waashk/dblp huggingface dataset:
a scalable and effective full-text search in p2p networks. we consider the problem of full-text search involving multi-term queries in a network of self-organizing, autonomous peers. existing approaches do not scale well with respect to the number of peers, because they either require access to a large number of peers or incur a high communication cost in order to achieve good query results. in this paper, we present a novel algorithmic framework for processing multi-term queries in p2p networks that achieves high recall while using (per-query) a small number of peers and a low communication cost, thereby enabling high query throughput. our approach is based on per-query peer-selection strategy using two-dimensional histograms of score distributions. a full utilization of the histograms incurs a high communication cost. we show how to drastically reduce this cost by employing a two-phase peer-selection algorithm. we also describe an adaptive approach to peer selection that further increases the recall. experiments on a large real-world collection show that the recall is indeed high while the number of involved peers and the communication cost are low.
a word clustering approach for language model-based sentence retrieval in question answering systems. in this paper we propose a term clustering approach to improve the performance of sentence retrieval in question answering (qa) systems. as the search in question answering is conducted over smaller segments of data than in a document retrieval task, the problems of data sparsity and exact matching become more critical. in this paper we propose language modeling (lm) techniques to overcome such problems and improve the sentence retrieval performance. our proposed methods include building class-based models by term clustering, and then employing higher order n-grams with the new class-based model. we report our experiments on the trec 2007 questions from qa track. the results show that the methods investigated here enhanced the mean average precision of sentence retrieval from 23.62% to 29.91%.
compressing tags to find interesting media groups. on photo sharing websites like flickr and zooomr, users are offered the possibility to assign tags to their uploaded pictures. using these tags to find interesting groups of semantically related pictures in the result set of a given query is a problem with obvious applications. we analyse this problem from a minimum description length (mdl) perspective and develop an algorithm that finds the most interesting groups. the method is based on krimp, which finds small sets of patterns that characterise the data using compression. these patterns are sets of tags, often assignedtogether to photos. the better a database compresses, the more structure it contains and thus the more homogeneous it is. following this observation we devise a compression-based measure. our experiments on flickr data show that the most interesting and homogeneous groups are found. we show extensive examples and compare to clusterings on the flickr website.
towards real-time measurement of customer satisfaction using automatically generated call transcripts. customer satisfaction is a very important indicator of how successful a contact center is at providing services to the customers. contact centers typically conduct a manual survey with a randomly selected group of customers to measure customer satisfaction. manual customer satisfaction surveys, however, provide limited values due to high cost and the time lapse between the service and the survey. in this paper, we demonstrate that it is possible to automatically measure customer satisfaction by analyzing call transcripts enabling companies to measure customer satisfaction for every call in near real-time. we have identified various features from multiple knowledge sources indicating prosodic, linguistic and behavioral aspects of the speakers, and built machine learning models that predict the degree of customer satisfaction with high accuracy. the machine learning algorithms used in this work include decision tree, naive bayes, logistic regression and support vector machines (svms). experiments were conducted for a 5-point satisfaction measurement and a 2-point satisfaction measurement using customer calls to an automotive company. the experimental results show that customer satisfaction can be measured quite accurately both at the end of calls and in the middle of calls. the best performing 5-point satisfaction classification yields an accuracy of 66.09% outperforming the dominantclass baseline by 15.16%. the best performing 2-point classification shows an accuracy of 89.42% and outperforms both the dominantclass baseline and the csrjudgment baseline by 17.7% and 3.3% respectively. furthermore, decision tree and svms achieve higher f-measure than the csrjudgment baseline in identifying both satisfied customers and dissatisfied customers.
graph-based seed selection for web-scale crawlers. one of the most important steps in web crawling is determining the starting points, or seed selection. this paper identifies and explores the problem of seed selection in web-scale incremental crawlers. we argue that seed selection is not a trivial but very important problem. selecting proper seeds can increase the number of pages a crawler will discover, and can result in a repository with more "good" and less "bad" pages. we propose a graph-based framework for crawler seed selection, and present several algorithms within this framework. evaluation on real web data showed significant improvements over heuristic seed selection approaches.
context-sensitive document ranking. ranking is a main research issue in ir-styled keyword search over a set of documents. in this paper, we study a new keyword search problem, called context-sensitive document ranking, which is to rank documents with an additional context that provides additional information about the application domain where the documents are to be searched and ranked. the work is motivated by the fact that additional information associated with the documents can possibly assist users to find more relevant documents when they are unable to find the needed documents from the documents alone. in this paper, a context is a multi-attribute graph, which can represent any information maintained in a relational database. the context-sensitive ranking is related to several research issues, how to score documents, how to evaluate the additional information obtained in the context that may contribute the document ranking, how to rank the documents by combining the scores/costs from the documents and the context. more importantly, the relationships between documents and the information stored in a relational database may be uncertain, because they are from different data sources and the relationships are determined systematically using similarity match which causes uncertainty. in this paper, we concentrate ourselves on these research issues, and provide our solution on how to rank the documents in a context where there exist uncertainty between the documents and the context. we confirm the effectiveness of our approaches by conducting extensive experimental studies using real datasets.
supervised semantic indexing. in this article we propose supervised semantic indexing (ssi), an algorithm that is trained on (query, document) pairs of text documents to predict the quality of their match. like latent semantic indexing (lsi), our models take account of correlations between words (synonymy, polysemy). however, unlike lsi our models are trained with a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. as the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as online advertising placement. dealing with models on all pairs of words features is computationally challenging. we propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, and correlated feature hashing (cfh). we provide an empirical study of all these methods on retrieval tasks based on wikipedia documents as well as an internet advertisement task. we obtain state-of-the-art performance while providing realistically scalable methods.
irank: an interactive ranking framework and its application in query-focused summarization. we address the problem of unsupervised ensemble ranking in this paper. traditional approaches either combine multiple ranking criteria into a unified representation to obtain an overall ranking score or to utilize certain rank fusion or aggregation techniques to combine the ranking results. beyond the aforementioned combine-then-rank and rank-then-combine approaches, we propose a novel rank-learn-combine ranking framework, called interactive ranking (irank), which allows two base rankers to "teach" each other before combination during the ranking process by providing their own ranking results as feedback to the others so as to boost the ranking performance. this mutual ranking refinement process continues until the two base rankers cannot learn from each other any more. the overall performance is improved by the enhancement of the base rankers through the mutual learning mechanism. we apply this framework to the sentence ranking problem in query-focused summarization and evaluate its effectiveness on the duc 2005 data set. the results are encouraging with consistent and promising improvements.
language-model-based ranking for queries on rdf-graphs. the success of knowledge-sharing communities like wikipedia and the advances in automatic information extraction from textual and web sources have made it possible to build large "knowledge repositories" such as dbpedia, freebase, and yago. these collections can be viewed as graphs of entities and relationships (er graphs) and can be represented as a set of subject-property-object (spo) triples in the semantic-web data model rdf. queries can be expressed in the w3c-endorsed sparql language or by similarly designed graph-pattern search. however, exact-match query semantics often fall short of satisfying the users' needs by returning too many or too few results. therefore, ir-style ranking models are crucially needed. in this paper, we propose a language-model-based approach to ranking the results of exact, relaxed and keyword-augmented graph pattern queries over rdf graphs such as er graphs. our method estimates a query model and a set of result-graph models and ranks results based on their kullback-leibler divergence with respect to the query model. we demonstrate the effectiveness of our ranking model by a comprehensive user study.
multi-aspect opinion polling from textual reviews. this paper presents an unsupervised approach to aspect-based opinion polling from raw textual reviews without explicit ratings. the key contribution of this paper is three-fold. first, a multi-aspect bootstrapping algorithm is proposed to learn from unlabeled data aspect-related terms of each aspect to be used for aspect identification. second, an unsupervised segmentation model is proposed to address the challenge of identifying multiple single-aspect units in a multi-aspect sentence. finally, an aspect-based opinion polling algorithm is presented. experiments on real chinese restaurant reviews show that our opinion polling method can achieve 75.5% precision performance.
rss watchdog: an instant event monitor on real online news streams. this paper introduces the rss watchdog system, which is capable of news clustering and instant event monitoring over multiple real and online rss news streams. we briefly mention software architecture design, technical implementation, and prototype demonstration. in addition, the results of real case studies are presented to notice the rss watchdog's functionality
a study of information retrieval on accumulative social descriptions using the generation features. this paper is concerned with the study of information retrieval (ir) on accumulative social descriptions (asds). asds refer to web texts that accumulated by many web users describing certain web resources, such as anchor texts, search logs and social annotations. there have been some studies working on leveraging asds for improving search performance in both internet and intranet. however, to the best of our knowledge, no prior study has concerned the specific generation features of asds, which are the focus point of this paper. specifically, we consider the generation features from two perspectives, the generation processes and the generated distributions. further, three probabilistic ir models are derived based on them. the three models are first demonstrated with one toy dataset and then empirically evaluated with two real datasets: an internet dataset consisting of 90,295 web pages, with 25,845,818 social annotations crawled from del.icio.us and 31,320,005 pieces of anchor texts crawled through yahoo! api, and an intranet dataset consisting of 179,835 web pages with 1,245,522 annotations dumped from the intranet tagging system in ibm, named as dogear. extensive experimental results show that the proposed methods, which fully leverage the generation features of asds, improve the performance of both internet and intranet search significantly.
privacy without noise. this paper presents several results on statistical database privacy. we first point out a serious vulnerability in a widely-accepted approach which perturbs query results with additive noise. we then show that for sum queries which aggregate across all records, when the dataset is sufficiently large, the inherent uncertainty associated with unknown quantities is enough to provide similar perturbation and the same privacy can be obtained without external noise. sum query is a surprisingly general primitive supporting a large number of data mining algorithms such as svd, pca, k-means, id3, svm, em, and all the algorithms in the statistical query model. we derive privacy conditions for sum queries and provide the first mathematical proof for the intuition that aggregates across a large number of individuals is private using a widely accepted notion of privacy. we also show how the results can be used to construct simulatable query auditing algorithms with stronger privacy.
efficient processing of twig pattern matching in fuzzy xml. in order to find all occurrences of a twig pattern in xml documents, a considerable amount of twig pattern matching algorithms have been proposed. at the same time, previous work mainly focuses on twig pattern query under the complete semantics. however, there is often a need to produce partial answers because xml data may have missing sub-elements. furthermore, the existed works fall short in their ability to support twig pattern query under different semantics in fuzzy xml. in this paper, we study the problem of twig matches in fuzzy xml. we begin by introducing the extended region scheme to accurately and effectively represent nodes information in fuzzy xml. we then discuss the fuzzy query semantics and compute the membership information by using einstein operator instead of zadeh's min-max technique. on the basis, we propose two efficient algorithms for querying twig under complete and incomplete semantics in fuzzy xml. the experimental results show that our proposed algorithms can perform on the fuzzy twig pattern matching efficiently.
mrm: an adaptive framework for xml searching. in order to deal with the diversified nature of xml documents as well as individual user preferences, we propose a novel multi-ranker model (mrm), which is able to abstract a spectrum of important xml properties and adapt the features to different xml search needs. the model consists of a novel three-level ranking structure and a training module called ranking support vector machine in a voting spy na¨1ve bayes framework (rssf). rssf is effective in learning search preference and then ranks the returned results adaptively. in this demonstration, we present our prototype developed from the model, which we call it the mrm xml search engine. the mrm engine employs only a list of simple xml tagged keywords as a user query for searching xml fragments from a collection of real xml documents. the demonstration presents an indepth analyses of the effectiveness of adaptive rankers, tailored xml rankers and a spectrum of low level ranking features.
query by analogical example: relational search using web search engine indices. we describe methods to search with a query by example in a known domain for information in an unknown domain by exploiting web search engines. relational search is an effective way to obtain information in an unknown field for users. for example, if an apple user searches for microsoft products, similar apple products are important clues for the search. even if the user does not know keywords to search for specific microsoft products, the relational search returns a product name by querying simply an example of apple products. more specifically, given a tuple containing three terms, such as (apple, ipod, microsoft), the term zune can be extracted from the web search results, where apple is to ipod what microsoft is to zune. as a previously proposed relational search requires a huge text corpus to be downloaded from the web, the results are not up-to-date and the corpus has a high construction cost. we introduce methods for relational search by using web search indices. we consider methods based on term co-occurrence, on lexico-syntactic patterns, and on combinations of the two approaches. our experimental results showed that the combination methods got the highest precision, and clarified the characteristics of the methods.
evaluating top-k queries over incomplete data streams. we study the problem of continuous monitoring of top-k queries over multiple non-synchronized streams. assuming a sliding window model, this general problem has been a well addressed research topic in recent years. most approaches, however, assume synchronized streams where all attributes of an object are known simultaneously to the query processing engine. in many streaming scenarios though, different attributes of an item are reported in separate non-synchronized streams which do not allow for exact score calculations. we present how the traditional notion of object dominance changes in this case such that the k dominance set still includes all and only those objects which have a chance of being among the top-k results in their life time. based on this, we propose an exact algorithm which builds on generating multiple instances of the same object in a way that enables efficient object pruning. we show that even with object pruning the necessary storage for exact evaluation of top-k queries is linear in the size of the sliding window. as data should reside in main memory to provide fast answers in an online fashion and cope with high stream rates, storing all this data may not be possible with limited resources. we present an approximate algorithm which leverages correlation statistics of pairs of streams to evict more objects while maintaining accuracy. we evaluate the efficiency of our proposed algorithms with extensive experiments.
a novel distributed p2p simulator architecture: d-p2p-sim. in this paper we introduce a novel distributed simulation environment with gui for p2p simulations (d-p2p-sim). the key aim is to provide the appropriate integrated set of tools in a single software solution to evaluate the performance of various protocols. the basic architecture of the distributed p2p simulator is based on a multi-threading, asynchronous, message passing and distributed environment with graphical user interface to facilitate ease of use by both researchers and programmers.
clustering queries for better document ranking. different queries require different ranking methods. it is however challenging to determine what queries are similar, and how to rank documents for them. in this paper, we propose a new method to cluster queries according to the similarity determined based on urls in their answers. we then train specific ranking models for each query cluster. in addition, a cluster-specific measure of authority is defined to favor documents from authoritative websites on the corresponding topics. the proposed approach is tested using data from a search engine. it turns out that our proposed topic-dependent models can significantly improve the search results of eight most popular categories of queries.
finding good feedback documents. pseudo-relevance feedback finds useful expansion terms from a set of top-ranked documents. it is often crucial to identify those good feedback documents from which useful expansion terms can be added to the query. in this paper, we propose to detect good feedback documents by classifying all feedback documents using a variety of features such as the distribution of query terms in the feedback document, the similarity between a single feedback document and all top-ranked documents, or the proximity between the expansion terms and the original query terms in the feedback document. by doing this, query expansion is only performed using a selected set of feedback documents, which are predicted to be good among all top-ranked documents. experimental results on standard trec test data show that query expansion on the selected feedback documents achieves statistically significant improvements over a strong pseudo-relevance feedback mechanism, which expands the query using all top-ranked documents.
collaborative resource discovery in social tagging systems. social tagging systems which allow users to create, edit and share collections of internet resources associated with tags in a collaborative fashion are growing in popularity in recent years. the rapidly growing amount of shared data in these folksonomies, i.e., taxonomies created by the folk, presents new technical challenges involved with discovering resources which are likely of interest to the user. social tags which reflect the meaning of resources from the user's points of view provide an opportunity to enhance the quality of retrieval. in this paper, we introduce a novel framework to search relevant resources to the user query by incorporating information obtained from folksonomies' underlying data structures consisting of a set of user/tag/resource triplets. in contrast to traditional retrieval and recommendation techniques which represent a collection by a matrix, we represent our data as a third-order tensor on which a novel cube latent semantic indexing (cubelsi) technique is proposed to capture latent semantic associations between tags. with the latent semantic representation we show how to rank relevant resources according to their relevance to user queries. the excellent performance of the method is demonstrated by an experimental evaluation on the deli.cio.us dataset.
fast shortest path distance estimation in large networks. in this paper we study approximate landmark-based methods for point-to-point distance estimation in very large networks. these methods involve selecting a subset of nodes as landmarks and computing offline the distances from each node in the graph to those landmarks. at runtime, when the distance between a pair of nodes is needed, it can be estimated quickly by combining the precomputed distances. we prove that selecting the optimal set of landmarks is an np-hard problem, and thus heuristic solutions need to be employed. we therefore explore theoretical insights to devise a variety of simple methods that scale well in very large networks. the efficiency of the suggested techniques is tested experimentally using five real-world graphs having millions of edges. while theoretical bounds support the claim that random landmarks work well in practice, our extensive experimentation shows that smart landmark selection can yield dramatically more accurate results: for a given target accuracy, our methods require as much as 250 times less space than selecting landmarks at random. in addition, we demonstrate that at a very small accuracy loss our techniques are several orders of magnitude faster than the state-of-the-art exact methods. finally, we study an application of our methods to the task of social search in large graphs.