waashk/dblp huggingface dataset:
preserving update semantics in schema integration. in this paper, we propose a methodology for schema integration where the semantics of updates is preserved during the view integration process. we propose to divide view integration into three steps: combination, restructuring, and optimization. in the view combination step, we define the combined schema that contains all original views, plus a new set of constraints that express how data in distinct views are interrelated. the restructuring step is devoted to normalizing the views so that merging becomes possible. the optimization step tries to reduce redundancy and the size of the schema. our methodology defines a set of transformation primitives that allows schema integration to be realized in a safe (information preserving) and algorithmic way. in the proposed transformation primitives, the relationship between the original and transformed schema is formally specified by the instance and update mappings. we introduce the notion of an update semantics preserving transformation, which guarantees that the relationships between each view and the global schema, originated during the view integration process, reflect exactly the relationships between the views as defined by the combined schema. in our approach, the view definition mappings and the view update translator can be directly defined from the instance and update mappings between the different intermediate schemas generated during the view integration process.
rotation invariant indexing of shapes and line drawings. we present data representations, distance measures and organizational structures for fast and efficient retrieval of similar shapes in image databases. using the hough transform we extract shape signatures that correspond to important features of an image. the new shape descriptor is robust against line discontinuities and takes into consideration not only the shape boundaries, but also the content inside the object perimeter. the object signatures are eventually projected into a space that renders them invariant to translation, scaling and rotation. in order to provide support for real-time query-by-content, we also introduce an index structure that hierarchically organizes compressed versions of the extracted object signatures. in this manner we can achieve a significant performance boost for multimedia retrieval. our experiments suggest that by exploiting the proposed framework, similarity search in a database of 100,000 images would require under 1 sec, using an off-the-shelf personal computer.
question answering in trec. traditional text retrieval systems return a ranked list of documents in response to a user's request. while a ranked list of documents can be an appropriate response for the user, frequently it is not. usually it would be better for the system to provide the answer itself instead of requiring the user to search for the answer in a set of documents. the text retrieval conference (trec) is sponsoring a question answering "track" to foster research on the problem of retrieving answers rather than document lists.trec is a workshop series sponsored by the national institute of standards and technology and the u.s. department of defense [7]. the purpose of the conference series is to encourage research on text retrieval for realistic applications by providing large test collections, uniform scoring procedures, and a forum for organizations interested in comparing results. the conference has focused primarily on the traditional ir problem of retrieving a ranked list of documents in response to a statement of information need, but has also included other tasks, called tracks, that focus on new areas or particularly difficult aspects of information retrieval. a question answering track was introduced in trec-8 1999. the track has generated wide-spread interest in the qa problem [2, 3, 4], and has documented significant improvements in question answering system effectiveness in its two-year history.this paper provides a brief summary of the findings of the trec question answering track to date and discusses the future directions of the track. the paper is extracted from a fuller description of the track given in "the trec question answering track" [8]. complete details about the trec question answering track can be found in the trec proceedings.
efficient and effective metasearch for a large number of text databases. metasearch engines can be used to facilitate ordinary users for retrieving information from multiple local sources (text databases). in a metasearch engine, the contents of each local database is represented by a representative. each user query is evaluated against the set of representatives of all databases in order to determine the appropriate databases to search. when the number of databases is very large, say in the order of tens of thousands or more, then a traditional metasearch engine may become inefficient as each query needs to be evaluated against too many database representatives. furthermore, the storage requirement on the site containing the metasearch engine can be very large. in this paper, we propose to use a hierarchy of database representatives to improve the efficiency. we provide an algorithm to search the hierarchy. we show that the retrieval effectiveness of our algorithm is the same as that of evaluating the user query against all database representatives. we also show that our algorithm is efficient. in addition, we propose an alternative way of allocating representatives to sites so that the storage burden on the site containing the metasearch engine is much reduced.
simple qsf-trees: an efficient and scalable spatial access method. the development of high-performance spatial access methods that can support complex operations of large spatial databases continues to attract considerable attention. this paper introduces qsf-trees, an efficient and scalable structure for indexing spatial objects, which has some important advantages over r*-trees. qsf-trees eliminate overlapping of index regions without forcing object clipping or sacrificing the selectivity of spatial operations. the method exploits the semantics of topological relations between spatial objects to further reduce the number of index nodes visited during the search. a series of experiments involving randomly-generated spatial objects was conducted to compare the structure with two variations of r*-trees. the experiments show qsf-trees to be more efficient and more scalable to the increase in the data-set size, the size of spatial objects, and the number of dimensions of the spatial universe.
person resolution in person search results: webhawk. finding information about people on the web using a search engine is difficult because there is a many-to-many mapping between person names and specific persons (i.e. referents). this paper describes a person resolution system, called webhawk. given a list of pages obtained by submitting a person query to a search engine, webhawk facilitates person search in three steps: first of all, a filter removes those pages that contain no information about any person. secondly, a cluster groups the remaining pages into different clusters, each for one specific person. to make the resulting clusters more meaningful, an extractor is used to induce query-oriented personal information from each page. finally, a namer generates an informative description for each cluster so that users can find any specific person easily. the architecture of webhawk is presented, and the four components are discussed in detail, with a separate evaluation of each component presented where appropriate. a user study shows that webhawk complements most existing search engines and successfully improves users' experience of person search on the web.
an agent-based approach to knowledge management. traditional approaches to knowledge management are essentially limited to document management. however, much knowledge in organizations or communities resides in an informal social network and may be accessed only by asking the right people. this paper describes mars, a multiagent referral system for knowledge management. mars assigns a software agent to each user. the agents facilitate their users' interactions and help manage their personal social networks. moreover, the agents cooperate with one another by giving and taking referrals to help their users find the right parties to contact for a specific knowledge need.
the earth mover's distance as a semantic measure for document similarity. different words are usually assumed to be semantically independent in most existing similarity measures, which is not often true in practice. the semantic relatedness between words cannot be conveniently employed in the existing measures. we propose a novel similarity measure based on the earth mover's distance (emd). in the proposed measure, the semantic distances between words are computed based on the electronic lexical database-wordnet and then the emd is employed to calculate the document similarity with a many-to-many matching between words. experiments and results demonstrate the effectiveness of the proposed similarity measure.
selecting relevant instances for efficient and accurate collaborative filtering. collaborative filtering uses a database about consumers' preferences to make personal product recommendations and is achieving widespread success in both e-commerce and information filtering applications nowadays. however, the traditional collaborative filtering algorithms do not scale well to the ever-growing number of consumers. the quality of the recommendation also needs to be improved in order to gain more trust from the consumers. in this paper, we present a novel method to improve the scalability and the accuracy of the collaborative filtering algorithm. we introduce an information theoretic approach to measure the relevance of a consumer (instance) for predicting the preference for the given product (target concept). the proposed method reduces the training data set by selecting only highly relevant instances. our experimental evaluation on the well-known eachmovie data set shows that our method doesn't only significantly speed up the prediction, but also results in a better accuracy.
removing redundancy and inconsistency in memory-based collaborative filtering. the application range of memory-based collaborative filtering (cf) is limited due to cf's high memory consumption and long runtime. the approach presented in this paper removes redundant and inconsistent instances (users) from the data. this paper aims to distinguish informative instances (users) from large raw user preference database and thus alleviate the memory and runtime cost of the widely used memory-based collaborative filtering (cf) algorithm. our work shows that a satisfactory accuracy can be achieved by using only a small portion of the original data set, thereby alleviating the storage and runtime cost of the cf algorithm. in our approach, we consider instance selection as the problem of selecting informative data that increase the we begin by discussing the instance selection problem in a general sense that is to increase a posteriori probability of the optimal model by selecting informative data. we evaluate the empirical performance of our approach pf on two real-world data sets and attain very promisingpositive experimental results. the ddata size and the prediction time are significantly reduced, while the prediction accuracy is on a par with almost the same as the results achieved by using the complete database.
text classification from positive and unlabeled documents. most existing studies of text classification assume that the training data are completely labeled. in reality, however, many information retrieval problems can be more accurately described as learning a binary classifier from a set of incompletely labeled examples, where we typically have a small number of labeled positive examples and a very large number of unlabeled examples. in this paper, we study such a problem of performing text classification without labeled negative data tc-won). in this paper, we explore an efficient extension of the standard support vector machine (svm) approach, called svmc (support vector mapping convergence) [17]for the tc-won tasks. our analyses show that when the positive training data is not too under-sampled, svmc significantly outperforms other methods because svmc basically exploits the natural "gap" between positive and negative documents in the feature space, which eventually corresponds to improving the generalization performance. in the text domain there are likely to exist many gaps in the feature space because a document is usually mapped to a sparse and high dimensional feature space. however, as the number of positive training data decreases, the boundary of svmc starts overfitting at some point and end up generating very poor results.this is because when the positive training data is too few, the boundary over-iterates and trespasses the natural gaps between positive and negative class in the feature space and thus ends up fitting tightly around the few positive training data.
towards estimating the number of distinct value combinations for a set of attributes. accurately and efficiently estimating the number of distinct values for some attribute(s) or sets of attributes in a data set is of critical importance to many database operations, such as query optimization and approximation query answering. previous work has focused on the estimation of the number of distinct values for a single attribute and most existing work adopts a data sampling approach. this paper addresses the equally important issue of estimating the number of distinct value combinations for multiple attributes which we call colscard (for column set cardinality). it also takes a different approach that uses existing statistical information (e.g., histograms) available on the individual attributes to assist estimation. we start with cases where exact frequency information on individual attributes is available, and present a pair of lower and upper bounds on colscard that are consistent with the available information, as well as an estimator of colscard based on probability. we then proceed to study the case where only partial information (in the form of histograms) is available on individual attributes, and show how the proposed estimator can be adapted to this case. we consider two types of widely used histograms and show how they can be constructed in order to obtain optimal approximation. an experimental evaluation of the proposed estimation method on synthetic as well as two real data sets is provided.
a framework for mining topological patterns in spatio-temporal databases. mining topological patterns in spatial databases has received a lot of attention. however, existing work typically ignores the temporal aspect and suffers from certain efficiency problems. they are not scalable for mining topological patterns in spatio-temporal databases. in this paper, we study the problem for mining topological patterns by incorporating the temporal aspect in the mining process. we introduce a summary-structure that records the instances' count information of a feature in a region within a time window. using this structure, we design an algorithm, topologyminer, to find interesting topological patterns without the need to generate candidates. experimental results show that topologyminer is effective and scalable in finding topological patterns and outperforms apriori-like algorithm by a few orders of magnitudes.
evaluating contents-link coupled web page clustering for web search results. clustering is currently one of the most crucial techniques for dealing (e.g. resources locating, information interpreting) with massive amount of heterogeneous information on the web. unlike clustering in other fields, web page clustering separates unrelated pages and clusters related pages (to a specific topic) into semantically meaningful groups, which is useful for discrimination, summarization, organization and navigation of unstructured web pages. we have proposed a contents-link coupled clustering algorithm that clusters web pages by combining contents and link analysis. in this paper, we particularly study the effects of out-links (from the web pages), in-links (to the web page) and terms on the final clustering results as well as how to effectively combine these three parts to improve the quality of clustering results. we apply it to cluster web search results. preliminary experiments and evaluations are conducted on various topics. as the experimental results show, the proposed clustering algorithm is effective and promising.
feature selection with conditional mutual information maximin in text categorization. feature selection is an important component of text categorization. this technique can both increase a classifier's computation speed, and reduce the overfitting problem. several feature selection methods, such as information gain and mutual information, have been widely used. although they greatly improve the classifier's performance, they have a common drawback, which is that they do not consider the mutual relationships among the features. in this situation, where one feature's predictive power is weakened by others, and where the selected features tend to bias towards major categories, such selection methods are not very effective. in this paper, we propose a novel feature selection method for text categorization called <i>conditional mutual information maximin</i> (cmim). it can select a set of individually discriminating and weakly dependent features. the experimental results show that cmim can perform much better than traditional feature selection methods.
a latent semantic classification model. latent semantic indexing (lsi) has been successfully applied to information retrieval and text classification. however, when lsi is used in classification, some important features for small classes may be ignored because of their small feature values. to solve this problem, we propose the latent semantic classification (lsc) model which extends the lsi model in the following way: the classification information of the training documents is introduced into the latent semantic structure via a second set of latent variables, so that both indexing and classification information can be taken into account during the classification process. our experiments on reuters show that our new model performs better than the existing classification methods such as knn and svm.
using lsi for text classification in the presence of background text. this paper presents work that uses latent semantic indexing (lsi) for text classification. however, in addition to relying on labeled training data, we improve classification accuracy by also using unlabeled data and other forms of available "background" text in the classification process. rather than performing lsi's singular value decomposition (svd) process solely on the training data, we instead use an expanded term-by-document matrix that includes both the labeled data as well as any available and relevant background text. we report the performance of this approach on data sets both with and without the inclusion of the background text, and compare our work to other efforts that can incorporate unlabeled data and other background text in the classification process.
efficient refreshment of materialized views with multiple sources. a data warehouse collects and maintains a large amount of data from multiple distributed and autonomous data sources. often the data in it is stored in the form of materialized views in order to provide fast access to the integrated data. however, maintaining a certain level consistency of warehouse data with the source data is challenging in a distributed multiple source environment. transactions containing multiple updates at one or more sources further complicate the consistency issue.following the four level consistency definition of view in a warehouse, we first present a complete consistency algorithm for maintaining spj-type materialized views incrementally. our algorithm speed-ups the view refreshment time, provided that some extra moderate space in the warehouse is available. we then give a variant of the proposed algorithm by taking the update frequencies of sources into account. we finally discuss the relationship between a view's certain level consistency and its refresh time. it is difficult to propose an incremental maintenance algorithm such that the view is always kept at a certain level consistency with the source data and the view's refresh time is as fast as possible. we trade-off these two factors by giving an algorithm with faster view refresh time, while the view maintained by the algorithm is strong consistency rather than complete consistency with the source data.
knowledge and information management: is it possible to do interesting and important research, get funded, be useful and appreciated? the survey of the cikm call for papers for the period 1998 - 2002 demonstrates that the cikm organizers very accurately "identify challenging problems facing the development of future knowledge and information systems [in] applied and theoretical research" [1998] and also play an important role fostering "bridging traditionally separated areas such as databases and information retrieval, or those that apply techniques from one area to another" [2001, 2002]. the presented cikm papers also indicate that researchers work on interesting problems. this talk will discuss some additional research topics for future consideration by the cikm community.in most cases, to achieve important results, research needs to be well supported. if you are not getting the funding you need, this talk may provide some pointers where you can look for finding. if you are well supported, i will try to convince you that you can be instrumental in improving the funding scenario for everybody, by mentoring the junior members of the cikm community, by forming collaborative (international, interdisciplinary) teams and by letting the funders know what you find conducive to your research and what you consider a hindrance. regardless if you are well funded or not, it is most helpful if you are active in identifying new research directions and also assist in evaluating the priorities.the most frequent problem for inadequate funding is lack of funds. however, researchers can help! this can be achieved in many different ways: thinking about long-term applications of fundamental research to societal needs; working with communities that can directly benefit from research; sharing the research results not only with the research colleagues, but also wider constituencies - at their appropriate levels; and informing your funders about your spectacular achievements, i.e., providing good reasons for increasing the research funding.cikm strives to bring together research communities that traditionally do not work together. providing a forum for interdisciplinary research is laudable and very important, as very often interdisciplinary or international research is not "appreciated". this talk will discuss how we could gradually change the discipline- and country-based "appraisal" cultures. we will also attempt to answer the question: "what is the ultimate appreciation?" (nobel prize? ...successful .com?... ???).
web page clustering enhanced by summarization. traditional web page clustering algorithms use the full-text in the documents to generate feature vectors. such methods often produce unsatisfactory results because there is much noisy information, such as decoration, interaction, and advertisement, in web pages. the varying-length problem of the web pages is also a significant negative factor affecting the performance. in this paper, we investigate the use of several summarization techniques to tackle these issues when clustering web pages. compared with the full-text representation of the web pages, our experimental results indicate that our proposed approach effectively solves the problems of noisy information and varying-length, and thus significantly boosts the clustering performance.