waashk/dblp huggingface dataset:
on domain similarity and effectiveness of adapting-to-rank. adapting to rank address the problem of insufficient domain-specific labeled training data in learning to rank. however, the initial study shows that adaptation is not always effective. in this paper, we investigate the relationship between the domain similarity and the effectiveness of domain adaptation with the help of two domain similarity measure: relevance correlation and sample distribution correlation.
identifying interesting assertions from the web. how can we cull the facts we need from the overwhelming mass of information and misinformation that is the web? the textrunner extraction engine represents one approach, in which people pose keyword queries or simple questions and textrunner returns concise answers based on tuples extracted from web text. unfortunately, the results returned by engines such as textrunner include both informative facts (e.g., the fda banned ephedra) and less useful statements (e.g., the fda banned products). this paper therefore investigates filtering textrunner results to enable people to better focus on interesting assertions. we first develop three distinct models of what assertions are likely to be interesting in response to a query. we then fully operationalize each of these models as a filter over textrunner results. finally, we develop a more sophisticated filter that combines the different models using relevance feedback. in a study of human ratings of the interestingness of textrunner assertions, we show that our approach substantially enhances the quality of textrunner results. our best filter raises the fraction of interesting results in the top thirty from 41.6% to 64.1%.
matching person names through name transformation. matching person names plays an important role in many applications, including bibliographic databases and indexing systems. name variations and spelling errors make exact string matching problematic; therefore, it is useful to develop methodologies that can handle variant forms for the same named entity. in this paper, a novel person name matching model is presented. common name variations in the english speaking world are formalized, and the concept of name transformation paths is introduced; name similarity is measured after the best transformation path has been selected. supervised techniques are used to learn a similarity function and a decision rule. experiments with three datasets show the method to be effective.
a unified relevance model for opinion retrieval. representing the information need is the greatest challenge for opinion retrieval. typical queries for opinion retrieval are composed of either just content words, or content words with a small number of cue "opinion" words. both are inadequate for retrieving opinionated documents. in this paper, we develop a general formal framework--the opinion relevance model--to represent an information need for opinion retrieval. we explore a series of methods to automatically identify the most appropriate opinion words for query expansion, including using query independent sentiment resources. we also propose a relevance feedback-based approach to extract opinion words. both query-independent and query-dependent methods can also be integrated into a more effective mixture relevance model. finally, opinion retrieval experiments are presented for the blog06 and coae08 text collections. the results show that, significant improvements can always be obtained by this opinion relevance model whether sentiment resources are available or not.
privacy and anonymization for very large datasets. with the increase of available public data sources and the interest for analyzing them, privacy issues are becoming the eye of the storm in many applications. the vast amount of data collected on human beings and organizations as a result of cyberinfrastructure advances, or that collected by statistical agencies, for instance, has made traditional ways of protecting social science data obsolete. this has given rise to different techniques aimed at tackling this problem and at the analysis of limitations in such environments, such as the seminal study by aggarwal of anonymization techniques and their dependency on data dimensionality. the growing accessibility to high-capacity storage devices allows keeping more detailed information from many areas. while this enriches the information and conclusions extracted from this data, it poses a serious problem for most of the previous work presented up to now regarding privacy, focused on quality and paying little attention to performance aspects. in this workshop, we want to gather researchers in the areas of data privacy and anonymization together with researchers in the area of high performance and very large data volumes management. we seek to collect the most recent advances in data privacy and anonymization (i.e. anonymization techniques, statistic disclosure techniques, privacy in machine learning algorithms, privacy in graphs or social networks, etc) and those in high performance and data management (i.e. algorithms and structures for efficient data management, parallel or distributed systems, etc).
m-cope: a multiple continuous query processing engine. a data stream management system (dsms) should support an efficient evaluation scheme for long-running continuous queries over infinite data streams. this demonstration presents a scalable query processing engine, m-cope (multiple continuous query processing engine) developed to evaluate multiple continuous queries efficiently. a multiple query optimization scheme implemented in the system generates a single network of operations as an execution plan for registered queries in order to maximize the reuse of the intermediate results of common sub-expressions in the queries adaptively. in this paper, we describe the overall architecture of m-cope along with its special features. network traffic flow streams are used to demonstrate the main features of m-cope.
detection of orthogonal concepts in subspaces of high dimensional data. in the knowledge discovery process, clustering is an established technique for grouping objects based on mutual similarity. however, in today's applications for each object very many attributes are provided. as multiple concepts described by different attributes are mixed in the same data set, clusters do not appear in all dimensions. in these high dimensional data spaces, each object can be clustered in several projections of the data. however, recent clustering techniques do not succeed in detection of these orthogonal concepts hidden in the data. they either miss multiple concepts for each object by partitioning approaches or provide redundant clusters in very similar subspaces. in this work we propose a novel clustering method aiming only at orthogonal concept detection in subspaces of the data. unlike existing clustering approaches, osclu (orthogonal subspace clustering) detects for each object the orthogonal concepts described by differing attributes while pruning similar concepts. thus, each detected cluster in an orthogonal subspace provides novel information about the hidden structure of the data. thorough experiments on real and synthetic data show that osclu yields substantial quality improvements over existing clustering approaches.
minimal common container of tree patterns. tree patterns represent important fragments of xpath. in this paper, we show that some classes of tree patterns exhibit such a property that, given a finite number of tree patterns p1, ..., pn, there exists another pattern p (tree pattern or dag-pattern) such that p1, ..., pn, are all contained in p, and for any tree pattern q belonging to a given class c, p1, ..., pn, are contained in q implies p is contained in q.
incremental similarity joins with edit distance constraints. with the dynamic increase of string data and the need to integrate data from multiple data sources, a challenging issue is to perform similarity joins on dynamically-augmented string sets. existing methods only exploit domain-oriented filters to speed up join processing on static datasets, which are inefficient for incremental data-generation scenarios. in this paper, an efficient approach called isj-ed (abbr for incremental similarity joins with edit distance constraints) is proposed to tackle similarity join problem on ever-growing string sets. we first design a distance-based filtering technique which exploits an incrementally-built index to improve the filtering capability. then, for the existent filters, we study the impact of their executing orders on total filtering cost and suggest dynamically-optimized filtering orders. all these optimization strategies work jointly with the existing domain-oriented filters in isj-ed, that is, they are complementary to those filter-based methods with edit-distance thresholds. experimental results demonstrate that on dynamically augmented string sets, our method is more efficient than those only leverage domain-oriented filters with a fixed filtering order.
active learning in partially supervised classification. positive example based learners reduce human annotation effort significantly by removing the burden of labeling the negative examples. various methods have been proposed in literature for building classifiers using positive and unlabeled examples. however, we empirically observe that classification accuracy of the state of the art methods degrades significantly as the number of labeled positive examples decreases. in this paper, we propose an active learning based method to address this issue. the proposed method learns starting from a handful of positively labeled examples and a large number of unlabeled examples. experimental results on benchmark datasets show that the proposed method performs better than the state of the art methods when the percentage of labeled positive examples is small.
rank-aware clustering of structured datasets. in online applications such as yahoo! personals and yahoo! real estate users define structured profiles in order to find potentially interesting matches. typically, profiles are evaluated against large datasets and produce thousands of matches. in addition to filtering, users also specify ranking in their profile, and matches are returned in a ranked list. top results in a list are typically homogeneous, which hinders data exploration. for example, a user looking for 1- or 2-bedroom apartments sorted by price will see a large number of cheap 1-bedrooms in undesirable neighborhoods before seeing a different apartment. an alternative to ranking is to group matches on common attribute values, e.g., cheap 1-bedrooms in good neighborhoods, 2-bedrooms with 2 baths, and choose groups in relationship with ranking. in this paper, we present a novel paradigm of rank-aware clustering, and demonstrate its effectiveness on a large dataset from yahoo! personals, a leading online dating site.
information extraction meets relation databases. information extraction from unstructured text has much in common with querying in databases systems. despite some differences on how data is modeled or represented, the general goal remains the same, i.e. to retrieve data or tag elements that satisfy some user-specified constraints. in recent years, the two paradigms have become much closer thanks to the large volume of data on the world wide web and the need for more automated search tools for information extraction and often the need for relating the extracted pieces. several developments have contributed to the growth of the area including the work on named entity recognition (marked by muc-6 and subsequent conferences) and natural language processing, web information retrieval and mining, and web query languages inspired by the query languages in the relational world. this panel explores the areas where the two paradigms overlap, the impacts and contributions they have had on each other and the areas that may be open for further research. the panel will bring together researchers who have worked in some established areas that closely relate to extracting structured information from unstructured text. in the first (role-playing) round, each panelist will strongly take a side on where the intersection is heading, arguing that one area will subsume the other area in near future. in the second round, the panelists will counter one or two others, pointing out the challenges that one area would be facing in subsuming the other and implications for future research directions.
mashup-based information retrieval for domain experts. in this paper, we tackle the problem of helping domain experts to construct, parameterize and deploy mashups of data and code. we view a mashup as a data processing flow, that describes how data is obtained from one or more sources, processed by one or more components, and finally sent to one or more sinks. our approach allows specifying patterns of flows, in a language called cascade. the patterns cover different possible variations of the flows, including variations in the structure of the flow, the components in the flow and the possible parameterizations of these components. we present a tool that makes use of this knowledge of flow patterns and associated metadata to allow domain experts to explore the space of possible flows described in the pattern. the tool uses an ai planning approach to automatically build a flow, belonging to the flow pattern, from a high-level goal, specified as a set of tags. we describe examples from the financial services domain to show the use of flow patterns in allowing domain experts to construct a large variety of mashups rapidly.
exploring multimedia databases via optimization-based relevance feedback and the earth mover's distance. determining similar objects is a fundamental operation both in data mining tasks such as clustering and in query-driven object retrieval. by definition of similarity search, query objects can only be imprecise descriptions of what users are looking for in a database, and even high-quality similarity measures can only be approximations of the users' notion of similarity. to overcome these shortcomings, iterative query refinement systems have been proposed. they utilize user feedback regarding the relevance of intermediate results to adapt the query object and/or the similarity measure. we propose an optimization-based relevance feedback approach for adaptable distance measures - focusing on the earth mover's distance. our technique enables quicker iterative database exploration as shown by our experiments.
a comparative study of methods for estimating query language models with pseudo feedback. we systematically compare five representative state-of-the-art methods for estimating query language models with pseudo feedback in ad hoc information retrieval, including two variants of the relevance language model, two variants of the mixture feedback model, and the divergence minimization estimation method. our experiment results show that a variant of relevance model and a variant of the mixture model tend to outperform other methods. we further propose several heuristics that are intuitively related to the good retrieval performance of an estimation method, and show that the variations in how these heuristics are implemented in different methods provide a good explanation of many empirical observations.
as-index: a structure for string search using n-grams and algebraic signatures. as-index is a new index structure for exact string search in disk resident databases. it uses hashing, unlike known alternatives, whether baesd on trees or tries. it typically indexes every n-gram in the database, though non-dense indexing is also possible. the hash function uses the algebraic signatures of all n-grams. use of hashing provides for constant index access time for arbitrarily long patterns, unlike other structures whose search cost is at best logarithmic. the storage overhead of as-index is basically 500 - 600%, similar to that of alternatives or smaller. we show the index structure, our use of algebraic signatures and the search algorithm. we discuss the design trade-offs and present the theoretical and experimental performance analysis. we compare the as-index to main alternatives. we conclude that as-index is an attractive structure and we indicate directions for future work.
finding the topical anchors of a context using lexical cooccurrence data. lexical cooccurrence in textual data is not uniformly random. the statistics inferred from the term-cooccurrence data enable us to model dependencies between terms as graphs, somewhat resembling the way semantic memory is organised in human beings. in this paper we look at cooccurrence patterns to identify topical anchors of a given context. topical anchors are those terms whose semantics represent the topic of the whole context. this work is based on computing a stationary distribution in the cooccurrence graph. topical anchors were computed on a set of 100 contexts and were also evaluated by 86 volunteers and the results show that the algorithm correctly identifies the topical anchors around 62% of the time.
group-by skyline query processing in relational engines. the skyline operator was first proposed in 2001 for retrieving interesting tuples from a dataset. since then, 100+ skyline-related papers have been published; however, we discovered that one of the most intuitive and practical type of skyline queries, namely, group-by skyline queries remains unaddressed. group-by skyline queries find the skyline for each group of tuples. in this paper, we present a comprehensive study on processing group-by skyline queries in the context of relational engines. specifically, we examine the composition of a query plan for a group-by skyline query and develop the missing cost model for the bbs algorithm. experimental results show that our techniques are able to devise the best query plans for a variety of group-by skyline queries. our focus is on algorithms that can be directly implemented in today's commercial database systems without the addition of new access methods (which would require addressing the associated challenges of maintenance with updates, concurrency control, etc.).
an efficient clustering algorithm for large-scale topical web pages. the clustering of topic-related web pages has been recognized as a foundational work in exploiting large sets of web pages such as the cases in search engines and web archive systems, which collect and preserve billions of web pages. however, this task faces great challenges both in efficiency and accuracy. in this paper we present a novel clustering algorithm for large scale topical web pages which achieves high efficiency together with considerately high accuracy. in our algorithm, a two-phase divide and conquer framework is developed to solve the efficiency problem, in which both link analysis and content analysis are utilized in mining the topical similarity between pages to achieve a high accuracy. a comprehensive experiment was conducted to evaluate our method in terms of its effectiveness, efficiency, and quality of result.
evidence of quality of textual features on the web 2.0. the growth of popularity of web 2.0 applications greatly increased the amount of social media content available on the internet. however, the unsupervised, user-oriented nature of this source of information, and thus, its potential lack of quality, have posed a challenge to information retrieval (ir) services. previous work focuses mostly only on tags, although a consensus about its effectiveness as supporting information for ir services has not yet been reached. moreover, other textual features of the web 2.0 are generally overseen by previous research. in this context, this work aims at assessing the relative quality of distinct textual features available on the web 2.0. towards this goal, we analyzed four features (title, tags, description and comments) in four popular applications (citeulike, last.fm, yahoo! video, and youtube). firstly, we characterized data from these applications in order to extract evidence of quality of each feature with respect to usage, amount of content, descriptive and discriminative power as well as of content diversity across features. afterwards, a series of classification experiments were conducted as a case study for quality evaluation. characterization and classification results indicate that: 1) when considered separately, tags is the most promising feature, achieving the best classification results, although its absence in a non-negligible fraction of objects may affect its potential use; and 2) each feature may bring different pieces of information, and combining their contents can improve classification.
relying on topic subsets for system ranking estimation. ranking a number of retrieval systems according to their retrieval effectiveness without relying on costly relevance judgments was first explored by soboroff et al [6]. over the years, a number of alternative approaches have been proposed. we perform a comprehensive analysis of system ranking estimation approaches on a wide variety of trec test collections and topics sets. our analysis reveals that the performance of such approaches is highly dependent upon the topic or topic subset, used for estimation. we hypothesize that the performance of system ranking estimation approaches can be improved by selecting the "right" subset of topics and show that using topic subsets improves the performance by 32% on average, with a maximum improvement of up to 70% in some cases.
large margin transductive transfer learning. recently there has been increasing interest in the problem of transfer learning, in which the typical assumption that training and testing data are drawn from identical distributions is relaxed. we specifically address the problem of transductive transfer learning in which we have access to labeled training data and unlabeled testing data potentially drawn from different, yet related distributions, and the goal is to leverage the labeled training data to learn a classifier to correctly predict data from the testing distribution. we have derived efficient algorithms for transductive transfer learning based on a novel viewpoint and the support vector machine (svm) paradigm, of a large margin hyperplane classifier in a feature space. we show that our method can out-perform some recent state-of-the-art approaches for transfer learning on several data sets, with the added benefits of model and data separation and the potential to leverage existing work on support vector machines.
probabilistic skyline queries. the ability to deal with uncertain information is becoming increasingly important for modern database applications. whereas a conventional (certain) object is usually represented by a vector from a multidimensional feature space, an uncertain object is represented by a multivariate probability density function (pdf). this pdf can be defined either discretely (e.g. by a histogram) or continuously in parametric form (e.g. by a gaussian mixture model). for a database of uncertain objects, the users expect similar data analysis techniques as for a conventional database of certain objects. an important analysis technique for certain objects is the skyline operator which finds maximal or minimal vectors with respect to any possible attribute weighting. in this paper, we propose the concept of probabilistic skylines, an extension of the skyline operator for uncertain objects. in addition, we propose efficient and effective methods for determining the probabilistic skyline of uncertain objects which are defined by a pdf in parametric form (e.g. a gaussian function or a gaussian mixture model). to further accelerate the search, we elaborate how the computation of the probabilistic skyline can be supported by an index structure for uncertain objects. an extensive experimental evaluation demonstrates both the effectiveness and the efficiency of our technique.