55,474 research outputs found
Generating User-centric Dynamic and Adaptable Knowledge Models for World Wide Web
GENERATING USER-CENTRIC DYNAMIC AND ADAPTABLE KNOWLEDGE MODELS FOR WORLD WIDE WEB By LEI LI JUNE, 2007 Committee Chair: Dr. Vijay Vaishnavi Major Department: Computer Information Systems In the current Internet age, more and more people, organizations, and businesses access the web to share and search for information. A web-based resource is often organized and presented based on its knowledge models (categorization structures). The static and inflexible knowledge models of web-based resources have become a major challenge for web users to successfully use and understand the information on the web. In this dissertation, I propose a research approach to generate user-centric dynamic and adaptable knowledge models for web-based resources. The user-centric feature means that a knowledge model is created based on a web user specified perspective for a web resource and that the user can provide feedback on the model building process. The dynamic feature means the knowledge models are built on the fly. The adaptable feature means the web user can have control of the user adaptation process by specifying his or her perspective for the web resource of interest. In this study, I apply a design science paradigm and follow the General Design Cycle (Vaishnavi and Kuechler 2004) during the course of research. A research prototype, Semantic Facilitator TM SM V2.0, has been implemented based on the proposed approach. A simulation-based experimentation is used to evaluate the research prototype. The experimental results show that the proposed research approach can effectively and efficiently create knowledge models on the fly based on a web user preferred perspective for the web resource. I found that incorporating user feedback into the modeling building process can greatly improve the quality of the knowledge models. At the end of the dissertation, I discuss the limitations and future directions of this research
Integrating Distributed Sources of Information for Construction Cost Estimating using Semantic Web and Semantic Web Service technologies
A construction project requires collaboration of several organizations such as owner, designer, contractor, and material supplier organizations. These organizations need to exchange information to enhance their teamwork. Understanding the information received from other organizations requires specialized human resources. Construction cost estimating is one of the processes that requires information from several sources including a building information model (BIM) created by designers, estimating assembly and work item information maintained by contractors, and construction material cost data provided by material suppliers. Currently, it is not easy to integrate the information necessary for cost estimating over the Internet. This paper discusses a new approach to construction cost estimating that uses Semantic Web technology. Semantic Web technology provides an infrastructure and a data modeling format that enables accessing, combining, and sharing information over the Internet in a machine processable format. The estimating approach presented in this paper relies on BIM, estimating knowledge, and construction material cost data expressed in a web ontology language. The approach presented in this paper makes the various sources of estimating data accessible as Simple Protocol and Resource Description Framework Query Language (SPARQL) endpoints or Semantic Web Services. We present an estimating application that integrates distributed information provided by project designers, contractors, and material suppliers for preparing cost estimates. The purpose of this paper is not to fully automate the estimating process but to streamline it by reducing human involvement in repetitive cost estimating activities
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
A Semantic Grid Oriented to E-Tourism
With increasing complexity of tourism business models and tasks, there is a
clear need of the next generation e-Tourism infrastructure to support flexible
automation, integration, computation, storage, and collaboration. Currently
several enabling technologies such as semantic Web, Web service, agent and grid
computing have been applied in the different e-Tourism applications, however
there is no a unified framework to be able to integrate all of them. So this
paper presents a promising e-Tourism framework based on emerging semantic grid,
in which a number of key design issues are discussed including architecture,
ontologies structure, semantic reconciliation, service and resource discovery,
role based authorization and intelligent agent. The paper finally provides the
implementation of the framework.Comment: 12 PAGES, 7 Figure
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A linked data-driven & service-oriented architecture for sharing educational resources
The two fundamental aims of managing educational resources are to enable resources to be reusable and interoperable and to enable Web-scale sharing of resources across learning communities. Currently, a variety of approaches have been proposed to expose and manage educational resources and their metadata on the Web. These are usually based on heterogeneous metadata standards and schemas, such as IEEE LOM or ADL SCORM, and diverse repository interfaces such as OAI-PMH or SQI. Also, there is still a lack of usage of controlled vocabularies and available data sets that could replace the widespread use of unstructured text for describing resources. On the other hand, the Linked Data approach has proven that it offers a set of successful principles that have the potential to alleviate the aforementioned issues. In this paper, we introduce an architecture and prototype which is fundamentally based on (a) Linked Data principles and (b) Service-orientation to resolve the integration issues for sharing educational resources
Semantic Modeling of Analytic-based Relationships with Direct Qualification
Successfully modeling state and analytics-based semantic relationships of
documents enhances representation, importance, relevancy, provenience, and
priority of the document. These attributes are the core elements that form the
machine-based knowledge representation for documents. However, modeling
document relationships that can change over time can be inelegant, limited,
complex or overly burdensome for semantic technologies. In this paper, we
present Direct Qualification (DQ), an approach for modeling any semantically
referenced document, concept, or named graph with results from associated
applied analytics. The proposed approach supplements the traditional
subject-object relationships by providing a third leg to the relationship; the
qualification of how and why the relationship exists. To illustrate, we show a
prototype of an event-based system with a realistic use case for applying DQ to
relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).Comment: Proceedings of the 2015 IEEE 9th International Conference on Semantic
Computing (IEEE ICSC 2015
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
Exploiting the user interaction context for automatic task detection
Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones
Higher-Order Process Modeling: Product-Lining, Variability Modeling and Beyond
We present a graphical and dynamic framework for binding and execution of
business) process models. It is tailored to integrate 1) ad hoc processes
modeled graphically, 2) third party services discovered in the (Inter)net, and
3) (dynamically) synthesized process chains that solve situation-specific
tasks, with the synthesis taking place not only at design time, but also at
runtime. Key to our approach is the introduction of type-safe stacked
second-order execution contexts that allow for higher-order process modeling.
Tamed by our underlying strict service-oriented notion of abstraction, this
approach is tailored also to be used by application experts with little
technical knowledge: users can select, modify, construct and then pass
(component) processes during process execution as if they were data. We
illustrate the impact and essence of our framework along a concrete, realistic
(business) process modeling scenario: the development of Springer's
browser-based Online Conference Service (OCS). The most advanced feature of our
new framework allows one to combine online synthesis with the integration of
the synthesized process into the running application. This ability leads to a
particularly flexible way of implementing self-adaption, and to a particularly
concise and powerful way of achieving variability not only at design time, but
also at runtime.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455
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