waashk/dblp huggingface dataset:
nugget discovery in visual exploration environments by query consolidation. queries issued by casual users or specialists exploring a dataset often point us to important subsets of the data, be it clusters, outliers or other meaningful features. capturing and caching such queries (henceforth called nuggets) has many potential benefits, including the optimization of the system performance and the search experience of users. unfortunately, current visual exploration systems have not yet tapped into this potential resource of identifying and sharing important queries. in this paper, we introduce a query consolidation strategy aimed at solving the general problem of isolating important queries from the potentially huge amount of queries submitted. our solution clusters redundant queries caused by exploration-style query specification, which is prevalent in data exploration systems. to measure the similarity between queries, we designed an effective distance metric that incorporates both the query specification and the actual query result. to overcome its high complexity when comparing queries with large result sets, we designed an approximation method, which is efficient while still providing excellent accuracy. a user study conducted on multivariate data sets comparing our proposed technique to others in the literature confirms that the proposed distance metric indeed matches well with users' intuition. as proof of feasibility, we integrated our proposed query consolidation solution into the nugget management system (nms) framework [22], which is based on a visual exploration system xmdvtool. a second user study indicates that both the efficiency and accuracy of users' visual exploration are enhanced when supported by nms.
ontology evaluation using wikipedia categories for browsing. ontology evaluation is a maturing discipline with methodologies and measures being developed and proposed. however, evaluation methods that have been proposed have not been applied to specific examples. in this paper, we present the state-of-the-art in ontology evaluation - current methodologies, criteria and measures, analyse appropriate evaluations that are important to our application - browsing in wikipedia, and apply these evaluations in the context of ontologies with varied properties. specifically, we seek to evaluate ontologies based on categories found in wikipedia.
ranking very many typed entities on wikipedia. we discuss the problem of ranking very many entities of different types. in particular we deal with a heterogeneous set of types, some being very generic and some very specific. we discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a web search engine to compute entity relevance. we evaluate these approaches on the real task of ranking wikipedia entities typed with a state-of-the-art named-entity tagger. results show that both approaches can greatly increase the performance of methods based only on passage retrieval.
recognition and classification of noun phrases in queries for effective retrieval. it has been shown that using phrases properly in the document retrieval leads to higher retrieval effectiveness. in this paper, we define four types of noun phrases and present an algorithm for recognizing these phrases in queries. the strengths of several existing tools are combined for phrase recognition. our algorithm is tested using a set of 500 web queries from a query log, and a set of 238 trec queries. experimental results show that our algorithm yields high phrase recognition accuracy. we also use a baseline noun phrase recognition algorithm to recognize phrases from the trec queries. a document retrieval experiment is conducted using the trec queries (1) without any phrases, (2) with the phrases recognized from a baseline noun phrase recognition algorithm, and (3) with the phrases recognized from our algorithm respectively. the retrieval effectiveness of (3) is better than that of (2), which is better than that of (1). this demonstrates that utilizing phrases in queries does improve the retrieval effectiveness, and better noun phrase recognition yields higher retrieval performance.
a constraint-based probabilistic framework for name disambiguation. this paper is concerned with the problem of name disambiguation. by name disambiguation, we mean distinguishing persons with the same name. it is a critical problem in many knowledge management applications. despite much research work has been conducted, the problem is still not resolved and becomes even more serious, in particular with the popularity of web 2.0. previously, name disambiguation was often undertaken in either a supervised or unsupervised fashion. this paper first gives a constraint-based probabilistic model for semi-supervised name disambiguation. specifically, we focus on investigating the problem in an academic researcher social network (http://arnetminer.org). the framework combines constraints and euclidean distance learning, and allows the user to refine the disambiguation results. experimental results on the researcher social network show that the proposed framework significantly outperforms the baseline method using unsupervised hierarchical clustering algorithm.
an efficient algorithm for approximate biased quantile computation in data streams. we propose an efficient algorithm for approximate biased quantile computation in large data streams. our algorithm computes decomposable biased quantile summaries on fixed sized blocks and dynamically maintains the biased quantile summary for the entire stream as the exponential histogram over the block-wise quantile summaries. the algorithm is computationally efficient and achieves an amortized computational cost of o(log(1⁄∈log(∈n))) and a space requirement of o(log3∈n↬∈). our algorithm does not assume prior knowledge of the stream sizes or the range of data values in the streams. in practice, our algorithm is able to efficiently maintain summaries over large data streams with over tens of millions of observations and achieves significant performance improvement over prior algorithms.
boolean representation based data-adaptive correlation analysis over time series streams. correlation analysis is a basic problem in the field of data stream mining. typical approaches add sliding window to data streams to get the recent results, but the window length defined by users is always fixed which is not suitable for the changing stream environment. we propose a boolean representation based data-adaptive method for correlation analysis among a large number of time series streams. the periodical trends of each stream series to are monitored to choose the most suitable window size and group the series with the same trends together. instead of adopting complex pair-wise calculation, we can also quickly get the correlation pairs of series at the optimal window sizes. all the processing is realized by simple boolean operations. both the theory analysis and the experimental evaluations show that our method has good computation efficiency with high accuracy.
opinion retrieval from blogs. opinion retrieval is a document retrieval process, which requires documents to be retrieved and ranked according to their opinions about a query topic. a relevant document must satisfy two criteria: relevant to the query topic, and contains opinions about the query, no matter if they are positive or negative. in this paper, we describe an opinion retrieval algorithm. it has a traditional information retrieval (ir) component to find topic relevant documents from a document set, an opinion classification component to find documents having opinions from the results of the ir step, and a component to rank the documents based on their relevance to the query, and their degrees of having opinions about the query. we implemented the algorithm as a working system and tested it using trec 2006 blog track data in automatic title-only runs. our result showed 28% to 32% improvements in map score over the best automatic runs in this 2006 track. our result is also 13% higher than a state-of-art opinion retrieval system, which is tested on the same data set.
effective top-k computation in retrieving structured documents with term-proximity support. modern web search engines are expected to return top-k results efficiently given a query. although many dynamic index pruning strategies have been proposed for efficient top-k computation, most of them are prone to ignore some especially important factors in ranking functions, e.g. term proximity (the distance relationship between query terms in a document). the inclusion of term proximity breaks the monotonicity of ranking functions and therefore leads to additional challenges for efficient query processing. this paper studies the performance of some existing top-k computation approaches using term-proximity-enabled ranking functions. our investigation demonstrates that, when term proximity is incorporated into ranking functions, most existing index structures and top-k strategies become quite inefficient. according to our analysis and experimental results, we propose two index structures and their corresponding index pruning strategies: structured and hybrid, which performs much better on the new settings. moreover, the efficiency of index building and maintenance would not be affected too much with the two approaches.
classifying networked entities with modularity kernels. statistical machine learning techniques for data classification usually assume that all entities are i.i.d. (independent and identically distributed). however, real-world entities often interconnect with each other through explicit or implicit relationships to form a complex network. although some graph-based classification methods have emerged in recent years, they are not really suitable for complex networks as they do not take the degree distribution of network into consideration. in this paper, we propose a new technique, modularity kernel, that can effectively exploit the latent community structure of networked entities for their classification. a number of experiments on hypertext datasets show that our proposed approach leads to excellent classification performance in comparison with the state-of-the-art methods.
the query-flow graph: model and applications. query logs record the queries and the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences, and the behavior of the users, as well as their implicit feedback to search engine results. mining the wealth of information available in the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more. in this paper we introduce the query-flow graph, a graph representation of the interesting knowledge about latent querying behavior. intuitively, in the query-flow graph a directed edge from query qi to query qj means that the two queries are likely to be part of the same "search mission". any path over the query-flow graph may be seen as a searching behavior, whose likelihood is given by the strength of the edges along the path. the query-flow graph is an outcome of query-log mining and, at the same time, a useful tool for it. we propose a methodology that builds such a graph by mining time and textual information as well as aggregating queries from different users. using this approach we build a real-world query-flow graph from a large-scale query log and we demonstrate its utility in concrete applications, namely, finding logical sessions, and query recommendation. we believe, however, that the usefulness of the query-flow graph goes beyond these two applications.
ranked feature fusion models for ad hoc retrieval. we introduce the ranked feature fusion framework for information retrieval system design. typical information retrieval formalisms such as the vector space model, the best-match model and the language model first combine features (such as term frequency and document length) into a unified representation, and then use the representation to rank documents. we take the opposite approach: documents are first ranked by the relevance of a single feature value and are assigned scores based on their relative ordering within the collection. a separate ranked list is created for every feature value and these lists are then fused to produce a final document scoring. this new "rank then combine" approach is extensively evaluated and is shown to be as effective as traditional "combine then rank" approaches. the model is easy to understand and contains fewer parameters than other approaches. finally, the model is easy to extend (integration of new features is trivial) and modify. this advantage includes but is not limited to relevance feedback and distribution flattening.
optimal proactive caching in peer-to-peer network: analysis and application. as a promising new technology with the unique properties like high efficiency, scalability and fault tolerance, peer-to-peer (p2p) technology is used as the underlying network to build new internet-scale applications. however, one of the well known issues in such an application (for example www) is that the distribution of data popularities is heavily tailed with a zipf-like distribution. with consideration of the skewed popularity we adopt a proactive caching approach to handle the challenge, and focus on two key problems: where (i.e. the placement strategy: where to place the replicas) and how (i.e. the degree problem: how many replicas are assigned to one specific content)? for the where problem, we propose a novel approach which can be generally applied to structured p2p networks. next, we solve two optimization objectives related to the how problem: max_perf and min_cost. our solution is called <b>popcache</b>, and we discover two interesting properties: (1) the number of replicas assigned to each content is proportional to its popularity; (2) the derived optimal solutions are related to the entropy of popularity. to our knowledge, none of the previous works has mentioned such results. finally, we apply the results of popcache to propose a p2p base web caching, called as web-popcache. by means of web cache trace driven simulation, our extensive evaluation results demonstrate the advantages of popcache and web-popcache.
bns feature scaling: an improved representation over tf-idf for svm text classification. in the realm of machine learning for text classification, tf-idf is the most widely used representation for real-valued feature vectors. however, idf is oblivious to the training class labels and naturally scales some features inappropriately. we replace idf with bi-normal separation (bns), which has been previously found to be excellent at ranking words for feature selection filtering. empirical evaluation on a benchmark of 237 binary text classification tasks shows substantially better accuracy and f-measure for a support vector machine (svm) by using bns scaling. a wide variety of other feature representations were later tested and found inferior, as well as binary features with no scaling. moreover, bns scaling yielded better performance without feature selection, obviating the need for feature selection.
energy-efficient skyline query processing and maintenance in sensor networks. the skyline query, as an important operator in databases for multi-preference analysis and decision making, has received much attention recently due to its wide application backgrounds. in this paper, we consider the skyline query problem in wireless sensor network with an objective to maximize the network lifetime by proposing filter-based distributed algorithms for skyline evaluation and maintenance. we also conduct preliminary experiments to evaluate the performance of the proposed algorithms. the experimental results demonstrate that the proposed algorithms significantly outperform existing algorithms on various datasets.
how evaluator domain expertise affects search result relevance judgments. traditional search evaluation approaches have often relied on domain experts to evaluate results for each query. unfortunately, the range of topics present in any representative sample of web queries makes it impractical to have expert evaluators for every topic. in this paper, we investigate the effect of using "generalist" evaluators instead of experts in the domain of queries being evaluated. empirically, we ind that for queries drawn from domains requiring high expertise, (1) generalists tend to give shallow, inaccurate ratings as compared to experts. (2) further experiments show that generalists disagree on the underlying meaning of these queries significantly more often than experts, and often appear to "give up'' and fall back on surface features such as keyword matching. (3) finally, by estimating the percentage of "expertise requiring'' queries in a web query sample, we estimate the impact of using generalists, versus the ideal of having domain experts for every "expertise requiring'' query.
efficient frequent pattern mining over data streams. this paper proposes a prefix-tree structure, called cps-tree (compact pattern stream tree) that efficiently discovers the exact set of recent frequent patterns from high-speed data stream. the cps-tree introduces the concept of dynamic tree restructuring technique in handling stream data that allows it to achieve highly compact frequency-descending tree structure at runtime and facilitates an efficient fp-growth-based [1] mining technique.
scalable community discovery on textual data with relations. every piece of textual data is generated as a method to convey its authors' opinion regarding specific topics. authors deliberately organize their writings and create links, i.e., references, acknowledgments, for better expression. thereafter, it is of interest to study texts as well as their relations to understand the underlying topics and communities. although many efforts exist in the literature in data clustering and topic mining, they are not applicable to community discovery on large document corpus for several reasons. first, few of them consider both textual attributes as well as relations. second, scalability remains a significant issue for large-scale datasets. additionally, most algorithms rely on a set of initial parameters that are hard to be captured and tuned. motivated by the aforementioned observations, a hierarchical community model is proposed in the paper which distinguishes community cores from affiliated members. we present our efforts to develop a scalable community discovery solution for large-scale document corpus. our proposal tries to quickly identify potential cores as seeds of communities through relation analysis. to eliminate the influence of initial parameters, an innovative attribute-based core merge process is introduced so that the algorithm promises to return consistent communities regardless initial parameters. experimental results suggest that the proposed method has high scalability to corpus size and feature dimensionality, with more than 15 topical precision improvement compared with popular clustering techniques.
boosting social annotations using propagation. this paper is concerned with the problem of boosting social annotations using propagation, which is also called social propagation. in particular, we focus on propagating social annotations of web pages (e.g., annotations in del.icio.us). although social annotations are developing fast, they cover only a small proportion of web pages on the world wide web. to alleviate the low coverage problem, a general propagation model based on random surfer is proposed. specifically, four steps are included: basic propagation, multiple-annotation propagation, multiple-link-type propagation, and constraint-guided propagation. experimental results show that the proposed model is very effective in increasing coverage of annotations as well as preserving property of social annotations.
search-based query suggestion. in this paper, we proposed a unified strategy to combine query log and search results for query suggestion. in this way, we leverage both the users' search intentions for popular queries and the power of search engines for unpopular queries. the suggested queries are also ranked according to their relevance and qualities; and each suggestion is described with a rich snippet including a photo and related description.
on quantifying changes in temporally evolving dataset. in this paper, we present a general framework to quantify changes in temporally evolving data. we focus on changes that materialize due to evolution and interactions of features extracted from the data. the changes are captured by the following key transformations: create, merge, split, continue, and cease. first, we identify various factors which influence the importance of each transformation. these factors are then combined using a weight vector. the weight vector encapsulates domain knowledge. we evaluate our algorithm using the following datasets: dblp, imdb, text and scientific dataset.
understanding the relationship between searchers' queries and information goals. we describe results from web search log studies aimed at elucidating user behaviors associated with queries and destination urls that appear with different frequencies. we note the diversity of information goals that searchers have and the differing ways that goals are specified. we examine rare and common information goals that are specified using rare or common queries. we identify several significant differences in user behavior depending on the rarity of the query and the destination url. we find that searchers are more likely to be successful when the frequencies of the query and destination url are similar. we also establish that the behavioral differences observed for queries and goals of varying rarity persist even after accounting for potential confounding variables, including query length, search engine ranking, session duration, and task difficulty. finally, using an information-theoretic measure of search difficulty, we show that the benefits obtained by search and navigation actions depend on the frequency of the information goal.
revisiting the relationship between document length and relevance. the scope hypothesis in information retrieval (ir) states that a relationship exists between document length and relevance, such that the likelihood of relevance increases with document length. a number of empirical studies have provided statistical evidence supporting the scope hypothesis. however, these studies make the implicit assumption that modern test collections are complete (i.e. all documents are assessed for relevance). as a consequence the observed evidence is misleading. in this paper we perform a deeper analysis of document length and relevance taking into account that test collections are incomplete. we first demonstrate that previous evidence supporting the scope hypothesis was an artefact of the test collection, where there is a bias towards longer documents in the pooling process. we evaluate whether this length bias affects system comparison when using incomplete test collections. the results indicate that test collections are problematic when considering map as a measure of effectiveness but are relatively robust when using bpref. the implications of the study indicate that retrieval models should not be tuned to favour longer documents, and that designers of new test collections should take measures against length bias during the pooling process in order to create more reliable and robust test collections.