Integrating Deep-Web Information Sources

Deep-web information sources are difficult to integrate into automated business processes if they only provide a search form. A wrapping agent is a piece of software that allows a developer to query such information sources without worrying about the details of interacting with such forms. Our goal is to help soft ware engineers construct wrapping agents that interpret queries written in high-level structured languages. We think that this shall definitely help reduce integration costs because this shall relieve developers from the burden of transforming their queries into low-level interactions in an ad-hoc manner. In this paper, we report on our reference framework, delve into the related work, and highlight current research challenges. This is intended to help guide future research efforts in this area.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-

On using high-level structured queries for integrating deep-web information sources

The actual value of the Deep Web comes from integrating the data its applications provide. Such applications offer human-oriented search forms as their entry points, and there exists a number of tools that are used to fill them in and retrieve the resulting pages programmatically. Solution that rely on these tools are usually costly, which motivated a number of researchers to work on virtual integration, also known as metasearch. Virtual integration abstracts away from actual search forms by providing a unified search form, i.e., a programmer fills it in and the virtual integration system translates it into the application search forms. We argue that virtual integration costs might be reduced further if another abstraction level is provided by issuing structured queries in high-level languages such as SQL, XQuery or SPARQL; this helps abstract away from search forms. As far as we know, there is not a proposal in the literature that addresses this problem. In this paper, we propose a reference framework called IntegraWeb to solve the problems of using high-level structured queries to perform deep-web data integration. Furthermore, we provide a comprehensive report on existing proposals from the database integration and the Deep Web research fields, which can be used in combination to address our problem within the previous reference framework.Ministerio de Ciencia y Tecnología TIN2007-64119Junta de Andalucía P07- TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010- 21744Ministerio de Economía, Industria y Competitividad TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-

WEIDJ: Development of a new algorithm for semi-structured web data extraction

In the era of industrial digitalization, people are increasingly investing in solutions that allow their process for data collection, data analysis and performance improvement. In this paper, advancing web scale knowledge extraction and alignment by integrating few sources by exploring different methods of aggregation and attention is considered in order focusing on image information. The main aim of data extraction with regards to semi-structured data is to retrieve beneficial information from the web. The data from web also known as deep web is retrievable but it requires request through form submission because it cannot be performed by any search engines. As the HTML documents start to grow larger, it has been found that the process of data extraction has been plagued with lengthy processing time. In this research work, we propose an improved model namely wrapper extraction of image using document object model (DOM) and JavaScript object notation data (JSON) (WEIDJ) in response to the promising results of mining in a higher volume of image from a various type of format. To observe the efficiency of WEIDJ, we compare the performance of data extraction by different level of page extraction with VIBS, MDR, DEPTA and VIDE. It has yielded the best results in Precision with 100, Recall with 97.93103 and F-measure with 98.9547

Integrating Authentic Digital Resources in Support of Deep, Meaningful Learning

"Integrating Authentic Digital Resources in Support of Deep, Meaningful Learning," a white paper prepared for the Smithsonian by Interactive Educational Systems Design Inc., describes instructional approaches that apply to successful teaching with the Smithsonian Learning Lab.After defining its use of terms such as deeper learning and authentic resources the authors review the research basis of three broad approaches that support integrating digital resources into the classroom:Project-based learningGuided exploration of concepts and principlesGuided development of academic skillsThese approaches find practical application in the last section of the paper, which includes seven case studies. Examples range from first-grade science, to middle-school English (including ELL strategy) to a high-school American government class. In each example, students study and analyze digital resources, going on to apply their knowledge and deepen their understanding of a range of topics and problems

A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge

We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.Comment: 25 pages, 10 figure

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version