waashk/dblp huggingface dataset:
a competition-based connectionist model for information retrieval using a merged thesaurus. this paper investigates a network-based information retrieval model using diagnostic inferencing techniques. a basic inference network in information retrieval consists of two component networks: the document component and the query component. in our approach, there is a layer of nodes corresponding to the documents, and a layer of nodes corresponding to the index terms extracted from the document set, with links connecting documents to the related index terms 1. a thesaurus is used to provide concept categories; these categories are represented by another layer of nodes, with links connecting the index terms and the related categories 2. the query component uses a symmetric structure. each query causes markings of category nodes, hence markings of the related index term nodes, in the document component of the network. in our previous work, we adapted a competition-based connectionist model for diagnostic problem solving to information retrieval. in this model, documents are treated as &ldquo;disorders&rdquo; and user information needs, represented by the marked index term nodes, as &ldquo;manifestations&rdquo;. a competitive activation mechanism is then used which converges to a set of disorders that best explain the given manifestations. our experiments showed that the retrieval performance of this model is comparable to or better than that of various information retrieval models reported in the literature. in this paper, we report further enhancements of the model by using a merged thesaurus.
associative document retrieval by query subtopic analysis and its application to invalidity patent search. we propose an associative document retrieval method, in which a document is used as a query to search for other similar documents. because a long document usually includes more than one topic, we first analyze a query document to extract multiple subtopics. for each subtopic element, a sub-query is produced and similar documents are retrieved with a relevance score. the relevance scores are weighted by the importance of each subtopic element and are integrated to determine the final relevant documents. in the calculation of the subtopic importance, the specificity of a query term is evaluated using entropy, which is the deviation degree of the appearances of the term in each subtopic element. we apply this method to an invalidity patent search. by exploiting certain unique features of japanese patent claims, we use features distinguishing the preamble and the essential portion in a query patent claim. to demonstrate the effectiveness of our method, we experimentally evaluated our associative document retrieval method on five years of patent documents.
from object evolution to object emergence. database applications which model aspects of the real world should be able to express as accurately as possible the different nuances of reality; that includes the need to evolve internally in response to signals of updates coming from the environment. these updates are not always supplied in an ideal and complete manner and are not always predefined or precisely defined. in practice, requirements for evolution generally occur during the manipulation of objects while running the database. it is frequently necessary to change individual objects, less frequently the database schema. database systems need to have mechanisms capable, whenever and as well as possible, of assimilating this new information correctly and diagnosing and implementing the changes necessary.this paper concerns the evolution of objects inside databases. our two main objectives are:to allow objects to evolve their structures dynamically during database maintenance and use, with all necessary impacts on the database schema;to allow, similarly, the creation and display of different plans for evolving the design, like ways of schema evolution, giving in this way a simulation tool for database design and maintenance.so, we propose a genetic evolution object model developed to have inherent capabilities for auto-adaptation between classes and instances.
a novel refinement approach for text categorization. in this paper we present a novel strategy, dragpushing, for improving the performance of text classifiers. the strategy is generic and takes advantage of training errors to successively refine the classification model of a base classifier. we describe how it is applied to generate two new classification algorithms; a refined centroid classifier and a refined naïve bayes classifier. we present an extensive experimental evaluation of both algorithms on three english collections and one chinese corpus. the results indicate that in each case, the refined classifiers achieve significant performance improvement over the base classifiers used. furthermore, the performance of the refined centroid classifier implemented is comparable, if not better, to that of state-of-the-art support vector machine (svm)-based classifier, but offers a much lower computational cost.
balancing performance and confidentiality in air index. studies on the performance issues (i.e., access latency and energy conservation) of wireless data broadcast have appeared in the literature. however, the important security issues have not been well addressed. this paper investigates the tradeoff between performance and security of signature-based air index schemes in wireless data broadcast. from the performance perspective, keeping low false drop probability helps clients retrieve the information from a broadcast channel efficiently. meanwhile, from the security perspective, achieving high false guess probability prevents the hacker from guessing the information easily. there is a tradeoff between these two aspects. an administrator of the wireless broadcast system may balance this tradeoff by carefully configuring the signatures used in broadcast. this study provides a guidance for parameter settings of the signature schemes in order to meet the performance and security requirements. experiments are performed to validate the analytical results and to obtain optimal signature configuration corresponding to different application criteria.
focused crawling for both topical relevance and quality of medical information. subject-specific search facilities on health sites are usually built using manual inclusion and exclusion rules. these can be expensive to maintain and often provide incomplete coverage of web resources. on the other hand, health information obtained through whole-of-web search may not be scientifically based and can be potentially harmful.to address problems of cost, coverage and quality, we built a focused crawler for the mental health topic of depression, which was able to selectively fetch higher quality relevant information. we found that the relevance of unfetched pages can be predicted based on link anchor context, but the quality cannot. we therefore estimated quality of the entire linking page, using a learned ir-style query of weighted single words and word pairs, and used this to predict the quality of its links. the overall crawler priority was determined by the product of link relevance and source quality.we evaluated our crawler against baseline crawls using both relevance judgments and objective site quality scores obtained using an evidence-based rating scale. both a relevance focused crawler and the quality focused crawler retrieved twice as many relevant pages as a breadth-first control. the quality focused crawler was quite effective in reducing the amount of low quality material fetched while crawling more high quality content, relative to the relevance focused crawler.analysis suggests that quality of content might be improved by post-filtering a very big breadth-first crawl, at the cost of substantially increased network traffic.
an iterative strategy for pattern discovery in high-dimensional data sets. high-dimensional data representation in which each data item (termed target object) is described by many features, is a necessary component of many applications. for example, in dna microarrays, each sample (target object) is represented by thousands of genes as features. pattern discovery of target objects presents interesting but also very challenging problems. the data sets are typically not task-specific, many features are irrelevant or redundant and should be pruned out or filtered for the purpose of classifying target objects to find empirical pattern. uncertainty about which features are relevant makes it difficult to construct an informative feature space. this paper proposes an iterative strategy for pattern discovery in high-dimensional data sets. in this approach, the iterative process consists of two interactive components: discovering patterns within target objects and pruning irrelevant features. the performance of the proposed method with various real data sets is also illustrated.
mining multiple phenotype structures underlying gene expression profiles. dna microarray technology is now widely used in basic biomedical research for mrna expression profiling and are increasingly being used to explore patterns of gene expression in clinical research. automatically detecting phenotype structures from gene expression profiles can provide deep insight into the nature of many diseases as well as lead in the development of new drugs. while most of the previous studies focus on only mining empirical phenotype structure which the experiment controls, it is also interesting to detect possible hidden phenotype structures underlying gene expression profiles.since the number of samples is usually limited, such data sets are very sparse in high-dimensional gene space. furthermore, most of the genes of interest are buried in large amount of noise. unsupervised phenotype structure discovery of such sparse high-dimensional data sets present interesting but challenging problems. in this paper, we propose the model of simultaneously mining both empirical and hidden phenotype structures from gene expression data. we demonstrate the effectiveness and efficiency of the proposed method on various real-world data sets.
queryable acyclic production systems. we pose a query problem about the behavior of a consultation system s: given a constraint formula q and a potential conclusion c for s, determine if there is a user input binding that satisfies q and causes s to conclude c. existing rule-based expert systems, both forward and backward chaining[3], implement a consultation mechanism s, but are not designed for these queries about s. for general production systems, the queries are undecidable. here we solve the problem for useful sublanguages of acyclic production systems.we implement a query tool in a datalog + constraints framework, and optimize for &ldquo;embedded decision trees&rdquo; in the rule system. our data complexity is &thgr;(n&middot;&fnof;(n)) in the size of the embedded trees, versus &thgr;(n&middot;&fnof;(n) + n2) for existing datalog evaluation algorithms, where &fnof;(n) is the cost of destructively conjoining a constraint of unit size into a conjunction of n constraints.
the power-method: a comprehensive estimation technique for multi-dimensional queries. existing estimation approaches for multi-dimensional databases often rely on the assumption that data distribution in a small region is uniform, which seldom holds in practice. moreover, their applicability is limited to specific estimation tasks under certain distance metric. this paper develops the power-method, a comprehensive technique applicable to a wide range of query optimization problems under various metrics. the power-method eliminates the local uniformity assumption and is accurate even in scenarios where existing approaches completely fail. furthermore, it performs estimation by evaluating only one simple formula with minimal computational overhead. extensive experiments confirm that the power-method outperforms previous techniques in terms of accuracy and applicability to various optimization scenarios.
scoring missing terms in information retrieval tasks. an usual approach to address mismatching vocabulary problem is to augment the original query using dictionaries and other lexical resources and/or by looking at pseudo-relevant documents. either way, terms are added to form a new query that will be used to score all documents in a subsequent retrieval pass, and as consequence the original query's focus may drift because of the newly added terms. we propose a new method to address the mismatching vocabulary problem, expanding original query terms only when necessary and complementing the user query for missing terms while scoring documents. it allows related semantic aspects to be included in a conservative and selective way, thus reducing the possibility of query drift. our results using replacements for the <i>missing query terms</i> in modified document and passages retrieval methods show significant improvement over the original ones.
semantic querying of tree-structured data sources using partially specified tree patterns. nowadays, huge volumes of data are organized or exported in a tree-structured form. querying capabilities are provided through queries that are based on branching path expression. even for a single knowledge domain structural differences raise difficulties for querying data sources in a uniform way. in this paper, we present a method for semantically querying tree-structured data sources using partially specified tree patterns. based on dimensions which are sets of semantically related nodes in tree structures, we define dimension graphs. dimension graphs can be automatically extracted from trees and abstract their structural information. they are semantically rich constructs that support the formulation of queries and their efficient evaluation. we design a tree-pattern query language to query multiple tree-structured data sources. a central feature of this language is that the structure can be specified fully, partially, or not at all in the queries. therefore, it can be used to query multiple trees with structural differences. %and we study the derivation of structural expressions in queries by introducing a set of inference rules for structural expressions. we define two types of query unsatisfiability and we provide necessary and sufficient conditions for checking each of them. our approach is validated through experimental evaluation.
query-sensitive similarity measures for the calculation of interdocument relationships. the application of document clustering to information retrieval has been motivated by the potential effectiveness gains postulated by the cluster hypothesis. the hypothesis states that relevant documents tend to be highly similar to each other, and therefore tend to appear in the same clusters. in this paper we propose that, for any given query, pairs of relevant documents will exhibit an inherent similarity which is dictated by the query itself. our research describes an attempt to devise means by which this similarity can be detected. we propose the use of query-sensitive similarity measures that bias interdocument relationships towards pairs of documents that jointly possess attributes that are expressed in a query. we experimentally tested query-sensitive measures against conventional ones that do not take the context of the query into account. we calculated interdocument relationships for varying numbers of top-ranked documents for five document collections. our results show a consistent and significant increase in the number of relevant documents that become nearest neighbours of any given relevant document when query-sensitive measures are used. these results suggest that the effectiveness of a cluster-based ir system has the potential to increase through the use of query-sensitive similarity measures.
calculating similarity between texts using graph-based text representation model. knowledge discovery from a large volumes of texts usually requires many complex analysis steps. the graph-based text representation model has been proposed to simplify the steps. the model represents texts in a formal manner, subject graphs, and provides text handling operations whose inputs and outputs are identical in form, i.e. a set of subject graphs, so they can be combined in any order. a subject graph uses node weight to represent the significance of each term, and link weight to represent that of each term-term association. this paper concentrates on the algorithms for making subject graphs and calculating the similarity between them. an evaluation shows that subject graphs can calculate the similarity between texts more precisely than term vectors, since they incorporate the significance of association between terms.
a new framework to combine descriptors for content-based image retrieval. in this paper, we propose a novel framework using genetic programming to combine image database descriptors for content-based image retrieval (cbir). our framework is validated through several experiments involving two image databases and specific domains, where the images are retrieved based on the shape of their objects.
visual structures for image browsing. content-based image retrieval (cbir) presents several challenges and has been subject to extensive research from many domains, such as image processing or database systems. database researchers are concerned with indexing and querying, whereas image processing experts worry about extracting appropriate image descriptors. comparatively little work has been done on designing user interfaces for cbir systems. this, in turn, has a profound effect on these systems since the concept of image similarity is strongly influenced by user perception. this paper describes an initial effort to fill this gap, combining recent research in cbir and information visualization, studied from a human-computer interface perspective. it presents two visualization techniques based on spiral and concentric rings implemented in a cbir system to explore query results. the approach is centered on keeping user focus on both the query image, and the most similar retrieved images. experiments conducted so far suggest that the proposed visualization strategies improves system usability.
vulnerabilities in similarity search based systems. similarity based queries are common in several modern database applications, such as multimedia, scientific, and biomedical databases. in most of these systems, database responds with the tuple with the closest match according to some metric. in this paper we investigate some important security issues related to similarity search in databases. we investigate the vulnerability of such systems against users who try to copy the database by sending automated queries. we analyze two models for similarity search, namely reply model and score model. reply model responds with the tuple with best match and score model responds with only the score of similarity search. for these models we analyze possible ways of attacks and strategies that can be used to detect attacks. our analysis shows that in score model it is much easier to plug the vulnerabilities than in reply model. sophisticated attacks can easily be used in reply model and the database is limited in capability to prevent such attacks.
text classification in a hierarchical mixture model for small training sets. documents are commonly categorized into hierarchies of topics, such as the ones maintained by yahoo! and the open directory project, in order to facilitate browsing and other interactive forms of information retrieval. in addition, topic hierarchies can be utilized to overcome the sparseness problem in text categorization with a large number of categories, which is the main focus of this paper. this paper presents a hierarchical mixture model which extends the standard naive bayes classifier and previous hierarchical approaches. improved estimates of the term distributions are made by differentiation of words in the hierarchy according to their level of generality/specificity. experiments on the newsgroups and the reuters-21578 dataset indicate improved performance of the proposed classifier in comparison to other state-of-the-art methods on datasets with a small number of positive examples.
how to improve the pruning ability of dynamic metric access methods. complex data retrieval is accelerated using index structures, which organize the data in order to prune comparisons between data during queries. in metric spaces, comparison operations can be specially expensive, so the pruning ability of indexing methods turns out to be specially meaningful. this paper shows how to measure the pruning power of metric access methods, and defines a new measurement, called "prunability," which indicates how well a pruning technique carries out the task of cutting down distance calculations at each tree level. it also presents a new dynamic access method, aiming to minimize the number of distance calculations required to answer similarity queries. we show that this novel structure is up to 3 times faster and requires less than 25% distance calculations to answer similarity queries, as compared to existing methods. this gain in performance is achieved by taking advantage of a set of global representatives. although our technique uses multiple representatives, the index structure still remains dynamic and balanced.
merging techniques for performing data fusion on the web. data fusion on the web refers to the merging, into a unified single list, of the ranked document lists, which are retrieved in response to a user query by more than one web search engine. it is performed by metasearch engines and their merging algorithms utilise the information present in the ranked lists of retrieved documents provided to them by the underlying search engines, such as the rank positions of the retrieved documents and their retrieval scores. in this paper, merging techniques are introduced that take into account not only the rank positions, but also the title and the summary accompanying the retrieved documents. furthermore, the data fusion process is viewed as being similar to the combination of belief in uncertain reasoning and is modelled using dempster-shafer's theory of evidence. our evaluation experiments indicate that the above merging techniques yield improvements in the effectiveness and that their effectiveness is comparable to that of the approach that merges the ranked lists by downloading and analysing the web documents.
managing ifc for civil engineering projects. the "industrial foundation classes" (ifc) are an iso norm to define all components of a building in a civil engineering project. ifc files are textual files whose size can reach 100 megabytes. several ifc files can coexist on the same civil engineering project. due to their size, their handling and sharing is a complex task. in this paper, we present an approach to automatically identify business objects in the ifc files and simplify their visualization and manipulation on the internet. we construct an ifc viewer which transforms the ifc file into a xml ifc tree manipulated through the 3d visualization of the building. the ifc viewer composed a web-based platform called active3d build server. this platform lets geographically dispersed project participants-from architects to electricians-directly use and exchange project documents in a centralized virtual environment during the life cycle of a civil engineering project.
structure-based query-specific document summarization. summarization of text documents is increasingly important with the amount of data available on the internet. the large majority of current approaches view documents as linear sequences of words and create query-independent summaries. however, ignoring the structure of the document degrades the quality of summaries. furthermore, the popularity of web search engines requires query-specific summaries. we present a method to create query-specific summaries by adding structure to documents by extracting associations between their fragments.