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A Bayesian network approach to searching web databases through keyword-based queries

INFORMATION PROCESSING & MANAGEMENT 2004年第5期40卷 773-790页

作者： Calado, P da Silva, AS Laender, AHF Ribeiro-Neto, BA Vieira, RC Univ Fed Minas Gerais Dept Comp Sci BR-30123970 Belo Horizonte MG Brazil Univ Fed Amazonas Dept Comp Sci BR-69077000 Manaus Amazonas Brazil

On-line information services have become widespread in the web nowadays. However, web users are non-specialized and have a great variety of interests. Interfaces for web databases must, therefore, be both simple and uniform. In this paper, we present a solution for querying web databases using keywords only. A Bayesian network model is used to generate a set of one or more plausible structured queries derived form the initial user input. These queries can then be submitted to web databases and the retrieved results presented as a set of ranked answers. To structure the user queries, full access to the database is not required. Instead, only a small portion of its content, extracted through a public web interface, is used by the network model. This approach not only reduces the complexity of existing on-line interfaces, but also offers a solution to the problem of querying several distinct web databases with a single interface. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： web databases Bayesian networks query structuring

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Using Linked Data for Integrating Educational Medical web databases Based on BioMedical Ontologies

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COMPUTER JOURNAL 2017年第3期60卷 369-388页

作者： Al Fayez, Reem Q. Joy, Mike Univ Jordan Dept Comp Informat Syst Amman 11942 Jordan Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England

Open data are playing a vital role in different communities, including governments, businesses and education. This revolution has had a high impact on the education field. Recently, Linked Data are being adopted for publishing and connecting data on the web by exposing and connecting data, which were not previously linked. In the context of education, applying Linked Data to the growing amount of open data used for learning is potentially highly beneficial. This paper proposes a system that tackles the challenges of data acquisition and integration from distributed web data sources into one linked data set. The application domain of this work is medical education, and the focus is on integrating educational content in the form of articles published in online educational libraries and web 2.0 content that can be used for education. The process of integrating a collection of heterogeneous resources is to create links that connect the resources collected from distributed web data sources based on their semantics. The proposed system harvests metadata from distributed web sources and enriches it with concepts from biomedical ontologies, such as Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT), that enable its linking. The final result of building this system is a linked data set of more than 10 000 resources collected from PubMed Library, YouTube channels and Blogging platforms. The final linked data set is evaluated by developing information retrieval methods that exploit the SNOMED CT hierarchical relations for accessing and querying the data set. Ontology-based browsing method has been developed for exploring the data set, and the browsing results have been clustered to evaluate its linkages. Furthermore, ontology-based query searching method has been developed and tested to enhance the discoverability of the data. The results were promising and had shown that using SNOMED CT for integrating distributed resources on the web is beneficial.

关键词： Linked Data biomedical ontologies medical education web databases data model data integration

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An ontology for semantic integration of life science web databases

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INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS 2003年第2期12卷 275-294页

作者： Ben Miled, Z webster, YW Liu, Y Li, NH Purdue Sch Engn & Technol Dept Elect & Comp Engn Indianapolis IN 46202 USA Purdue Sch Sci Indianapolis IN 46202 USA

The incompatibilities among complex data formats and various schema used by biological databases that house these data are becoming a bottleneck in biological research. For example, biological data format varies from simple words (e.g. gene name), numbers (e.g. molecular weight) to sequence strings (e.g. nucleic acid sequence), to even more complex data formats such as taxonomy trees. Some information is embedded in narrative text, such as expert comments and publications. Some other information is expressed as graphs or images (e.g. pathways networks). The confederation of heterogeneous web databases has become a crucial issue in today's biological research. In other words, interoperability has to be archieved among the biological web databases and the heterogeneity of the web databases has to be resolved. This paper presents a biological ontology, BAO, and discusses its advantages in supporting the semantic integration of biological web databases are discussed.

关键词： ontology biology query translation integration web databases interoperability

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QProber: A system for automatic classification of hidden-web databases

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2003年第1期21卷 1-41页

作者： Gravano, L Ipeirotis, PG Sahami, M Columbia Univ Dept Comp Sci New York NY 10027 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA

The contents of many valuable web-accessible databases are only available through search interfaces and are hence invisible to traditional web "crawlers." Recently, commercial web sites have started to manually organize web-accessible databases into Yahoo!-like hierarchical classification schemes. Here we introduce QProber, a modular system that automates this classification process by using a small number of query probes, generated by document classifiers. QProber can use a variety of types of classifiers to generate the probes. To classify a database, QProber does not retrieve or inspect any documents or pages from the database, but rather just exploits the number of matches that each query probe generates at the database in question. We have conducted an extensive experimental evaluation of QProber over collections of real documents, experimenting with different types of document classifiers and retrieval models. We have also tested our system with over one hundred web-accessible databases. Our experiments show that our system has low overhead and achieves high classification accuracy across a variety of databases.

关键词： algorithms experimentation performance database classification web databases hidden web

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SMARTINT: using mined attribute dependencies to integrate fragmented web databases

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2012年第3期38卷 575-599页

作者： Gummadi, Ravi Khulbe, Anupam Kalavagattu, Aravind Salvi, Sanil Kambhampati, Subbarao Arizona State Univ Dept Comp Sci & Engn Tempe AZ 85287 USA

Many web databases can be seen as providing partial and overlapping information about entities in the world. To answer queries effectively, we need to integrate the information about the individual entities that are fragmented over multiple sources. At first blush this is just the inverse of traditional database normalization problem-rather than go from a universal relation to normalized tables, we want to reconstruct the universal relation given the tables (sources). The standard way of reconstructing the entities will involve joining the tables. Unfortunately, because of the autonomous and decentralized way in which the sources are populated, they often do not have Primary Key-Foreign Key relations. While tables may share attributes, naive joins over these shared attributes can result in reconstruction of many spurious entities thus seriously compromising precision. Our system, SI is aimed at addressing the problem of data integration in such scenarios. Given a query, our system uses the Approximate Functional Dependencies (AFDs) to piece together a tree of relevant tables to answer it. The result tuples produced by our system are able to strike a favorable balance between precision and recall.

关键词： web databases Loss of PK/FK Information integration

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Personalized ranking in web databases: establishing and utilizing an appropriate workload

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DISTRIBUTED AND PARALLEL databases 2013年第1期31卷 47-70页

作者： Telang, Aditya Chakravarthy, Sharma Li, Chengkai Univ Texas Arlington Dept Comp Sci & Engg Arlington TX 76019 USA

The emergence of the deep web has given a new connotation to the concept of ranking database query results. Earlier approaches for ranking either resorted to analyzing frequencies of database values and query logs or establishing user profiles. In contrast, an integrated approach, based on the notion of a similarity model, for holistically supporting user- and query-dependent ranking has been recently proposed (Telang et al. in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2011). An important component of this framework is a workload consisting of ranking functions, wherein each function represents an individual user's preferences towards the results of a specific query. At the time of answering a query for which no prior ranking function exists, the similarity model is employed, and is expected to ensure a good quality of ranking as long as a ranking function for a very similar user-query pair exists in this workload. In this paper, we address the problem of determining an appropriate set of user-query pairs to form a workload of ranking functions to support user- and query-dependent ranking for web databases. We propose a novel metric, termed workload goodness, that quantifies the notion of a "good" workload into an absolute value. The process of finding such a workload of optimal goodness is a combinatorially explosive problem;therefore, we propose a heuristic solution, and advance three approaches for determining an acceptable workload, in a static as well as a dynamic environment. We discuss the effectiveness of our proposal analytically as well as experimentally over two web databases.

关键词： Ranking web databases Similarity model Workload

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One Size Does Not Fit All: Toward User- and Query-Dependent Ranking for web databases

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2012年第9期24卷 1671-1685页

作者： Telang, Aditya Li, Chengkai Chakravarthy, Sharma Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

With the emergence of the deep web, searching web databases in domains such as vehicles, real estate, etc., has become a routine task. One of the problems in this context is ranking the results of a user query. Earlier approaches for addressing this problem have used frequencies of database values, query logs, and user profiles. A common thread in most of these approaches is that ranking is done in a user-and/or query-independent manner. This paper proposes a novel query- and user-dependent approach for ranking query results in web databases. We present a ranking model, based on two complementary notions of user and query similarity, to derive a ranking function for a given user query. This function is acquired from a sparse workload comprising of several such ranking functions derived for various user-query pairs. The model is based on the intuition that similar users display comparable ranking preferences over the results of similar queries. We define these similarities formally in alternative ways and discuss their effectiveness analytically and experimentally over two distinct web databases.

关键词： Automated ranking web databases user similarity query similarity workload

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A Tree-Based WQI Modeling Approach for Integrating web databases 17

A Tree-Based WQI Modeling Approach for Integrating Web Datab...

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17th International Conference on Information Fusion (FUSION)

作者： Marin-Castro, Heidy M. Sosa-Sosa, Victor J. Lopez-Arevalo, Ivan Natl Polytech Inst Ctr Res & Adv Studies Informat Technol Lab Victoria City Tamaulipas Mexico

ISBN: (纸本)9781479916344

Everyday, more and more specialized databases (car rental, hotels, airfares, etc.) are available on the web and can be only queried by means of a web Query Interface (WQI). Since in the web is increasing the number of domain-specific databases, it is getting very complicated for end users to explore the information stored in them. In this context, research efforts are focused on building a single (unified) specific-domain WQI that allows user to query and integrate information available in different web databases. The construction of such integrated WQI, for a given domain, involves several complex tasks, specially the extraction, representation, understanding and mapping of semantic content of each individual WQI associated to a web database. Previous approaches have considered hierarchical models to build integrated WQI, preserving the ancestor-descendant relationships in individual WQIs. In this work, we propose a novel tree-based approach for automatic construction of a hierarchical model of visual content of WQIs, representing their components in a clear and concise form. In the proposed approach, the Document Object Model(DOM) tree of each WQI considered in the integration process is processed by a specialized web resource to obtain relevant visual information in the WQI such as fields (UIs), groups of UIs and super-groups as well as their corresponding labels. This process is guided by a set of 8 design heuristic rules for the right identification of labels and components. Experiments to evaluate the proposed strategy were conducted on the ICQ and Tel-8 datasets of UIUC repository. Our results showed that the proposed tree-based approach for representing the visual components in a WQI has more than 94% of accuracy, improving current reported approaches and making easier the integration process of domain-specific WQIs.

关键词： databases Engines HTML Rendering (computer graphics) Semantics Vectors Visualization Water quality index web databases HyperText Markup Language Engines Database Semantics Visualization webs World Wide web congenital web hierarchical models web resources

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Breaking the Top-k Barrier of Hidden web databases

Breaking the Top-k Barrier of Hidden Web Databases

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29th IEEE International Conference on Data Engineering (ICDE)

作者： Thirumuruganathan, Saravanan Zhang, Nan Das, Gautam Univ Texas Arlington Arlington TX 76019 USA Qatar Comp Res Inst Rayyan Qatar

ISBN: (纸本)9781467349093;9781467349086

A large number of web databases are only accessible through proprietary form-like interfaces which require users to query the system by entering desired values for a few attributes. A key restriction enforced by such an interface is the top-k output constraint -i.e., when there are a large number of matching tuples, only a few (top-k) of them are preferentially selected and returned by the website, often according to a proprietary ranking function. Since most web database owners set k to be a small value, the top-k output constraint prevents many interesting third-party (e. g., mashup) services from being developed over real-world web databases. In this paper we consider the novel problem of "digging deeper" into such web databases. Our main contribution is the meta-algorithm GetNext that can retrieve the next ranked tuple from the hidden web database using only the restrictive interface of a web database without any prior knowledge of its ranking function. This algorithm can then be called iteratively to retrieve as many top ranked tuples as necessary. We develop principled and efficient algorithms that are based on generating and executing multiple reformulated queries and inferring the next ranked tuple from their returned results. We provide theoretical analysis of our algorithms, as well as extensive experimental results over synthetic and real-world databases that illustrate the effectiveness of our techniques.

关键词： web databases deep web Database tuple websites Prior Learning database owner Theoretical analysis

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Automatic Structured web databases Classification

Automatic Structured Web Databases Classification

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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： XiaoJun Cui ZhongSheng Ren Le Xu HongYu Xiao Wenzhou Vocational College of Science and Technology Wenzhou China State Key Laboratory of Software College of Mathematics and Computer Science Fujian Normal University Fuzhou China Department of Information and Technology Wenzhou Vocational College of Science and Technology Wenzho

The growing structured web databases on the web, making large-scale Deep web data integration faces enormous challenges. Organizing such structured web databases into a hierarchy directory tree is one of critical step towards the large-scale integration of Deep web. In this paper, a method for automatic classification of web database is addressed. Firstly, the method for calculating the semantic similarities among the web databases based on their interface schemas is proposed and be translated to the problem of extended optimal matching for bipartite graph. Then based on the achieved similarity matrix, an agglomerative hierarchical clustering algorithm is proposed, which can organize the web databases into a hierarchy tree automatically. Theoretical analysis and experimental results show that the method is efficient.

关键词： web databases interface schema bipartite graph matching hierarchical clustering

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