Carlos M. Lorenzetti

Mining for Topics to Suggest Knowledge Model Extensions

Electronic concept maps, interlinked with other concept maps and multimedia resources, can provide rich knowledge models to capture and share human knowledge. This article presents and evaluates methods to support experts as they extend existing knowledge models, by suggesting new context-relevant topics mined from Web search engines. The task of generating topics to support knowledge model extension raises two research questions: first, how to extract topic descriptors and discriminators from concept maps; and second, how to use these topic descriptors and discriminators to identify candidate topics on the Web with the right balance of novelty and relevance. To address these questions, this article first develops the theoretical framework required for a “topic suggester” to aid information search in the context of a knowledge model under construction. It then presents and evaluates algorithms based on this framework and applied in Extender, an implemented tool for topic suggestion. Extender has been developed and tested within CmapTools, a widely used system for supporting knowledge modeling using concept maps. However, the generality of the algorithms makes them applicable to a broad class of knowledge modeling systems, and to Web search in general.

Intelligent algorithms for improving communication patterns in thematic P2P search

We present algorithms for reducing query propagation in unstructured decentralized P2P networks.The proposed algorithms attempt to learn about other peers interests.Extensive simulations indicate that the most evolved algorithms reduce query propagation and maximize the clustering coefficient of the emergent logical network.The emergence of semantic communities can be observed in the resulting logical network. The Internet is a cooperative and decentralized network built out of millions of participants that store and share large amounts of information with other users. Peer-to-peer systems go hand-in-hand with this huge decentralized network, where each individual node can serve content as well as request it. In this scenario, the analysis, development and testing of distributed search algorithms is a key research avenue. In particular, thematic search algorithms should lead to and benefit from the emergence of semantic communities that are the result of the interaction among participants. As a result, intelligent algorithms for neighbor selection should give rise to a logical network topology reflecting efficient communication patterns. This paper presents a series of algorithms which are specifically aimed at reducing the propagation of queries in the network, by applying a novel approach for learning peers interests. These algorithms were constructed in an incremental way so that each new algorithm presents some improvements over the previous ones. Several simulations were completed to analyze the connectivity and query propagation patterns of the emergent logical networks. The results indicate that the algorithms with better behavior are those that induce greater collaboration among peers.

Topic relevance and diversity in information retrieval from large datasets: A multi-objective evolutionary algorithm approach

Abstract Enabling effective information search is an increasing problem, as technology enhances the ability to publish information rapidly, and large quantities of information are instantly available for retrieval. In this scenario, topical search is the process of searching for material that is relevant to a given topic. Multi-objective Evolutionary Algorithms have demonstrated great potential for addressing the topical search problem in very large datasets. In an evolutionary approach to topical search, a population of queries is automatically generated from a given topic, and the population of queries then evolves towards successively better candidate queries. Despite the promise of this approach, previous studies have revealed a common genotypic phenomenon: throughout evolution, the population tends to converge to almost identical sets of terms. This situation reduces the solution set to a few queries and leads to the exploration of a very limited region of the search space, which constitutes a limitation when users require different options from a topical search tool. This paper proposes and evaluates strategies to favor diversity in evolutionary topical search. These strategies rely on novel fitness functions, different parameterization for the crossover and mutation rates, and the use of multiple populations to favor diversity preservation. Experimental results conducted using these strategies in combination with the NSGA-II algorithm on a dataset consisting of more than 350,000 labeled web pages indicate that the proposed strategies show great promise for searching very large datasets, by helping to achieve query and search result diversity without giving up precision.

Multi-objective Query Optimization Using Topic Ontologies

Formulating search queries based on a thematic context is a challenging problem due to the large number of combinations in which terms can be used to reflect the topic of interest. This paper presents a novel approach to learn topical queries that simultaneously satisfy multiple retrieval objectives. The proposed method consists in using a topic ontology to train an Evolutionary Algorithm that incrementally moves a population of queries towards the proposed objectives. We present an analysis of different single- and multi-objective strategies, discuss their strengths and limitations and test the most promising strategies on a large set of labeled Web pages. Our evaluations indicate that the tested strategies that apply multi-objective Evolutionary Algorithms are significantly superior to a baseline approach that attempts to generate queries directly from a topic description.