16918412
Ranking function optimization for effective Web search by genetic programming: an empirical study
Web search engines have become indispensable in our daily life to help us find the information we need. Although search engines are very fast in search response time, their effectiveness in finding useful and relevant documents at the top of the search hit list needs to be improved. In this paper, we report our experience applying genetic programming (GP) to the ranking function discovery problem leveraging the structural information of HTML documents. Our empirical experiments using the Web track data from recent TREC conferences show that we can discover better ranking functions than existing well-known ranking strategies from IR, such as Okapi, Ptfidf. The performance is even comparable to those obtained by support vector machine.
234966
Prototyping Digital Libraries Handling Heterogeneous Data Sources - The ETANA-DL Case Study
Information systems used in archaeology have several needs: interoperability among heterogeneous systems, making information available without significant delay, long-term preservation of data, and providing a suite of services to users. In this paper, we show how digital library techniques can be employed to provide solutions to three of these problems. We show this by describing a prototype for an archaeological Digital Library (ETANA-DL). First, ETANA-DL applies and extends the metadata harvesting approach to address some of the needs interoperability, rapid access to data, and data preservation. Second, we show that availability of a pool of components that implement common DL services has helped in rapidly creating the prototype, which was subsequently used for requirements elicitation. However, understanding complex archaeological information systems is a difficult task. Third, therefore, we describe our efforts to model these systems using the 5S framework, and show how the partially developed model has been used to implement complex services helping users carry out key tasks with the integrated data.
10630201
Tapping the power of text mining
Sifting through vast collections of unstructured or semistructured data beyond the reach of data mining tools, text mining tracks information sources, links isolated concepts in distant documents, maps relationships between activities, and helps answer questions.
9624007
Mining the web for answers to natural language questions
The web is now becoming one of the largest information and knowledge repositories. Many large scale search engines (Google, Fast, Northern Light, etc.) have emerged to help users find information. In this paper, we study how we can effectively use these existing search engines to mine the Web and discover the "correct" answers to factual natural language questions.We propose a probabilistic algorithm called QASM (Question Answering using Statistical Models) that learns the best query paraphrase of a natural language question. We validate our approach for both local and web search engines using questions from the TREC evaluation. We also show how this algorithm can be combined with another algorithm (AnSel) to produce precise answers to natural language questions.
4216648
Optimizing web search using web click-through data
The performance of web search engines may often deteriorate due to the diversity and noisy information contained within web pages. User click-through data can be used to introduce more accurate description (metadata) for web pages, and to improve the search performance. However, noise and incompleteness, sparseness, and the volatility of web pages and queries are three major challenges for research work on user click-through log mining. In this paper, we propose a novel iterative reinforced algorithm to utilize the user click-through data to improve search performance. The algorithm fully explores the interrelations between queries and web pages, and effectively finds "virtual queries" for web pages and overcomes the challenges discussed above. Experiment results on a large set of MSN click-through log data show a significant improvement on search performance over the naive query log mining algorithm as well as the baseline search engine.
1990595
IMMC: incremental maximum margin criterion
Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or large-scale data. To meet this desirability, Incremental Principal Component Analysis (IPCA) algorithm has been well established, but it is an unsupervised subspace learning approach and is not optimal for general classification tasks, such as face recognition and Web document categorization. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Maximum Margin Criterion (IMMC), to infer an adaptive subspace by optimizing the Maximum Margin Criterion. We also present the proof for convergence of the proposed algorithm. Experimental results on both synthetic dataset and real world datasets show that IMMC converges to the similar subspace as that of batch approach.
10449065
Beyond XML Query Languages
A query language is essential, if XML is to serve effectively as an exchange medium for large data sets. The design of query languages for XML is in its infancy, and the choice of a standard may be governed more by user acceptance than by any understanding of underlying principles. One would hope that expressive power, performance, and compatibility with other languages will be considered in choosing among alternatives, but it is likely that several contenders will co-exist for some time. It is worth observing that, during the 20-year development of relational query languages, several competing languages were developed; and even today there are several relational query language standards. In spite of this, a great deal of technology was developed that was independent of the surface syntax of a query language. This included technology "below" the language such as efficient execution models and work "above" the level of language such as techniques for view definition and maintenance, triggers, etc. At Penn we are working on some of these language-independent issues. We include a summary of them here. They include execution and data models to support XML and semistructured query languages; the use of schemas and constraints in optimizing XML query languages; and tools for extracting data form existing sources and presenting it as XML. Comments Postprint version. Published in Query Language Workshop, November 1998. Publisher URL: http://www.w3.org/TandS/QL/QL98/pp/penn.html Author(s) Peter Buneman, Alin Deutsch, Wenfei Fan, Hartmut Liefke, Arnaud Sahuguet, and Wang-Chiew Tan This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/db_research/14 Beyond XML Query Languages Peter Buneman Alin Deutsch Wenfei Fan Hartmut Liefke Arnaud Sahuguet Wang-Chiew Tan University of Pennsylvania fpeter,adeutsch,wfan,liefke,sahuguet,wctang@saul.cis.upenn.edu
5459818
Putting context into schema matching
Attribute-level schema matching has proven to be an important first step in developing mappings for data exchange, integration, restructuring and schema evolution. In this paper we investigate contextual schema matching, in which selection conditions are associated with matches by the schema matching process in order to improve overall match quality. We define a general space of matching techniques, and within this framework we identify a variety of novel, concrete algorithms for contextual schema matching. Furthermore, we show how common schema mapping techniques can be generalized to take more effective advantage of contextual matches, enabling automatic construction of mappings across certain forms of schema heterogeneity. An experimental study examines a wide variety of quality and performance issues. In addition, it demonstrates that contextual schema matching is an effective and practical technique to further automate the definition of complex data transformations.
8618813
Reasoning about Keys for XML
We study absolute and relative keys for XML, and investigate their associated decision problems. We argue that these keys are important to many forms of hierarchically structured data including XML documents. In contrast to other proposals of keys for XML, these keys can be reasoned about efficiently. We show that the (finite) satisfiability problem for these keys is trivial, and their (finite) implication problem is finitely axiomatizable and decidable in PTIME in the size of keys.
1927184
Local manifold matching for face recognition
In this paper, we propose a novel classification method, called local manifold matching (LMM), for face recognition. LMM has great representational capacity of available prototypes and is based on the local linearity assumption that each data point and its k nearest neighbors from the same class lie on a linear manifold locally embedded in the image space. We present a supervised local manifold learning algorithm for learning all locally linear manifold structures. Then we propose the nearest manifold criterion for the classification in which the query feature point is assigned to the most matching face manifold. Experimental results show that kernel PCA incorporated with the LMM classifier achieves the best face recognition performance.
53305032
Reasoning about keys for XML
We study absolute and relative keys for XML, and investigate their associated decision problems. We argue that these keys are important to many forms of hierarchically structured data including XML documents. In contrast to other proposals of keys for XML, we show that these keys are always (finitely) satisfiable, and their (finite) implication problem is finitely axiomatizable. Furthermore, we provide a polynomial time algorithm for determining (finite) implication in the size of keys. Our results also demonstrate, among other things, that the analysis of XML keys is far more intricate than its relational counterpart.