1978214
A Language Modeling Approach to Passage Question Answering
This paper reports our efforts on developing a language modeling approach to passage question answering. In particular, we address the following two problems: (i) generalized language modeling for question classification; (ii) constrained language modeling for passage retrieval.
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.
2001196
Web taxonomy integration using support vector machines
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. In this paper we attempt to use a powerful classification method, Support Vector Machine (SVM), to attack this problem. 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. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose a method, Cluster Shrinkage (CS), to further enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
18688530
Web taxonomy integration through co-bootstrapping
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 learn a classifier that can classify objects from the source taxonomy into categories of the master taxonomy. The key insight is that the availability of the source taxonomy data could be helpful to build better classifiers for the master taxonomy if their categorizations have some semantic overlap. In this paper, we propose a new approach, co-bootstrapping, to enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
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.
36919888
Optimizing spatial Min/Max aggregations
Abstract.Aggregate computation over a collection of spatial objects appears in many real-life applications. Aggregates are computed on values (weights) associated with spatial objects, for example, the temperature or rainfall over the area covered by the object. In this paper we concentrate on MIN/MAX aggregations: “given a query rectangle, find the minimum/maximum weight among all objects intersecting the query rectangle.” Traditionally such queries have been performed as range searches. Assuming that objects are indexed by a spatial access method (SAM), the MIN/MAX is computed while retrieving those objects intersecting the query interval. This requires effort proportional to the number of objects satisfying the query, which may be large. A better approach is to maintain aggregate information among the index nodes of the spatial access method; then various index paths can be eliminated during the range search. Yet another approach is to build a specialized index that maintains the aggregate incrementally. We propose four novel optimizations for improving the performance of MIN/MAX queries when an index structure (traditional or specialized) is present. Moreover, we introduce the MR-tree, an R-tree-like dynamic specialized index that incorporates all four optimizations. Our experiments show that the MR-tree offers drastic performance improvement over previous solutions. As a byproduct of this work we present an optimized version of the MSB-tree, an index that has been proposed for the MIN/MAX computation over 1D interval objects.
10744441
Autonomy of Autonomous Agents
Autonomy is one of the most notable attributes of agency, and this paper presents a formal framework for modelhng it. By representing the mental states of agents, we provide an analysis of some attributes of autonomy. In particular, we define three sorts of autonomous agents and examine their structure. We also outline features of higher autonomy. Our framework can be a basis for cleissifying hierarchies of autonomy of agency.
10114115
The Optimal-Location Query
We propose and solve the optimal-location query in spatial databases. Given a set S of sites, a set O of weighted objects, and a spatial region Q, the optimal-location query returns a location in Q with maximum influence. Here the influence of a location l is the total weight of its RNNs, i.e. the total weight of objects in O that are closer to l than to any site in S. This new query has practical applications, but is very challenging to solve. Existing work on computing RNNs assumes a single query location, and thus cannot be used to compute optimal locations. The reason is that there are infinite candidate locations in Q. If we check a finite set of candidate locations, the result can be inaccurate, i.e. the revealed location may not have maximum influence. This paper proposes three methods that accurately compute optimal locations. The first method uses a standard R*-tree. To compute an optimal location, the method retrieves certain objects from the R*-tree and sends them as a stream to a plane-sweep algorithm, which uses a new data structure called the aSB-tree to ensure query efficiency. The second method is based on a new index structure called the OL-tree, which novelly extends the k-d-B-tree to store segmented rectangular records. The OL-tree is only of theoretical usage for it is not space efficient. The most practical approach is based on a new index structure called the Virtual OL-tree. These methods are theoretically and experimentally evaluated.
7615477
Efficient mining of max frequent patterns in a generalized environment
This poster paper summarizes our solution for mining max frequent generalized itemsets (g-itemsets), a compact representation for frequent patterns in the generalized environment.
8809044
Adaptive Vision for Playing Table Soccer
For real time object recognition and tracking often color-based methods are used. While these methods are very efficient, they usually dependent heavily on lighting conditions. In this paper we present a robust and efficient vision system for the table soccer robot KiRo. By exploiting knowledge about invariant characteristics of the table soccer game, the system is able to adapt to changing lighting conditions dynamically and to detect relevant objects on the table within a few milliseconds. We give experimental evidence for the robustness and efficiency of our approach.