Krishnaprasad Thirunarayan

The Semantic Web - ISWC 2009

Research Track.- Queries to Hybrid MKNF Knowledge Bases through Oracular Tabling.- Automatically Constructing Semantic Web Services from Online Sources.- Exploiting User Feedback to Improve Semantic Web Service Discovery.- A Generic Approach for Large-Scale Ontological Reasoning in the Presence of Access Restrictions to the Ontologys Axioms.- OntoCase-Automatic Ontology Enrichment Based on Ontology Design Patterns.- Graph-Based Ontology Construction from Heterogenous Evidences.- DOGMA: A Disk-Oriented Graph Matching Algorithm for RDF Databases.- Semantically-Aided Business Process Modeling.- Task Oriented Evaluation of Module Extraction Techniques.- A Decomposition-Based Approach to Optimizing Conjunctive Query Answering in OWL DL.- Goal-Directed Module Extraction for Explaining OWL DL Entailments.- Analysis of a Real Online Social Network Using Semantic Web Frameworks.- Coloring RDF Triples to Capture Provenance.- TripleRank: Ranking Semantic Web Data by Tensor Decomposition.- What Four Million Mappings Can Tell You about Two Hundred Ontologies.- Modeling and Query Patterns for Process Retrieval in OWL.- Context and Domain Knowledge Enhanced Entity Spotting in Informal Text.- Using Naming Authority to Rank Data and Ontologies for Web Search.- Executing SPARQL Queries over the Web of Linked Data.- Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes.- Decidable Order-Sorted Logic Programming for Ontologies and Rules with Argument Restructuring.- Semantic Web Service Composition in Social Environments.- XLWrap - Querying and Integrating Arbitrary Spreadsheets with SPARQL.- Optimizing QoS-Aware Semantic Web Service Composition.- Synthesizing Semantic Web Service Compositions with jMosel and Golog.- A Practical Approach for Scalable Conjunctive Query Answering on Acyclic Knowledge Base.- Learning Semantic Query Suggestions.- Investigating the Semantic Gap through Query Log Analysis.- Towards Lightweight and Robust Large Scale Emergent Knowledge Processing.- On Detecting High-Level Changes in RDF/S KBs.- Efficient Query Answering for OWL 2.- Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data.- A Conflict-Based Operator for Mapping Revision.- Functions over RDF Language Elements.- Policy-Aware Content Reuse on the Web.- Exploiting Partial Information in Taxonomy Construction.- Actively Learning Ontology Matching via User Interaction.- Optimizing Web Service Composition While Enforcing Regulations.- A Weighted Approach to Partial Matching for Mobile Reasoning.- Scalable Distributed Reasoning Using MapReduce.- Discovering and Maintaining Links on the Web of Data.- Concept and Role Forgetting in Ontologies.- Parallel Materialization of the Finite RDFS Closure for Hundreds of Millions of Triples.- Semantic Web In Use.- Live Social Semantics.- RAPID: Enabling Scalable Ad-Hoc Analytics on the Semantic Web.- LinkedGeoData: Adding a Spatial Dimension to the Web of Data.- Enrichment and Ranking of the YouTube Tag Space and Integration with the Linked Data Cloud.- Produce and Consume Linked Data with Drupal!.- Extracting Enterprise Vocabularies Using Linked Open Data.- Reasoning about Resources and Hierarchical Tasks Using OWL and SWRL.- Using Hybrid Search and Query for E-discovery Identification.- Bridging the Gap between Linked Data and the Semantic Desktop.- Vocabulary Matching for Book Indexing Suggestion in Linked Libraries - A Prototype Implementation and Evaluation.- Semantic Web Technologies for the Integration of Learning Tools and Context-Aware Educational Services.- Semantic Enhancement for Enterprise Data Management.- Lifting Events in RDF from Interactions with Annotated Web Pages.- A Case Study in Integrating Multiple E-commerce Standards via Semantic Web Technology.- Supporting Multi-view User Ontology to Understand Company Value Chains.- Doctoral Consortium.- EXPRESS: EXPressing REstful Semantic Services Using Domain Ontologies.- A Lexical-Ontological Resource for Consumer Heathcare.- Semantic Web for Search.- Towards Agile Ontology Maintenance.- Ontologies for User Interface Integration.- Semantic Usage Policies for Web Services.- Ontology-Driven Generalization of Cartographic Representations by Aggregation and Dimensional Collapse.- Invited Talks.- Populating the Semantic Web by Macro-reading Internet Text.- Search 3.0: Present, Personal, Precise.

The Semantic Web - ISWC 2008

Research Track.- Involving Domain Experts in Authoring OWL Ontologies.- Supporting Collaborative Ontology Development in Protege.- Identifying Potentially Important Concepts and Relations in an Ontology.- RoundTrip Ontology Authoring.- nSPARQL: A Navigational Language for RDF.- An Experimental Comparison of RDF Data Management Approaches in a SPARQL Benchmark Scenario.- Anytime Query Answering in RDF through Evolutionary Algorithms.- The Expressive Power of SPARQL.- Integrating Object-Oriented and Ontological Representations: A Case Study in Java and OWL.- Extracting Semantic Constraint from Description Text for Semantic Web Service Discovery.- Enhancing Semantic Web Services with Inheritance.- Using Semantic Distances for Reasoning with Inconsistent Ontologies.- Statistical Learning for Inductive Query Answering on OWL Ontologies.- Optimization and Evaluation of Reasoning in Probabilistic Description Logic: Towards a Systematic Approach.- Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning.- Comparison between Ontology Distances (Preliminary Results).- Folksonomy-Based Collabulary Learning.- Combining a DL Reasoner and a Rule Engine for Improving Entailment-Based OWL Reasoning.- Improving an RCC-Derived Geospatial Approximation by OWL Axioms.- OWL Datatypes: Design and Implementation.- Laconic and Precise Justifications in OWL.- Learning Concept Mappings from Instance Similarity.- Instanced-Based Mapping between Thesauri and Folksonomies.- Collecting Community-Based Mappings in an Ontology Repository.- Algebras of Ontology Alignment Relations.- Scalable Grounded Conjunctive Query Evaluation over Large and Expressive Knowledge Bases.- A Kernel Revision Operator for Terminologies - Algorithms and Evaluation.- Description Logic Reasoning with Decision Diagrams.- RDF123: From Spreadsheets to RDF.- Evaluating Long-Term Use of the Gnowsis Semantic Desktop for PIM.- Bringing the IPTC News Architecture into the Semantic Web.- RDFS Reasoning and Query Answering on Top of DHTs.- An Interface-Based Ontology Modularization Framework for Knowledge Encapsulation.- On the Semantics of Trust and Caching in the Semantic Web.- Semantic Web Service Choreography: Contracting and Enactment.- Formal Model for Semantic-Driven Service Execution.- Efficient Semantic Web Service Discovery in Centralized and P2P Environments.- Exploring Semantic Social Networks Using Virtual Reality.- Semantic Grounding of Tag Relatedness in Social Bookmarking Systems.- Semantic Modelling of User Interests Based on Cross-Folksonomy Analysis.- ELP: Tractable Rules for OWL 2.- Term Dependence on the Semantic Web.- Semantic Relatedness Measure Using Object Properties in an Ontology.- Semantic Web in Use Track.- Thesaurus-Based Search in Large Heterogeneous Collections.- Deploying Semantic Web Technologies for Work Integrated Learning in Industry - A Comparison: SME vs. Large Sized Company.- Creating and Using Organisational Semantic Webs in Large Networked Organisations.- An Architecture for Semantic Navigation and Reasoning with Patient Data - Experiences of the Health-e-Child Project.- Requirements Analysis Tool: A Tool for Automatically Analyzing Software Requirements Documents.- OntoNaviERP: Ontology-Supported Navigation in ERP Software Documentation.- Market Blended Insight: Modeling Propensity to Buy with the Semantic Web.- DogOnt - Ontology Modeling for Intelligent Domotic Environments.- Introducing IYOUIT.- A Semantic Data Grid for Satellite Mission Quality Analysis.- A Process Catalog for Workflow Generation.- Inference Web in Action: Lightweight Use of the Proof Markup Language.- Supporting Ontology-Based Dynamic Property and Classification in WebSphere Metadata Server.- Towards a Multimedia Content Marketplace Implementation Based on Triplespaces.- Doctoral Consortium Track.- Semantic Enrichment of Folksonomy Tagspaces.- Contracting and Copyright Issues for Composite Semantic Services.- Parallel Computation Techniques for Ontology Reasoning.- Towards Semantic Mapping for Casual Web Users.- Interactive Exploration of Heterogeneous Cultural Heritage Collections.- End-User Assisted Ontology Evolution in Uncertain Domains.- Learning Methods in Multi-grained Query Answering.

SemSOS: Semantic sensor Observation Service

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 applications smarter or make the data smarter. We propose the latter option and accomplish this by leveraging semantic technologies in order to provide and apply more meaningful representation of sensor data. More specifically, we are modeling the domain of sensors and sensor observations in a suite of ontologies, adding semantic annotations to the sensor data, using the ontology models to reason over sensor observations, and extending an open source SOS implementation with our semantic knowledge base. This semantically enabled SOS, or SemSOS, provides the ability to query high-level knowledge of the environment as well as low-level raw sensor data.

Comparative trust management with applications: Bayesian approaches emphasis

Trust relationships occur naturally in many diverse contexts such as collaborative systems, e-commerce, interpersonal interactions, social networks, and semantic sensor web. As agents providing content and services become increasingly removed from the agents that consume them, the issue of robust trust inference and update becomes critical. There is a need to find online substitutes for traditional (direct or face-to-face) cues to derive measures of trust, and create efficient and robust systems for managing trust in order to support decision-making. Unfortunately, there is neither a universal notion of trust that is applicable to all domains nor a clear explication of its semantics or computation in many situations. We motivate the trust problem, explain the relevant concepts, summarize research in modeling trust and gleaning trustworthiness, and discuss challenges confronting us. The goal is to provide a comprehensive broad overview of the trust landscape, with the nitty-gritties of a handful of approaches. We also provide details of the theoretical underpinnings and comparative analysis of Bayesian approaches to binary and multi-level trust, to automatically determine trustworthiness in a variety of reputation systems including those used in sensor networks, e-commerce, and collaborative environments. Ultimately, we need to develop expressive trust networks that can be assigned objective semantics.

A graph-based recovery and decomposition of Swanson's hypothesis using semantic predications

OBJECTIVES This paper presents a methodology for recovering and decomposing Swansons Raynaud Syndrome-Fish Oil hypothesis semi-automatically. The methodology leverages the semantics of assertions extracted from biomedical literature (called semantic predications) along with structured background knowledge and graph-based algorithms to semi-automatically capture the informative associations originally discovered manually by Swanson. Demonstrating that Swansons manually intensive techniques can be undertaken semi-automatically, paves the way for fully automatic semantics-based hypothesis generation from scientific literature. METHODS Semantic predications obtained from biomedical literature allow the construction of labeled directed graphs which contain various associations among concepts from the literature. By aggregating such associations into informative subgraphs, some of the relevant details originally articulated by Swanson have been uncovered. However, by leveraging background knowledge to bridge important knowledge gaps in the literature, a methodology for semi-automatically capturing the detailed associations originally explicated in natural language by Swanson, has been developed. RESULTS Our methodology not only recovered the three associations commonly recognized as Swansons hypothesis, but also decomposed them into an additional 16 detailed associations, formulated as chains of semantic predications. Altogether, 14 out of the 19 associations that can be attributed to Swanson were retrieved using our approach. To the best of our knowledge, such an in-depth recovery and decomposition of Swansons hypothesis has never been attempted. CONCLUSION In this work therefore, we presented a methodology to semi-automatically recover and decompose Swansons RS-DFO hypothesis using semantic representations and graph algorithms. Our methodology provides new insights into potential prerequisites for semantics-driven Literature-Based Discovery (LBD). Based on our observations, three critical aspects of LBD include: (1) the need for more expressive representations beyond Swansons ABC model; (2) an ability to accurately extract semantic information from text; and (3) the semantic integration of scientific literature and structured background knowledge.

Power of Clouds in Your Pocket: An Efficient Approach for Cloud Mobile Hybrid Application Development

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 Language (DSL) based methodology to develop these applications. We define Cloud-mobile hybrid as a collective application that has a Cloud based back-end and a mobile device front-end. Using a single DSL script, our toolkit is capable of generating a variety of CMH applications. These applications are composed of multiple combinations of native Cloud and mobile applications. Our approach not only reduces the learning curve but also shields developers from the complexities of the target platforms. We provide a detailed description of our language and present the results obtained using our prototype generator implementation. We also present a list of extensions that will enhance the various aspects of this platform.

Cursing in English on twitter

Cursing is not uncommon during conversations in the physical world: 0.5% to 0.7% of all the words we speak are curse words, given that 1% of all the words are first-person plural pronouns (e.g., we, us, our). On social media, people can instantly chat with friends without face-to-face interaction, usually in a more public fashion and broadly disseminated through highly connected social network. Will these distinctive features of social media lead to a change in peoples cursing behavior? In this paper, we examine the characteristics of cursing activity on a popular social media platform - Twitter, involving the analysis of about 51 million tweets and about 14 million users. In particular, we explore a set of questions that have been recognized as crucial for understanding cursing in offline communications by prior studies, including the ubiquity, utility, and contextual dependencies of cursing.

Situation awareness via abductive reasoning from Semantic Sensor data: A preliminary report

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 and develop a meta-interpreter in Prolog to explain Weather data. This preliminary work illustrates synthesis of high-level, reliable information for situation awareness by querying low-level sensor data.

A theory of nonmonotonic inheritance based on annotated logic

Abstract We propose a logical language for representing networks with nonmonotonic multiple inheritance. The language is based on a variant of annotated logic studied in [5, 6, 17–21]. The use of annotated logic provides a rich setting that allows to disambiguate networks whose topology does not provide enough information to decide how properties are to be inherited. The proposed formalism handles inheritance via strict as well as defeasible links. We provide a formal account of the language, describe its semantics, and show how a unique intended model can be associated with every inheritance specification written in the language. Finally, we present an algorithm that correctly propagates inherited properties according to the given semantics. The algorithm is also complete in the sense that it computes the set of all properties that must be inherited by any given individual object, and then terminates.

Extracting City Traffic Events from Social Streams

Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology-enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services, such as traffic, public transport, water supply, weather, sewage, and public safety, as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance-level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over 4 months from the San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.