Prueba de concepto de expansión de consultas basada en ontologías de dominio financiero

Este trabajo presenta el uso de una ontología en el dominio financiero para la expansión de consultas con el fin de mejorar los resultados de un sistema de recuperación de información (RI) financiera. Este sistema está compuesto por una ontología y un índice de Lucene que permite recuperación de conceptos identificados mediante procesamiento de lenguaje natural. Se ha llevado a cabo una evaluación con un conjunto limitado de consultas y los resultados indican que la ambigüedad sigue siendo un problema al expandir la consulta. En ocasiones, la elección de las entidades adecuadas a la hora de expandir las consultas (filtrando por sector, empresa, etc.) permite resolver esa ambigüedad.

COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres

In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, single- and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractive-oriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.

Factores predictores de pérdida de peso y grasa en el tratamiento dietético: sexo, edad, índice de masa corporal y consulta de asistencia

%WL: porcentaje de pérdida de peso; %FL: porcentaje de pérdida de grasa; PNLWF: pacientes que pierden peso o grasa; PLWF: pacientes que pierden peso y grasa. Objetivo: evaluar si el %WL y el %FL en el tratamiento dietético, se vieron afectados por el género, la edad, el IMC y la asistencia a la consulta. Método: 4.700 consultas, 670 pacientes (IMC ≥ 25), en el sur-este de España (2006-12). Se utilizó la dieta equilibrada e hipocalórica. Dos tipos de pacientes: PNLWF y PLWF (91,9%). Resultados: en los PLWF, los hombres y los que asisten en mayor número de ocasiones a la consulta han mostrado una mayor pérdida, frente a las mujeres (%FL: 23,0 vs 14,3%, p = 0,000; %WL: 7,7 vs 6,6%, p = 0,020), y los que asisten con menor frecuencia (%FL: 19,1 vs 7,3%, p = 0,000; %WL: 7,8 vs 2,9%, p = 0,000). El análisis de regresión multinomial (PNLWF/ PLWF) indica que solo el asistir a más de mes y medio a la consulta es un factor que influye en la pérdida, OR 8,3 (IC 95% 4,5-15,1; p = 0,000). Conclusión: la medición de la grasa corporal proporciona una información adicional al peso perdido; la mayoría de los pacientes que asisten más de mes y medio obtienen un elevado %FL; la asistencia es un factor predictor de la pérdida; el %FL indica que el tratamiento dietético juega un papel principal en la resolución de esta patología; se recomienda diseñar esquemas prácticos del proceso de actuación de los nutricionistas en función del IMCi y el variable.

An authoring tool for decision support systems in context questions of ecological knowledge

Decision support systems (DSS) support business or organizational decision-making activities, which require the access to information that is internally stored in databases or data warehouses, and externally in the Web accessed by Information Retrieval (IR) or Question Answering (QA) systems. Graphical interfaces to query these sources of information ease to constrain dynamically query formulation based on user selections, but they present a lack of flexibility in query formulation, since the expressivity power is reduced to the user interface design. Natural language interfaces (NLI) are expected as the optimal solution. However, especially for non-expert users, a real natural communication is the most difficult to realize effectively. In this paper, we propose an NLI that improves the interaction between the user and the DSS by means of referencing previous questions or their answers (i.e. anaphora such as the pronoun reference in “What traits are affected by them?”), or by eliding parts of the question (i.e. ellipsis such as “And to glume colour?” after the question “Tell me the QTLs related to awn colour in wheat”). Moreover, in order to overcome one of the main problems of NLIs about the difficulty to adapt an NLI to a new domain, our proposal is based on ontologies that are obtained semi-automatically from a framework that allows the integration of internal and external, structured and unstructured information. Therefore, our proposal can interface with databases, data warehouses, QA and IR systems. Because of the high NL ambiguity of the resolution process, our proposal is presented as an authoring tool that helps the user to query efficiently in natural language. Finally, our proposal is tested on a DSS case scenario about Biotechnology and Agriculture, whose knowledge base is the CEREALAB database as internal structured data, and the Web (e.g. PubMed) as external unstructured information.

Particulars of the trial and execution of George Partridge, aged 21: who was executed at Bury-St.-Edmund's, on Monday last, for the wilful murder of George Ansell, aged 9 years, within a few miles of Polstead, the late scene of Maria Martin's murder. Also, the prisoner's confession

Date of imprint from execution date, April 13, 1829 -- Capital Punishment UK site via WWW, viewed December 14, 2007.

Desempenho comparativo do BLAST e do mpiBLAST

Em Bioinformática são frequentes problemas cujo tratamento necessita de considerável poder de processamento/cálculo e/ou grande capacidade de armazenamento de dados e elevada largura de banda no acesso aos mesmos (de forma não comprometer a eficiência do seu processamento). Um exemplo deste tipo de problemas é a busca de regiões de similaridade em sequências de amino-ácidos de proteínas, ou em sequências de nucleótidos de DNA, por comparação com uma dada sequência fornecida (query sequence). Neste âmbito, a ferramenta computacional porventura mais conhecida e usada é o BLAST (Basic Local Alignment Search Tool) [1]. Donde, qualquer incremento no desempenho desta ferramenta tem impacto considerável (desde logo positivo) na atividade de quem a utiliza regularmente (seja para investigação, seja para fins comerciais). Precisamente, desde que o BLAST foi inicialmente introduzido, foram surgindo diversas versões, com desempenho melhorado, nomeadamente através da aplicação de técnicas de paralelização às várias fases do algoritmo (e. g., partição e distribuição das bases de dados a pesquisar, segmentação das queries, etc. ), capazes de tirar partido de diferentes ambientes computacionais de execução paralela, como: máquinas multi-core (BLAST+ 2), clusters de nós multi-core (mpiBLAST3J e, mais recentemente, co-processadores aceleradores como GPUs" ou FPGAs. É também possível usar as ferramentas da família BLAST através de um interface/sítio WEB5, que permite, de forma expedita, a pesquisa de uma variedade de bases de dados conhecidas (e em permanente atualização), com tempos de resposta suficientemente pequenos para a maioria dos utilizadores, graças aos recursos computacionais de elevado desempenho que sustentam o seu backend. Ainda assim, esta forma de utilização do BLAST poderá não ser a melhor opção em algumas situações, como por exemplo quando as bases de dados a pesquisar ainda não são de domínio público, ou, sendo-o, não estão disponíveis no referido sitio WEB. Adicionalmente, a utilização do referido sitio como ferramenta de trabalho regular pressupõe a sua disponibilidade permanente (dependente de terceiros) e uma largura de banda de qualidade suficiente, do lado do cliente, para uma interacção eficiente com o mesmo. Por estas razões, poderá ter interesse (ou ser mesmo necessário) implantar uma infra-estrutura BLAST local, capaz de albergar as bases de dados pertinentes e de suportar a sua pesquisa da forma mais eficiente possível, tudo isto levando em conta eventuais constrangimentos financeiros que limitam o tipo de hardware usado na implementação dessa infra-estrutura. Neste contexto, foi realizado um estudo comparativo de diversas versões do BLAST, numa infra-estrutura de computação paralela do IPB, baseada em componentes commodity: um cluster de 8 nós (virtuais, sob VMWare ESXi) de computação (com CPU Í7-4790K 4GHz, 32GB RAM e 128GB SSD) e um nó dotado de uma GPU (CPU Í7-2600 3.8GHz, 32GB RAM, 128 GB SSD, 1 TB HD, NVIDIA GTX 580). Assim, o foco principal incidiu na avaliação do desempenho do BLAST original e do mpiBLAST, dado que são fornecidos de base na distribuição Linux em que assenta o cluster [6]. Complementarmente, avaliou-se também o BLAST+ e o gpuBLAST no nó dotado de GPU. A avaliação contemplou diversas configurações de recursos, incluindo diferentes números de nós utilizados e diferentes plataformas de armazenamento das bases de dados (HD, SSD, NFS). As bases de dados pesquisadas correspondem a um subconjunto representativo das disponíveis no sitio WEB do BLAST, cobrindo uma variedade de dimensões (desde algumas dezenas de MBytes, até à centena de GBytes) e contendo quer sequências de amino-ácidos (env_nr e nr), quer de nucleótidos (drosohp. nt, env_nt, mito. nt, nt e patnt). Para as pesquisas foram 'usadas sequências arbitrárias de 568 letras em formato FASTA, e adoptadas as opções por omissão dos vários aplicativos BLAST. Salvo menção em contrário, os tempos de execução considerados nas comparações e no cálculo de speedups são relativos à primeira execução de uma pesquisa, não sendo assim beneficiados por qualquer efeito de cache; esta opção assume um cenário real em que não é habitual que uma mesma query seja executada várias vezes seguidas (embora possa ser re-executada, mais tarde). As principais conclusões do estudo comparativo realizado foram as seguintes: - e necessário acautelar, à priori, recursos de armazenamento com capacidade suficiente para albergar as bases de dados nas suas várias versões (originais/compactadas, descompactadas e formatadas); no nosso cenário de teste a coexistência de todas estas versões consumiu 600GBytes; - o tempo de preparação (formataçâo) das bases de dados para posterior pesquisa pode ser considerável; no nosso cenário experimental, a formatação das bases de dados mais pesadas (nr, env_nt e nt) demorou entre 30m a 40m (para o BLAST), e entre 45m a 55m (para o mpiBLAST); - embora economicamente mais onerosos, a utilização de discos de estado sólido, em alternativa a discos rígidos tradicionais, permite melhorar o tempo da formatação das bases de dados; no entanto, os benefícios registados (à volta de 9%) ficam bastante aquém do inicialmente esperado; - o tempo de execução do BLAST é fortemente penalizado quando as bases de dados são acedidas através da rede, via NFS; neste caso, nem sequer compensa usar vários cores; quando as bases de dados são locais e estão em SSD, o tempo de execução melhora bastante, em especial com a utilização de vários cores; neste caso, com 4 cores, o speedup chega a atingir 3.5 (sendo o ideal 4) para a pesquisa de BDs de proteínas, mas não passa de 1.8 para a pesquisa de BDs de nucleótidos; - o tempo de execução do mpiBLAST é muito prejudicado quando os fragmentos das bases de dados ainda não se encontram nos nós do cluster, tendo que ser distribuídos previamente à pesquisa propriamente dita; após a distribuição, a repetição das mesmas queries beneficia de speedups de 14 a 70; porém, como a mesma base de dados poderá ser usada para responder a diferentes queries, então não é necessário repetir a mesma query para amortizar o esforço de distribuição; - no cenário de teste, a utilização do mpiBLAST com 32+2 cores, face ao BLAST com 4 cores, traduz-se em speedups que, conforme a base de dados pesquisada (e previamente distribuída), variam entre 2 a 5, valores aquém do máximo teórico de 6.5 (34/4), mas ainda assim demonstradores de que, havendo essa possibilidade, compensa realizar as pesquisas em cluster; explorar vários cores) e com o gpuBLAST, realizada no nó com GPU (representativo de uma workstation típica), permite aferir qual a melhor opção no caso de não serem possíveis pesquisas em cluster; as observações realizadas indicam que não há diferenças significativas entre o BLAST e o BLAST+; adicionalmente, o desempenho do gpuBLAST foi sempre pior (aproximadmente em 50%) que o do BLAST e BLAST+, o que pode encontrar explicação na longevidade do modelo da GPU usada; - finalmente, a comparação da melhor opção no nosso cenário de teste, representada pelo uso do mpiBLAST, com o recurso a pesquisa online, no site do BLAST5, revela que o mpiBLAST apresenta um desempenho bastante competitivo com o BLAST online, chegando a ser claramente superior se se considerarem os tempos do mpiBLAST tirando partido de efeitos de cache; esta assunção acaba por se justa, Já que BLAST online também rentabiliza o mesmo tipo de efeitos; no entanto, com tempos de pequisa tão reduzidos (< 30s), só é defensável a utilização do mpiBLAST numa infra-estrutura local se o objetivo for a pesquisa de Bds não pesquisáveis via BLAS+ online.

Learning representations for Information Retrieval

La recherche d'informations s'intéresse, entre autres, à répondre à des questions comme: est-ce qu'un document est pertinent à une requête ? Est-ce que deux requêtes ou deux documents sont similaires ? Comment la similarité entre deux requêtes ou documents peut être utilisée pour améliorer l'estimation de la pertinence ? Pour donner réponse à ces questions, il est nécessaire d'associer chaque document et requête à des représentations interprétables par ordinateur. Une fois ces représentations estimées, la similarité peut correspondre, par exemple, à une distance ou une divergence qui opère dans l'espace de représentation. On admet généralement que la qualité d'une représentation a un impact direct sur l'erreur d'estimation par rapport à la vraie pertinence, jugée par un humain. Estimer de bonnes représentations des documents et des requêtes a longtemps été un problème central de la recherche d'informations. Le but de cette thèse est de proposer des nouvelles méthodes pour estimer les représentations des documents et des requêtes, la relation de pertinence entre eux et ainsi modestement avancer l'état de l'art du domaine. Nous présentons quatre articles publiés dans des conférences internationales et un article publié dans un forum d'évaluation. Les deux premiers articles concernent des méthodes qui créent l'espace de représentation selon une connaissance à priori sur les caractéristiques qui sont importantes pour la tâche à accomplir. Ceux-ci nous amènent à présenter un nouveau modèle de recherche d'informations qui diffère des modèles existants sur le plan théorique et de l'efficacité expérimentale. Les deux derniers articles marquent un changement fondamental dans l'approche de construction des représentations. Ils bénéficient notamment de l'intérêt de recherche dont les techniques d'apprentissage profond par réseaux de neurones, ou deep learning, ont fait récemment l'objet. Ces modèles d'apprentissage élicitent automatiquement les caractéristiques importantes pour la tâche demandée à partir d'une quantité importante de données. Nous nous intéressons à la modélisation des relations sémantiques entre documents et requêtes ainsi qu'entre deux ou plusieurs requêtes. Ces derniers articles marquent les premières applications de l'apprentissage de représentations par réseaux de neurones à la recherche d'informations. Les modèles proposés ont aussi produit une performance améliorée sur des collections de test standard. Nos travaux nous mènent à la conclusion générale suivante: la performance en recherche d'informations pourrait drastiquement être améliorée en se basant sur les approches d'apprentissage de représentations.

Integrazione di contenuti multimediali in basi di conoscenza strutturate: il caso DBpedia

Viene presentato l’approccio Linked Data, che si serve di descrizioni scritte in linguaggio RDF per rendere espliciti ai calcolatori i legami semantici esistenti tra le risorse che popolano il Web. Si descrive quindi il progetto DBpedia, che si propone di riorganizzare le informazioni disponibili su Wikipedia in formato Linked Data, così da renderle più facilmente consultabili dall’utente e da rendere possibile l’esecuzione di query complesse. Si discute quindi della sfida riguardante l’integrazione di contenuti multimediali (immagini, file audio, video…) su DBpedia e si analizzano tre progetti rivolti in tal senso: Multipedia, DBpedia Commons e IMGpedia. Vengono infine sottolineate l’importanza e le potenzialità legate alla creazione di un Web Semantico.

Learning representations for Information Retrieval

La recherche d'informations s'intéresse, entre autres, à répondre à des questions comme: est-ce qu'un document est pertinent à une requête ? Est-ce que deux requêtes ou deux documents sont similaires ? Comment la similarité entre deux requêtes ou documents peut être utilisée pour améliorer l'estimation de la pertinence ? Pour donner réponse à ces questions, il est nécessaire d'associer chaque document et requête à des représentations interprétables par ordinateur. Une fois ces représentations estimées, la similarité peut correspondre, par exemple, à une distance ou une divergence qui opère dans l'espace de représentation. On admet généralement que la qualité d'une représentation a un impact direct sur l'erreur d'estimation par rapport à la vraie pertinence, jugée par un humain. Estimer de bonnes représentations des documents et des requêtes a longtemps été un problème central de la recherche d'informations. Le but de cette thèse est de proposer des nouvelles méthodes pour estimer les représentations des documents et des requêtes, la relation de pertinence entre eux et ainsi modestement avancer l'état de l'art du domaine. Nous présentons quatre articles publiés dans des conférences internationales et un article publié dans un forum d'évaluation. Les deux premiers articles concernent des méthodes qui créent l'espace de représentation selon une connaissance à priori sur les caractéristiques qui sont importantes pour la tâche à accomplir. Ceux-ci nous amènent à présenter un nouveau modèle de recherche d'informations qui diffère des modèles existants sur le plan théorique et de l'efficacité expérimentale. Les deux derniers articles marquent un changement fondamental dans l'approche de construction des représentations. Ils bénéficient notamment de l'intérêt de recherche dont les techniques d'apprentissage profond par réseaux de neurones, ou deep learning, ont fait récemment l'objet. Ces modèles d'apprentissage élicitent automatiquement les caractéristiques importantes pour la tâche demandée à partir d'une quantité importante de données. Nous nous intéressons à la modélisation des relations sémantiques entre documents et requêtes ainsi qu'entre deux ou plusieurs requêtes. Ces derniers articles marquent les premières applications de l'apprentissage de représentations par réseaux de neurones à la recherche d'informations. Les modèles proposés ont aussi produit une performance améliorée sur des collections de test standard. Nos travaux nous mènent à la conclusion générale suivante: la performance en recherche d'informations pourrait drastiquement être améliorée en se basant sur les approches d'apprentissage de représentations.

Ex ante evaluations of alternate data structures for end user queries: Theory and experimental test

The data structure of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. This research develops a methodology for evaluating, ex ante, the relative desirability of alternative data structures for end user queries. This research theorizes that the data structure that yields the lowest weighted average complexity for a representative sample of information requests is the most desirable data structure for end user queries. The theory was tested in an experiment that compared queries from two different relational database schemas. As theorized, end users querying the data structure associated with the less complex queries performed better Complexity was measured using three different Halstead metrics. Each of the three metrics provided excellent predictions of end user performance. This research supplies strong evidence that organizations can use complexity metrics to evaluate, ex ante, the desirability of alternate data structures. Organizations can use these evaluations to enhance the efficient and effective retrieval of information by creating data structures that minimize end user query complexity.

Cultural and gender influences on age identification

Age identification plays a significant role in young adults" mass, interpersonal, intergenerational, and intercultural communication. This research examines cultural and gender influences on young people's age identity by measuring the social age identity of male and female young adult members of five cultures varying in individualism/collectivism (Laos, Thailand, Spain, Australia, and the U.S.A.). We found cultural influences on age identity to be both unexpected in nature and modest in effect. American and Laotian respondents had similar and nominally higher levels of age identity than Australian, Thai, and Spanish respondents, with all having a markedly different age identities than those of Japanese respondents as reported by other researchers. No direct effect for gender on age identity emerged, though American females were more age identified than all other respondents. Across cultures, the social identity scale was found to be a reasonably adequate measure of age identity.

Selecting optimal instantiations of data models - Theory and validation of an ex ante approach

The schema of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. Obtaining quickly the appropriate data increases the likelihood that an organization will make good decisions and respond adeptly to challenges. This research presents and validates a methodology for evaluating, ex ante, the relative desirability of alternative instantiations of a model of data. In contrast to prior research, each instantiation is based on a different formal theory. This research theorizes that the instantiation that yields the lowest weighted average query complexity for a representative sample of information requests is the most desirable instantiation for end-user queries. The theory was validated by an experiment that compared end-user performance using an instantiation of a data structure based on the relational model of data with performance using the corresponding instantiation of the data structure based on the object-relational model of data. Complexity was measured using three different Halstead metrics: program length, difficulty, and effort. For a representative sample of queries, the average complexity using each instantiation was calculated. As theorized, end users querying the instantiation with the lower average complexity made fewer semantic errors, i.e., were more effective at composing queries. (c) 2005 Elsevier B.V. All rights reserved.

Hierarchical indexing structure for efficient similarity search in video retrieval

With the rapid increase in both centralized video archives and distributed WWW video resources, content-based video retrieval is gaining its importance. To support such applications efficiently, content-based video indexing must be addressed. Typically, each video is represented by a sequence of frames. Due to the high dimensionality of frame representation and the large number of frames, video indexing introduces an additional degree of complexity. In this paper, we address the problem of content-based video indexing and propose an efficient solution, called the Ordered VA-File (OVA-File) based on the VA-file. OVA-File is a hierarchical structure and has two novel features: 1) partitioning the whole file into slices such that only a small number of slices are accessed and checked during k Nearest Neighbor (kNN) search and 2) efficient handling of insertions of new vectors into the OVA-File, such that the average distance between the new vectors and those approximations near that position is minimized. To facilitate a search, we present an efficient approximate kNN algorithm named Ordered VA-LOW (OVA-LOW) based on the proposed OVA-File. OVA-LOW first chooses possible OVA-Slices by ranking the distances between their corresponding centers and the query vector, and then visits all approximations in the selected OVA-Slices to work out approximate kNN. The number of possible OVA-Slices is controlled by a user-defined parameter delta. By adjusting delta, OVA-LOW provides a trade-off between the query cost and the result quality. Query by video clip consisting of multiple frames is also discussed. Extensive experimental studies using real video data sets were conducted and the results showed that our methods can yield a significant speed-up over an existing VA-file-based method and iDistance with high query result quality. Furthermore, by incorporating temporal correlation of video content, our methods achieved much more efficient performance.

Approximate processing of massive continuous quantile queries over high-speed data streams

Quantile computation has many applications including data mining and financial data analysis. It has been shown that an is an element of-approximate summary can be maintained so that, given a quantile query d (phi, is an element of), the data item at rank [phi N] may be approximately obtained within the rank error precision is an element of N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different phi and is an element of poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.

Summarizing level-two topological relations in large spatial datasets

Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this article, we present several novel techniques to effectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important level-two topological relations: contains, contained, overlap, and disjoint. We first present a novel framework to construct a multiscale Euler histogram in 2D space with the guarantee of the exact summarization results for aligned windows in constant time. To minimize the storage space in such a multiscale Euler histogram, an approximate algorithm with the approximate ratio 19/12 is presented, while the problem is shown NP-hard generally. To conform to a limited storage space where a multiscale histogram may be allowed to have only k Euler histograms, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy in approximately summarizing aligned windows. Then, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. We also investigate the problem of nonaligned windows and the problem of effectively partitioning the data space to support nonaligned window queries. Finally, we extend our techniques to 3D space. Our extensive experiments against both synthetic and real world datasets demonstrate that the approximate multiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost efficiency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for many popular real datasets.