waashk/dblp huggingface dataset:
efficient interactive query expansion with complete search. we present an efficient realization of the following interactive search engine feature: as the user is typing the query, words that are related to the last query word and that would lead to good hits are suggested, as well as selected such hits. the realization has three parts: (i) building clusters of related terms, (ii) adding this information as artificial words to the index such that (iii) the described feature reduces to an instance of prefix search and completion. an efficient solution for the latter is provided by the completesearch engine, with which we have integrated the proposed feature. for building the clusters of related terms we propose a variant of latent semantic indexing that, unlike standard approaches, is completely transparent to the user. by experiments on two large test-collections, we demonstrate that the feature is provided at only a slight increase in query processing time and index size.
query taxonomy generation for web search. we propose an approach that organizes the search-result clusters into a hierarchical structure, called a query taxonomy, from the user's perspective. the proposed approach is based on an unsupervised classification method, which uses the dynamic web as the training corpus. with query taxonomy, users can browse relevant web documents more conveniently and comprehensibly. our experimental results verify the feasibility and the effectiveness of the proposed approach to query taxonomy generation in web search.
ranking web objects from multiple communities. vertical search is a promising direction as it leverages domain-specific knowledge and can provide more precise information for users. in this paper, we study the web object-ranking problem, one of the key issues in building a vertical search engine. more specifically, we focus on this problem in cases when objects lack relationships between different web communities, and take high-quality photo search as the test bed for this investigation. we proposed two score fusion methods that can automatically integrate as many web communities (web forums) with rating information as possible. the proposed fusion methods leverage the hidden links discovered by a duplicate photo detection algorithm, and aims at minimizing score differences of duplicate photos in different forums. both intermediate results and user studies show the proposed fusion methods are practical and efficient solutions to web object ranking in cases we have described. though the experiments were conducted on high-quality photo ranking, the proposed algorithms are also applicable to other ranking problems, such as movie ranking and music ranking.
finding and linking incidents in news. news reports are being produced and disseminated in overwhelming volume, making it difficult to keep up with the newest information. most previous research in automatic news organization treated news topics as a flat list, ignoring the intrinsic connection among individual reports. we argue that more contextual information within and across the topics will benefit users in their news understanding process. a news organization infrastructure, incident threading, is proposed in this article. all text snippets describing the occurrence of a real-world happening are combined into a news incident, and a network is composed of incidents that are interconnected by links in certain types. a limited vocabulary of connection types is defined and corresponding rules are established based upon the human experience of news understanding. the incident threading system is implemented with two different algorithms. one starts from clustering of text passages and then creates links with pre-built rules. the other method defines a global score function over the whole collection and solves the optimization problem with simulated annealing. the former achieves higher accuracy in the identification of incidents and the latter generates better links, which is preferred since the links are more important for the formation of the incident network.
web search: from information retrieval to microeconomic modeling. in scarcely a decade, web search has gone from simply scaling traditional information retrieval, to a groundswell of new opportunities that are changing marketing as we know it. in this lecture, we begin by reviewing the progress, pointing out that web search is no longer a purely computer sceince problem. we then hint at the role of other disciplines in this ongoing revolution and a number of directions for research.
parameter sensitivity in the probabilistic model for ad-hoc retrieval. the term frequency normalisation parameter sensitivity is an important issue in the probabilistic model for information retrieval. a high parameter sensitivity indicates that a slight change of the parameter value may considerably affect the retrieval performance. therefore, a weighting model with a high parameter sensitivity is not robust enough to provide a consistent retrieval performance across different collections and queries. in this paper, we suggest that the parameter sensitivity is due to the fact that the query term weights are not adequate enough to allow informative query terms to differ from non-informative ones. we show that query term reweighing, which is part of the relevance feedback process, can be successfully used to reduce the parameter sensitivity. experiments on five text retrieval conference (trec) collections show that the parameter sensitivity does remarkably decrease when query terms are reweighed.
summarizing local context to personalize global web search. the pc desktop is a very rich repository of personal information, efficiently capturing user's interests. in this paper we propose a new approach towards an automatic personalization of web search in which the user specific information is extracted from such local desktops, thus allowing for an increased quality of user profiling, while sharing less private information with the search engine. more specifically, we investigate the opportunities to select personalized query expansion terms for web search using three different desktop oriented approaches: summarizing the entire desktop data, summarizing only the desktop documents relevant to each user query, and applying natural language processing techniques to extract dispersive lexical compounds from relevant desktop resources. our experiments with the google api showed at least the latter two techniques to produce a very strong improvement over current web search.
detecting distance-based outliers in streams of data. in this work a method for detecting distance-based outliers in data streams is presented. we deal with the sliding window model, where outlier queries are performed in order to detect anomalies in the current window. two algorithms are presented. the first one exactly answers outlier queries, but has larger space requirements. the second algorithm is directly derived from the exact one, has limited memory requirements and returns an approximate answer based on accurate estimations with a statistical guarantee. several experiments have been accomplished, confirming the effectiveness of the proposed approach and the high quality of approximate solutions.
query optimization using restructured views. we study optimization of relational queries using materialized views, where views may be regular or restructured. in a restructured view, some data from the base table(s) are represented as metadata - that is, schema information, such as table and attribute names - or vice versa.using restructured views in query optimization opens up a new spectrum of views that were not previously available, and can result in significant additional savings in query-evaluation costs. these savings can be obtained due to a significantly larger set of views to choose from, and may involve reduced table sizes, elimination of self-joins, clustering produced by restructuring, and horizontal partitioning.in this paper we propose a general query-optimization framework that treats regular and restructured views in a uniform manner and is applicable to sql select-project-join queries and views with or without aggregation. within the framework we provide (1) algorithms to determine when a view (regular or restructured) is usable in answering a query, and (2) algorithms to rewrite a query using usable views.semantic information, such as knowledge of the key of a view, can be used to further optimize a rewritten query. within our general query-optimization framework, we develop techniques for determining the key of a (regular or restructured) view, and show how this information can be used to further optimize a rewritten query. it is straightforward to integrate all our algorithms and techniques into standard query-optimization algorithms.
communities in graphs and hypergraphs. in this paper we define a type of cohesive subgroups - called communities - in hypergraphs, based on the edge connectivity of subhypergraphs. we describe a simple algorithm for the construction of these sets and show, based on examples from image segmentation and information retrieval, that these groups may be useful for the analysis and accessibility of large graphs and hypergraphs.
validating associations in biological databases. erroneous data can often be found in databases, and detecting it is normally a non-trivial task. for example, to cope with the large amount of biological sequences being produced, a significant number of genes and proteins have been annotated by automated tools. a protein annotation is an association between a protein and a term describing its role. these tools have produced a significant number of misannotations that are now present in biological databases. this paper proposes a new method for automatically scoring associations by comparing them to preexisting curated associations. an association is a pair that links two entities. the score can be used to filter incorrect or uncommon associations.we evaluated the method using the automated protein annotations submitted to biocreative, an international evaluation of state-of-the-art text-mining systems in biology. the method scored each of these annotations and those scored below a certain threshold were discarded. the results have shown a small trade-off in recall for a large improvement in precision. for example, we were able to discard 44.6%, 66.8% and 81% of the misannotations, maintaining 96.9%, 84.2%, and 47.8% of the correct annotations, respectively. moreover, we were able to outperform each individual submission to biocreative by proper adjustment of the threshold.
computing explanations for unlively queries in databases. a query is unlively if it always returns an empty answer. debugging a database schema requires not only determining unlively queries, but also fixing them. to the best of our knowledge, the existing methods do not provide the designer with an explanation of why a query is not lively. in this paper, we propose a method for computing explanations that is independent of the particular method used to determine liveliness. it provides three levels of search: one explanation, a maximal set of non-overlapping explanations, and all explanations. the first two levels require only a linear number of calls to the underlying method. we also propose a filter to reduce the number of these calls, and experimentally compare our method with the best known method for finding unsatisfiable subsets of constraints.
efficient and effective link analysis with precomputed salsa maps. salsa is a link-based ranking algorithm that takes the result set of a query as input, extends the set to include additional neighboring documents in the web graph, and performs a random walk on the induced subgraph. the stationary probability distribution of this random walk, used as a relevance score, is significantly more effective for ranking purposes than popular query-independent link-based ranking algorithms such as pagerank. unfortunately, this requires significant effort at query-time, to access the link graph and compute the stationary probability distribution. in this paper, we explore whether it is possible to perform most of the computation off-line, prior to the arrival of any queries. the off-line phase of our approach computes a "score map" for each node in the web graph by performing a salsa-like algorithm on the neighborhood of that node and retaining the scores of the most promising nodes in the neighborhood graph. the on-line phase takes the results to a query, retrieves the score map of each result, and returns for each result a score that is the sum of the matching scores from each score map. we evaluated this algorithm on a collection of about 28,000 queries with partially labeled results, and found that it is significantly more effective than pagerank, although not quite as effective as salsa. we also studied the trade-off between ranking effectiveness and space requirements.
workload-based optimization of integration processes. the efficient execution of integration processes between distributed, heterogeneous data sources and applications is a challenging research area of data management. these integration processes are an abstraction for workflow-based integration tasks, used in eai servers and wfms. the major problem are significant workload changes during runtime. the performance of integration processes strongly depends on those dynamic workload characteristics, and hence workload-based optimization is important. however, existing approaches of workflow optimization only address the rule-based optimization and disregard changing workload characteristics. to overcome the problem of inefficient process execution in the presence of workload shifts, here, we present an approach for the workload-based optimization of instance-based integration processes and show that significant execution time reductions are possible.
entity-based query reformulation using wikipedia. many real world applications increasingly involve both structured data and text, and entity based retrieval is an important problem in this realm. in this paper, we present an automatic query reformulation approach based on entities detected in each query. the aim is to utilize semantics associated with entities for enhancing document retrieval. this is done by expanding a query with terms/phrases related to entities in the query. we exploit wikipedia as a large repository of entity information. our reformulated approach consists of three major steps : (1) detect representative entity in a query; (2) expand the query with entity related terms/phrases; and (3) facilitate term dependency features. we evaluate our approach in ad-hoc retrieval task on four trec collections, including two large web collections. experiments results show that significant improvement is possible by utilizing entity corresponding information.
automatic online news topic ranking using media focus and user attention based on aging theory. news topics, which are constructed from news stories using the techniques of topic detection and tracking (tdt), bring convenience to users who intend to see what is going on through the internet. however, it is almost impossible to view all the generated topics, because of the large amount. so it will be helpful if all topics are ranked and the top ones, which are both timely and important, can be viewed with high priority. generally, topic ranking is determined by two primary factors. one is how frequently and recently a topic is reported by the media; the other is how much attention users pay to it. both media focus and user attention varies as time goes on, so the effect of time on topic ranking has already been included. however, inconsistency exists between both factors. in this paper, an automatic online news topic ranking algorithm is proposed based on inconsistency analysis between media focus and user attention. news stories are organized into topics, which are ranked in terms of both media focus and user attention. experiments performed on practical web datasets show that the topic ranking result reflects the influence of time, the media and users. the main contributions of this paper are as follows. first, we present the quantitative measure of the inconsistency between media focus and user attention, which provides a basis for topic ranking and an experimental evidence to show that there is a gap between what the media provide and what users view. second, to the best of our knowledge, it is the first attempt to synthesize the two factors into one algorithm for automatic online topic ranking.
evaluating topic models for information retrieval. we explore the utility of different types of topic models, both probabilistic and not, for retrieval purposes. we show that: (1) topic models are effective for document smoothing; (2) more elaborate topic models that capture topic dependencies provide no additional gains; (3) smoothing documents by using their similar documents is as effective as smoothing them by using topic models; (4) topics discovered on the whole corpus are too coarse-grained to be useful for query expansion. experiments to measure topic models' ability to predict held-out likelihood confirm past results on small corpora, but suggest that simple approaches to topic model are better for large corpora.
"more like these": growing entity classes from seeds. we present a corpus-based approach to the class expansion task. for a given set of seed entities we use co-occurrence statistics taken from a text collection to define a membership function that is used to rank candidate entities for inclusion in the set. we describe an evaluation framework that uses data from wikipedia. the performance of our class extension method improves as the size of the text collection increases.
reasoning about vague topological information. topological information plays a fundamental role in the human perception of spatial configurations and is thereby one of the most prominent geographical features in natural language. as vagueness abounds in geography, flexible formalisms with the ability to capture vague topological information are often needed in practice. while such formalisms have already been introduced by various authors, complete reasoning procedures are usually not discussed. in this paper, we show how many interesting reasoning tasks, such as consistency checking and entailment checking, can be supported in a generalization of the well-known rcc-8 calculus. in particular, we present decision procedures based on linear programming, solving all reasoning tasks of interest. we furthermore show how deciding the consistency of vague topological information can be reduced to the consistency problem of the original rcc-8.
web search personalization with ontological user profiles. every user has a distinct background and a specific goal when searching for information on the web. the goal of web search personalization is to tailor search results to a particular user based on that user's interests and preferences. effective personalization of information access involves two important challenges: accurately identifying the user context and organizing the information in such a way that matches the particular context. we present an approach to personalized search that involves building models of user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. a spreading activation algorithm is used to maintain the interest scores based on the user's ongoing behavior. our experiments show that re-ranking the search results based on the interest scores and the semantic evidence in an ontological user profile is effective in presenting the most relevant results to the user.
protecting location privacy against location-dependent attack in mobile services. privacy preservation has recently received considerable attention for location-based mobile services. in this paper, we present location-dependent attack resulting from continuous and dependent location updates and propose an incremental clique-based cloaking algorithm, called icliquecloak, to defend against location-dependent attack. the main idea is to incrementally maintain maximal cliques for location cloaking in an un-directed graph that takes into consideration the effect of continuous location updates.
identifying opinion leaders in the blogosphere. opinion leaders are those who bring in new information, ideas, and opinions, then disseminate them down to the masses, and thus influence the opinions and decisions of others by a fashion of word of mouth. opinion leaders capture the most representative opinions in the social network, and consequently are important for understanding the massive and complex blogosphere. in this paper, we propose a novel algorithm called influencerank to identify opinion leaders in the blogosphere. the influencerank algorithm ranks blogs according to not only how important they are as compared to other blogs, but also how novel the information they can contribute to the network. experimental results indicate that our proposed algorithm is effective in identifying influential opinion leaders.
ranking with semi-supervised distance metric learning and its application to housing potential estimation. this paper proposes a semi-supervised distance metric learning algorithm for the ranking problem. instead of giving the computer what are the important factors that affect the final rank value, we only give several most certainly ranked points which implicitly contain the knowledge of the ranking factors. then the computer can automatically use the most certain points and plenty of unlabeded data to learn an informative metric for ranking. this metric not only can help to regress an order in the observed data, but also can be used to retrieve the data by querying new test points. moreover, the lower-rank distance metric can be used to visualize high-dimensional data. we also present an application to the housing potential estimation problem. it is shown that the algorithm is efficient to help consultants to refine their consulting work.
evaluation of partial path queries on xml data. xml query languages typically allow the specification of structural patterns of elements. finding the occurrences of such patterns in an xml tree is the key operation in xml query processing. many algorithms have been presented for this operation. these algorithms focus mainly on the evaluation of path-pattern or tree-pattern queries. in this paper, we define a partial path-pattern query language, and we address the problem of its efficient evaluation on xml data. in order to process partial path-pattern queries, we introduce a set of sound and complete inference rules to characterize structural relationship derivation. we provide necessary and sufficient conditions for detecting query unsatisfiability and node redundancy. we show how partial path-pattern queries can be equivalently put in a canonical directed acyclic graph form. we developed two stack-based algorithms for the evaluation of partial path-pattern queries, partialmj and partialpathstack. partialmj computes answers to the query by merge-joining the results of the root-to-leaf paths of a spanning tree of the query. partialpathstack exploits a topological order of the nodes of the query graph to match the query pattern as a whole to the xml tree. the experimental evaluation of our algorithms shows that partialpathstack is independent of intermediate results and largely outperforms partialmj.