waashk/dblp huggingface dataset:
towards non-directional xpath evaluation in a rdbms. xml query languages use directional path expressions to locate data in an xml data collection. they are tightly coupled to the structure of a data collection, and can fail when evaluated on the same data in a different structure. this paper extends path expressions with a new non-directional axis called the rank-distance axis. given a context node and two positive integers α and β, the rank-distance axis returns those nodes that are ranked between α and β in terms of closeness from the context node in any direction. this paper shows how to evaluate the rank-distance axis in a tree-unaware xml database. a tree-unaware implementation does not invade the database kernel to support xml queries, instead it uses an existing rdbms such as microsoft's sql server as a back-end and provides a front-end layer to translate xml queries to sql. this paper presents an overview of an algorithm that translates queries with a rank-distance axis to sql.
scalable learning of collective behavior based on sparse social dimensions. the study of collective behavior is to understand how individuals behave in a social network environment. oceans of data generated by social media like facebook, twitter, flickr and youtube present opportunities and challenges to studying collective behavior in a large scale. in this work, we aim to learn to predict collective behavior in social media. in particular, given information about some individuals, how can we infer the behavior of unobserved individuals in the same network? a social-dimension based approach is adopted to address the heterogeneity of connections presented in social media. however, the networks in social media are normally of colossal size, involving hundreds of thousands or even millions of actors. the scale of networks entails scalable learning of models for collective behavior prediction. to address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. with sparse social dimensions, the social-dimension based approach can efficiently handle networks of millions of actors while demonstrating comparable prediction performance as other non-scalable methods.
adaptive geospatially focused crawling. location information on the web is a precious asset for a multitude of applications and is becoming an increasingly important dimension in web search. even though more and more web pages carry location information, they form only a small share of all pages and are scattered over the web. to efficiently find and index location-related web content, we propose an efficient crawling strategy that retrieves precisely those pages that are geospatially relevant while minimizing the amount of the non-spatially-relevant pages within the crawled pages. we propose to address this challenge by expanding the technique of focused crawling to exploit location references on web pages to specifically retrieve geospatial topics on the web. in this paper, we describe the design and development of a focused crawler with an adaptive geospatial focus that efficiently retrieves and identifies location-relevant documents on the web. drawing from geospatial features of both web pages and the link graph, a crawl strategy based on bayesian classifiers prioritizes promising links and pages, leading to a faster coverage of the desired geospatial topic as a means for fast creation of precise geospatial web indexes. we present evaluations of the system's performance and share our findings on the geospatial web graph and the distribution of location references on the web.
efficient join processing on uncertain data streams. join processing in the streaming environment has many practical applications such as data cleaning and outlier detection. due to the inherent uncertainty in the real-world data, it has become an increasingly important problem to consider the join processing on uncertain data streams, where the incoming data at each timestamp are uncertain and imprecise. different from the static databases, processing uncertain data streams has its own requirements such as the limited memory, small response time, and so on. to tackle the challenges with respect to efficiency and effectiveness, in this paper, we formalize the problem of join on uncertain data streams (usj), which can guarantee the accuracy of usj answers over uncertain data, and propose effective pruning methods to filter out false alarms. we integrate the pruning methods into an efficient query procedure for incrementally maintaining usj answers. extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approaches.
the use of categorization information in language models for question retrieval. community question answering (cqa) has emerged as a popular type of service meeting a wide range of information needs. such services enable users to ask and answer questions and to access existing question-answer pairs. cqa archives contain very large volumes of valuable user-generated content and have become important information resources on the web. to make the body of knowledge accumulated in cqa archives accessible, effective and efficient question search is required. question search in a cqa archive aims to retrieve historical questions that are relevant to new questions posed by users. this paper proposes a category-based framework for search in cqa archives. the framework embodies several new techniques that use language models to exploit categories of questions for improving question-answer search. experiments conducted on real data from yahoo! answers demonstrate that the proposed techniques are effective and efficient and are capable of outperforming baseline methods significantly.
helping people to choose for whom to vote. a web information system for the 2009 european elections. we demonstrate a web information system created for the european elections in june 2009. based on their speeches in the eu parliament and their written questions, we created language models for each of the 736 members of the eu parliament. these language models were used to search for politicians responsible for a given topic, similar to expert search applications. users prefer to see some kind of evidence for returning a hit after a search. we created a profile of each eu parlementarian by comparing her personal language model to the language model created from all eu parlementarians. the top 50 words best separating the individual from the avarage were shown as a wordcloud. these top 50 words and their scores were derived from a parsimonious language model.
a study of selective collection enrichment for enterprise search. enterprise intranets are often sparse in nature, with limited use of alternative lexical representations between authors, making query expansion (qe) ineffective. hence, for some enterprise search queries, it can be advantageous to instead use the well-known collection enrichment (ce) method to gather higher quality pseudo-feedback documents from a more diverse external resource. however, it is not always clear for which queries the collection enrichment technique should be applied. in this paper, we study two different approaches, namely a predictor-based approach and a divergence-based approach, to decide on when to apply ce. we thoroughly evaluate both approaches on the trec enterprise track cerc test collection and its corresponding topic sets, in combination with three different external resources and nine different query performance predictors. our results show that both approaches are effective to selectively apply ce for enterprise search. in particular, the divergence-based approach leads to consistent and marked retrieval improvements over the systematic application of qe or ce on all external resources.
vrifa: a nonlinear svm visualization tool using nomogram and localized radial basis function (lrbf) kernels. prediction problems are prevalent in medical domains. for example, computer-aided diagnosis or prognosis is a key component in a cdss (clinical decision support system). svms, especially svms with nonlinear kernels such rbf kernels, have shown superior accuracy in prediction problems. however, they are not favorably used by physicians for medical prediction problems because nonlinear svms are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. nomogram was proposed to visualize svm classification models. however, it cannot visualize nonlinear svm models. localized rbf (lrbf) kernel was proposed which shows comparable accuracy as the rbf kernel while the lrbf kernel is easier to interpret since it can be linearly decomposed. this paper presents a new tool named vrifa, which integrates the nomogram and lrbf kernel to provide users with an interactive visualization of nonlinear svm models. vrifa graphically exposes the internal structure of nonlinear svm models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. vrifa also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. the tool has been used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases. vrifa is accessible at http://dm.postech.ac.kr/vrifa .
efficient information retrieval in mobile peer-to-peer networks. mobile devices have become indispensable in daily life, and hence how to take advantage of these portable and powerful facilities to share resources and information begins to emerge as an interesting problem. in this paper, we investigate the problem of information retrieval in a mobile peer-to-peer network. the prevailing approach to information retrieval is to apply flooding methods because of its quick response and easy maintenance. obviously, this kind of approach wastes a huge amount of communication bandwidth which greatly affects the availability of the network, and the battery power which significantly shortens the serving time of mobile devices in the network. to tackle this problem, we propose a novel approach by mimicking different human behaviors of social networks, which takes advantages of intelligence accuracy (ia) mechanism that evaluates the distance from a node to certain resources in the network. extensive experimental results show the efficiency and effectiveness of our approach as well as its scalability in a volatile environment.
clustering web queries. despite the wide applicability of clustering methods, their evaluation remains a problem. in this paper, we present a metric for the evaluation of clustering methods. the data set to be clustered is viewed as a sample from a larger population, with clustering quality measured in terms of our predicted ability to discriminate between members of this population. we measure this property by training a classifier to recognize each cluster and measuring the accuracy of this classifier, normalized by a notion of expected accuracy. to demonstrate the applicability of this metric we apply it to web queries. we investigated a commercially oriented data set of 1700 queries and a general data set of 4000 queries. both sets are taken from the logs of a commercial web search engine. clustering is based on the contents of search engine result pages generated by executing the queries on the search engine from which they were taken. multiple clustering algorithms are crossed with various weighting schemes to produce multiple clusterings of each query set. our metric is used evaluate these clusterings. the results on the commercially oriented data set are compared to two pre-existing manual labelings, and are also used in an ad clickthrough experiment.
pure spreading activation is pointless. almost every application of spreading activation is accompanied by its own set of often heuristic restrictions on the dynamics. we show that in constraint-free scenarios spreading activation would actually yield query-independent results, so that the specific choice of restrictions is not only a pragmatic computational issue, but crucially determines the outcome.
automatic web data extraction using tree alignment. this paper investigates the automatic extraction of data from forums, blogs and news web sites. web pages are increasingly dynamically generated using a common template populated with data from databases. this paper proposes a novel method that uses tree alignment to automatically extract data from these types of web pages. a new tree alignment algorithm is presented for determining the optimal matching structure of the input web pages. based on the alignment, the trees are merged into one union tree whose nodes record statistical information obtained from multiple web pages. a heuristic method is employed for determining the most probable content block and the alignment algorithm detects repeating patterns on the union tree. a wrapper built on the most probable content block and the repeating patterns extracts data from web pages. experimental results show that the method achieves high extraction accuracy and has steady performance.
frequent subgraph pattern mining on uncertain graph data. graph data are subject to uncertainties in many applications due to incompleteness and imprecision of data. mining uncertain graph data is semantically different from and computationally more challenging than mining exact graph data. this paper investigates the problem of mining frequent subgraph patterns from uncertain graph data. the frequent subgraph pattern mining problem is formalized by designing a new measure called expected support. an approximate mining algorithm is proposed to find an approximate set of frequent subgraph patterns by allowing an error tolerance on the expected supports of the discovered subgraph patterns. the algorithm uses an efficient approximation algorithm to determine whether a subgraph pattern can be output or not. the analytical and experimental results show that the algorithm is very efficient, accurate and scalable for large uncertain graph databases.
online community search using thread structure. online communities are valuable information sources where knowledge is accumulated by interactions between people. search services provided by online community sites such as forums are often, however, quite poor. to address this, we investigate retrieval techniques that exploit the hierarchical thread structures in community sites. since these structures are sometimes not explicit or accurately annotated, we use structure discovery techniques. we then make use of thread structures in retrieval experiments. our results show that using thread structures that have been accurately annotated can lead to significant improvements in retrieval performance compared to strong baselines.
selc: a self-supervised model for sentiment classification. this paper presents the selc model (self-supervised, (lexicon-based and (corpus-based model) for sentiment classification. the selc model includes two phases. the first phase is a lexicon-based iterative process. in this phase, some reviews are initially classified based on a sentiment dictionary. then more reviews are classified through an iterative process with a negative/positive ratio control. in the second phase, a supervised classifier is learned by taking some reviews classified in the first phase as training data. then the supervised classifier applies on other reviews to revise the results produced in the first phase. experiments show the effectiveness of the proposed model. selc totally achieves 6.63% f1-score improvement over the best result in previous studies on the same data (from 82.72% to 89.35%). the first phase of the selc model independently achieves 5.90% improvement (from 82.72% to 88.62%). moreover, the standard deviation of f1-scores is reduced, which shows that the selc model could be more suitable for domain-independent sentiment classification.
hddbrs middleware for implementing highly available distributed databases. our demo presents hddbrs, a middle tier offering to clients a highly available distributed database interface using reed solomon codes to compute parity data. parity data is stored in dedicated parity db backends, is synchronously updated and allows recovering from multiple db backend unavailability. hddbrs middle tier is implemented in java using standard technology, and is designed to be interoperable with any database engine that provides a jdbc driver and implements x/open xa protocol.
exploit the tripartite network of social tagging for web clustering. in this poster, we investigate how to enhance web clustering by leveraging the tripartite network of social tagging systems. we propose a clustering method, called "tripartite clustering", which cluster the three types of nodes (resources, users and tags) simultaneously based on the links in the social tagging network. the proposed method is experimented on a real-world social tagging dataset sampled from del.icio.us. we also compare the proposed clustering approach with k-means. all the clustering results are evaluated against a human-maintained web directory. the experimental results show that tripartite clustering significantly outperforms the content-based k-means approach and achieves performance close to that of social annotation-based k-means whereas generating much more useful information.
event detection from flickr data through wavelet-based spatial analysis. detecting events from web resources has attracted increasing research interests in recent years. our focus in this paper is to detect events from photos on flickr, an internet image community website. the results can be used to facilitate user searching and browsing photos by events. the problem is challenging considering: (1) flickr data is noisy, because there are photos unrelated to real-world events; (2) it is not easy to capture the content of photos. this paper presents our effort in detecting events from flickr photos by exploiting the tags supplied by users to annotate photos. in particular, the temporal and locational distributions of tag usage are analyzed in the first place, where a wavelet transform is employed to suppress noise. then, we identify tags related with events, and further distinguish between tags of aperiodic events and those of periodic events. afterwards, event-related tags are clustered such that each cluster, representing an event, consists of tags with similar temporal and locational distribution patterns as well as with similar associated photos. finally, for each tag cluster, photos corresponding to the represented event are extracted. we evaluate the performance of our approach using a set of real data collected from flickr. the experimental results demonstrate that our approach is effective in detecting events from the flickr photo collection.
l2 norm regularized feature kernel regression for graph data. features in many real world applications such as cheminformatics, bioinformatics and information retrieval have complex internal structure. for example, frequent patterns mined from graph data are graphs. such graph features have different number of nodes and edges and usually overlap with each other. in conventional data mining and machine learning applications, the internal structure of features are usually ignored. in this paper we consider a supervised learning problem where the features of the data set have intrinsic complexity, and we further assume that the feature intrinsic complexity may be measured by a kernel function. we hypothesize that by regularizing model parameters using the information of feature complexity, we can construct simple yet high quality model that captures the intrinsic structure of the data. towards the end of testing this hypothesis, we focus on a regression task and have designed an algorithm that incorporate the feature complexity in the learning process, using a kernel matrix weighted l2 norm for regularization, to obtain improved regression performance over conventional learning methods that does not consider the additional information of the feature. we have tested our algorithm using 5 different real-world data sets and have demonstrate the effectiveness of our method.
label correspondence learning for part-of-speech annotation transformation. the performance of machine learning methods heavily depends on the volume of used training data. for the purpose of dataset enlargement, it is of interest to study the problem of unifying multiple labeled datasets with different annotation standards. in this paper, we focus on the case of unifying datasets for sequence labeling problems with natural language part-of-speech (pos) tagging as an examplar application. to this end, we propose a probabilistic approach to transforming the annotations of one dataset to the standard specified by another dataset. the key component of the approach, named as label correspondence learning, serves as a bridge of annotations from the datasets. two methods designed from distinct perspectives are proposed to attack this sub-problem. experiments on two large-scale part-of-speech datasets demonstrate the efficacy of the transformation and label correspondence learning methods.
effective and efficient structured retrieval. search engines that support structured documents typically support structure created by the author (e.g., title, section), and may also support structure added by an annotation process (e.g., part of speech, named entity, semantic role). exploiting such structure can be difficult. query structure may fail to match structure in a relevant document for a variety of reasons, thus structured queries, although containing more information than keyword queries, are often less effective than unstructured queries. this paper studies retrieval of sentences with annotations for a question answering task. three problems of structured retrieval are identified and solutions proposed. structural mismatch is addressed by query structure expansion of predicted relevant structures. lack of presence of all key aspects of a question is solved by boolean filtering of result sentences. the score variations of the annotator generated fields with all the different lengths are accounted for by using field specific smoothing. experiments show that each solution incrementally improves structured retrieval, and a combination of boolean filtering, structural expansion, and keyword queries outperforms keyword and simple structured retrieval baselines.
a translation model for matching reviews to objects. we develop a generic method for the review matching problem, which is to match unstructured text reviews to a list of objects, where each object has a set of attributes. to this end, we propose a translation model for generating reviews from a structured description of objects. we develop an em-based method to estimate the model parameters and use this model to find, given a review, the object most likely to be the topic of the review. we conduct extensive experiments on two large-scale datasets: a collection of restaurant reviews from yelp and a collection of movie reviews from imdb. the experiments show that our translation model-based method is superior to traditional tf-idf based methods as well as a recent mixture model-based method for the review matching problem.