waashk/dblp huggingface dataset:
using multiple ontologies in information extraction. ontology-based information extraction (obie) has recently emerged as a subfield of information extraction (ie). here, ontologies - which provide formal and explicit specifications of conceptualizations - play a crucial role in the information extraction process. several obie systems have been implemented previously but all of them use a single ontology although multiple ontologies have been designed for many domains. we have studied the theoretical basis for using multiple ontologies in information extraction and have developed information extraction systems that use them. these systems investigate the two major scenarios for having multiple ontologies for the same domain: specializing in sub-domains and providing different perspectives. the domain of universities has been used for the former scenario through a corpus collected from university websites. for the latter, the domain of terrorist attacks and a corpus used by a previous message understanding conference (muc) have been used. the results from these two case studies indicate that using multiple ontologies in information extraction has led to a clear improvement in performance measures.
ming: mining informative entity relationship subgraphs. many modern applications are faced with the task of knowledge discovery in entity-relationship graphs, such as domain-specific knowledge bases or social networks. mining an "informative" subgraph that can explain the relations between k(>= 2) given entities of interest is a frequent knowledge discovery scenario on such graphs. we present ming, a principled method for extracting an informative subgraph for given query nodes. ming builds on a new notion of informativeness of nodes. this is used in a random-walk-with-restarts process to compute the informativeness of entire subgraphs.
who tags the tags?: a framework for bookmark weighting. in this work we propose a novel framework for bookmark weighting which allows us to estimate the effectiveness of each of the bookmarks individually. we show that by weighting bookmarks according to their estimated quality we can significantly improve search effectiveness. using empirical evaluation on real data gathered from two large bookmarking systems, we demonstrate the effectiveness of the new framework for search enhancement.
probabilistic latent preference analysis for collaborative filtering. a central goal of collaborative filtering (cf) is to rank items by their utilities with respect to individual users in order to make personalized recommendations. traditionally, this is often formulated as a rating prediction problem. however, it is more desirable for cf algorithms to address the ranking problem directly without going through an extra rating prediction step. in this paper, we propose the probabilistic latent preference analysis (plpa) model for ranking predictions by directly modeling user preferences with respect to a set of items rather than the rating scores on individual items. from a user's observed ratings, we extract his preferences in the form of pairwise comparisons of items which are modeled by a mixture distribution based on bradley-terry model. an em algorithm for fitting the corresponding latent class model as well as a method for predicting the optimal ranking are described. experimental results on real world data sets demonstrated the superiority of the proposed method over several existing cf algorithms based on rating predictions in terms of ranking performance measure ndcg.
post-rank reordering: resolving preference misalignments between search engines and end users. no search engine is perfect. a typical type of imperfection is the preference misalignment between search engines and end users, e.g., from time to time, web users skip higher-ranked documents and click on lower-ranked ones. although search engines have been aggressively incorporating clickthrough data in their ranking, it is hard to eliminate such misalignments across millions of queries. therefore, we, in this paper, propose to accompany a search engine with an "always-on" component that reorders documents on a per-query basis, based on user click patterns. because of positional bias and dependencies between clicks, we show that a simple sort based on click counts (and its variants), albeit intuitive and useful, is not precise enough. in this paper, we put forward a principled approach to reordering documents by leveraging existing click models. specifically, we compute the preference probability that a lower-ranked document is preferred to a higher-ranked one from the click chain model (ccm), and propose to swap the two documents if the probability is sufficiently high. because ccm models positional bias and dependencies between clicks, this method readily accounts for many twisted heuristics that have to be manually encoded in sort-based approaches. for this approach to be practical, we further devise two approximation schemes that make online computation of the preference probability feasible. we carried out a set of experiments based on real-world data from a major search engine, and the result clearly demonstrates the effectiveness of the proposed approach.
workload-aware trie indices for xml. well-designed indices can dramatically improve query performance. including query workload information can produce indices that yield better overall throughput while balancing the space and performance trade-off at the core of index design. in the context of xml, structural indices have proven to be particularly effective in supporting xpath queries by capturing the structural correlation between data components in an xml document. in this paper, we propose a family of novel workload-aware indices by taking advantage of the disk-based ρ[k]-trie index framework, which indexes node pairs of an xml document to facilitate index-only evaluation plans. our indices are designed to be optimal for answering frequent path queries in one index lookup and efficient for answering non-frequent path queries using an index-only plan. experimental results prove that our indices outperform the apex index in overall throughput and excel in answering non-frequent queries, queries with predicates, and queries that yield empty results.
usage based effectiveness measures: monitoring application performance in information retrieval. the aim of an information retrieval (ir) application is to support the user accessing relevant information effectively and efficiently. it is well known that system performance, in terms of finding relevant information is heavily dependent upon the ir application (i.e. the ir system exposed through the application's interface), as well as how the application is used by the user (i.e. how the user interacts with the system through the interface). thus, a very pragmatic evaluation question that arises at the application level is: what is the effectiveness experienced by the user during the usage of the application? to be able to answer this question, we represent the usage of an application by the stream of documents the user encounters while interacting with the application. this representation enables us to monitor and track the performance over time and usage. by taking a stream-based, time-centric view of the ir process, instead of a rank-list, topic/task centric view, the evaluation can be performed on any ir based application. to illustrate the difference and the utility of this approach, we demonstrate how a new suite of usage based effectiveness measures can be applied. this work provides the conceptual foundations for measuring, monitoring and modeling the performance of any ir application which needs to be evaluated over time and in context.
ds-cuber: an integrated olap environment for data streams. most of emerging applications deal with an infinite data stream in an incessant, immense and volatile manner. consequently, it is very important to analyze not only the varying characteristics of a source data stream in a short-term period but also those in a long-term period. for this purpose, this paper demonstrates an olap system, ds-cuber (data stream cuber) for the analysis of data streams. the proposed system consists of two analytic components: short-term and long-term, so that it can provide an integrated analysis environment for infinite data streams. furthermore, each of these two components supports diversified exception detection methods which can be used for the automatic identification of abnormality in the data elements of a data stream in order to guide the data cube navigation of a user effectively. network traffic flow streams are used to demonstrate the features of the ds-cube system.
(not) yet another matcher. discovering correspondences between schema elements is a crucial task for data integration. most schema matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). they mainly use several methods to combine and aggregate similarity measures. however, their quality results often decrease when one requires to integrate a new similarity measure or when matching particular domain schemas. this paper describes yam (yet another matcher), which is a schema matcher factory. indeed, it enables the generation of a dedicated matcher for a given schema matching scenario, according to user inputs. our approach is based on machine learning since schema matchers can be seen as classifiers. several bunches of experiments run against matchers generated by yam and traditional matching tools show how our approach is able to generate the best matcher for a given scenario.
efficient processing of group-oriented connection queries in a large graph. we study query processing in large graphs that are fundamental data model underpinning various social networks and web structures. given a set of query nodes, we aim to find the groups which the query nodes belong to, as well as the best connection among the groups. such a query is useful to many applications but the query processing is extremely costly. we define a new notion of correlation group (cg), which is a set of nodes that are strongly correlated in a large graph g. we then extract the subgraph from g that gives the best connection for the nodes in a cg. to facilitate query processing, we develop an efficient index built upon the cgs. our experiments show that the cgs are meaningful as groups and importantly, the meaningfulness of the query results are justifiable. we also demonstrate the high efficiency of cg computation, index construction and query processing.
an empirical study on using hidden markov model for search interface segmentation. this paper describes a hidden markov model (hmm) based approach to perform search interface segmentation. automatic processing of an interface is a must to access the invisible contents of deep web. this entails automatic segmentation, i.e., the task of grouping related components of an interface together. while it is easy for a human to discern the logical relationships among interface components, machine processing of an interface is difficult. in this paper, we propose an approach to segmentation that leverages the probabilistic nature of the interface design process. the design process involves choosing components based on the underlying database query requirements, and organizing them into suitable patterns. we simulate this process by creating an "artificial designer" in the form of a 2-layered hmm. the learned hmm acquires the implicit design knowledge required for segmentation. we empirically study the effectiveness of the approach across several representative domains of deep web. in terms of segmentation accuracy, the hmm-based approach outperforms an existing state-of-the-art approach by at least 10% in most cases. furthermore, our cross-domain investigation shows that a single hmm trained on data having varied and frequent design patterns can accurately segment interfaces from multiple domains.
constructing evolutionary taxonomy of collaborative tagging systems. collaborative tagging systems allow users to label online resources. the tags are generally correlated and evolving according to the change of web contents, and the popularity of tags represent evolution of social interests. tag taxonomy is a promising solution to organize the data in tagging systems. in this demonstration, we propose to construct the evolutionary taxonomy which incorporates the correlation and evolution of tags, as user generated tags grow and change temporally. we demonstrate that our approach is intuitive and efficient in tag organization which exploits the evolving characteristic of collaborative tagging systems.
pqc: personalized query classification. query classification (qc) is a task that aims to classify web queries into topical categories. since queries are usually short in length and ambiguous, the same query may need to be classified to different categories according to different people's perspectives. in this paper, we propose the personalized query classification (pqc) task and develop an algorithm based on user preference learning as a solution. users' preferences that are hidden in clickthrough logs are quite helpful for search engines to improve their understandings of users' queries. we propose to connect query classification with users' preference learning from clickthrough logs for pqc. to tackle the sparseness problem in clickthrough logs, we propose a collaborative ranking model to leverage similar users' information. experiments on a real world clickthrough log data show that our proposed pqc algorithm can gain significant improvement compared with general qc as well as natural baselines. our method can be applied to a wide range of applications including personalized search and online advertising.
automatic retrieval of similar content using search engine query interface. we consider the coverage testing problem where we are given a document and a corpus with a limited query interface and asked to find if the corpus contains a near-duplicate of the document. this problem has applications in search engines for competitive coverage testing. to solve this problem, we propose approaches that work in three main steps: generate a query signature from the document, query the corpus using the query signature and scrape the returned results, and validate the similarity between the input document and the returned results. we discuss techniques to control and bound the performance of these methods. we perform large-scale experimental validation and show that these methods perform well across different search engine corpora and documents in multiple languages. they also are robust against performance parameter variations.
improving retrievability of patents with cluster-based pseudo-relevance feedback documents selection. high findability of documents within a certain cut-off rank is considered an important factor in recall-oriented application domains such as patent or legal document retrieval. findability is hindered by two aspects, namely the inherent bias favoring some types of documents over others introduced by the retrieval model, and the failure to correctly capture and interpret the context of conventionally rather short queries. in this paper, we analyze the bias impact of different retrieval models and query expansion strategies. we furthermore propose a novel query expansion strategy based on document clustering to identify dominant relevant documents. this helps to overcome limitations of conventional query expansion strategies that suffer strongly from the noise introduced by imperfect initial query results for pseudo-relevance feedback documents selection. experiments with different collections of patent documents suggest that clustering based document selection for pseudo-relevance feedback is an effective approach for increasing the findability of individual documents and decreasing the bias of a retrieval system.
probabilistic models for topic learning from images and captions in online biomedical literatures. biomedical images and captions are one of the major sources of information in online biomedical publications. they often contain the most important results to be reported, and provide rich information about the main themes in published papers. in the data mining and information retrieval community, there has been much effort on using text mining and language modeling algorithms to extract knowledge from the text content of online biomedical publications; however, the problem of knowledge extraction from biomedical images and captions has not been fully studied yet. in this paper, a hierarchical probabilistic topic model with background distribution (hpb) is introduced to uncover the latent semantic topics from the co-occurrence patterns of caption words, visual words and biomedical concepts. with downloaded biomedical figures, restricted captions are extracted with regard to each individual image panel. during the indexing stage, the 'bag-of-words' representation of captions is supplemented by an ontology-based concept indexing to alleviate the synonym and polysemy problems. as the visual counterpart of text words, the visual words are extracted and indexed from corresponding image panels. the model is estimated via collapsed gibbs sampling algorithm. we compare the performance of our model with the extension of the correspondence lda (corr-lda) model under the same biomedical image annotation scenario using cross-validation. experimental results demonstrate that our model is able to accurately extract latent patterns from complicated biomedical image-caption pairs and facilitate knowledge organization and understanding in online biomedical literatures.
suffix trees for very large genomic sequences. a suffix tree is a fundamental data structure for string searching algorithms. unfortunately, when it comes to the use of suffix trees in real-life applications, the current methods for constructing suffix trees do not scale for large inputs. all the existing practical algorithms perform random access to the input string, thus requiring that the input be small enough to be kept in main memory. we are the first to present an algorithm which is able to construct suffix trees for input sequences significantly larger than the size of the available main memory. as a proof of concept, we show that our method allows to build the suffix tree for 12gb of real dna sequences in 26 hours on a single machine with 2gb of ram. this input is four times the size of the human genome, and the construction of suffix trees for inputs of such magnitude was never reported before.
translating relevance scores to probabilities for contextual advertising. information retrieval systems conventionally assess document relevance using the bag of words model. consequently, relevance scores of documents retrieved for different queries are often difficult to compare, as they are computed on different (or even disjoint) sets of textual features. many tasks, such as federation of search results or global thresholding of relevance scores, require that scores be globally comparable. to achieve this, in this paper we propose methods for non-monotonic transformation of relevance scores into probabilities for a contextual advertising selection engine that uses a vector space model. the calibration of the raw scores is based on historical click data.
clouddb workshop summary. this is the first workshop in cikm conference that addresses the challenge of large data management based on cloud computing infrastructure. this workshop will bring together researchers and practitioners in cloud computing and data-intensive system design, programming, parallel algorithms, data management, scientific applications, and information-based applications to maximize performance, minimize cost and improve the scale of their endeavors. totally this workshop attracted 11 submissions from asia, canada, europe, and the united states. the program committee accepted 8 papers, among which there are 5 full papers and 3 short papers. topics of accepted papers includes query processing & index, service in the cloud, platform availability, cloud system implementation, data replication and so on. these proceedings will serve as a valuable reference for cloud data management researchers and developers.
personalized social search based on the user's social network. this work investigates personalized social search based on the user's social relations -- search results are re-ranked according to their relations with individuals in the user's social network. we study the effectiveness of several social network types for personalization: (1) familiarity-based network of people related to the user through explicit familiarity connection; (2) similarity-based network of people "similar" to the user as reflected by their social activity; (3) overall network that provides both relationship types. for comparison we also experiment with topic-based personalization that is based on the user's related terms, aggregated from several social applications. we evaluate the contribution of the different personalization strategies by an off-line study and by a user survey within our organization. in the off-line study we apply bookmark-based evaluation, suggested recently, that exploits data gathered from a social bookmarking system to evaluate personalized retrieval. in the on-line study we analyze the feedback of 240 employees exposed to the alternative personalization approaches. our main results show that both in the off-line study and in the user survey social network based personalization significantly outperforms non-personalized social search. additionally, as reflected by the user survey, all three sn-based strategies significantly outperform the topic-based strategy.
rose: retail outlet site evaluation by learning with both sample and feature preference. it is critical for retail enterprises to select good sites or locations to open their stores, especially in current competitive retail market. however, evaluating the goodness of sites in real business applications is a complex problem. that is, how to judge whether the market around a store site is good? we don't know the exact mechanism of how a site can be good and it is hard to have correct site goodness values as supervised labels. the retail outlet site evaluation (rose) tool is designed to learn the site evaluation model by integrating city geographic & demographic data and two kinds of expert knowledge: sample preference and feature preference. the feature preference information can help greatly reduce the required number of sample preferences. it enables our application practicable because it is almost impossible to give such amount of sample preference pairs manually by experts when ranking hundreds of data points. in the experiment and case study part, we show that the rose tool can achieve good results and useful for users to do site evaluation work in real cases.
boosting knn text classification accuracy by using supervised term weighting schemes. the increasing availability of digital documents in the last decade has prompted the development of machine learning techniques to automatically classify and organize text documents. the majority of text classification systems rely on the vector space model, which represents the documents as vectors in the term space. each vector component is assigned a weight that reflects the importance of the term in the document. typically, these weights are assigned using an information retrieval (ir) approach, such as the famous tf-idf function. in this work, we study two weighting schemes based on information gain and chi-square statistics. these schemes take advantage of the category label information to weight the terms according to their distributions across the different categories. we show that using these supervised weights instead of conventional unsupervised weights can greatly improve the performance of the k-nearest neighbor (knn) classifier. experimental evaluations, carried out on multiple text classification tasks, demonstrate the benefits of this approach in creating accurate text classifiers.