An argument-based approach to cope with trust and pluralism in web news reports

Due to the huge amount of multi-source news that are available on the Web at any time, it is crucial to provide intelligent mechanisms to select and rank news reports. Over the last few years, a number of approaches based on criteria such as freshness, relevance and viewer profile have been proposed. However, most existing news processing services do not deal with credibility from a qualitative perspective, and do not provide mechanisms to cope with controversial news reports. To fill this gap, this paper proposes a news service framework that brings the notions of trust and pluralism into play. The proposed framework is based on a set of basic postulates characterizing the nature of trust. In our proposal, trust is modeled using Defeasible Logic Programming, a general-purpose defeasible argumentation formalism based on logic programming. Our approach helps identify antagonism among sources of news and facilitates the analysis of opposing positions. This allows us to integrate dialectical reasoning into a news recommender system, which has the capability of providing a reasoned basis for the news presented to the viewer.Red de Universidades con Carreras en Informática (RedUNCI

Exploiting user context and preferences for intelligent web search

Seeking information relevant to a topic of interest has become a common task in our daily activities. However, searching the Web using current technologies still presents many limitations. One of the main limitations is that existing tools for searching the Web restrict user queries to a small number of terms. As a result, a single query may not reflect the user information needs at a sufficient level of detail. In addition, even if longer queries were allowed, the user may not find the right terms to supply appropriate queries, or may not be willing to put the effort required to explicitly describe his or her information needs. Another limitation of today’s search tools is that they are not capable of performing qualitative inference on the suggestions they offer. For certain domains, such as news or scientific articles, a good amount of structural information can be usefully exploited to extract meaningful content. This can help sort out the material returned by a search engine and to perform a qualitative analysis to warrant some of the search results. This paper shows how to enhance current search engines capabilities by (1) taking advantage of the user context, and (2) ranking search results based on preferential criteria provided by the user. We describe ongoing research on the use of context-specific terms to refine Web search and on the use of a defeasible argumentation framework to prioritize search results.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

An argument-based approach to cope with trust and pluralism in web news reports

Due to the huge amount of multi-source news that are available on the Web at any time, it is crucial to provide intelligent mechanisms to select and rank news reports. Over the last few years, a number of approaches based on criteria such as freshness, relevance and viewer profile have been proposed. However, most existing news processing services do not deal with credibility from a qualitative perspective, and do not provide mechanisms to cope with controversial news reports. To fill this gap, this paper proposes a news service framework that brings the notions of trust and pluralism into play. The proposed framework is based on a set of basic postulates characterizing the nature of trust. In our proposal, trust is modeled using Defeasible Logic Programming, a general-purpose defeasible argumentation formalism based on logic programming. Our approach helps identify antagonism among sources of news and facilitates the analysis of opposing positions. This allows us to integrate dialectical reasoning into a news recommender system, which has the capability of providing a reasoned basis for the news presented to the viewer.Red de Universidades con Carreras en Informática (RedUNCI

Exploiting user context and preferences for intelligent web search

Seeking information relevant to a topic of interest has become a common task in our daily activities. However, searching the Web using current technologies still presents many limitations. One of the main limitations is that existing tools for searching the Web restrict user queries to a small number of terms. As a result, a single query may not reflect the user information needs at a sufficient level of detail. In addition, even if longer queries were allowed, the user may not find the right terms to supply appropriate queries, or may not be willing to put the effort required to explicitly describe his or her information needs. Another limitation of today’s search tools is that they are not capable of performing qualitative inference on the suggestions they offer. For certain domains, such as news or scientific articles, a good amount of structural information can be usefully exploited to extract meaningful content. This can help sort out the material returned by a search engine and to perform a qualitative analysis to warrant some of the search results. This paper shows how to enhance current search engines capabilities by (1) taking advantage of the user context, and (2) ranking search results based on preferential criteria provided by the user. We describe ongoing research on the use of context-specific terms to refine Web search and on the use of a defeasible argumentation framework to prioritize search results.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Incremental methods for context-basedWeb retrieval

Intelligent search depends on effective methods for identifying the information needs of a user and making relevant information resources available when needed. Reflecting user context has long been recognized as a key aspect to realizing the potential of intelligent Web search. This paper proposes a theoretical basis for better understanding the role of context in Web retrieval. It addresses the problem of identifying context-specific terms, finding relevant information sources, and automatically formulating and refining queries. We describe ongoing research on the use of incremental methods to retrieve relevant content through two main approaches. The first, feed-based, periodically checks for new relevant items in specific websites by accessing RSS feeds. The second, query-based, incrementally formulates queries, which are submitted to search interfaces (e.g., major search engines or individual search forms). We discuss the technical challenges imposed by these approaches, outline our system architecture, and present preliminary evaluations of the proposed techniques.VII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI