waashk/dblp huggingface dataset:
data-driven compound splitting method for english compounds in domain names. significant amount of literature is available on compound splitting of long words albeit for non-english languages- especially european. not surprisingly, there has been not much work for english as it is not a compounding language like some of its european counterparts. however, internet domain names in general are compound english words, e.g. bankofamerica.com". compound splitting can be effectively employed to extract information from domain names. in this paper, an data-driven learning technique for splitting english compound words is described which among others uses features like normalized frequency, length of parts and n-gram. the splitting f-measure is higher than the published approaches. we applied this technique on a real life web search application where the queries are mistyped domain names routed through sources like isps and browsers. relevant and meaningful keywords were extracted out and shown to the user as a value added search option. results show a very high click-through rate and increased commercial value.
sdoc: exploring social wisdom for document enhancement in web mining. web document could be seen to be composed of textual content as well as social metadata of various forms (e.g., anchor text, search query and social annotation), both of which are valuable to indicate the semantic content of the document. however, due to the free nature of the web, the two streams of web data suffer from the serious problems of noise and sparseness, which have actually become the major challenges to the success of many web mining applications. previous work has shown that it could enhance the content of web document by integrating anchor text and search query. in this paper, we study the problem of exploring emergent social annotation for document enhancement and propose a novel reinforcement framework to generate "social representation" of document. distinguishing from prior work, textual content and social annotation are enhanced simultaneously in our framework, which is achieved by exploiting a kind of mutual reinforcement relationship behind them. two convergent models, social content model and social annotation model, are symmetrically derived from the framework to represent enhanced textual content and enhanced social annotation respectively. the enhanced document is referred to as social document or sdoc in that it could embed complementary viewpoints from many web authors and many web visitors. in this sense, the document semantics is enhanced exactly by exploring social wisdom. we build the framework on a large del.icio.us data and evaluate it through three typical web mining applications: annotation, classification and retrieval. experimental results demonstrate that social representation of web document could boost the performance of these applications significantly.
using opinion-based features to boost sentence retrieval. opinion mining has become recently a major research topic. a wide range of techniques have been proposed to enable opinion-oriented information seeking systems. however, little is known about the ability of opinion-related information to improve regular retrieval tasks. our hypothesis is that standard retrieval methods might benefit from the inclusion of opinion-based features. a sentence retrieval scenario is a natural choice to evaluate this claim. we propose here a formal method to incorporate some opinion-based features of the sentences as query-independent evidence. we show that this incorporation leads to retrieval methods whose performance is significantly better than the the performance of state of the art sentence retrieval models.
the gardener's problem for web information monitoring. we introduce and theoretically study the gardener's problem that well models many web information monitoring scenarios, where numerous dynamically changing web sources are monitored and local information needs to be periodically updated under communication and computation capacity constraints. typical such examples include maintenance of inverted indexes for search engines and maintenance of extracted structures for unstructured data management systems. we formulate a corresponding multicriteria optimization problem and propose heuristic solutions.
learning to rank graphs for online similar graph search. many applications in structure matching require the ability to search for graphs that are similar to a query graph, i.e., similarity graph queries. prior works, especially in chemoinformatics, have used the maximum common edge subgraph (mceg) to compute the graph similarity. this approach is prohibitively slow for real-time queries. in this work, we propose an algorithm that extracts and indexes subgraph features from a graph dataset. it computes the similarity of graphs using a linear graph kernel based on feature weights learned offline from a training set generated using mceg. we show empirically that our proposed algorithm of learning to rank graphs can achieve higher normalized discounted cumulative gain compared with existing optimal methods based on mceg. the running time of our algorithm is orders of magnitude faster than these existing methods.
compact full-text indexing of versioned document collections. we study the problem of creating highly compressed full-text index structures for versioned document collections, that is, collections that contain multiple versions of each document. important examples of such collections are wikipedia or the web page archive maintained by the internet archive. a straightforward indexing approach would simply treat each document version as a separate document, such that index size scales linearly with the number of versions. however, several authors have recently studied approaches that exploit the significant similarities between different versions of the same document to obtain much smaller index sizes. in this paper, we propose new techniques for organizing and compressing inverted index structures for such collections. we also perform a detailed experimental comparison of new techniques and the existing techniques in the literature. our results on an archive of the english version of wikipedia, and on a subset of the internet archive collection, show significant benefits over previous approaches.
answering xml queries using materialized views revisited. answering queries using views is a well-established technique in databases. in this context, two outstanding problems can be formulated. the first one consists in deciding whether a query can be answered exclusively using one or multiple materialized views. given the many alternative ways to compute the query from the materialized views, the second problem consists in finding the best way to compute the query from the materialized views. in the realm of xml, there is a restricted number of contributions in the direction of these problems due to the many limitations associated with the use of materialized views in traditional xml query evaluation models. in this paper, we adopt a recent evaluation model, called inverted lists model, and holistic algorithms which together have been established as the prominent technique for evaluating queries on large persistent xml data, and we address the previous two problems. this new context revises these problems since it requires new conditions for view usability and new techniques for computing queries from materialized views. we suggest an original approach for materializing views which stores for every view node only the list of xml nodes necessary for computing the answer of the view. we specify necessary and sufficient conditions for answering a tree-pattern query using one or multiple materialized views in terms of homomorphisms from the views to the query. in order to efficiently answer queries using materialized views, we design a stack-based algorithm which compactly encodes in polynomial time and space all the homomorphisms from a view to a query. we further propose space and time optimizations by using bitmaps to encode view materializations and by employing bitwise operations to minimize the evaluation cost of the queries. finally, we conducted an extensive experimentation which demonstrates that our approach yields impressive query hit rates in the view pool, achieves significant time and space savings and shows smooth scalability.
mining linguistic cues for query expansion: applications to drug interaction search. given a drug under development, what are other drugs or biochemical compounds that it might interact with? early answers to this question, by mining the literature, are valuable for pharmaceutical companies, both monetarily and in avoiding public relations nightmares. inferring drug-drug interactions is also important in designing combination therapies for complex diseases including cancers. we study this problem as one of mining linguistic cues for query expansion. by using (only) positive instances of drug interactions, we show how we can extract linguistic cues which can then be used to expand and reformulate queries to improve the effectiveness of drug interaction search. our approach integrates many learning paradigms: partially supervised classification, association measures for collocation mining, and feature selection in supervised learning. we demonstrate compelling results on using positive examples from the drugbank database to seed medline searches for drug interactions. in particular, we show that purely data-driven linguistic cues can be effectively mined and applied to realize a successful domain-specific query expansion framework.
ensembles in adversarial classification for spam. the standard method for combating spam, either in email or on the web, is to train a classifier on manually labeled instances. as the spammers change their tactics, the performance of such classifiers tends to decrease over time. gathering and labeling more data to periodically retrain the classifier is expensive. we present a method based on an ensemble of classifiers that can detect when its performance might be degrading and retrain itself, all without manual intervention. experiments with a real-world dataset from the blog domain show that our methods can significantly reduce the number of times classifiers are retrained when compared to a fixed retraining schedule, and they maintain classification accuracy even in the absence of manually labeled examples.
efficient algorithms for approximate member extraction using signature-based inverted lists. we study the problem of approximate membership extraction (ame), i.e., how to efficiently extract substrings in a text document that approximately match some strings in a given dictionary. this problem is important in a variety of applications such as named entity recognition and data cleaning. we solve this problem in two steps. in the first step, for each substring in the text, we filter away the strings in the dictionary that are very different from the substring. in the second step, each candidate string is verified to decide whether the substring should be extracted. we develop an incremental algorithm using signature-based inverted lists to minimize the duplicate list-scan operations of overlapping windows in the text. our experimental study of the proposed algorithms on real and synthetic datasets showed that our solutions significantly outperform existing methods in the literature.
yam: a schema matcher factory. in this paper, we present yam, a schema matcher factory. yam (yet another matcher) is not (yet) another schema matching system as it enables the generation of a la carte schema matchers according to user requirements. these requirements include a preference for recall or precision, a training data set (schemas already matched) and provided expert correspondences. yam uses a knowledge base that includes a (possibly large) set of similarity measures and classifiers. based on the user requirements, yam learns how to best apply these tools (similarity measures and classifiers) in concert to achieve the best matching quality. in our demonstration, we will let users apply yam to build the best schema matcher for different user requirements.
socializing or knowledge sharing?: characterizing social intent in community question answering. knowledge sharing communities, such as wikipedia or yahoo! answers, add greatly to the wealth of information available on the web. they represent complex social ecosystems that rely on user paricipation and the quality of users' contributions to prosper. however, quality is harder to achieve when knowledge sharing is facilitated through a high degree of personal interactions. the individuals' objectives may change from knowledge sharing to socializing, with a profound impact on the community and the value it delivers to the broader population of web users. in this paper we provide new insights into the types of content that is shared through community question answering (cqa) services. we demonstrate an approach that combines in-depth content analysis with social network analysis techniques. we adapted the undirected inductive coding method to analyze samples of user questions and arrive at a comprehensive typology of the user intent. in our analysis we focused on two types of intent, social vs. non-social, and define measures of social engagement to characterize the users' participation and content contributions. our approach is applicable to a broad class of online communities and can be used to monitor the dynamics of community ecosystems.
context sensitive synonym discovery for web search queries. we propose a simple yet effective approach to context sensitive synonym discovery for web search queries based on co-click analysis; i.e., analyzing queries leading to clicking same documents. in addition to deriving word based synonyms, we also derive concept based synonyms with the help of query segmentation. evaluation results show that this approach dramatically outperforms the thesaurus based synonym replacement method in keeping search intent, from accuracy of 40% to above 80%.
fuzzy semantic web ontology learning from fuzzy uml model. how to quickly and cheaply construct web ontologies has become a key technology to enable the semantic web. classical ontologies are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. in this paper, we propose an approach for constructing fuzzy ontologies from fuzzy uml models, in which the fuzzy ontology consists of fuzzy ontology structure and instances. firstly, the fuzzy uml model is investigated in detail, and a kind of formal definition of fuzzy uml models is proposed. then, a kind of fuzzy ontology called fuzzy owl dl ontology is introduced. furthermore, we consider the fuzzy uml model and the corresponding fuzzy uml instantiations (i.e., object diagrams) simultaneously, and translate them into the fuzzy ontology structure and the fuzzy ontology instances, respectively. in addition, since a fuzzy owl dl ontology is equivalent to a fuzzy description logic f-shoin(d) knowledge base, how the reasoning problems of fuzzy uml models (e.g., consistency, subsumption, equivalence, and redundancy) may be reasoned through reasoning mechanism of f-shoin(d) is investigated, which can help to construct fuzzy ontologies more exactly.
automatic query generation for patent search. patent search is the task of finding relevant existing patents, which is an important part of the patent's examiner's process of validating a patent application. in this paper, we studied how to transform a query patent (the application) into search queries. three types of search features are explored for automatic query generation for patent search. furthermore, different types of features are combined with a learning to rank method. experiments based on a uspto patent collection demonstrate that the single best search feature is the combination of words and noun-phrases from the summary field and the retrieval performance can be significantly improved by combining three types of search features.
user-induced links in collaborative tagging systems. collaborative tagging systems allow users to use tags to describe their favourite online documents. two documents that are maintained in the collection of the same user and/or assigned similar sets of tags can be considered as related from the perspective of the user, even though they may not be connected by hyperlinks. we call this kind of implicit relations user-induced links between documents. we consider two methods of identifying user-induced links in collaborative tagging, and compare these links with existing hyperlinks on the web. our analyses show that user-induced links have great potentials to enrich the existing link structure of the web. we also propose to use these links as a basis for predicting how documents would be tagged. our experiments show that they achieve much higher accuracy than existing hyperlinks. this study suggests that by studying the collective behaviour of users we are able to enhance navigation and organisation of web documents.
provenance query evaluation: what's so special about it? while provenance has been extensively studied in the literature, the efficient evaluation of provenance queries remains an open problem. traditional query optimization techniques, like the use of general-purpose indexes, or the materialization of provenance data, fail on different fronts to address the problem. therefore, the need to develop provenance-aware access methods becomes apparent. this paper starts by identifying some key requirements that are to a large extent specific to provenance queries and are necessary for their efficient evaluation. the first such property, called duality, requires that a single access method is used to evaluate both backward provenance queries (which input items of some analysis generate an output item) and forward provenance queries (which outputs of some analysis does an input item generate). the second property, called locality, guarantees that provenance query evaluation times should depend mainly on the size of the provenance query results and should be largely independent of the total size of provenance data. motivated by the above, we identify proper data structures with the aforementioned properties, we implement them, and through a detailed set of experiments, we illustrate their effectiveness on the evaluation of provenance queries.
online anonymity for personalized web services. to receive personalized web services, the user has to provide personal information and preferences, in addition to the query itself, to the web service. however, detailed personal information could identify the sender of sensitive queries, thus compromise user privacy. we propose the notion of online anonymity to enable users to issue personalized queries to an untrusted web service while with their anonymity preserved. the challenge for providing online anonymity is dealing with unknown and dynamic web users who can get online and offline at any time. we define this problem, discuss its implications and differences from the problems in the literature, and propose a solution.
url normalization for de-duplication of web pages. presence of duplicate documents in the world wide web adversely affects crawling, indexing and relevance, which are the core building blocks of web search. in this paper, we present a set of techniques to mine rules from urls and utilize these learnt rules for de-duplication using just url strings without fetching the content explicitly. our technique is composed of mining the crawl logs and utilizing clusters of similar pages to extract specific rules from urls belonging to each cluster. preserving each mined rules for de-duplication is not efficient due to the large number of specific rules. we present a machine learning technique to generalize the set of rules, which reduces the resource footprint to be usable at web-scale. the rule extraction techniques are robust against web-site specific url conventions. we demonstrate the effectiveness of our techniques through experimental evaluation.
heterogeneous cross domain ranking in latent space. traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. however, in many real-world applications, in particular with the rapid growth of the web 2.0, ranking over multiple interrelated (heterogeneous) domains becomes a common situation, where in some domains we may have a large amount of training data while in some other domains we can only collect very little. one important question is: "if there is not sufficient supervision in the domain of interest, how could one borrow labeled information from a related but heterogenous domain to build an accurate model?". this paper explores such an approach by bridging two heterogeneous domains via the latent space. we propose a regularized framework to simultaneously minimize two loss functions corresponding to two related but different information sources, by mapping each domain onto a "shared latent space", capturing similar and transferable oncepts. we solve this problem by optimizing the convex upper bound of the non-continuous loss function and derive its generalization bound. experimental results on three different genres of data sets demonstrate the effectiveness of the proposed approach.
ranking model adaptation for domain-specific search. recently, various domain-specific search engines emerge, which are restricted to specific topicalities or document formats, and vertical to the broad-based search. simply applying the ranking model trained for the broad-based search to the verticals cannot achieve a sound performance due to the domain differences, while building different ranking models for each domain is both laborious for labeling sufficient training samples and time-consuming or the training process. in this paper, to address the above difficulties, we investigate two problems: (1) whether we can adapt the ranking model learned for existing web page search or verticals, to the new domain, so that the amount of labeled data and the training cost is reduced, while the performance requirement is still satisfied; and (2) how to adapt the ranking model from auxiliary domains to a new target domain. we address the second problem from the regularization framework and an algorithm called ranking adaptation svm is proposed. our algorithm is flexible enough, which needs only the prediction from the existing ranking model, rather than the internal representation of the model or the data from auxiliary domains. the first problem is addressed by the proposed ranking adaptability measurement, which quantitatively estimates if an existing ranking model can be adapted to the new domain. extensive experiments are performed over letor benchmark dataset and two large scale datasets crawled from different domains through a commercial internet search engine, where the ranking model learned for one domain will be adapted to the other. the results demonstrate the applicabilities of the proposed ranking model adaptation algorithm and the ranking adaptability measurement.