As cyberbullying becomes more and more frequent in social networks, automatically detecting it and pro-actively acting upon it becomes of the utmost importance. In this work, we study how a recent technique with proven success in similar tasks, Fuzzy Fingerprints, performs when detecting textual cyberbullying in social networks. Despite being commonly treated as binary classification task, we argue that this is in fact a retrieval problem where the only relevant performance is that of retrieving cyberbullying interactions. Experiments show that the Fuzzy Fingerprints slightly outperforms baseline classifiers when tested in a close to real life scenario, where cyberbullying instances are rarer than those without cyberbullying.

Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics.

Memory-based Collaborative filtering solutions are dominant in the Recommendation Systems domain, due to their low implementation effort and service maintenance, when compared to Model-based approaches. Memory-based systems often rely on similarity metrics to compute similarities between items (or users) using ratings, in what is often named neighbor-based Collaborative filtering. This paper applies Fuzzy Fingerprints to create a novel similarity metric. In it, the Fuzzy Fingerprint of each item is described with a ranking of users ratings, combined with words obtained from the items' description. This allows the presented similarity metric to use fewer neighbors than other well-known metrics such as Cosine similarity or Pearson Correlation. Our proposal is able to reduce RMSE by at least 0.030 and improve NDCG@10 by at least 0.017 when compared with the best baseline here presented.

User‐generated content is one of the most interesting phenomena of current published media, as users are now able not only to consume, but also to produce content in a much faster and easier manner. However, such freedom also carries concerns about content quality. In this work, we propose an automatic framework to assess the quality of collaboratively generated content. Quality is addressed as a multidimensional concept, modeled as a combination of independent assessments, each regarding different quality dimensions. Accordingly, we adopt a machine‐learning (ML)‐based multiview approach to assess content quality. We perform a thorough analysis of our framework on two different domains: Questions and Answer Forums and Collaborative Encyclopedias. This allowed us to better understand when and how the proposed multiview approach is able to provide accurate quality assessments. Our main contributions are: (a) a general ML multiview framework that takes advantage of different views of quality indicators; (b) the improvement (up to 30%) in quality assessment over the best state‐of‐the‐art baseline methods; (c) a thorough feature and view analysis regarding impact, informativeness, and correlation, based on two distinct domains.

Online social media has become an essential part of our life. This media is often characterized by its diverse content, which is produced by ordinary users. The potential to easily express ideas and opinions has made social media a source of valuable information on a variety of topics. In particular, information containing comments about consumer products has become prevalent. Here, we are interested in linking products mentioned in unstructured user-generated content, namely open discussion forums, to their respective entities in consumer product catalogs. Among the issues associated with this task, ambiguity is a particularly hard problem, as users typically refer to the same product using many different forms and different products may share the same form. We argue that this problem can be effectively solved using a set of evidences that can be easily extracted from social media content and product descriptions. To achieve this, we show which features should be used, how they can be extracted, and then how to combine them through machine learning techniques. Experiments in three different product categories and two different datasets demonstrate that all the sources of evidence here proposed are important, while contextual information is fundamental to achieve higher levels of precision. In fact, our method, although straightforward, was able to achieve an average improvement of 0.17 in precision and 0.13 in F1, when compared to the current state-of-the-art solution.

Abstract Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state‐of‐the‐art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph‐structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state‐of‐the‐art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.

Collaborative web sites, such as collaborative encyclopedias, blogs, and forums, are characterized by a loose edit control, which allows anyone to freely edit their content. As a consequence, the quality of this content raises much concern. To deal with this, many sites adopt manual quality control mechanisms. However, given their size and change rate, manual assessment strategies do not scale and content that is new or unpopular is seldom reviewed. This has a negative impact on the many services provided, such as ranking and recommendation. To tackle with this problem, we propose a learning to rank (L2R) approach for ranking answers in Q&A forums. In particular, we adopt an approach based on Random Forests and represent query and answer pairs using eight different groups of features. Some of these features are used in the Q&A domain for the first time. Our L2R method was trained to learn the answer rating, based on the feedback users give to answers in Q&A forums. Using the proposed method, we were able (i) to outperform a state of the art baseline with gains of up to 21% in NDCG, a metric used to evaluate rankings; we also conducted a comprehensive study of the features, showing that (ii) review and user features are the most important in the Q&A domain although text features are useful for assessing quality of new answers; and (iii) the best set of new features we proposed was able to yield the best quality rankings.

Although there is a long line of work on identifying duplicates in relational data, only a few solutions focus on duplicate detection in more complex hierarchical structures, like XML data. In this paper, we present a novel method for XML duplicate detection, called XMLDup. XMLDup uses a Bayesian network to determine the probability of two XML elements being duplicates, considering not only the information within the elements, but also the way that information is structured. In addition, to improve the efficiency of the network evaluation, a novel pruning strategy, capable of significant gains over the unoptimized version of the algorithm, is presented. Through experiments, we show that our algorithm is able to achieve high precision and recall scores in several data sets. XMLDup is also able to outperform another state-of-the-art duplicate detection solution, both in terms of efficiency and of effectiveness.

Search engines are essential tools for web users today. They rely on a large number of features to compute the rank of search results for each given query. The estimated reputation of pages is among the effective features available for search engine designers, probably being adopted by most current commercial search engines. Page reputation is estimated by analyzing the linkage relationships between pages. This information is used by link analysis algorithms as a query‐independent feature, to be taken into account when computing the rank of the results. Unfortunately, several types of links found on the web may damage the estimated page reputation and thus cause a negative effect on the quality of search results. This work studies alternatives to reduce the negative impact of such noisy links. More specifically, the authors propose and evaluate new methods that deal with noisy links, considering scenarios where the reputation of pages is computed using the PageRank algorithm. They show, through experiments with real web content, that their methods achieve significant improvements when compared to previous solutions proposed in the literature.

Detecting and eliminating duplicates in databases is a task of critical importance in many applications. Although solutions for traditional models, such as relational data, have been widely studied, recently there has been some focus on solutions for more complex hierarchical structures as, for instance, XML data. Such data presents many different challenges, among which is the issue of how to exploit the schema structure to determine if two objects are duplicates. In this paper, we argue that structure can indeed have a significant impact on the process of duplicate detection. We propose a novel method that automatically restructures database objects in order to take full advantage of the relations between its attributes. This new structure reflects the relative importance of the attributes in the database and avoids the need to perform a manual selection. To test our approach we applied it to an existing duplicate detection system. Experiments performed on several datasets show that, using the new learned structure, we consistently outperform both the results obtained with the original database structure and those obtained by letting a knowledgeable user manually choose the attributes to compare.

This article discusses a novel approach developed for static index pruning that takes into account the locality of occurrences of words in the text. We use this new approach to propose and experiment on simple and effective pruning methods that allow a fast construction of the pruned index. The methods proposed here are especially useful for pruning in environments where the document database changes continuously, such as large-scale web search engines. Extensive experiments are presented showing that the proposed methods can achieve high compression rates while maintaining the quality of results for the most common query types present in modern search engines, namely, conjunctive and phrase queries. In the experiments, our locality-based pruning approach allowed reducing search engine indices to 30% of their original size, with almost no reduction in precision at the top answers. Furthermore, we conclude that even an extremely simple locality-based pruning method can be competitive when compared to complex methods that do not rely on locality information.

Fuzzy duplicate detection aims at identifying multiple representations of real-world objects stored in a data source, and is a task of critical practical relevance in data cleaning, data mining, or data integration. It has a long history for relational data stored in a single table (or in multiple tables with equal schema). Algorithms for fuzzy duplicate detection in more complex structures, e.g., hierarchies of a data warehouse, XML data, or graph data have only recently emerged. These algorithms use similarity measures that consider the duplicate status of their direct neighbors, e.g., children in hierarchical data, to improve duplicate detection effectiveness. In this paper, we propose a novel method for fuzzy duplicate detection in hierarchical and semi-structured XML data. Unlike previous approaches, it not only considers the duplicate status of children, but rather the probability of descendants being duplicates. Probabilities are computed efficiently using a Bayesian network. Experiments show the proposed algorithm is able to maintain high precision and recall values, even when dealing with data containing a high amount of errors and missing information. Our proposal is also able to outperform a state-of-the-art duplicate detection system on three different XML databases.

In this paper, we propose a new method to discover collection-adapted ranking functions based on Genetic Programming (GP). Our Combined Component Approach (CCA)is based on the combination of several term-weighting components (i.e.,term frequency, collection frequency, normalization) extracted from well-known ranking functions. In contrast to related work, the GP terminals in our CCA are not based on simple statistical information of a document collection, but on meaningful, effective, and proven components. Experimental results show that our approach was able to outper form standard TF-IDF, BM25 and another GP-based approach in two different collections. CCA obtained improvements in mean average precision up to 40.87% for the TREC-8 collection, and 24.85% for the WBR99 collection (a large Brazilian Web collection), over the baseline functions. The CCA evolution process also was able to reduce the overtraining, commonly found in machine learning methods, especially genetic programming, and to converge faster than the other GP-based approach used for comparison.

The currently booming search engine industry has determined many online organizations to attempt to artificially increase their ranking in order to attract more visitors to their web sites. In the same time, the growth of the web has also inherently generated several navigational hyperlink structures which have a negative impact on the importance measures employed by current search engines. In this paper we propose and evaluate algorithms for identifying all these noisy links over the web graph, may them be spam or simple relationships between real world entities represented by sites, replication of content, etc. Unlike prior work, we target a different type of noisy link structures, residing at the site level, instead of the page level. We thus investigate and annihilate site level mutual reinforcement relationships, abnormal support coming from one site towards another, as well as complex link alliances between web sites. Our experiments with the link database of the TodoBR search engine show a very strong increase in the quality of the output rankings after having applied our techniques.

Abstract Traditional text‐based document classifiers tend to perform poorly on the Web. Text in Web documents is usually noisy and often does not contain enough information to determine their topic. However, the Web provides a different source that can be useful to document classification: its hyperlink structure. In this work, the authors evaluate how the link structure of the Web can be used to determine a measure of similarity appropriate for document classification. They experiment with five different similarity measures and determine their adequacy for predicting the topic of a Web page. Tests performed on a Web directory show that link information alone allows classifying documents with an average precision of 86%. Further, when combined with a traditional text‐based classifier, precision increases to values of up to 90%, representing gains that range from 63 to 132% over the use of text‐based classification alone. Because the measures proposed in this article are straightforward to compute, they provide a practical and effective solution for Web classification and related information retrieval tasks. Further, the authors provide an important set of guidelines on how link structure can be used effectively to classify Web documents.

In this paper, we propose a novel framework using Genetic Programming to combine image database descriptors for content-based image retrieval (CBIR). Our framework is validated through several experiments involving two image databases and specific domains, where the images are retrieved based on the shape of their objects.

This paper shows how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity -- five derived from the citation information of the collection, and three derived from the structural content -- and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than GA or other fusion techniques.

This paper shows how different measures of similarity derived from the citation information and the structural content (e.g., title, abstract) of the collection can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than other fusion techniques.

The unarguably fast, and continuous, growth of the volume of indexed (and indexable) documents on the Web poses a great challenge for search engines. This is true regarding not only search effectiveness but also time and space efficiency. In this paper we present an index pruning technique targeted for search engines that addresses the latter issue without disconsidering the former. To this effect, we adopt a new pruning strategy capable of greatly reducing the size of search engine indices. Experiments using a real search engine show that our technique can reduce the indices' storage costs by up to 60% over traditional lossless compression methods, while keeping the loss in retrieval precision to a minimum. When compared to the indices size with no compression at all, the compression rate is higher than 88%, i.e., less than one eighth of the original size. More importantly, our results indicate that, due to the reduction in storage overhead, query processing time can be reduced to nearly 65% of the original time, with no loss in average precision. The new method yields significative improvements when compared against the best known static pruning method for search engine indices. In addition, since our technique is orthogonal to the underlying search algorithms, it can be adopted by virtually any search engine.

This paper discusses how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity derived from the citation structure and the structural content of the collection, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM Digital Library and the ACM Computing Classification System show that we can discover similarity functions that work better than using evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers.

The World Wide Web is the largest publicly available image repository and a natural source of attention. An immediate consequence is that searching for images on the Web has become a current and important task. To search for images of interest, the most direct approach is keyword-based searching. However, since images on the Web are poorly labeled, direct application of standard keyword-based image searching techniques frequently yields poor results. We propose a comprehensive solution to this problem. In our approach, multiple sources of evidence related to the images are considered. To allow combining these distinct sources of evidence, we introduce an image retrieval model based on Bayesian belief networks. To evaluate our approach, we perform experiments on a reference collection composed of 54000 Web images. Our results indicate that retrieval using an image surrounding text passages is as effective as standard retrieval based on HTML tags. This is an interesting result because current image search engines in the Web usually do not take text passages into consideration. Most important, according to our results, the combination of information derived from text passages with information derived from HTML tags leads to improved retrieval, with relative gains in average precision figures of roughly 50 percent, when compared to the results obtained by the use of each source of evidence in isolation.

This paper studies how link information can be used to improve classification results for Web collections. We evaluate four different measures of subject similarity, derived from the Web link structure, and determine how accurate they are in predicting document categories. Using a Bayesian network model, we combine these measures with the results obtained by traditional content-based classifiers. Experiments on a Web directory show that best results are achieved when links from pages outside the directory are considered. Link information alone is able to obtain gains of up to 46 points in F1, when compared to a traditional content-based classifier. The combination with content-based methods can further improve the results, but too much noise may be introduced, since the text of Web pages is a much less reliable source of information. This work provides an important insight on which measures derived from links are more appropriate to compare Web documents and how these measures can be combined with content-based algorithms to improve the effectiveness of Web classification.

With the growing availability of online information systems, a need for user interfaces that are flexible and easy to use has arisen. For such type of systems, an interface that allows the formulation of approximate queries can be of great utility since these allow the user to quickly explore the database contents even when he is unaware of the exact values of the database instances. Our work focuses on this problem, presenting a new model for ranking approximate answers and a new algorithm to compute the semantic similarity between attribute values, based on information retrieval techniques. To demonstrate the utility and usefulness of the approach, we perform a series of usability tests. The results suggest that our approach allows the retrieval of more relevant answers with less effort by the user.

Information derived from the cross-references among the documents in a hyperlinked environment, usually referred to as link information, is considered important since it can be used to effectively improve document retrieval. Depending on the retrieval strategy, link information can be local or global. Local link information is derived from the set of documents returned as answers to the current user query. Global link information is derived from all the documents in the collection. In this work, we investigate how the use of local link information compares to the use of global link information. For the comparison, we run a series of experiments using a large document collection extracted from the Web. For our reference collection, the results indicate that the use of local link information improves precision by 74%. When global link information is used, precision improves by 35%. However, when only the first 10 documents in the ranking are considered, the average gain in precision obtained with the use of global link information is higher than the gain obtained with the use of local link information. This is an interesting result since it provides insight and justification for the use of global link information in major Web search engines, where users are mostly interested in the first 10 answers. Furthermore, global information can be computed in the background, which allows speeding up query processing.

The Web contains a huge volume of information, almost all unstructured and, therefore, difficult to manage. In Digital Libraries, however, information is explicitly organized, described, and managed. In this paper, we propose an architecture that allows the construction of digital libraries from the Web, using standard protocols and archival technologies, and incorporating powerful digital library and data extraction tools, thus benefiting from the breadth of the Web contents, but supporting services and organization available in digital libraries. The proposed architecture was applied to the Networked Digital Library of Theses and Dissertations, providing an important first step toward rapid construction of large DLs from the Web, as well as a large-scale solution for interoperability between independent digital libraries.

On-line information services have become widespread in the Web nowadays. However, Web users are non-specialized and have a great variety of interests. Thus, interfaces for Web databases must be simple and uniform. In this paper we present an approach, based on Bayesian networks, for querying Web databases using keywords only. According to this approach, the user inputs a query through a simple search-box interface. From the input query, one or more plausible structured queries are derived and submitted to Web databases. The results are then retrieved and presented to the user as ranked answers. Our approach reduces the complexity of existing on-line interfaces and offers a solution to the problem of querying several distinct Web databases with a single interface. The applicability of the proposed approach was demonstrated by experimental results with 3 databases, obtained with a prototype search system that implements it. We have found that from 77% to 95% of the time, one of the top three resulting structured queries is the proper one. Further, when the user selects one of these three top queries for processing, the ranked answers present average precision figures from 60% to about 100%.

This work presents an information retrieval model developed to deal with hyperlinked environments. The model is based on belief networks and provides a framework for combining information extracted from the content of the documents with information derived from cross-references among the documents. The information extracted from the content of the documents is based on statistics regarding the keywords in the collection and is one of the basis for traditional information retrieval (IR) ranking algorithms. The information derived from cross-references among the documents is based on link references in a hyperlinked environment and has received increased attention lately due to the success of the Web. We discuss a set of strategies for combining these two types of sources of evidential information and experiment with them using a reference collection extracted from the Web. The results show that this type of combination can improve the retrieval performance without requiring any extra information from the users at query time. In our experiments, the improvements reach up to 59% in terms of average precision figures.