waashk/dblp huggingface dataset:
question answering from the web using knowledge annotation and knowledge mining techniques. we present a strategy for answering fact-based natural language questions that is guided by a characterization of real-world user queries. our approach, implemented in a system called aranea, extracts answers from the web using two different techniques: knowledge annotation and knowledge mining. knowledge annotation is an approach to answering large classes of frequently occurring questions by utilizing semi\-structured and structured web sources. knowledge mining is a statistical approach that leverages massive amounts of web data to overcome many natural language processing challenges. we have integrated these two different paradigms into a question answering system capable of providing users with concise answers that directly address their information needs.
an evaluation of the incorporation of a semantic network into a multidimensional retrieval engine. this paper describes a new method for incorporating a hierarchical category dimension into an information retrieval framework. the approach is to use the synonym sets and the hyponym ("is-a") relations defined within wordnet in order to derive a conceptual hierarchical category dimension. the hierarchical nature of a category dimension not only provides an overview of a set of documents but also facilitates the effectiveness and the efficiency of searching documents. an evaluation is performed on two different types of models and the multidimensional approach shows a significant reduction in the number of page accesses over a large document collection.
handling frequent updates of moving objects. a critical issue in moving object databases is to develop appropriate indexing structures for continuously moving object locations so that queries can still be performed efficiently. however, such location changes typically cause a high volume of updates, which in turn poses serious problems on maintaining index structures. in this paper we propose a lazy group update (lgu) algorithm for disk-based index structures of moving objects. lgu contains two key additional structures to group ``similar'' updates so that they can be performed together: a disk-based insertion buffer (i-buffer) for each internal node, and a memory-based deletion table (d-table) for the entire tree. different strategies of ``pushing down'' an overflow i-buffer to the next level are studied. comprehensive empirical studies over uniform and skewed datasets, as well as simulated street traffic data show that lgu achieves a significant improvement on update throughput while allowing a reasonable performance for queries.
using path information for query processing in object-oriented database systems. this paper argues that most queries in object-oriented databases require traversing from one object to another in the aggregation hierarchy. thus, the connections between objects through object identifiers are essential to the efficiency of query processing and should be represented separately from the database. we introduce the concept of path dictionary and describe how it supports queries of different types. we evaluate the storage overhead, query and update costs of the path dictionary. compared to the path index, the path dictionary has better overall query and update performance and lower storage overhead.
zbroker: a query routing broker for z39.50 databases. a query routing broker is a software agent that determines from a large set of accessing information sources the ones most relevant to a user's information need. as the number of information sources on the internet increases dramatically, future users will have to rely on query routing brokers to decide a small number of information sources to query without incurring too much query processing overheads. in this paper, we describe a query routing broker known as zbroker developed for bibliographic database servers that support the z39.50 protocol. zbroker samples the content of each bibliographic database by using training queries and their results, and summarizes the bibliographic database content into a knowledge base. we present the design and implementation of zbroker and describe its web-based user interface.
sliding-window filtering: an efficient algorithm for incremental mining. we explore in this paper an effective sliding-window filtering (abbreviatedly as swf) algorithm for incremental mining of association rules. in essence, by partitioning a transaction database into several partitions, algorithm swf employs a filtering threshold in each partition to deal with the candidate itemset generation. under swf, the cumulative information of mining previous partitions is selectively carried over toward the generation of candidate itemsets for the subsequent partitions. algorithm swf not only significantly reduces i/o and cpu cost by the concepts of cumulative filtering and scan reduction techniques but also effectively controls memory utilization by the technique of sliding-window partition. algorithm swf is particularly powerful for efficient incremental mining for an ongoing time-variant transaction database. by utilizing proper scan reduction techniques, only one scan of the incremented dataset is needed by algorithm swf. the i/o cost of swf is, in orders of magnitude, smaller than those required by prior methods, thus resolving the performance bottleneck. experimental studies are performed to evaluate performance of algorithm swf. it is noted that the improvement achieved by algorithm swf is even more prominent as the incremented portion of the dataset increases and also as the size of the database increases.
unsupervised question answering data acquisition from local corpora. data-driven approaches in question answering (qa) are increasingly common. since availability of training data for such approaches is very limited, we propose an unsupervised algorithm that generates high quality question-answer pairs from local corpora. the algorithm is ontology independent, requiring very small seed data as its starting point. two alternating views of the data make learning possible: 1) question types are viewed as relations between entities and 2) question types are described by their corresponding question-answer pairs. these two aspects of the data allow us to construct an unsupervised algorithm that acquires high precision question-answer pairs. we show the quality of the acquired data for different question types and perform a task-based evaluation. with each iteration, pairs acquired by the unsupervised algorithm are used as training data to a simple qa system. performance increases with the number of question-answer pairs acquired confirming the robustness of the unsupervised algorithm. we introduce the notion of <i>semantic drift</i> and show that it is a desirable quality in training data for question answering systems.
design and evaluation rules for building adaptive schema in an object-oriented data and knowledge base system. we develop a selection of design and evaluation rules for building an adaptive schema in an object-oriented data and knowledge base system. this set of style rules include not only those which we use to preserve validity and minimality of an object-oriented schema, but also those which help us to promote extensibility, reusability and adaptiveness of an object-oriented schema against future requirement changes. we encourage to use the set of style rules proposed as a means for validating quality of a schema, and for transforming an object-oriented schema into a better style regarding to adaptiveness and robustness, rather than as a user-oriented method solely for designing the schema.
passage retrieval based on language models. previous research has shown that passage-level evidence can bring added benefits to document retrieval when documents are long or span different subject areas. recent developments in language modeling approach to ir provided a new effective alternative to traditional retrieval models. these two streams of research motivate us to examine the use of passages in a language model framework. this paper reports on experiments using passages in a simple language model and a relevance model, and compares the results with document-based retrieval. results from the inquery search engine, which is not based on a language modeling approach, are also given for comparison. test data include two heterogeneous and one homogeneous document collections. our experiments show that passage retrieval is feasible in the language modeling context, and more importantly, it can provide more reliable performance than retrieval based on full documents.
net & cot: translating relational schemas to xml schemas using semantic constraints. two algorithms, called net and cot, to translate relational schemas to xml schemas using various semantic constraints are presented. the xml schema representation we use is a language-independent formalism named xschema, that is both precise and concise. a given xschema can be mapped to a schema in any of the existing xml schema language proposals. our proposed algorithms have the following characteristics: (1) net derives a nested structure from a flat relational model by repeatedly applying the nest operator on each table so that the resulting xml schema becomes hierarchical, and (2) cot considers not only the structure of relational schemas, but also semantic constraints such as inclusion dependencies during the translation. it takes as input a relational schema where multiple tables are interconnected through inclusion dependencies and converts it into a good xschema. to validate our proposals, we present experimental results using both real schemas from the uci repository and synthetic schemas from tpc-h.
batch data warehouse maintenance in dynamic environments. data warehouse view maintenance is an important issue due to the growing use of warehouse technology for information integration and data analysis. given the dynamic nature of modern distributed environments, both data updates and schema changes are likely to occur in different data sources. in applications that the real-time refreshment of data warehouse extent under source changes is not critical, the source updates are usually maintained in a batch fashion to reduce the maintenance overhead. however, most prior work can only deal with batch source data updates. in this paper, we provide a solution strategy that is capable of batching both source data updates and schema changes. we propose techniques to first preprocess the initial source updates to summarize delta changes for each source. we then design a view adaptation algorithm to adapt the warehouse view under these delta changes. we have implemented our solutions and incorporated into an existing data warehouse prototype system. the experimental studies demonstrate excellent performance achievable by our batch techniques.
intelligent knowledge discovery in peer-to-peer file sharing. emerging peer-to-peer computing provides new possibilities but also challenges for distributed applications. despite their significant potential, current peer-to-peer networks lack efficient knowledge discovery and management. this paper addresses this deficiency and proposes the intelligent file sharing framework, which provides an effective and flexible query for p2p file sharing. the ifs is based on powerful schema and flexible inference, as well as efficiently integrated and extensible retrieval algorithms. experimental results have provided evidence of the high performance and scalability of the intelligent file sharing (ifs) system in peer-to-peer environments.
a singer identification technique for content-based classification of mp3 music objects. as there is a growing amount of mp3 music data available on the internet today, the problems related to music classification and content-based music retrieval are getting more attention recently. in this paper, we propose an approach to automatically classify mp3 music objects according to their singers. first, the coefficients extracted from the output of the polyphase filters are used to compute the mp3 features for segmentation. based on these features, an mp3 music object can be decomposed into a sequence of notes (or phonemes). then for each mp3 phoneme in the training set, its mp3 feature is extracted and used to train an mp3 classifier which can identify the singer of an unknown mp3 music object. experiments are performed and analyzed to show the effectiveness of the proposed method.
efficient incremental view maintenance in data warehouses. in the data warehouse environment, the concept of a materialized view is nowadays common and important in an objective of efficiently supporting olap query processing. materialized views are generally derived from select-project-join of several base relations. these materialized views need to be updated when the base relations change. since the propagation of updates to the views may impose a significant overhead, it is very important to update the warehouse views efficiently. though various view maintenance strategies have been discussed so far, they typically require too much access to base relations, resulting in the performance degradation.in this paper we propose an efficient incremental view maintenance strategy called delta propagation that can minimize the total size of base relations accessed by analyzing the properties of base relations. we first define the delta expression and a delta propagation tree which are core concepts of the strategy. then, a dynamic programming algorithm that can find the optimal delta expression are proposed. we also present various experimental results that show the usefulness and efficiency of the strategy.
computer image retrieval by features: selecting the best facial features for suspect identification systems. correct suspect identification of known offenders by witnesses deteriorates rapidly as more are examined in mugshot albums. feature approaches, where mugshots are displayed in order of similarity to witnesses' descriptions, increase identification success by reducing this number. system performance depends on selection of system features. four methods of selecting features are evaluated empirically: theory, random, hill-climbing algorithm, and hybrid. the theory asserts success depends on five properties of system features: informativeness, orthogonality, sufficiency, consistency, and observability. comparing system performance on the best 10 features selected (from a pool of 90) by each method supports our contention. in four experimental tests of a system with 1000 official mugshots, over 90% of witness searches resulted in photos of target suspects retrieved in the first ten mugshots displayed for examination (using all 90 system features). on average, suspects were retrieved in the first 54, 7, 22, and 70 mugshots when using only the best 10 model features. hybrid and hill-climbing algorithms did not improve on this performance, and performance of randomly selected sets of 10 features was poor.
xclust: clustering xml schemas for effective integration. it is increasingly important to develop scalable integration techniques for the growing number of xml data sources. a practical starting point for the integration of large numbers of document type definitions (dtds) of xml sources would be to first find clusters of dtds that are similar in structure and semantics. reconciling similar dtds within such a cluster will be an easier task than reconciling dtds that are different in structure and semantics as the latter would involve more restructuring. we introduce xclust, a novel integration strategy that involves the clustering of dtds. a matching algorithm based on the semantics, immediate descendents and leaf-context similarity of dtd elements is developed. our experiments to integrate real world dtds demonstrate the effectiveness of the xclust approach.
algebraic equivalences among nested relational expressions. algebraic optimization is both theoretically and practically important for query processing in (nested) relational databases. in this paper, we consider this issue and investigate some algebraic properties concerning the nested relational operators. we also outline a heuristic optimization algorithm for nested relational expressions by adopting algebraic transformation rules developed in this paper and previous related work.
the egg/yolk reliability hierarchy : semantic data integration using sorts with prototypes. integration of disparate heterogeneous databases requires translation of types. because a type in one system often has no exact counterpart in the others, fully reliable integration requires deep understanding of the subject domain, with conceptual analysis of type meanings. so far, reliable translation has had to be done by hand. in practice, few types are so crucial as to require full reliability. the egg/yolk hierarchy ranks types by the tolerable rashness in translation, based on prototypes in each type. each defined class (egg) has a subclass of typical members (yolk) defined. we exploit cui, cohn and randell's qualitative spatial simulation program to create the hierarchy of all possible relations between source and target egg/yolk types, ranked by reliability. our eventual ranking is based on a poset combining four different preference criteria.
restructuring batch view maintenance efficiently. materialized views defined over distributed data sources are a well recognized technology for modern applications. state-of the-art incremental view maintenance requires o(<i>n</i><sup>2</sup>) or more maintenance queries to remote data sources with <i>n</i> being the number of data sources in the view definition. in this poster, we illustrate basic ideas of novel view maintenance strategies that dramatically reduce the number of maintenance queries. such reduction brings the tradeoff between the number of maintenance queries and the complexity of each query. these algorithms have been implemented in a working prototype system. experimental studies illustrate major performance improvement in terms of total processing time compared with existing batch algorithms.
content-based retrieval of mp3 music objects. in recent years, the searching and indexing techniques for multimedia data are getting more attention in the area of multimedia databases. as many research works were done on the content-based retrieval of image and video data, less attention was received to the content-based retrieval of audio data. in this paper, we propose an approach to retrieve mp3 music objects based on their content. in our approach, the coefficients extracting from the output of the polyphase filters are used to compute the mp3 features for indexing the mp3 objects. we also propose an mp3 similarity measuring function to provide users the ability to approximately retrieve the desired mp3 objects. experiments are performed and analyzed to show the efficiency and the effectiveness of the proposed method.
detecting changes on unordered xml documents using relational databases: a schema-conscious approach. several relational approaches have been proposed to detect the changes to xml documents by using relational databases. these approaches store the xml documents in the relational database and issue sql queries (whenever appropriate) to detect the changes. all of these relational-based approaches use the schema-oblivious xml storage strategy for detecting the changes. however, there is growing evidence that schema-conscious storage approaches perform significantly better than schema-oblivious approaches as far as xml query processing is concerned. in this paper, we study a relational-based unordered xml change detection technique (called h<small>elios</small>) that uses a schema-conscious approach (shared-inlining) as the underlying storage strategy. h<small>elios</small> is up to 52 times faster than x-diff [7] for large datasets (more than 1000 nodes). it is also up to 6.7 times faster than x<small>andy</small> [4]. the result quality of deltas detected by h<small>elios</small> is comparable to the result quality of deltas detected by xandy.
maintaining views in object-relational databases. view materialization is an important way of improving the performance of query processing. when an update occurs to the source data from which a materialized view is derived, the materialized view has to be updated so that it is consistent with the source data. this update process is called view maintenance . the incremental method of view maintenance, which computes the new view using the old view and the update to the source data, is widely preferred to full view recomputation when the update is small in size. in this paper we investigate how to incrementally maintain views in object-relational (or) databases. the investigation focuses on maintaining views defined in or-sql, a language containing the features of object referencing, inheritance, collection, and aggregate functions including user-defined set aggregate functions. we propose an architecture and algorithms for incremental or view maintenance. we implement all algorithms and analyze the performance of them in comparison with full view recomputation. the analysis shows that the algorithms significantly reduce the cost of updating a view when the size of an update to the source data is relatively small.
fast on-line index construction by geometric partitioning. inverted index structures are the mainstay of modern text retrieval systems. they can be constructed quickly using off-line merge-based methods, and provide efficient support for a variety of querying modes. in this paper we examine the task of on-line index construction -- that is, how to build an inverted index when the underlying data must be continuously queryable, and the documents must be indexed and available for search as soon they are inserted. when straightforward approaches are used, document insertions become increasingly expensive as the size of the database grows. this paper describes a mechanism based on controlled partitioning that can be adapted to suit different balances of insertion and querying operations, and is faster and scales better than previous methods. using experiments on 100gb of web data we demonstrate the efficiency of our methods in practice, showing that they dramatically reduce the cost of on-line index construction.
extracting a website's content structure from its link structure. hierarchical models are commonly used to organize a website's content. a website's content structure can be represented by a topic hierarchy, a directed tree rooted at a website's homepage in which the vertices and edges correspond to web pages and hyperlinks. in this work, we propose an algorithm for extracting a website's topic hierarchy from its link structure. the proposed algorithm consists of a construction stage and a refining stage, in which we analyze the semantic relationships between web pages based on link structure, web page content and directory structure. we've done extensive experiments using different websites and obtained very promising results.
evaluating document clustering for interactive information retrieval. we consider the problem of organizing and browsing the top ranked portion of the documents returned by an information retrieval system. we study the effectiveness of a document organization in helping a user to locate the relevant material among the retrieved documents as quickly as possible. in this context we examine a set of clustering algorithms and experimentally show that a clustering of the retrieved documents can be significantly more effective than traditional ranked list approach. we also show that the clustering approach can be as effective as the interactive relevance feedback based on query expansion while retaining an important advantage -- it provides the user with a valuable sense of control over the feedback process.