Hacia una integración de un sistema de búsqueda de respuestas sobre la inteligencia empresarial mediante el uso de ontologías

El objetivo general de las aplicaciones de inteligencia empresarial (Business Intelligence, a partir de ahora BI) es permitir a sus usuarios entender y analizar los datos existentes en sus organizaciones para adquirir conocimiento útil y lograr así una mejor toma de decisiones. El corazón de las aplicaciones de BI son los almacenes de datos (Data Warehouse, a partir de ahora DW), los cuales integran diferentes recursos de datos, principalmente bases de datos estructuradas. Sin embargo, una nueva tendencia a utilizar la Web como fuente de información sobre el entorno de las organizaciones ha emergido. Como parte de esta línea de investigación, estamos trabajando en la aplicación de un sistema de búsqueda de respuesta (Question Answering) como herramienta vinculante a los DW para la obtención de información que ayude en la toma de decisiones, continuando, de esta manera, con los avances obtenidos.Eje: Agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Enrichment of the Phenotypic and Genotypic Data Warehouse analysis using Question Answering systems to facilitate the decision making process in cereal breeding programs

Currently there are an overwhelming number of scientific publications in Life Sciences, especially in Genetics and Biotechnology. This huge amount of information is structured in corporate Data Warehouses (DW) or in Biological Databases (e.g. UniProt, RCSB Protein Data Bank, CEREALAB or GenBank), whose main drawback is its cost of updating that makes it obsolete easily. However, these Databases are the main tool for enterprises when they want to update their internal information, for example when a plant breeder enterprise needs to enrich its genetic information (internal structured Database) with recently discovered genes related to specific phenotypic traits (external unstructured data) in order to choose the desired parentals for breeding programs. In this paper, we propose to complement the internal information with external data from the Web using Question Answering (QA) techniques. We go a step further by providing a complete framework for integrating unstructured and structured information by combining traditional Databases and DW architectures with QA systems. The great advantage of our framework is that decision makers can compare instantaneously internal data with external data from competitors, thereby allowing taking quick strategic decisions based on richer data.This paper has been partially supported by the MESOLAP (TIN2010-14860) and GEODAS-BI (TIN2012-37493-C03-03) projects from the Spanish Ministry of Education and Competitivity. Alejandro Maté is funded by the Generalitat Valenciana under an ACIF grant (ACIF/2010/298)

Hacia una integración de un sistema de búsqueda de respuestas sobre la inteligencia empresarial mediante el uso de ontologías

El objetivo general de las aplicaciones de inteligencia empresarial (Business Intelligence, a partir de ahora BI) es permitir a sus usuarios entender y analizar los datos existentes en sus organizaciones para adquirir conocimiento útil y lograr así una mejor toma de decisiones. El corazón de las aplicaciones de BI son los almacenes de datos (Data Warehouse, a partir de ahora DW), los cuales integran diferentes recursos de datos, principalmente bases de datos estructuradas. Sin embargo, una nueva tendencia a utilizar la Web como fuente de información sobre el entorno de las organizaciones ha emergido. Como parte de esta línea de investigación, estamos trabajando en la aplicación de un sistema de búsqueda de respuesta (Question Answering) como herramienta vinculante a los DW para la obtención de información que ayude en la toma de decisiones, continuando, de esta manera, con los avances obtenidos.Eje: Agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Sistemas multiagentes en ambientes dinámicos

La meta fundamental de este proyecto es el desarrollo de conocimiento especializado en el área de Inteligencia Artificial Distribuida, estudiando técnicas de representación del conocimiento y razonamiento, junto con métodos de planificación y tecnologías del lenguaje natural aplicadas al desarrollo de sistemas multiagentes. En la línea Planificación, la temática de investigación es el desarrollo de una arquitectura para agentes que soporte tanto control reactivo como deliberativo, de forma tal que el agente pueda actuar de manera competente y efectiva en un ambiente real. Uno de los objetivos de esta investigación es el intento de dotar a un agente inteligente de ambas capacidades. Esto brindará la posibilidad de elegir cuál sería la mejor forma de actuar frente a un problema determinado. Por otro lado, las otras líneas se basan en técnicas de procesamiento del lenguaje natural (PLN). La información textual disponible en la web podría ser categorizada en expresiones de hecho y de opinión. Las expresiones de hechos están relacionadas a entidades, eventos y sus propiedades. Por otro lado, las de opinión son usualmente expresiones subjetivas que describen algún sentimiento sobre las personas, valoraciones o sentimientos hacia las entidades, eventos y sus propiedades. Siguiendo con esto, cada línea de investigación, dentro del PLN, está orientada a tratar con una de estas categorías. Es así que la línea de Opinion Mining se centra en las expresiones de opinión. Mientras que la línea de investigación sobre la inteligencia empresarial (Business Intelligence), en esta primera etapa, está orientada a trabajar solamente con expresiones de hechos.Eje: Agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

An overview of the linguistic resources used in cross-language question answering systems in CLEF Conference

The development of the Semantic Web requires great economic and human effort. Consequently, it is very useful to create mechanisms and tools that facilitate its expansion. From the standpoint of information retrieval (hereafter IR), access to the contents of the Semantic Web can be favored by the use of natural language, as it is much simpler and faster for the user to engage in his habitual form of expression. The growing popularity of Internet and the wide availability of web informative resources for general audiences are a fairly recent phenomenon, although man´s need to hurdle the language barrier and communicate with others is as old as the history of mankind. The World Wide Web, also known as WWW, together with the growing globalization of companies and organizations, and the increase of the non-English speaking audience, entails the demand for tools allowing users to secure information from a wide range of resources. Yet the underlying linguistic restrictions are often overlooked by researchers and designers. Against this background, a key characteristic to be evaluated in terms of the efficiency of IR systems is its capacity to allow users find a corpus of documents in different languages, and to facilitate the relevant information despite limited linguistic competence regarding the target language

An overview of the linguistic resources used in cross-language question answering systems in CLEF Conference

The development of the Semantic Web requires great economic and human effort. Consequently, it is very useful to create mechanisms and tools that facilitate its expansion. From the standpoint of information retrieval (hereafter IR), access to the contents of the Semantic Web can be favored by the use of natural language, as it is much simpler and faster for the user to engage in his habitual form of expression. The growing popularity of Internet and the wide availability of web informative resources for general audiences are a fairly recent phenomenon, although man´s need to hurdle the language barrier and communicate with others is as old as the history of mankind. The World Wide Web, also known as WWW, together with the growing globalization of companies and organizations, and the increase of the non-English speaking audience, entails the demand for tools allowing users to secure information from a wide range of resources. Yet the underlying linguistic restrictions are often overlooked by researchers and designers. Against this background, a key characteristic to be evaluated in terms of the efficiency of IR systems is its capacity to allow users find a corpus of documents in different languages, and to facilitate the relevant information despite limited linguistic competence regarding the target language

An Overview of the Linguistic Resources used in Cross-Language Question Answering Systems in CLEF Conference

The development of the Semantic Web requires great economic and human effort. Consequently, it is very useful to create mechanisms and tools that facilitate its expansion. From the standpoint of information retrieval (hereafter IR), access to the contents of the Semantic Web can be favored by the use of natural language, as it is much simpler and faster for the user to engage in his habitual form of expression. The growing popularity of Internet and the wide availability of web informative resources for general audiences are a fairly recent phenomenon, although man´s need to hurdle the language barrier and communicate with others is as old as the history of mankind. The World Wide Web, also known as WWW, together with the growing globalization of companies and organizations, and the increase of the non-English speaking audience, entails the demand for tools allowing users to secure information from a wide range of resources. Yet the underlying linguistic restrictions are often overlooked by researchers and designers. Against this background, a key characteristic to be evaluated in terms of the efficiency of IR systems is its capacity to allow users find a corpus of documents in different languages, and to facilitate the relevant information despite limited linguistic competence regarding the target language

Automatic generation of question answering systems in restricted domains

Question answering (QA) applications can be considered as the potential successors to the traditional information retrieval on the Web. However, QA systems should be adapted to restricted domains for the sake of precision. This adaptation is not a trivial task, since several heterogeneous resources related to a restricted domain must be integrated into existing QA systems. This paper presents the Maraqa tool, whose novelty is the use of software engineering techniques such as model driven development to automate the adaptation process. It is worth noting that Maraqa has been evaluated through a set of experiments (within the agricultural domain) that demonstrate its applicability: the precision of the adapted QA system showed 29.5% improvement

A framework for enriching Data Warehouse analysis with Question Answering systems

Business Intelligence (BI) applications allow their users to query, understand, and analyze existing data within their organizations in order to acquire useful knowledge, thus making better strategic decisions. The core of BI applications is a Data Warehouse (DW), which integrates several heterogeneous structured data sources in a common repository of data. However, there is a common agreement in that the next generation of BI applications should consider data not only from their internal data sources, but also data from different external sources (e.g. Big Data, blogs, social networks, etc.), where relevant update information from competitors may provide crucial information in order to take the right decisions. This external data is usually obtained through traditional Web search engines, with a significant effort from users in analyzing the returned information and in incorporating this information into the BI application. In this paper, we propose to integrate the DW internal structured data, with the external unstructured data obtained with Question Answering (QA) techniques. The integration is achieved seamlessly through the presentation of the data returned by the DW and the QA systems into dashboards that allow the user to handle both types of data. Moreover, the QA results are stored in a persistent way through a new DW repository in order to facilitate comparison of the obtained results with different questions or even the same question with different dates.This paper has been partially supported by the MESOLAP (TIN2010-14860), GEODASBI (TIN2012-37493-C03-03), LEGOLANG-UAGE (TIN2012-31224) and DIIM2.0 (PROMETEOII/2014/001) projects from the Spanish Ministry of Education and Competitivity. Alejandro Maté is funded by the Generalitat Valenciana under an ACIF grant (ACIF/2010/298)