding text phrases in the documents. Specifically, we present both qualitative and quantitative characterization of searching News documents: former in terms of the nature of the content and the queries, and latter in terms of a metric obtained by adapting the notions of precision and recall. Specifically, the approach tries to reduce the manual effort required to analyze the News documents to compare …
Extending The Network Life Time In Wsn Using Energy Efficient Algorithm, Venkata Sesha Sai Koundinya Goparaju
Extending The Network Life Time In Wsn Using Energy Efficient Algorithm, Venkata Sesha Sai Koundinya Goparaju
Electrical & Computer Engineering Theses & Dissertations
Wireless Sensor networks have many potential applications. These wireless sensor networks necessitate specific design requirements of which energy efficiency is vital. The sensor networks consist of sensor nodes that operate on battery power, and replacement of these batteries is very often a strenuous task, since networks are deployed in areas where the act of replacing the batteries proves impractical.
With the limited available energy of sensor nodes, most of the energy is drained during communication. An energy efficient routing algorithm can prolong the lifetime of the network by gradually depleting the nodes in the network. Many of the routing protocols …
A Framework For Trust And Distrust Networks, Krishnaprasad Thirunarayan
A Framework For Trust And Distrust Networks, Krishnaprasad Thirunarayan
Kno.e.sis Publications
In this age of internet and electronic commerce it is becoming increasingly important to have and to manipulate information about the trustworthiness of the content or service providers in order to make informed decisions. This paper explores realistic models of trust and distrust based on partially ordered discrete values and proposes a framework, which is sensitive to local, relative ordering of values rather than their magnitudes. The framework distinguishes between direct and inferred trust, preferring direct information over possibly conflicting inferred information. It also represents ambiguity or inconsistency explicitly. The framework is capable of handling general trust and belief networks …
A Forgetting-Based Approach For Reasoning With Inconsistent Distributed Ontologies, Guilin Qi, Yimin Wang, Peter Haase, Pascal Hitzler
A Forgetting-Based Approach For Reasoning With Inconsistent Distributed Ontologies, Guilin Qi, Yimin Wang, Peter Haase, Pascal Hitzler
Computer Science and Engineering Faculty Publications
In the context of multiple distributed ontologies, we are often confronted with the problem of dealing with inconsistency. In this paper, we propose an approach for reasoning with inconsistent distributed ontologies based on concept forgetting. We firstly define concept forgetting in description logics. We then adapt the notions of recoveries and preferred recoveries in propositional logic to description logics. Two consequence relations are then defined based on the preferred recoveries.
Defeasible Inference With Circumscriptive Owl Ontologies, Stephan Grimm, Pascal Hitzler
Defeasible Inference With Circumscriptive Owl Ontologies, Stephan Grimm, Pascal Hitzler
Computer Science and Engineering Faculty Publications
The Web Ontology Language (OWL) adheres to the openworld assumption and can thus not be used for forms of nonmonotonic reasoning or defeasible inference, an acknowledged desirable feature in open Semantic Web environments. We investigate the use of the formalism of circumscriptive description logics (DLs) to realise defeasible inference within the OWL framework. By example, we demonstrate how reasoning with (restricted) circumscribed OWL ontologies facilitates various forms of defeasible inference, also in comparison to alternative approaches. Moreover, we sketch an extension to DL tableaux for handling the circumscriptive case and report on a preliminary implementation.