Multipedia: Enriching DBpedia with Multimedia Information

Enriching knowledge bases with multimedia information makes it possible to complement textual descriptions with visual and audio information. Such complementary information can help users to understand the meaning of assertions, and in general improve the user experience with the knowledge base. In this paper we address the problem of how to enrich ontology instances with candidate images retrieved from existing Web search engines. DBpedia has evolved into a major hub in the Linked Data cloud, interconnecting millions of entities organized under a consistent ontology. Our approach taps into the Wikipedia corpus to gather context information for DBpedia instances and takes advantage of image tagging information when this is available to calculate semantic relatedness between instances and candidate images. We performed experiments with focus on the particularly challenging problem of highly ambiguous names. Both methods presented in this work outperformed the baseline. Our best method leveraged context words from Wikipedia, tags from Flickr and type information from DBpedia to achieve an average precision of 80%.

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature

Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. Results: We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. Conclusions: CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems.

A formalism and method for representing and reasoning with process models authored by subject matter experts

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic in order to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82% were well-formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25% and 30% with respect to the base case, respectively.

La investigación en Bibliometría en la Argentina : Quiénes son y qué producen los autores argentinos que realizan estudios bibliométricos

Trabajo que tiene por objetivo observar y describir el panorama de la investigación bibliométrica en la Argentina en el período comprendido entre los años 1984 y 2012, a partir del análisis de las publicaciones de autores de instituciones argentinas localizadas en buscadores web, repositorios temáticos y bases de datos regionales e internacionales. Interpreta las formas que revisten los ítems estudiados enfocando la atención en el volumen y evolución de la producción, tipo de literatura, idioma, temas y unidades de análisis. Calcula el índice de coautoría y tasas de colaboración nacional e internacional. Identifica los autores más productivos y las instituciones de afiliación más frecuentes. Determina la existencia de algunos grupos de investigación, caracterizando sus temáticas de investigación, las revistas donde publican y los congresos más frecuentes en los que participan

A general proposal to multilingual information access based on syntactic-semantic patterns

In the last few years, there has been a wide development in the research on textual information systems. The goal is to improve these systems in order to allow an easy localization, treatment and access to the information stored in digital format (Digital Databases, Documental Databases, and so on). There are lots of applications focused on information access (for example, Web-search systems like Google or Altavista). However, these applications have problems when they must access to cross-language information, or when they need to show information in a language different from the one of the query. This paper explores the use of syntactic-sematic patterns as a method to access to multilingual information, and revise, in the case of Information Retrieval, where it is possible and useful to employ patterns when it comes to the multilingual and interactive aspects. On the one hand, the multilingual aspects that are going to be studied are the ones related to the access to documents in different languages from the one of the query, as well as the automatic translation of the document, i.e. a machine translation system based on patterns. On the other hand, this paper is going to go deep into the interactive aspects related to the reformulation of a query based on the syntactic-semantic pattern of the request.

Herramientas e indicadores SEO: características y aplicación al análisis de cibermedios

La búsqueda es una de las actividades centrales en el mundo digital y, por tanto, uno de los elementos clave en el análisis de cibermedios, ya que una parte de sus audiencias y de sus ingresos procede de las páginas de resultados de los buscadores (SERP). En este trabajo, presentamos algunas de las herramientas de análisis de posicionamiento SEO más utilizadas con el fin de considerar su aplicación en estudios académicos sobre cibermedios. Aplicamos los nueve indicadores más importantes de estas herramientas a la página principal de cuatro cibermedios generalistas espanoles con el fin de estimar su viabilidad como indicadores alternativos al PageRank y otros indicadores de Google.

La investigación en Bibliometría en la Argentina : Quiénes son y qué producen los autores argentinos que realizan estudios bibliométricos

Trabajo que tiene por objetivo observar y describir el panorama de la investigación bibliométrica en la Argentina en el período comprendido entre los años 1984 y 2012, a partir del análisis de las publicaciones de autores de instituciones argentinas localizadas en buscadores web, repositorios temáticos y bases de datos regionales e internacionales. Interpreta las formas que revisten los ítems estudiados enfocando la atención en el volumen y evolución de la producción, tipo de literatura, idioma, temas y unidades de análisis. Calcula el índice de coautoría y tasas de colaboración nacional e internacional. Identifica los autores más productivos y las instituciones de afiliación más frecuentes. Determina la existencia de algunos grupos de investigación, caracterizando sus temáticas de investigación, las revistas donde publican y los congresos más frecuentes en los que participan

La investigación en Bibliometría en la Argentina : Quiénes son y qué producen los autores argentinos que realizan estudios bibliométricos

Trabajo que tiene por objetivo observar y describir el panorama de la investigación bibliométrica en la Argentina en el período comprendido entre los años 1984 y 2012, a partir del análisis de las publicaciones de autores de instituciones argentinas localizadas en buscadores web, repositorios temáticos y bases de datos regionales e internacionales. Interpreta las formas que revisten los ítems estudiados enfocando la atención en el volumen y evolución de la producción, tipo de literatura, idioma, temas y unidades de análisis. Calcula el índice de coautoría y tasas de colaboración nacional e internacional. Identifica los autores más productivos y las instituciones de afiliación más frecuentes. Determina la existencia de algunos grupos de investigación, caracterizando sus temáticas de investigación, las revistas donde publican y los congresos más frecuentes en los que participan

Semantic annotation for knowledge management:requirements and a survey of the state of the art

While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In the emerging Semantic Web, search, interpretation and aggregation can be addressed by ontology-based semantic mark-up. In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems. This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress.

Internet computing with distributed software agent

The phenomenonal growth of the Internet has connected us to a vast amount of computation and information resources around the world. However, making use of these resources is difficult due to the unparalleled massiveness, high communication latency, share-nothing architecture and unreliable connection of the Internet. In this dissertation, we present a distributed software agent approach, which brings a new distributed problem-solving paradigm to the Internet computing researches with enhanced client-server scheme, inherent scalability and heterogeneity. Our study discusses the role of a distributed software agent in Internet computing and classifies it into three major categories by the objects it interacts with: computation agent, information agent and interface agent. The discussion of the problem domain and the deployment of the computation agent and the information agent are presented with the analysis, design and implementation of the experimental systems in high performance Internet computing and in scalable Web searching. ^ In the computation agent study, high performance Internet computing can be achieved with our proposed Java massive computation agent (JAM) model. We analyzed the JAM computing scheme and built a brutal force cipher text decryption prototype. In the information agent study, we discuss the scalability problem of the existing Web search engines and designed the approach of Web searching with distributed collaborative index agent. This approach can be used for constructing a more accurate, reusable and scalable solution to deal with the growth of the Web and of the information on the Web. ^ Our research reveals that with the deployment of the distributed software agent in Internet computing, we can have a more cost effective approach to make better use of the gigantic scale network of computation and information resources on the Internet. The case studies in our research show that we are now able to solve many practically hard or previously unsolvable problems caused by the inherent difficulties of Internet computing. ^

Discovery and efficient reuse of technology pictures using Wikimedia infrastructures. A proposal

Multimedia objects, especially images and figures, are essential for the visualization and interpretation of research findings. The distribution and reuse of these scientific objects is significantly improved under open access conditions, for instance in Wikipedia articles, in research literature, as well as in education and knowledge dissemination, where licensing of images often represents a serious barrier. Whereas scientific publications are retrievable through library portals or other online search services due to standardized indices there is no targeted retrieval and access to the accompanying images and figures yet. Consequently there is a great demand to develop standardized indexing methods for these multimedia open access objects in order to improve the accessibility to this material. With our proposal, we hope to serve a broad audience which looks up a scientific or technical term in a web search portal first. Until now, this audience has little chance to find an openly accessible and reusable image narrowly matching their search term on first try - frustratingly so, even if there is in fact such an image included in some open access article.

Navigating a corpus of journal papers using Handles

For some years now the Internet and World Wide Web communities have envisaged moving to a next generation of Web technologies by promoting a globally unique, and persistent, identifier for identifying and locating many forms of published objects . These identifiers are called Universal Resource Names (URNs) and they hold out the prospect of being able to refer to an object by what it is (signified by its URN), rather than by where it is (the current URL technology). One early implementation of URN ideas is the Unicode-based Handle technology, developed at CNRI in Reston Virginia. The Digital Object Identifier (DOI) is a specific URN naming convention proposed just over 5 years ago and is now administered by the International DOI organisation, founded by a consortium of publishers and based in Washington DC. The DOI is being promoted for managing electronic content and for intellectual rights management of it, either using the published work itself, or, increasingly via metadata descriptors for the work in question. This paper describes the use of the CNRI handle parser to navigate a corpus of papers for the Electronic Publishing journal. These papers are in PDF format and based on our server in Nottingham. For each paper in the corpus a metadata descriptor is prepared for every citation appearing in the References section. The important factor is that the underlying handle is resolved locally in the first instance. In some cases (e.g. cross-citations within the corpus itself and links to known resources elsewhere) the handle can be handed over to CNRI for further resolution. This work shows the encouraging prospect of being able to use persistent URNs not only for intellectual property negotiations but also for search and discovery. In the test domain of this experiment every single resource, referred to within a given paper, can be resolved, at least to the level of metadata about the referred object. If the Web were to become more fully URN aware then a vast directed graph of linked resources could be accessed, via persistent names. Moreover, if these names delivered embedded metadata when resolved, the way would be open for a new generation of vastly more accurate and intelligent Web search engines.

Contextual text mining

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the author(s) of a biomedical publication, or implicit, such as the positive or negative sentiment that an author had when she wrote a product review; there may also be complex context such as the social network of the authors. Many applications require analysis of topic patterns over different contexts. For instance, analysis of search logs in the context of the user can reveal how we can improve the quality of a search engine by optimizing the search results according to particular users; analysis of customer reviews in the context of positive and negative sentiments can help the user summarize public opinions about a product; analysis of blogs or scientific publications in the context of a social network can facilitate discovery of more meaningful topical communities. Since context information significantly affects the choices of topics and language made by authors, in general, it is very important to incorporate it into analyzing and mining text data. In general, modeling the context in text, discovering contextual patterns of language units and topics from text, a general task which we refer to as Contextual Text Mining, has widespread applications in text mining. In this thesis, we provide a novel and systematic study of contextual text mining, which is a new paradigm of text mining treating context information as the ``first-class citizen.'' We formally define the problem of contextual text mining and its basic tasks, and propose a general framework for contextual text mining based on generative modeling of text. This conceptual framework provides general guidance on text mining problems with context information and can be instantiated into many real tasks, including the general problem of contextual topic analysis. We formally present a functional framework for contextual topic analysis, with a general contextual topic model and its various versions, which can effectively solve the text mining problems in a lot of real world applications. We further introduce general components of contextual topic analysis, by adding priors to contextual topic models to incorporate prior knowledge, regularizing contextual topic models with dependency structure of context, and postprocessing contextual patterns to extract refined patterns. The refinements on the general contextual topic model naturally lead to a variety of probabilistic models which incorporate different types of context and various assumptions and constraints. These special versions of the contextual topic model are proved effective in a variety of real applications involving topics and explicit contexts, implicit contexts, and complex contexts. We then introduce a postprocessing procedure for contextual patterns, by generating meaningful labels for multinomial context models. This method provides a general way to interpret text mining results for real users. By applying contextual text mining in the ``context'' of other text information management tasks, including ad hoc text retrieval and web search, we further prove the effectiveness of contextual text mining techniques in a quantitative way with large scale datasets. The framework of contextual text mining not only unifies many explorations of text analysis with context information, but also opens up many new possibilities for future research directions in text mining.

Towards near-threshold server processors

The popularity of cloud computing has led to a dramatic increase in the number of data centers in the world. The ever-increasing computational demands along with the slowdown in technology scaling has ushered an era of power-limited servers. Techniques such as near-threshold computing (NTC) can be used to improve energy efficiency in the post-Dennard scaling era. This paper describes an architecture based on the FD-SOI process technology for near-threshold operation in servers. Our work explores the trade-offs in energy and performance when running a wide range of applications found in private and public clouds, ranging from traditional scale-out applications, such as web search or media streaming, to virtualized banking applications. Our study demonstrates the benefits of near-threshold operation and proposes several directions to synergistically increase the energy proportionality of a near-threshold server.

Query routing in cooperative semi-structured peer-to-peer information retrieval networks

Conventional web search engines are centralised in that a single entity crawls and indexes the documents selected for future retrieval, and the relevance models used to determine which documents are relevant to a given user query. As a result, these search engines suffer from several technical drawbacks such as handling scale, timeliness and reliability, in addition to ethical concerns such as commercial manipulation and information censorship. Alleviating the need to rely entirely on a single entity, Peer-to-Peer (P2P) Information Retrieval (IR) has been proposed as a solution, as it distributes the functional components of a web search engine – from crawling and indexing documents, to query processing – across the network of users (or, peers) who use the search engine. This strategy for constructing an IR system poses several efficiency and effectiveness challenges which have been identified in past work. Accordingly, this thesis makes several contributions towards advancing the state of the art in P2P-IR effectiveness by improving the query processing and relevance scoring aspects of a P2P web search. Federated search systems are a form of distributed information retrieval model that route the user’s information need, formulated as a query, to distributed resources and merge the retrieved result lists into a final list. P2P-IR networks are one form of federated search in routing queries and merging result among participating peers. The query is propagated through disseminated nodes to hit the peers that are most likely to contain relevant documents, then the retrieved result lists are merged at different points along the path from the relevant peers to the query initializer (or namely, customer). However, query routing in P2P-IR networks is considered as one of the major challenges and critical part in P2P-IR networks; as the relevant peers might be lost in low-quality peer selection while executing the query routing, and inevitably lead to less effective retrieval results. This motivates this thesis to study and propose query routing techniques to improve retrieval quality in such networks. Cluster-based semi-structured P2P-IR networks exploit the cluster hypothesis to organise the peers into similar semantic clusters where each such semantic cluster is managed by super-peers. In this thesis, I construct three semi-structured P2P-IR models and examine their retrieval effectiveness. I also leverage the cluster centroids at the super-peer level as content representations gathered from cooperative peers to propose a query routing approach called Inverted PeerCluster Index (IPI) that simulates the conventional inverted index of the centralised corpus to organise the statistics of peers’ terms. The results show a competitive retrieval quality in comparison to baseline approaches. Furthermore, I study the applicability of using the conventional Information Retrieval models as peer selection approaches where each peer can be considered as a big document of documents. The experimental evaluation shows comparative and significant results and explains that document retrieval methods are very effective for peer selection that brings back the analogy between documents and peers. Additionally, Learning to Rank (LtR) algorithms are exploited to build a learned classifier for peer ranking at the super-peer level. The experiments show significant results with state-of-the-art resource selection methods and competitive results to corresponding classification-based approaches. Finally, I propose reputation-based query routing approaches that exploit the idea of providing feedback on a specific item in the social community networks and manage it for future decision-making. The system monitors users’ behaviours when they click or download documents from the final ranked list as implicit feedback and mines the given information to build a reputation-based data structure. The data structure is used to score peers and then rank them for query routing. I conduct a set of experiments to cover various scenarios including noisy feedback information (i.e, providing positive feedback on non-relevant documents) to examine the robustness of reputation-based approaches. The empirical evaluation shows significant results in almost all measurement metrics with approximate improvement more than 56% compared to baseline approaches. Thus, based on the results, if one were to choose one technique, reputation-based approaches are clearly the natural choices which also can be deployed on any P2P network.