8536303
Web Based Question Answering with Aggregation Strategy
The Web is apparently an ideal source of answers to a large variety of questions, due to the tremendous amount of information available online. This paper describes a question answering system LAMP, which can instantly find answers from the Web. LAMP is publicly accessible on the Web, and its performance is comparable to the state-of-the-art question answering systems. One novel characteristic of this system is that its answer selection method is based on aggregation, which is shown to be more effective than the common methods based on individual or redundancy.
30422301
Web Taxonomy Integration Using Spectral Graph Transducer
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to train a classifier for each category in the master taxonomy, and then classify objects from the source taxonomy into these categories. Our key insight is that the availability of the source taxonomy data could be helpful to build better classifiers in this scenario, therefore it would be beneficial to do transductive learning rather than inductive learning, i.e., learning to optimize classification performance on a particular set of test examples. In this paper, we attempt to use a powerful transductive learning algorithm, Spectral Graph Transducer (SGT), to attack this problem. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose to further enhance SGT classifiers by incorporating the affinity information present in the taxonomy data. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
15034258
Semantic, Hierarchical, Online Clustering of Web Search Results
We propose a Semantic, Hierarchical, Online Clustering (SHOC) approach to automatically organizing Web search results into groups. SHOC combines the power of two novel techniques, key phrase discovery and orthogonal clustering, to generate clusters which are both reasonable and readable. Moreover, SHOC can work for multiple languages: not only English but also oriental languages like Chinese. The main contribution of this paper includes the following. (1) The benefits of using key phrases as Web document features are discussed. A key phrase discovery algorithm based on suffix array is presented. This algorithm is highly effective and efficient no matter how large the language’s alphabet is. (2) The concept of orthogonal clustering is proposed for general clustering problems. The reason why matrix Singular Value Decomposition (SVD) can provide solution to orthogonal clustering is strictly proved. The orthogonal clustering has a solid mathematics foundation and many advantages over traditional heuristic clustering algorithms.
42579325
A data model and algebra for the Web
The recent emergence of XML as a standard for representation and exchange of data on the Web has drawn significant attention. But existing semistructured data models, such as OEM, cannot support XML naturally and fully. A new data model named XOM is proposed in this paper as the common data model for the Web data source. Based on rooted connected directed graphs both cyclic and acyclic, whose nodes are labeled and edges are ranked, XOM can describe the aggregation relationship as well as the general reference relationship (linkage) between XML elements. Through ranked closure operation, both tree-oriented and graph-oriented models can be unified in XOM. As the formal foundation for query processing and optimization for Web data, a new algebra called XOM algebra, which includes six operations, i.e., object union, difference, select, project, paste and cut, is also proposed. In comparison with relational algebra, XOM algebra is more flexible and powerful.
18058823
A novel Web usage mining approach for search engines
Abstract Web usage mining can be very useful to search engines. This paper proposes a novel effective approach to exploit the relationships among users, queries and resources based on the search engine's log. How this method can be applied is illustrated by a Chinese image search engine.
206133548
An efficient algorithm to rank Web resources
Abstract How to rank Web resources is critical to Web Resource Discovery (Search Engine). This paper not only points out the weakness of current approaches, but also presents in-depth analysis of the multidimensionality and subjectivity of rank algorithms. From a dynamics viewpoint, this paper abstracts a user's Web surfing action as a Markov model. Based on this model, we propose a new rank algorithm. The result of our rank algorithm, which synthesizes the relevance, authority, integrativity and novelty of each Web resource, can be computed efficiently not by iteration but through solving a group of linear equations.
129481
On Multidimensional k-Anonymity with Local Recoding Generalization
This paper presents the first theoretical study, on using local-recoding generalization (LRG) to compute a k-anonymous table with quality guarantee. First, we prove that it is NP-hard both to find the table with the maximum quality, and to discover a solution with an approximation ratio at most 5/4. Then, we develop an algorithm with good balance between the approximation ratio and time complexity. The quality of our solution is verified by experiments.
9693532
Negotiation mechanism for TAC SCM component market
This paper presents a new negotiation mechanism and a new supplier model for Trading Agent Competition Supply Chain Management(TAC SCM). Under the new negotiation mechanism, an agent is allowed to negotiate with component suppliers on price, delivery date and supply quantity while a supplier can autonomously vary its production capacity with market demands and allocate its products to buyers through auction. A mathematical analysis is given to ensure the new negotiation mechanism and supplier model effectively solve the existing problems while keeping most good features of the original TAC SCM game model.
3129003
Efficient computation of temporal aggregates with range predicates
A temporal aggregation query is an important but costly operation for applications that maintain time-evolving data (data warehouses, temporal databases, etc.). Due to the large volume of such data, performance improvements for temporal aggregation queries are critical. In this paper we examine techniques to compute temporal aggregates that include key-range predicates (range temporal aggregates). In particular we concentrate on SUM, COUNT and AVG aggregates. This problem is novel; to handle arbitrary key ranges, previous methods would need to keep a separate index for every possible key range. We propose an approach based on a new index structure called the Multiversion SB-Tree, which incorporates features from both the SB-Tree and the Multiversion B-Tree, to handle arbitrary key-range temporal SUM, COUNT and AVG queries. We analyze the performance of our approach and present experimental results that show its efficiency.
15692531
Molecular caps for full quantum mechanical computation of peptide-water interaction energy
We present a systematic study of numerical accuracy of various forms of molecular caps that are employed in a recently developed molecular fractionation scheme for full quantum mechanical computation of protein-molecule interaction energy. A previously studied pentapeptide (Gly-Ser-Ala-Asp-Val) or P5 interacting with a water molecule is used as a benchmark system for numerical testing. One-dimensional potential energy curves are generated for a number of peptide-water interaction pathways. Our study shows that various forms of caps all give consistently accurate energies compared to the corresponding full system calculation with only small deviations. We also tested the accuracy of cutting peptide backbone at different positions and comparisons of results are presented.
9840052
Finding Fastest Paths on A Road Network with Speed Patterns
This paper proposes and solves the Time-Interval All Fastest Path (allFP) query. Given a user-defined leaving or arrival time interval I, a source node s and an end node e, allFP asks for a set of all fastest paths from s to e, one for each sub-interval of I. Note that the query algorithm should find a partitioning of I into sub-intervals. Existing methods can only be used to solve a very special case of the problem, when the leaving time is a single time instant. A straightforward solution to the allFP query is to run existing methods many times, once for every time instant in I. This paper proposes a solution based on novel extensions to the A* algorithm. Instead of expanding the network many times, we expand once. The travel time on a path is kept as a function of leaving time. Methods to combine travel-time functions are provided to expand a path. A novel lower-bound estimator for travel time is proposed. Performance results reveal that our method is more efficient and more accurate than the discrete-time approach.