6066153
Kernel Methods for Document Filtering
This paper describes the algorithms inplemented by the KerMIT consortium for its participation in the Trec 2002 Filtering track. The consortium submitted runs for the routing task using a linear SVM, for the batch task using the same SVM in combination with an innovation threshold-selection mechanism, and for the adaptive task using both a second-order perceptron and a combination of SVM and perceptron with uneven margin. Results seem to indicate that these algorithm performed relatively well on the extensive TREC benchmark.
5886737
Using KCCA for Japanese–English cross-language information retrieval and document classification
Kernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two variables in a kernel defined feature space. A machine learning algorithm based on KCCA is studied for cross-language information retrieval. We apply the algorithm in Japanese–English cross-language information retrieval. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. Computational complexity is an important issue when applying KCCA to large dataset as in information retrieval. We experimentally evaluate several methods to alleviate the problem of applying KCCA to large datasets. We also investigate cross-language document classification using KCCA as well as other methods. Our results show that it is feasible to use a classifier learned in one language to classify the documents in other languages.
11166931
Automatic Extraction of Hierarchical Relations from Text
Automatic extraction of semantic relationships between entity instances in an ontology is useful for attaching richer semantic metadata to documents. In this paper we propose an SVM based approach to hierarchical relation extraction, using features derived automatically from a number of GATE-based open-source language processing tools. In comparison to the previous works, we use several new features including part of speech tag, entity subtype, entity class, entity role, semantic representation of sentence and WordNet synonym set. The impact of the features on the performance is investigated, as is the impact of the relation classification hierarchy. The results show there is a trade-off among these factors for relation extraction and the features containing more information such as semantic ones can improve the performance of the ontological relation extraction task.
14153782
SVM Based Learning System for Information Extraction
This paper presents an SVM-based learning system for information extraction (IE). One distinctive feature of our system is the use of a variant of the SVM, the SVM with uneven margins, which is particularly helpful for small training datasets. In addition, our approach needs fewer SVM classifiers to be trained than other recent SVM-based systems. The paper also compares our approach to several state-of-the-art systems (including rule learning and statistical learning algorithms) on three IE benchmark datasets: CoNLL-2003, CMU seminars, and the software jobs corpus. The experimental results show that our system outperforms a recent SVM-based system on CoNLL-2003, achieves the highest score on eight out of 17 categories on the jobs corpus, and is second best on the remaining nine.
6980198
A combined approach to checking web ontologies
The understanding of Semantic Web documents is built upon ontologies that define concepts and relationships of data. Hence, the correctness of ontologies is vital. Ontology reasoners such as RACER and FaCT have been developed to reason ontologies with a high degree of automation. However, complex ontology-related properties may not be expressible within the current web ontology languages, consequently they may not be checkable by RACER and FaCT. We propose to use the software engineering techniques and tools, i.e., Z/EVES and Alloy Analyzer, to complement the ontology tools for checking Semantic Web documents.In this approach, Z/EVES is first applied to remove trivial syntax and type errors of the ontologies. Next, RACER is used to identify any ontological inconsistencies, whose origins can be traced by Alloy Analyzer. Finally Z/EVES is used again to express complex ontology-related properties and reveal errors beyond the modeling capabilities of the current web ontology languages. We have successfully applied this approach to checking a set of military plan ontologies.
12666680
Semantic Web Languages - Towards an Institutional Perspective
The Semantic Web (SW) is viewed as the next generation of the Web that enables intelligent software agents to process and aggregate data autonomously. Ontology languages provide basic vocabularies to semantically markup data on the SW. We have witnessed an increase of numbers of SW languages in the last years. These languages, such as RDF, RDF Schema (RDFS), the OWL suite of languages, the OWL− − suite, SWRL, are based on different semantics, such as the RDFS-based, description logic-based, Datalog-based semantics. The relationship among the various semantics poses a challenge for the SW community for making the languages interoperable. Institutions provide a means of reasoning about software specifications regardless of the logical system. This makes it an ideal candidate to represent and reason about the various languages in the Semantic Web. In this paper, we construct institutions for the SW languages and use institution morphisms to relate them. We show that RDF framework together with the RDF serializations of SW languages form an indexed institution. This allows the use of Grothendieck institutions to combine Web ontologies described in various languages.
5296800
Adaptive box-based efficient fault-tolerant routing in 3D torus
In this paper, we propose efficient fault-tolerant routing algorithms for 3D torus with possible large number of faulty nodes. There is no any presumption on the number and the distribution of faulty nodes. The proposed algorithms find a fault-free path between any two nonfaulty nodes with high probability in linear time by using only the local faulty information of the network. The results of our empirical analysis through simulations show that the algorithms can find a fault-free path between any two nonfaulty nodes with a probability higher than 90% in a 3D torus with the number of faulty nodes up to 30%.
16074029
Using Grid Computing for Distributed Software Testing
Grid computing, which is characterized by largescale sharing and collaboration of dynamic resources, has quickly become a mainstream technology in distributed computing. In this article, we present a grid-based software testing framework, which takes advantage of the large-scale and cost-efficient computational grid resources as a software testing testbed to support automated large-scale and complicated software test. Within this test framework, a dynamic bag-of-tasks model is used to manage test suites on the grid. Grid workflow is used to accommodate various test units within the software test task. Overall, we expect that the grid-based unit test framework can significantly reduce test cost in complex software systems and accelerate the software testing process.
18611349
Online Adaptive Fault-Tolerant Routing in 2D Torus
In this paper, we propose efficient routing algorithms for 2D torus with possible large number of faulty nodes. There is no presumption on the number and the distribution of faulty nodes. The proposed algorithms find a fault-free path between any two nonfaulty nodes with high probability in linear time by using only the local routing information of the network. The results of our empirical analysis through simulations show that the algorithms can find a fault-free path between any two nonfaulty nodes with high probability. For example, in a torus of size up to 128×128, where, the number of faulty nodes up to 15%, the heuristuc-square routing algorithm finds a fault-free path with a probability of 90% or higher. The experimental results are impressive for 2D torus with only four links per node.