28087802
ETANA-DL: a digital library for integrated handling of heterogeneous archaeological data
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.
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.
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.
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.
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.
123606281
Combining null space-based Gabor features for face recognition
We propose a face recognition strategy combining various discriminating Gabor features in multi-scales and multi-orientations. A bank of well-chosen Gabor filters is applied on the image to construct a group of feature vectors, and then the Null Space-based LDA (NLDA) is performed simultaneously on each orientation channel and the original image to give 5 component classifier outputs, which are then combined to increase the final recognition rate. Experimental results on the FERET database demonstrate the effectiveness and flexibility of our proposed method.
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.
5543178
On XML integrity constraints in the presence of DTDs
The paper investigates XML document specifications with DTDs and integrity constraints, such as keys and foreign keys. We study the consistency problem of checking whether a given specification is meaningful: that is, whether there exists an XML document that both conforms to the DTD and satisfies the constraints. We show that DTDs interact with constraints in a highly intricate way and as a result, the consistency problem in general is undecidable. When it comes to unary keys and foreign keys, the consistency problem is shown to be NP-complete. This is done by coding DTDs and integrity constraints with linear constraints on the integers. We consider the variations of the problem (by both restricting and enlarging the class of constraints), and identify a number of tractable cases, as well as a number of additional NP-complete ones. By incorporating negations of constraints, we establish complexity bounds on the implication problem, which is shown to be coNP-complete for unary keys and foreign keys.