14132755
Efficient Name Disambiguation for Large-Scale Databases
Name disambiguation can occur when one is seeking a list of publications of an author who has used different name variations and when there are multiple other authors with the same name. We present an efficient integrative framework for solving the name disambiguation problem: a blocking method retrieves candidate classes of authors with similar names and a clustering method, DBSCAN, clusters papers by author. The distance metric between papers used in DBSCAN is calculated by an online active selection support vector machine algorithm (LASVM), yielding a simpler model, lower test errors and faster prediction time than a standard SVM. We prove that by recasting transitivity as density reachability in DBSCAN, transitivity is guaranteed for core points. For evaluation, we manually annotated 3,355 papers yielding 490 authors and achieved 90.6% pairwise-F1. For scalability, authors in the entire CiteSeer dataset, over 700,000 papers, were readily disambiguated.
3032576
Generative models for name disambiguation
Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or evenshare the same name with other people. In this paper, we present an efficient framework by using two novel topic-based models, extended from Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. Experiments indicate that our approach consistently outperforms other unsupervised methods including spectral and DBSCAN clustering. Scalability is addressed by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.
33920606
Hot-Spot Passage Retrieval in Question Answering
Question Answering has been the recent focus of information retrieval research; many systems just incorporate a search engine as a black box and most effort has been devoted to the question analyzer and the answer identifier. In the context of QA, however, passage provides an ideal medium between the document collection and an exact answer. And passage retrieval is a finer-grain approach than the traditional document retrieval both for the answer identifier and a human reader. In this paper, distinctions are first made between document retrieval and passage retrieval. And the Hot-Spot Passage Retrieval algorithm, which takes into account the measures of blurred BM25, coverage and height, is examined in detail. For evaluation, an isolated test is conducted and the algorithm gains 18.3% better answer redundancy and 4.8% better coverage rate than Okapi's original passage retrieval algorithm.
474823
Boosting the Feature Space: Text Classification for Unstructured Data on the Web
The issue of seeking efficient and effective methods for classifying unstructured text in large document corpora has received much attention in recent years. Traditional document representation like bag-of-words encodes documents as feature vectors, which usually leads to sparse feature spaces with large dimensionality, thus making it hard to achieve high classification accuracies. This paper addresses the problem of classifying unstructured documents on the Web. A classification approach is proposed that utilizes traditional feature reduction techniques along with a collaborative filtering method for augmenting document feature spaces. The method produces feature spaces with an order of magnitude less features compared with a baseline bag-of-words feature selection method. Experiments on both real-world data and benchmark corpus indicate that our approach improves classification accuracy over the traditional methods for both support vector machines and AdaBoost classifiers.
16266978
Ontology Reconciliation for Service-Oriented Computing
Service-oriented computing (SOC) is viewed as the computing paradigm of the near future, allowing for the dynamic interaction of services provided by distributed business partners. Being a declarative knowledge representation model, ontologies serve as a foundation for SOC. Due to the heterogeneous nature of independently designed ontologies, it is problematic for partners to understand the concepts adopted in ontologies from other sources. In order for partners to achieve seamless collaboration of services, they need to reconcile their ontologies with each other. During the alignment process and the following service interactions, compatibility is an important measurement that has been neglected in most research work. We extend a vector system to encode ontology compatibility. In addition, we present a new model - probabilistic center ontology - for better recording and maintenance of ontology alignment results. Our precise and efficient approach is verified by both theoretic proofs and experimental results
1414584
Broadcast program generation for unordered queries with data replication
We study in this paper the problem of broadcasting dependent data for unordered queries. However, most prior studies on dependent data broadcasting are limited to the premise of no data replication. Different from other prior studies, we investigate the effect of data replication in this paper. Specifically, we first derive several theoretical properties for the average access time by analyzing the model of dependent data broadcasting. On the basis of the theoretical results, we develop a genetic algorithm to generate broadcast programs with replication. In order to compare the performance of the proposed algorithm and the prior studies, several experiments are conducted. Our experimental results show that with the analytical results derived, the theoretical results derived are able to guide the search of the genetic algorithm very effectively, and lead to solution broadcast programs of higher quality than those of the prior studies.
37411387
Automatic generation of memory built-in self-test cores for system-on-chip
Memory testing is becoming the dominant factor in testing a system-on-chip (SoC), with the rapid growth of the size and density of embedded memories. To minimize the test effort, we present an automatic generation framework of memory built-in self-test (BIST) cores for SoC designs. The BIST generation framework is a much improved one of our previous work. Test integration of heterogeneous memory architectures and clusters of memories are focused on. The automatic test grouping and scheduling optimize the overhead in test time, performance, power consumption, etc. Furthermore, with our novel BIST architecture, the BIST cores can be accessed via an on-chip bus interface (e.g., AMBA), which eases the control of testing and diagnosis in a typical SoC scenario. With a configurable and extensible architecture, the proposed framework facilitates easy memory test integration for core providers as well as system integrators.
14351696
A sufficient condition for a semicomplete multipartite digraph to be Hamiltonian
Abstract A multipartite tournament is an orientation of a complete k -partite graph for some k ⩾ 2. A factor of a digraph D is a collection of vertex disjoint cycles covering all the vertices of D . We show that there is no degree of strong connectivity which together with the existence of a factor will guarantee that a multipartite tournament is Hamiltonian. Our main result is a sufficient condition for a multipartite tournament to be Hamiltonian. We show that this condition is general enough to provide easy proofs of many existing results on paths and cycles in multipartite tournaments. Using this condition, we obtain a best possible lower bound on the length of a longest cycle in any strongly connected multipartite tournament.
6888787
Distributed data management for large volume visualization
We propose a distributed data management scheme for large data visualization that emphasizes efficient data sharing and access. To minimize data access time and support users with a variety of local computing capabilities, we introduce an adaptive data selection method based on an "enhanced time-space partitioning" (ETSP) tree that assists with effective visibility culling, as well as multiresolution data selection. By traversing the tree, our data management algorithm can quickly identify the visible regions of data, and, for each region, adaptively choose the lowest resolution satisfying user-specified error tolerances. Only necessary data elements are accessed and sent to the visualization pipeline. To further address the issue of sharing large-scale data among geographically distributed collaborative teams, we have designed an infrastructure for integrating our data management technique with a distributed data storage system provided by logistical networking (LoN). Data sets at different resolutions are generated and uploaded to LoN for wide-area access. We describe a parallel volume rendering system that verifies the effectiveness of our data storage, selection and access scheme.