Mining officially unrecognized side effects of drugs by combining web search and machine learning
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: 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: Schema-Free XQuery
abstract: The widespread adoption of XML holds out the promise that document structure can be exploited to specify precise database queries. However, the user may have only a limited knowledge of the XML structure, and hence may be unable to produce a correct XQuery, especially in the context of a heterogeneous information collection. The default is to use keyword-based search and we are all too familiar with how difficult it is to obtain precise answers by these means. We seek to address these problems by introducing the notion of Meaningful Lowest Common Ancestor Structure (MLCAS) for finding related nodes within an XML document. By automatically computing MLCAS and expanding ambiguous tag names, we add new functionality to XQuery and enable users to take full advantage of XQuery in querying XML data precisely and efficiently without requiring (perfect) knowledge of the document structure. Such a Schema-Free XQuery is potentially of value not just to casual users with partial knowledge of schema, but also to experts working in a data integration or data evolution context. In such a context, a schema-free query, once written, can be applied universally to multiple data sources that supply similar content under different schemas, and applied "forever" as these schemas evolve. Our experimental evaluation found that it was possible to express a wide variety of queries in a schema-free manner and have them return correct results over a broad diversity of schemas. Furthermore, the evaluation of a schema-free query is not expensive using a novel stack-based algorithm we develop for computing MLCAS: from 1 to 4 times the execution time of an equivalent schema-aware query.

title: A multimodal learning interface for grounding spoken language in sensory perceptions
abstract: Most speech interfaces are based on natural language processing techniques that use pre-defined symbolic representations of word meanings and process only linguistic information. To understand and use language like their human counterparts in multimodal human-computer interaction, computers need to acquire spoken language and map it to other sensory perceptions. This paper presents a multimodal interface that learns to associate spoken language with perceptual features by being situated in users' everyday environments and sharing user-centric multisensory information. The learning interface is trained in unsupervised mode in which users perform everyday tasks while providing natural language descriptions of their behaviors. We collect acoustic signals in concert with multisensory information from non-speech modalities, such as user's perspective video, gaze positions, head directions and hand movements. The system firstly estimates users' focus of attention from eye and head cues. Attention, as represented by gaze fixation, is used for spotting the target object of user interest. Attention switches are calculated and used to segment an action sequence into action units which are then categorized by mixture hidden Markov models. A multimodal learning algorithm is developed to spot words from continuous speech and then associate them with perceptually grounded meanings extracted from visual perception and action. Successful learning has been demonstrated in the experiments of three natural tasks: "unscrewing a jar", "stapling a letter" and "pouring water".

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.

title: Effective Integration of Protein Data through Better Data Modeling
abstract: Protein data, from sequence and structure to interaction, is being generated through many diverse methodologies; it is stored and reported in numerous forms and multiple places. The magnitude of the data limits researchers abilities to utilize all information generated. Effective integration of protein data can be accomplished through better data modeling. We demonstrate this through the MIPD project.

title: Efficient discovery of XML data redundancies
abstract: As XML becomes widely used, dealing with redundancies in XML data has become an increasingly important issue. Redundantly stored information can lead not just to a higher data storage cost, but also to increased costs for data transfer and data manipulation. Furthermore, such data redundancies can lead to potential update anomalies, rendering the database inconsistent.One way to avoid data redundancies is to employ good schema design based on known functional dependencies. In fact, several recent studies have focused on defining the notion of XML Functional Dependencies (XML FDs) to capture XML data redundancies. We observe further that XML databases are often "casually designed" and XML FDs may not be determined in advance. Under such circumstances, discovering XML data redundancies (in terms of FDs) from the data itself becomes necessary and is an integral part of the schema refinement process.In this paper, we present the design and implementation of the first system, DiscoverXFD, for effcient discovery of XML data redundancies. It employs a novel XML data structure and introduces a new class of partition based algorithms. DiscoverXFD can not only be used for the previous definitions of XML functional dependencies, but also for a more comprehensive notion we develop in this paper, capable of detecting redundancies involving set elements while maintaining clear semantics. Experimental evaluations using real life and benchmark datasets demonstrate that our system is practical and scales well with increasing data size.