A Methodology to Retrieve Text Documents from Multiple Databases
This paper presents a methodology for finding the n most similar documents across multiple text databases for any given query and for any positive integer n. This methodology consists of two steps. First, the contents of databases are indicated approximately by database representatives. Databases are ranked using their representatives with respect to the given query. We provide a necessary and sufficient condition to rank the databases optimally. In order to satisfy this condition, we provide three estimation methods. One estimation method is intended for short queries; the other two are for all queries. Second, we provide an algorithm, OptDocRetrv, to retrieve documents from the databases according to their rank and in a particular way. We show that if the databases containing the n most similar documents for a given query are ranked ahead of other databases, our methodology will guarantee the retrieval of the n most similar documents for the query. When the number of databases is large, we propose to organize database representatives into a hierarchy and employ a best-search algorithm to search the hierarchy. It is shown that the effectiveness of the best-search algorithm is the same as that of evaluating the user query against all database representatives.

title: Determining Text Databases to Search in the Internet
abstract: Text data in the Internet can be partitioned into many databases naturally. Efficient retrieval of desired data can be achieved if we can accurately predict the usefulness of each database, because with such information, we only need to retrieve potentially useful documents from useful databases. In this paper, we propose two new methods for estimating the usefulness of text databases. For a given query, the usefulness of a text database in this paper is defined to be the number of documents in the database that are sufficiently similar to the query. Such a usefulness measure enables naive-users to make informed decision about which databases to search. We also consider the collection fusion problem. Because local databases may employ similarity functions that are different from that used by the global database, the threshold used by a local database to determine whether a document is potentially useful may be different from that used by the global database. We provide techniques that determine the best threshold for a given local database.

title: Personalized Web search for improving retrieval effectiveness
abstract: Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.

title: WISE-Integrator: An Automatic Integrator of Web Search Interfaces for E-Commerce
abstract: More and more databases are becoming Web accessible through form-based search interfaces, and many of these sources are E-commerce sites. Providing a unified access to multiple E-commerce search engines selling similar products is of great importance in allowing users to search and compare products from multiple sites with ease. One key task for providing such a capability is to integrate the Web interfaces of these E-commerce search engines so that user queries can be submitted against the integrated interface. Currently, integrating such search interfaces is carried out either manually or semi-automatically, which is inefficient and difficult to maintain. In this paper, we present WISE-Integrator - a tool that performs automatic integration of Web Interfaces of Search Engines. WISE-Integrator employs sophisticated techniques to identify matching attributes from different search interfaces for integration. It also resolves domain differences of matching attributes. Our experimental results based on 20 and 50 interfaces in two different domains indicate that WISE-Integrator can achieve high attribute matching accuracy and can produce high-quality integrated search interfaces without human interactions.

title: Fully automatic wrapper generation for search engines
abstract: When a query is submitted to a search engine, the search engine returns a dynamically generated result page containing the result records, each of which usually consists of a link to and/or snippet of a retrieved Web page. In addition, such a result page often also contains information irrelevant to the query, such as information related to the hosting site of the search engine and advertisements. In this paper, we present a technique for automatically producing wrappers that can be used to extract search result records from dynamically generated result pages returned by search engines. Automatic search result record extraction is very important for many applications that need to interact with search engines such as automatic construction and maintenance of metasearch engines and deep Web crawling. The novel aspect of the proposed technique is that it utilizes both the visual content features on the result page as displayed on a browser and the HTML tag structures of the HTML source file of the result page. Experimental results indicate that this technique can achieve very high extraction accuracy.

title: Mining officially unrecognized side effects of drugs by combining web search and machine learning
abstract: We consider the problem of finding officially unrecognized side effects of drugs. By submitting queries to the Web involving a given drug name, it is possible to retrieve pages concerning the drug. However, many retrieved pages are irrelevant and some relevant pages are not retrieved. More relevant pages can be obtained by adding the active ingredient of the drug to the query. In order to eliminate irrelevant pages, we propose a machine learning process to filter out the undesirable pages. The process is shown experimentally to be very effective. Since obtaining training data for the machine learning process can be time consuming and expensive, we provide an automatic method to generate the training data. The method is also shown to be very accurate. The side effects of three drugs which are not recognized by FDA are validated by an expert. We believe that the same approach can be applied to many real life problems and will yield high precision. Thus, this could lead a new way to perform retrieval with high accuracy.

title: Constraint-based XML query rewriting for data integration
abstract: We study the problem of answering queries through a target schema, given a set of mappings between one or more source schemas and this target schema, and given that the data is at the sources. The schemas can be any combination of relational or XML schemas, and can be independently designed. In addition to the source-to-target mappings, we consider as part of the mapping scenario a set of target constraints specifying additional properties on the target schema. This becomes particularly important when integrating data from multiple data sources with overlapping data and when such constraints can express data merging rules at the target. We define the semantics of query answering in such an integration scenario, and design two novel algorithms, basic query rewrite and query resolution, to implement the semantics. The basic query rewrite algorithm reformulates target queries in terms of the source schemas, based on the mappings. The query resolution algorithm generates additional rewritings that merge related information from multiple sources and assemble a coherent view of the data, by incorporating target constraints. The algorithms are implemented and then evaluated using a comprehensive set of experiments based on both synthetic and real-life data integration scenarios.

title: On the Integration of Grounding Language and Learning Objects
abstract: This paper presents a multimodal learning system that can ground spoken names of objects in their physical referents and learn to recognize those objects simultaneously from naturally co-occurring multisensory input. There are two technical problems involved: (1) the correspondence problem in symbol grounding - how to associate words (symbols) with their perceptually grounded meanings from multiple cooccurrences between words and objects in the physical environment. (2) object learning - how to recognize and categorize visual objects. We argue that those two problems can be fundamentally simplified by considering them in a general system and incorporating the spatio-temporal and cross-modal constraints of multimodal data. The system collects egocentric data including image sequences as well as speech while users perform natural tasks. It is able to automatically infer the meanings of object names from vision, and categorize objects based on teaching signals potentially encoded in speech. The experimental results reported in this paper reveal the effectiveness of using multimodal data and integrating heterogeneous techniques in machine learning, natural language processing and computer vision.

title: Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together
abstract: Protein interaction data exists in a number of repositories. Each repository has its own data format, molecule identifier and supplementary information. Michigan Molecular Interactions (MiMI) assists scientists searching through this overwhelming amount of protein interaction data. MiMI gathers data from well-known protein interaction databases and deep-merges the information. Utilizing an identity function, molecules that may have different identifiers but represent the same real-world object are merged. Thus, MiMI allows the users to retrieve information from many different databases at once, highlighting complementary and contradictory information. To help scientists judge the usefulness of a piece of data, MiMI tracks the provenance of all data. Finally, a simple yet powerful user interface aids users in their queries, and frees them from the onerous task of knowing the data format or learning a query language. MiMI allows scientists to query all data, whether corroborative or contradictory, and specify which sources to utilize. MiMI is part of the National Center for Integrative Biomedical Informatics (NCIBI) and is publicly available at: .

title: Making database systems usable
abstract: Database researchers have striven to improve the capability of a database in terms of both performance and functionality. We assert that the usability of a database is as important as its capability. In this paper, we study why database systems today are so difficult to use. We identify a set of five pain points and propose a research agenda to address these. In particular, we introduce a presentation data model and recommend direct data manipulation with a schema later approach. We also stress the importance of provenance and of consistency across presentation models.

title: Querying structured text in an XML database
abstract: XML databases often contain documents comprising structured text. Therefore, it is important to integrate "information retrieval style" query evaluation, which is well-suited for natural language text, with standard "database style" query evaluation, which handles structured queries efficiently. Relevance scoring is central to information retrieval. In the case of XML, this operation becomes more complex because the data required for scoring could reside not directly in an element itself but also in its descendant elements.In this paper, we propose a bulk-algebra, TIX, and describe how it can be used as a basis for integrating information retrieval techniques into a standard pipelined database query evaluation engine. We develop new evaluation strategies essential to obtaining good performance, including a stack-based TermJoin algorithm for efficiently scoring composite elements. We report results from an extensive experimental evaluation, which show, among other things, that the new TermJoin access method outperforms a direct implementation of the same functionality using standard operators by a large factor.