Semantic wrapping for heterogeneous database management

Query processing is a commonly performed procedure and a vital and integral part of information processing. It is therefore important and necessary for information processing applications to continuously improve the accessibility of data sources as well as the ability to perform queries on those data sources. ^ It is well known that the relational database model and the Structured Query Language (SQL) are currently the most popular tools to implement and query databases. However, a certain level of expertise is needed to use SQL and to access relational databases. This study presents a semantic modeling approach that enables the average user to access and query existing relational databases without the concern of the database's structure or technicalities. This method includes an algorithm to represent relational database schemas in a more semantically rich way. The result of which is a semantic view of the relational database. The user performs queries using an adapted version of SQL, namely Semantic SQL. This method substantially reduces the size and complexity of queries. Additionally, it shortens the database application development cycle and improves maintenance and reliability by reducing the size of application programs. Furthermore, a Semantic Wrapper tool illustrating the semantic wrapping method is presented. ^ I further extend the use of this semantic wrapping method to heterogeneous database management. Relational, object-oriented databases and the Internet data sources are considered to be part of the heterogeneous database environment. Semantic schemas resulting from the algorithm presented in the method were employed to describe the structure of these data sources in a uniform way. Semantic SQL was utilized to query various data sources. As a result, this method provides users with the ability to access and perform queries on heterogeneous database systems in a more innate way. ^

Everglades Online Thesaurus: A Standard Vocabulary for the South Florida Ecosystem

The Everglades Online Thesaurus is a structured vocabulary of concepts and terms relating to the south Florida environment. Designed as an information management tool for both researchers and metadata creators, the Thesaurus is intended to improve information retrieval across the many disparate information systems, databases, and web sites that provide Everglades-related information. The vocabulary provided by the Everglades Online Thesaurus expresses each relevant concept using a single ‘preferred term’, whereas in natural language many terms may exist to express that same concept. In this way, the Thesaurus offers the possibility of standardizing the terminology used to describe Everglades-related information — an important factor in predictable and successful resource discovery.

Integration and querying of heterogeneous, autonomous, distributed database systems

Today, databases have become an integral part of information systems. In the past two decades, we have seen different database systems being developed independently and used in different applications domains. Today's interconnected networks and advanced applications, such as data warehousing, data mining & knowledge discovery and intelligent data access to information on the Web, have created a need for integrated access to such heterogeneous, autonomous, distributed database systems. Heterogeneous/multidatabase research has focused on this issue resulting in many different approaches. However, a single, generally accepted methodology in academia or industry has not emerged providing ubiquitous intelligent data access from heterogeneous, autonomous, distributed information sources. This thesis describes a heterogeneous database system being developed at Highperformance Database Research Center (HPDRC). A major impediment to ubiquitous deployment of multidatabase technology is the difficulty in resolving semantic heterogeneity. That is, identifying related information sources for integration and querying purposes. Our approach considers the semantics of the meta-data constructs in resolving this issue. The major contributions of the thesis work include: (i.) providing a scalable, easy-to-implement architecture for developing a heterogeneous multidatabase system, utilizing Semantic Binary Object-oriented Data Model (Sem-ODM) and Semantic SQL query language to capture the semantics of the data sources being integrated and to provide an easy-to-use query facility; (ii.) a methodology for semantic heterogeneity resolution by investigating into the extents of the meta-data constructs of component schemas. This methodology is shown to be correct, complete and unambiguous; (iii.) a semi-automated technique for identifying semantic relations, which is the basis of semantic knowledge for integration and querying, using shared ontologies for context-mediation; (iv.) resolutions for schematic conflicts and a language for defining global views from a set of component Sem-ODM schemas; (v.) design of a knowledge base for storing and manipulating meta-data and knowledge acquired during the integration process. This knowledge base acts as the interface between integration and query processing modules; (vi.) techniques for Semantic SQL query processing and optimization based on semantic knowledge in a heterogeneous database environment; and (vii.) a framework for intelligent computing and communication on the Internet applying the concepts of our work.

Representing clinical documents to support automatic retrieval of evidence from the Cochrane Library

The overall aim of our research is to develop a clinical information retrieval system that retrieves systematic reviews and underlying clinical studies from the Cochrane Library to support physician decision making. We believe that in order to accomplish this goal we need to develop a mechanism for effectively representing documents that will be retrieved by the application. Therefore, as a first step in developing the retrieval application we have developed a methodology that semi-automatically generates high quality indices and applies them as descriptors to documents from The Cochrane Library. In this paper we present a description and implementation of the automatic indexing methodology and an evaluation that demonstrates that enhanced document representation results in the retrieval of relevant documents for clinical queries. We argue that the evaluation of information retrieval applications should also include an evaluation of the quality of the representation of documents that may be retrieved. ©2010 IEEE.

Design of information architecture of the web site of the Faculty of Industrial Engineering and Tourism of the Universidad Central “Marta Abreu” de Las Villas (Cuba)

The information architecture supports information retrieval by users in Web environment. The design should be center in the information user, favoring usability. The Faculty of Industrial Engineering and Tourism of the Universidad Central "Marta Abreu" de Las Villas, lacks a site that enhances the disclosure of information to its members. Are presented as objectives of the study: 1) conduct a user survey to identify information needs of users, 2) establish guidelines for information architecture for the institution focused on users, 3) designing the information architecture for the institution and 4) designed to evaluate the proposal. Are presented as objectives of the study: 1) to realize a user study to identify the information needs of users, 2) establish guidelines for information architecture for the institution focused on users, 3) to design the information architecture for the institution and 4) to evaluate the proposal designed. To obtain results are used methods in the theoretical and empirical levels. Besides, are use techniques that favored the design and evaluation. Is designed the intranet of the Faculty of Industrial Engineering and Tourism. Is evaluated the proposed design for the validation of the results.

D6.3 – Semantic Content Annotation Support

The Semantic Annotation component is a software application that provides support for automated text classification, a process grounded in a cohesion-centered representation of discourse that facilitates topic extraction. The component enables the semantic meta-annotation of text resources, including automated classification, thus facilitating information retrieval within the RAGE ecosystem. It is available in the ReaderBench framework (http://readerbench.com/) which integrates advanced Natural Language Processing (NLP) techniques. The component makes use of Cohesion Network Analysis (CNA) in order to ensure an in-depth representation of discourse, useful for mining keywords and performing automated text categorization. Our component automatically classifies documents into the categories provided by the ACM Computing Classification System (http://dl.acm.org/ccs_flat.cfm), but also into the categories from a high level serious games categorization provisionally developed by RAGE. English and French languages are already covered by the provided web service, whereas the entire framework can be extended in order to support additional languages.

Gestengestützte Schema- und Datenexploration

Der Zugang zu Datenbanken über die universelle Abfragesprache SQL stellt für Nicht-Spezialisten eine große Herausforderung dar. Als eine benutzerfreundliche Alternative wurden daher seit den 1970er-Jahren unterschiedliche visuelle Abfragesprachen (Visual Query Languages, kurz VQLs) für klassische PCs erforscht. Ziel der vorliegenden Arbeit ist es, eine generische VQL zu entwickeln und zu erproben, die eine gestenbasierte Exploration von Datenbanken auf Schema- und Instanzdatenebene für mobile Endgeräte, insbesondere Tablets, ermöglicht. Dafür werden verschiedene Darstellungsformen, Abfragestrategien und visuelle Hints für Fremdschlüsselbeziehungen untersucht, die den Benutzer bei der Navigation durch die Daten unterstützen. Im Rahmen einer Anforderungsanalyse erwies sich die Visualisierung der Daten und Beziehungen mittels einer platzsparenden geschachtelten NF2-Darstellung als besonders vorteilhaft. Zur Steuerung der Datenbankexploration wird eine geeignete Gestensprache, bestehend aus Stroke-, Multitouch- und Mid-Air-Gesten, vorgestellt. Das Gesamtkonzept aus Darstellung und Gestensteuerung wurde anhand des im Rahmen dieser Arbeit entwickelten GBXT-Prototyps auf seine reale Umsetzbarkeit hin, als plattformunabhängige Single-Page-Application für verschiedene mobile Endgeräte mittels JavaScript und HTML5/CSS3 untersucht.

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Microblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking News

While news stories are an important traditional medium to broadcast and consume news, microblogging has recently emerged as a place where people can dis- cuss, disseminate, collect or report information about news. However, the massive information in the microblogosphere makes it hard for readers to keep up with these real-time updates. This is especially a problem when it comes to breaking news, where people are more eager to know “what is happening”. Therefore, this dis- sertation is intended as an exploratory effort to investigate computational methods to augment human effort when monitoring the development of breaking news on a given topic from a microblog stream by extractively summarizing the updates in a timely manner. More specifically, given an interest in a topic, either entered as a query or presented as an initial news report, a microblog temporal summarization system is proposed to filter microblog posts from a stream with three primary concerns: topical relevance, novelty, and salience. Considering the relatively high arrival rate of microblog streams, a cascade framework consisting of three stages is proposed to progressively reduce quantity of posts. For each step in the cascade, this dissertation studies methods that improve over current baselines. In the relevance filtering stage, query and document expansion techniques are applied to mitigate sparsity and vocabulary mismatch issues. The use of word embedding as a basis for filtering is also explored, using unsupervised and supervised modeling to characterize lexical and semantic similarity. In the novelty filtering stage, several statistical ways of characterizing novelty are investigated and ensemble learning techniques are used to integrate results from these diverse techniques. These results are compared with a baseline clustering approach using both standard and delay-discounted measures. In the salience filtering stage, because of the real-time prediction requirement a method of learning verb phrase usage from past relevant news reports is used in conjunction with some standard measures for characterizing writing quality. Following a Cranfield-like evaluation paradigm, this dissertation includes a se- ries of experiments to evaluate the proposed methods for each step, and for the end- to-end system. New microblog novelty and salience judgments are created, building on existing relevance judgments from the TREC Microblog track. The results point to future research directions at the intersection of social media, computational jour- nalism, information retrieval, automatic summarization, and machine learning.

Top-K Query Processing in Edge-Labeled Graph Data

Edge-labeled graphs have proliferated rapidly over the last decade due to the increased popularity of social networks and the Semantic Web. In social networks, relationships between people are represented by edges and each edge is labeled with a semantic annotation. Hence, a huge single graph can express many different relationships between entities. The Semantic Web represents each single fragment of knowledge as a triple (subject, predicate, object), which is conceptually identical to an edge from subject to object labeled with predicates. A set of triples constitutes an edge-labeled graph on which knowledge inference is performed. Subgraph matching has been extensively used as a query language for patterns in the context of edge-labeled graphs. For example, in social networks, users can specify a subgraph matching query to find all people that have certain neighborhood relationships. Heavily used fragments of the SPARQL query language for the Semantic Web and graph queries of other graph DBMS can also be viewed as subgraph matching over large graphs. Though subgraph matching has been extensively studied as a query paradigm in the Semantic Web and in social networks, a user can get a large number of answers in response to a query. These answers can be shown to the user in accordance with an importance ranking. In this thesis proposal, we present four different scoring models along with scalable algorithms to find the top-k answers via a suite of intelligent pruning techniques. The suggested models consist of a practically important subset of the SPARQL query language augmented with some additional useful features. The first model called Substitution Importance Query (SIQ) identifies the top-k answers whose scores are calculated from matched vertices' properties in each answer in accordance with a user-specified notion of importance. The second model called Vertex Importance Query (VIQ) identifies important vertices in accordance with a user-defined scoring method that builds on top of various subgraphs articulated by the user. Approximate Importance Query (AIQ), our third model, allows partial and inexact matchings and returns top-k of them with a user-specified approximation terms and scoring functions. In the fourth model called Probabilistic Importance Query (PIQ), a query consists of several sub-blocks: one mandatory block that must be mapped and other blocks that can be opportunistically mapped. The probability is calculated from various aspects of answers such as the number of mapped blocks, vertices' properties in each block and so on and the most top-k probable answers are returned. An important distinguishing feature of our work is that we allow the user a huge amount of freedom in specifying: (i) what pattern and approximation he considers important, (ii) how to score answers - irrespective of whether they are vertices or substitution, and (iii) how to combine and aggregate scores generated by multiple patterns and/or multiple substitutions. Because so much power is given to the user, indexing is more challenging than in situations where additional restrictions are imposed on the queries the user can ask. The proposed algorithms for the first model can also be used for answering SPARQL queries with ORDER BY and LIMIT, and the method for the second model also works for SPARQL queries with GROUP BY, ORDER BY and LIMIT. We test our algorithms on multiple real-world graph databases, showing that our algorithms are far more efficient than popular triple stores.

IVIS: Search and visualization on heterogeneous information networks

We build a system to support search and visualization on heterogeneous information networks. We first build our system on a specialized heterogeneous information network: DBLP. The system aims to facilitate people, especially computer science researchers, toward a better understanding and user experience about academic information networks. Then we extend our system to the Web. Our results are much more intuitive and knowledgeable than the simple top-k blue links from traditional search engines, and bring more meaningful structural results with correlated entities. We also investigate the ranking algorithm, and we show that the personalized PageRank and proposed Hetero-personalized PageRank outperform the TF-IDF ranking or mixture of TF-IDF and authority ranking. Our work opens several directions for future research.

Using Kullback-Leibler Distance for Text Categorization

International audience

A supervised machine learning method for word sense disambiguation of Portuguese nouns

Dissertação de Mestrado, Ciências da Linguagem, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2010

Medical-miner at TREC 2011 medical records track

This paper presents work done at Medical Minner Project on the TREC-2011 Medical Records Track. The paper proposes four models for medical information retrieval based on Lucene index approach. Our retrieval engine used an Lucen Index scheme with traditional stopping and stemming, enhanced with entity recognition software on query terms. Our aim in this first competition is to set a broader project that involves the develop of a configurable Apache Lucene-based framework that allows the rapid development of medical search facilities. Results around the track median have been achieved. In this exploratory track, we think that these results are a good beginning and encourage us for future developments.

Versioning Concept Schemes for Persistent Retrieval

Things change. Words change, meaning changes and use changes both words and meaning. In information access systems this means concept schemes such as thesauri or clas- sification schemes change. They always have. Concept schemes that have survived have evolved over time, moving from one version, often called an edition, to the next. If we want to manage how words and meanings - and as a conse- quence use - change in an effective manner, and if we want to be able to search across versions of concept schemes, we have to track these changes. This paper explores how we might expand SKOS, a World Wide Web Consortium (W3C) draft recommendation in order to do that kind of tracking.The Simple Knowledge Organization System (SKOS) Core Guide is sponsored by the Semantic Web Best Practices and Deployment Working Group. The second draft, edited by Alistair Miles and Dan Brickley, was issued in November 2005. SKOS is a “model for expressing the basic structure and content of concept schemes such as thesauri, classification schemes, subject heading lists, taxonomies, folksonomies, other types of controlled vocabulary and also concept schemes embedded in glossaries and terminologies” in RDF. How SKOS handles version in concept schemes is an open issue. The current draft guide suggests using OWL and DCTERMS as mechanisms for concept scheme revision.As it stands an editor of a concept scheme can make notes or declare in OWL that more than one version exists. This paper adds to the SKOS Core by introducing a tracking sys- tem for changes in concept schemes. We call this tracking system vocabulary ontogeny. Ontogeny is a biological term for the development of an organism during its lifetime. Here we use the ontogeny metaphor to describe how vocabularies change over their lifetime. Our purpose here is to create a conceptual mechanism that will track these changes and in so doing enhance information retrieval and prevent document loss through versioning, thereby enabling persistent retrieval.