Manuel de Buenaga

Multidocument summarization: An added value to clustering in interactive retrieval

A more and more generalized problem in effective information access is the presence in the same corpus of multiple documents that contain similar information. Generally, users may be interested in locating, for a topic addressed by a group of similar documents, one or several particular aspects. This kind of task, called instance or aspectual retrieval, has been explored in several TREC Interactive Tracks. In this article, we propose in addition to the classification capacity of clustering techniques, the possibility of offering a indicative extract about the contents of several sources by means of multidocument summarization techniques. Two kinds of summaries are provided. The first one covers the similarities of each cluster of documents retrieved. The second one shows the particularities of each document with respect to the common topic in the cluster. The document multitopic structure has been used in order to determine similarities and differences of topics in the cluster of documents. The system is independent of document domain and genre. An evaluation of the proposed system with users proves significant improvements in effectiveness. The results of previous experiments that have compared clustering algorithms are also reported.A more and more generalized problem in effective information access is the presence in the same corpus of multiple documents that contain similar information. Generally, users may be interested in locating, for a topic addressed by a group of similar documents, one or several particular aspects. This kind of task, called instance or aspectual retrieval, has been explored in several TREC Interactive Tracks. In this article, we propose in addition to the classification capacity of clustering techniques, the possibility of offering a indicative extract about the contents of several sources by means of multidocument summarization techniques. Two kinds of summaries are provided. The first one covers the similarities of each cluster of documents retrieved. The second one shows the particularities of each document with respect to the common topic in the cluster. The document multitopic structure has been used in order to determine similarities and differences of topics in the cluster of documents. The system is independent of document domain and genre. An evaluation of the proposed system with users proves significant improvements in effectiveness. The results of previous experiments that have compared clustering algorithms are also reported.

Integrating linguistic resources in TC through WSD

Information access methods must be improved to overcome theinformation overload that most professionals face nowadays. Textclassification tasks, like Text Categorization, help the usersto access to the great amount of text they find in the Internetand their organizations.TC is the classification of documents into a predefined set ofcategories. Most approaches to automatic TC are based on theutilization of a training collection, which is a set of manuallyclassified documents. Other linguistic resources that areemerging, like lexical databases, can also be used forclassification tasks. This article describes an approach to TCbased on the integration of a training collection (Reuters-21578)and a lexical database (WordNet 1.6) as knowledge sources.Lexical databases accumulate information on the lexical items ofone or several languages. This information must be filtered inorder to make an effective use of it in our model of TC. Thisfiltering process is a Word Sense Disambiguation task. WSDis the identification of the sense of words in context. This taskis an intermediate process in many natural language processingtasks like machine translation or multilingual informationretrieval. We present the utilization of WSD as an aid for TC. Ourapproach to WSD is also based on the integration of two linguisticresources: a training collection (SemCor and Reuters-21578) and alexical database (WordNet 1.6).We have developed a series of experiments that show that: TC andWSD based on the integration of linguistic resources are veryeffective; and, WSD is necessary to effectively integratelinguistic resources in TC.

BioAnnote: A software platform for annotating biomedical documents with application in medical learning environments

Automatic term annotation from biomedical documents and external information linking are becoming a necessary prerequisite in modern computer-aided medical learning systems. In this context, this paper presents BioAnnote, a flexible and extensible open-source platform for automatically annotating biomedical resources. Apart from other valuable features, the software platform includes (i) a rich client enabling users to annotate multiple documents in a user friendly environment, (ii) an extensible and embeddable annotation meta-server allowing for the annotation of documents with local or remote vocabularies and (iii) a simple client/server protocol which facilitates the use of our meta-server from any other third-party application. In addition, BioAnnote implements a powerful scripting engine able to perform advanced batch annotations.

Big Data and IoT for Chronic Patients Monitoring

Recent data of the European Union reveals that the main chronic pathologies are the Cardiovascular Disease (CVD), the main cause of death in Europe, and respiratory diseases, specially the Chronic Obstructive Pulmonary Disease (COPD). Each year CVD causes over 4 million deaths in Europe alone and over 1.9 million deaths in the European Union (EU). According to the WHO (World Health Organization), in 2030 COPD will be the third leading cause of death, and the first cause of sanitary costs in Europe, due to the profiles of the expenses in health sector and the long time expenses by age groups and their important associate morbidity. New medical applications based on remote monitoring can help treat those chronic diseases but significantly will increase the volume of health information to manage, including data from medical and biological sensors, being then necessary to process this huge volume of data using techniques from Big Data. In this paper we propose one potential solution for creating those new services, based on Big Data processing and IoT concepts.

Personalized Health Care System with Virtual Reality Rehabilitation and Appropriate Information for Seniors

The concept of the information society is now a common one, as opposed to the industrial society that dominated the economy during the last years. It is assumed that all sectors should have access to information and reap its benefits. Elderly people are, in this respect, a major challenge, due to their lack of interest in technological progress and their lack of knowledge regarding the potential benefits that information society technologies might have on their lives. The Naviga Project (An Open and Adaptable Platform for the Elderly and Persons with Disability to Access the Information Society) is a European effort, whose main goal is to design and develop a technological platform allowing elder people and persons with disability to access the internet and the information society. Naviga also allows the creation of services targeted to social networks, mind training and personalized health care. In this paper we focus on the health care and information services designed on the project, the technological platform developed and details of two representative elements, the virtual reality hand rehabilitation and the health information intelligent system.

The SINAMED and ISIS projects: applying text mining techniques to improve access to a medical digital library

Intelligent information access systems integrate text mining and content analysis capabilities as a relevant element in an increasing way. In this paper we present our work focused on the integration of text categorization and summarization to improve information access on a specific medical domain, patient clinical records and related scientific documentation, in the framework of two different research projects: SINAMED and ISIS, developed by a consortium of two research groups from two universities, one hospital and one software development firm. SINAMED has a basic research orientation and its goal is to design new text categorization and summarization algorithms based on the utilization of lexical resources in the biomedical domain. ISIS is a R&D project with a more applied and technology-transfer orientation, focused on more direct practical aspects of the utilization in a concrete public health institution.

A learning support tool with clinical cases based on concept maps and medical entity recognition

The search for truthful health information through Internet is an increasingly complex process due to the growing amount of resources. Access to information can be difficult to control even in environments where the goal pursued is well-defined, as in the case of learning activities with medical students. In this paper, we present a computer tool devised to ease the process of understanding medical concepts from information in clinical case histories. To this end, it automatically constructs concept maps and presents reliable information from different ontologies and knowledge bases. The two main components of the system are an Intelligent Information Access interface and a Concept Map Graph that retrieves medical concepts from a text input, and provides rich information and semantically related concepts. The paper includes a user evaluation of the first component and a systematic assessment for the second component. Results show that our proposal can be efficient and useful for students in a medical learning environment.

In the development of a spanish metamap

MetaMap is an online application that allows mapping text to UMLS Metathesaurus concepts, which is very useful interoperability among different languages and systems within the biomedical domain. MetaMap Transfer (MMTx) is a Java program that makes MetaMap available to biomedical researchers. Currently there is no Spanish version of MetaMap, which difficults the use of UMLS Metathesaurus to extract concepts from Spanish biomedical texts. Our ongoing research is mainly focused on using biomedical concepts for cross-lingual text classification and retrieval [3]. In this context the use of concepts instead of bag of words representation allows us to face text classification tasks abstracting from the language [4]. In this paper we evaluate the possibility of combining automatic translation techniques with the use of biomedical ontologies to produce an English text that can be processed by MMTx.

Thinking precedes action: using software engineering for the development of a terminology database to improve access to biomedical documentation

Relational databases have been used to represent lexical knowledge since the days of machine-readable dictionaries. However, although software engineering provides a methodological framework for the construction of databases, most developing efforts focus on content, implementation and time-saving issues, and forget about the software engineering aspects of software and database construction. We have defined a methodology for the development of lexical resources that covers this and other aspects, by following a sound software engineering approach to formally represent knowledge. Nonetheless, the conceptual model from which it departs has some major limitations that need to be overcome. Based on a short analysis of common problems in existing lexical resources, we present an upgraded conceptual model as a first step towards the methodological development of a hierarchically organized concept-based terminology database, to improve the access to medical information as part of the SINAMED and ISIS projects.

Conceptual design for domain and task specific ontology-based linguistic resources

Regardless of the knowledge representation schema chosen to implement a linguistic resource, conceptual design is an important step in its development However, it is normally put aside by developing efforts as they focus on content, implementation and time-saving issues rather than on the software engineering aspects of the construction of linguistic resources Based on an analysis of common problems found in linguistic resources, we present a reusable conceptual model which incorporates elements that give ontology developers the possibility to establish formal semantic descriptions for concepts and relations, and thus avoiding the aforementioned common problems The model represents a step forward in our efforts to define a complete methodology for the design and implementation of ontology-based linguistic resources using relational databases and a sound software engineering approach for knowledge representation.