3269549
Intelligent GP fusion from multiple sources for text classification
This paper shows how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity -- five derived from the citation information of the collection, and three derived from the structural content -- and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than GA or other fusion techniques.
15362027
ETANA-DL: managing complex information applications - an archaeology digital library
Summary form only given. Archaeological research results in the generation of large quantities of heterogeneous information managed by different projects using custom information systems. We demonstrate a prototype digital library (DL) for integrating and managing archaeological data and providing services useful to various user communities. ETANA-DL is a model-based, componentized, extensible, archaeological DL that manages complex information sources using the client-server paradigm of the open archives initiative protocol for metadata harvesting (OAI-PMH). We present a prototype of ETANA-DL that builds on the 5S meta-model for developing for archaeological systems. We demonstrate our approach, used to integrate and handle highly heterogeneous data and the various services that ETANA-DL currently supports - searching, browsing, personal collections, workflows, discussions, annotations, etc. We are working on the structural aspects of the meta-model, and have used the results to develop advanced cross-collection searching and browsing services.
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.
15496678
Discovery of context-specific ranking functions for effective information retrieval using genetic programming
The Internet and corporate intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. We argue that the ranking strategy should be context specific and we propose a , new systematic method that can automatically generate ranking strategies for different contexts based on genetic programming (GP). The new method was tested on TREC data and the results are very promising.
16272457
SimFusion: measuring similarity using unified relationship matrix
In this paper we use a Unified Relationship Matrix (URM) to represent a set of heterogeneous data objects (e.g., web pages, queries) and their interrelationships (e.g., hyperlinks, user click-through sequences). We claim that iterative computations over the URM can help overcome the data sparseness problem and detect latent relationships among heterogeneous data objects, thus, can improve the quality of information applications that require com- bination of information from heterogeneous sources. To support our claim, we present a unified similarity-calculating algorithm, SimFusion. By iteratively computing over the URM, SimFusion can effectively integrate relationships from heterogeneous sources when measuring the similarity of two data objects. Experiments based on a web search engine query log and a web page collection demonstrate that SimFusion can improve similarity measurement of web objects over both traditional content based algorithms and the cutting edge SimRank algorithm.
14252554
Discovering and Reconciling Semantic Conflicts: A Data Mining Perspective
Current approaches to semantic interoperability require human intervention in detecting potential conflicts and in defining how those conflicts may be resolved. This is a major impedance to achieving “logical connectivity”, especially when the dumber of disparate sources is large. In this paper, we demonstrate that the detection and reconciliation of semantic conflicts can be automated using tools and techniques developed by the data mining community. We describe a process for discovering such rules that represent the relationships among semanticaly related attributes and illustrate the effectiveness of our approach with examples.
2869108
Propagating XML constraints to relations
We present a technique for refining the design of relational storage for XML data. The technique is based on XML key propagation: given a set of keys on XML data and a mapping (transformation) from the XML data to relations, what functional dependencies must hold on the relations produced by the mapping? With the functional dependencies one can then convert the relational design into, e.g. 3NF, BCNF, and thus develop efficient relational storage for XML data. We provide several algorithms for computing XML key propagation. One algorithm is to check whether a functional dependency is propagated from a set of XML keys via a predefined mapping; this allows one to determine whether or not the relational design is in a normal form. The others are to compute a minimum cover for all functional dependencies that are propagated from a set of XML keys and hold on a universal relation; these provide guidance for how to design a relational schema for storing XML data. These algorithms show that XML key propagation and its associated minimum cover can be computed in polynomial time. Our experimental results verify that these algorithms are efficient in practice. We also investigate the complexity of propagating other XML constraints to relations. The ability to compute XML key propagation is a first step toward establishing a connection between XML data and its relational representation at the semantic level.
11711193
Video-Based Face Recognition Using Bayesian Inference Model
There has been a flurry of works on video sequence-based face recognition in recent years. One of the hard problems in this area is how to effectively combine the facial configuration and temporal dynamics for the recognition task. The proposed method treats this problem in two steps. We first construct several view specific appearance sub-manifolds learned from the training video frames using locally linear embedding (LLE). A general Bayesian inference model is then fit on the recognition task, transforming the complicated maximum likelihood estimation to some elegant distance measures in the learned sub-manifolds. Experimental results on a middle-scale video database demonstrate the effectiveness and flexibility of our proposed method.
6139666
Capturing both types and constraints in data integration
We propose a framework for integrating data from multiple relational sources into an XML document that both conforms to a given DTD and satisfies predefined XML constraints. The framework is based on a specification language, AIG, that extends a DTD by (1) associating element types with semantic attributes (inherited and synthesized, inspired by the corresponding notions from Attribute Grammars), (2) computing these attributes via parameterized SQL queries over multiple data sources, and (3) incorporating XML keys and inclusion constraints. The novelty of AIG consists in semantic attributes and their dependency relations for controlling context-dependent, DTD-directed construction of XML documents, as well as for checking XML constraints in parallel with document-generation. We also present cost-based optimization techniques for efficiently evaluating AIGs, including algorithms for merging queries and for scheduling queries on multiple data sources. This provides a new grammar-based approach for data integration under both syntactic and semantic constraints.
553416
Path Constraints in Semistructured Databases
We investigate a class of path constraints that is of interest in connection with both semistructured and structured data. In standard database systems, constraints are typically expressed as part of the schema, but in semistructured data there is no explicit schema and path constraints provide a natural alternative. As with structured data, path constraints on semistructured data express integrity constraints associated with the semantics of data and are important in query optimization. We show that in semistructured databases, despite the simple syntax of the constraints, their associated implication problem is r.e. complete and finite implication problem is co-r.e. complete. However, we establish the decidability of the implication and finite implication problems for several fragments of the path constraint language and demonstrate that these fragments suffice to express important semantic information such as extent constraints, inverse relationships, and local database constraints commonly found in object-oriented databases.
38519945
Propagating XML constraints to relations
We present a technique for refining the design of relational storage for XML data based on XML key propagation. Three algorithms are presented: one checks whether a given functional dependency is propagated from XML keys via a predefined view; the others compute a minimum cover for all functional dependencies on a universal relation given XML keys. Experimental results show that these algorithms are efficient in practice. We also investigate the complexity of propagating other XML constraints to relations, and the effect of increasing the power of the transformation language. Computing XML key propagation is a first step toward establishing a connection between XML data and its relational representation at the semantic level.