975 resultados para Structured data


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Currently there is no structured data standard for representing elements commonly found in transmedia fictional universes. There are websites dedicated to individual universes, however, information found on these sites separates the various formats into books, movies, comics, etc.; concentrate on only the bibliographic aspects of the material; and are only full-text searchable. We have created an ontological model that will allow researchers, fans, brand managers, and creators to search for and retrieve the information contained in these worlds based on how they are structured. We conducted a domain analysis and user studies based on the contents of Harry Potter, Lord of the Rings, the Marvel Universe, and Star Wars in order to build a new model using the Ontology Web Language (OWL) and an artificial intelligence reasoning engine. This model can infer connections between characters, elements of power, items, places, events, etc. This model will facilitate better search and retrieval of the information contained within these vast story universes for all users interested in them. The result of this project is and OWL ontology that is intuitive for users; can be used by AI systems; and has been updated to reflect real user needs based on user research.

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This paper discusses the use of the non-parametric free disposal hull (FDH) and the parametric multi-level model (MLM) as alternative methods for measuring pupil and school attainment where hierarchical structured data are available. Using robust FDH estimates, we show how to decompose the overall inefficiency of a unit (a pupil) into a unit specific and a higher level (a school) component. By a sample of entry and exit attainments of 3017 girls in British ordinary single sex schools, we test the robustness of the non-parametric and parametric estimates. Finally, the paper uses the traditional MLM model in a best practice framework so that pupil and school efficiencies can be computed.

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Kernel methods provide a convenient way to apply a wide range of learning techniques to complex and structured data by shifting the representational problem from one of finding an embedding of the data to that of defining a positive semidefinite kernel. One problem with the most widely used kernels is that they neglect the locational information within the structures, resulting in less discrimination. Correspondence-based kernels, on the other hand, are in general more discriminating, at the cost of sacrificing positive-definiteness due to their inability to guarantee transitivity of the correspondences between multiple graphs. In this paper we generalize a recent structural kernel based on the Jensen-Shannon divergence between quantum walks over the structures by introducing a novel alignment step which rather than permuting the nodes of the structures, aligns the quantum states of their walks. This results in a novel kernel that maintains localization within the structures, but still guarantees positive definiteness. Experimental evaluation validates the effectiveness of the kernel for several structural classification tasks. © 2014 Springer-Verlag Berlin Heidelberg.

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Kernel methods provide a way to apply a wide range of learning techniques to complex and structured data by shifting the representational problem from one of finding an embedding of the data to that of defining a positive semidefinite kernel. In this paper, we propose a novel kernel on unattributed graphs where the structure is characterized through the evolution of a continuous-time quantum walk. More precisely, given a pair of graphs, we create a derived structure whose degree of symmetry is maximum when the original graphs are isomorphic. With this new graph to hand, we compute the density operators of the quantum systems representing the evolutions of two suitably defined quantum walks. Finally, we define the kernel between the two original graphs as the quantum Jensen-Shannon divergence between these two density operators. The experimental evaluation shows the effectiveness of the proposed approach. © 2013 Springer-Verlag.

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Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. ^ There are two issues in using HLPNs—modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. ^ For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. ^ For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. ^ The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.^

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Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used factorized approximation to the posterior and provide generic applicability to a broad class of probabilistic models with minimal model-specific derivations. In the second part of this dissertation, we design probabilistic graphical models to accommodate multimodal data, describe dynamical behaviors and account for task heterogeneity. In particular, the sparse latent factor model is able to reveal common low-dimensional structures from high-dimensional data. We demonstrate the effectiveness of the proposed statistical learning methods on both synthetic and real-world data.

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Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. There are two issues in using HLPNs - modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.

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In this article, the change in examinee effort during an assessment, which we will refer to as persistence, is modeled as an effect of item position. A multilevel extension is proposed to analyze hierarchically structured data and decompose the individual differences in persistence. Data from the 2009 Program of International Student Achievement (PISA) reading assessment from N = 467,819 students from 65 countries are analyzed with the proposed model, and the results are compared across countries. A decrease in examinee effort during the PISA reading assessment was found consistently across countries, with individual differences within and between schools. Both the decrease and the individual differences are more pronounced in lower performing countries. Within schools, persistence is slightly negatively correlated with reading ability; but at the school level, this correlation is positive in most countries. The results of our analyses indicate that it is important to model and control examinee effort in low-stakes assessments. (DIPF/Orig.)

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Conhecer os requisitos ambientais por parte das espécies demonstrou ser essencial em disciplinas como a ecologia e a biologia da conservação. O presente estudo visa estudar as respostas e dependência das comunidades de aves em relação às galerias ripícolas mediterrânicas. Para tal utilizaram-se dados recolhidos pelo River Habitat Survey (RHS) e por censos por pontos de escuta, em três ribeiras no sul de Portugal. Os dados ambientais foram estruturados em matrizes de acordo com as características físicas da ribeira e das margens, e modificações antropogénicas. Enquanto os dados biológicos foram agrupados em guildas: alimentação e ocupação vertical do habitat ("estrato"). Através de análises canónicas aos a dos estruturados obtiveram-se correlações válidas entre as matrizes ambientais e as guildas, nomeadamente para indivíduos directamente dependentes da água e planadores ("aéreas"), provando a validade da metodologia e o potencial da combinação destas duas técnicas. ABSTRACT; Understanding species habitat requirements has proved to be essential in ecology and conservation biology. The present report aims to examine the responses and dependence we used data collected by River Habitat Survey (RHS) and point count censuses in three rivers in southern Portugal. The environmental data were structured in matrices according to physical characteristics of the stream, the banks and anthropogenic modifications, whilst biological data was grouped into guilds: foraging and occupation ("estate"). Through canonical analysis to structured data we obtained valid correlations between the environmental variables and species guilds, particularly for those directly dependent on water and gliders ("aéreas"), proving the validity of the methodology and the potential of these two techniques working together.

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The ICES Working Group for the Bay of Biscay and the Iberic waters Ecoregion (WGBIE) met in Copenhagen, Denmark during 13–14 May 2016. There were 22 stocks in its remit distributed from ICES Divisions 3.a–4.a though mostly distributed in Sub Areas 7, 8 and 9. There were 21 participants, some of whom joined the meeting re-motely. The group was tasked with conducting assessments of stock status for 22 stocks using analytical, forecast methods or trends indicators to provide catch forecasts for eight stocks and provide a first draft of the ICES advice for 2016 for fourteen stocks. For the remaining stocks, the group had to update catch information and indices of abundance where needed. Depending on the result of this update, namely if it would change the perception of the stock, the working group drafted new advice. Analytical assessments using age-structured models were conducted for the northern and southern stocks of megrim and the Bay of Biscay sole. The two hake stocks and one southern stock of anglerfish were assessed using models that allow the use of only length-structured data (no age data). A surplus-production model, without age or length structure, was used to assess the second southern stocks of anglerfish. No ana-lytical assessments have been provided for the northern stocks of anglerfish after 2006. This is mostly due to ageing problems and to an increase in discards in recent years, for which there is no reliable data at the stock level. The state of stocks for which no analytical assessment could be performed was inferred from examination of commer-cial LPUE or CPUE data and from survey information. Three nephrops stocks from the Bay of Biscay and the Iberian waters are scheduled for benchmark assessments in October 2016. The WGBIE meeting spent some time review-ing the progress towards the benchmark (see Annex 6) together with longer term benchmarks (2017 and after, see section 1.) for sea bass in the Bay of Biscay, all an-glerfish and hake stocks assessed by the WG. For the northern megrim stock, the sched-ule an inter-benchmark meeting was completed successfully and the group reviewed the outcome and accepted the category 1 update assessment. A recurrent issue significantly constrained the group’s ability to address the terms of reference this year. Despite an ICES data call with a deadline of six weeks before the meeting, data for several stocks were resubmitted during the meeting which lead to increased workloads during the working group, as in that case, the assessments could not be carried out in National Laboratories prior to the meeting as mentioned in the ToRs. This is an important matter of concerns for the group members. Section 1 of the report presents a summary by stock and discusses general issues. Sec-tion 2 provides descriptions of the relevant fishing fleets and surveys used in the as-sessment of the stocks. Sections 3–18 contains the single stock assessments.

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My doctoral research is about the modelling of symbolism in the cultural heritage domain, and on connecting artworks based on their symbolism through knowledge extraction and representation techniques. In particular, I participated in the design of two ontologies: one models the relationships between a symbol, its symbolic meaning, and the cultural context in which the symbol symbolizes the symbolic meaning; the second models artistic interpretations of a cultural heritage object from an iconographic and iconological (thus also symbolic) perspective. I also converted several sources of unstructured data, a dictionary of symbols and an encyclopaedia of symbolism, and semi-structured data, DBpedia and WordNet, to create HyperReal, the first knowledge graph dedicated to conventional cultural symbolism. By making use of HyperReal's content, I showed how linked open data about cultural symbolism could be utilized to initiate a series of quantitative studies that analyse (i) similarities between cultural contexts based on their symbologies, (ii) broad symbolic associations, (iii) specific case studies of symbolism such as the relationship between symbols, their colours, and their symbolic meanings. Moreover, I developed a system that can infer symbolic, cultural context-dependent interpretations from artworks according to what they depict, envisioning potential use cases for museum curation. I have then re-engineered the iconographic and iconological statements of Wikidata, a widely used general-domain knowledge base, creating ICONdata: an iconographic and iconological knowledge graph. ICONdata was then enriched with automatic symbolic interpretations. Subsequently, I demonstrated the significance of enhancing artwork information through alignment with linked open data related to symbolism, resulting in the discovery of novel connections between artworks. Finally, I contributed to the creation of a software application. This application leverages established connections, allowing users to investigate the symbolic expression of a concept across different cultural contexts through the generation of a three-dimensional exhibition of artefacts symbolising the chosen concept.

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Given the importance the concept of productive efficiency has on analyzing the human development process, which is complex and multidimensional, this study conducts a literature review on the research works that have used the data envelopment analysis (DEA) to measure and analyze the development process. Therefore, we researched the databases of Scopus and Web of Science, and considered the following analysis dimensions: bibliometrics, scope, DEA models and extensions used, interfaces with other techniques, units analyzed and depth of analysis. In addition to a brief summary, the main gaps in each analysis dimension were assessed, which may serve to guide future researches. (C) 2015 Elsevier Ltd. All rights reserved.

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The recent liberalization of the German energy market has forced the energy industry to develop and install new information systems to support agents on the energy trading floors in their analytical tasks. Besides classical approaches of building a data warehouse giving insight into the time series to understand market and pricing mechanisms, it is crucial to provide a variety of external data from the web. Weather information as well as political news or market rumors are relevant to give the appropriate interpretation to the variables of a volatile energy market. Starting from a multidimensional data model and a collection of buy and sell transactions a data warehouse is built that gives analytical support to the agents. Following the idea of web farming we harvest the web, match the external information sources after a filtering and evaluation process to the data warehouse objects, and present this qualified information on a user interface where market values are correlated with those external sources over the time axis.

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In the paper we report on the results of our experiments on the construction of the opinion ontology. Our aim is to show the benefits of publishing in the open, on the Web, the results of the opinion mining process in a structured form. On the road to achieving this, we attempt to answer the research question to what extent opinion information can be formalized in a unified way. Furthermore, as part of the evaluation, we experiment with the usage of Semantic Web technologies and show particular use cases that support our claims.