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Detection of cyber-attacks is a major responsibility for network managers and security specialists. Most existing Network Intrusion Detection systems rely on inspecting individual packets, an increasingly resource consuming task in today's high speed networks due to the overhead associated with accessing packet content. An alternative approach is to detect attack patterns by investigating IP flows...
Semantic models of data sources describe the meaning of the data in terms of the concepts and relationships defined by a domain ontology. Building such models is an important step toward integrating data from different sources, where we need to provide the user with a unified view of underlying sources. In this paper, we present a scalable approach to automatically learn semantic models of a structured...
The business world has become more dynamic than ever before. Global competition and today's rapid pace of development in many fields has led to shorter time-to-market intervals, as well as more complex products and services. These developments do often imply impromptu changes to existing business processes. These dynamics are aggravated when unforeseen paths have to be taken like it is often the case...
In recent years, developing needed software applications via the technique Web Service Composition (WSC) has been more and more popular. Moreover, benefit from the Semantic Web Services (SWSs) technology, it is possible to even automatically conduct WSC, i.e. the Automated Web Service composition (AWSC). Currently the AWSC is a well-studied research subject and which means the existence of a large...
Process constraint modeling and development, focusing on how to enforce the conformity of process constraints throughout its lifecycle, including design, deployment and runtime execution, remains a big challenge in the research area of model-driven development, especially when such constraints are considered in the composite Web services and workflow applications. By extending our previous work in...
Ontologies are a vital component of most knowledge-based applications, including semantic web search, intelligent information integration, and natural language processing. In particular, we need effective tools for generating in-depth ontologies that achieve comprehensive converge of specific application domains of interest, while minimizing the time and cost of this process. Therefore we cannot rely...
The emerging Internet of Things technologies enable enterprises to collect a variety of real-time data from the physical world, making a case for accessing, combining, interpreting, and distributing such data in real-time too. Enterprise Information Integration (EII) aims at providing tools for integrating data from multiple sources without having to first load all the data into a central warehouse,...
Crowd sourcing is an emerging paradigm to exploit the notion of human-computation for solving various computational problems, which cannot be accurately solved solely by the machine-based solutions. We use crowd sourcing for large-scale link management in the Semantic Web. More specifically, we develop Crowd Link, which utilizes crowd workers for verification and creation of triples in Linking Open...
To address the problem of semantic heterogeneity, there has been a large body of research directed to the study of semantic mapping technologies. Although various semantic mapping technologies have been investigated, facilitating the process for domain experts to perform a semantic data integration task is not an easy task. This is because one is required not only to possess domain expertise but also...
Vector Symbolic Architectures (VSA) are approaches to representing symbols and structured combinations of symbols as high-dimensional vectors. They have applications in machine learning and for understanding information processing in neurobiology. VSAs are typically described in an abstract mathematical form in terms of vectors and operations on vectors. In this work, we show that a machine learning...
This paper presents an approach for semantic word comparison by coupling natural text descriptions with semi-structured knowledge for revealing more precise context information. The goal of the study is to present how popularity of a word's sense can affect semantic relatedness when two words are compared.
We propose a statistical semantic analysis method for Chinese terms. We use words, part-of-speech (POS) tags, word distances, word contexts and the first sememe of a word in HowNet as features to train a Support Vector Machine (SVM) model for analyzing term semantics. The model is used to identify dependencies embedded inside a term. A Conditional Random Field (CRF) model is used afterwards to incorporate...
There is a growing need to make sense of all the raw data available on the Internet, hence, the purpose of this study is to explore the capabilities of data mining algorithms applied to social networks. We propose a system to mine public Twitter data for information relevant to obesity and health as an initial case study. This paper details the findings of our project and critiques the use of social...
We have developed a large semi-synthetic, semantically rich dataset, modeled after the medical record of a large medical institution. Using the highly diverse data.gov data repository and a multivariate data augmentation strategy, we can generate arbitrarily large semi-synthetic datasets which can be used to test new algorithms and computational platforms. The construction process and basic data characterization...
Multimedia documents like PowerPoint presentations or Flash documents are widely adopted in the Internet and exist in context of lots of different topics. However, so far there is no user friendly way to explore and search for this content. The aim of this work is to address this issue by developing a new, easy-to-use user interface approach and prototype search engine. Our system is called fulgeo...
Technologies based on the internet are improving continuously and so are the harmful websites such as pornography or illegal gambling websites. In addition, it is the characteristics of websites that the changes made to the web address or its contents take effect almost instantaneously. Therefore, it is not easy to identify harmful websites from those that are not. There are two ways to make such...
We present a simple approach to handle recursive SPARQL queries, that is, nested queries that may contain references to the query itself. This powerful feature is obtained by implementing a custom SPARQL function that takes a SPARQL query as a parameter and executes it over a specified endpoint. The behaviour is similar to the SPARQL 1.1 SERVICE clause, with a few fundamental differences: (1) the...
In many Semantic Web applications, having RDF predicates sorted by significance is of primarily importance to improve usability and performance. In this paper we focus on predicates available on DBpedia, the most important Semantic Web source of data counting 470 million english triples. Although there is plenty of work in literature dealing with ranking entities or RDF query results, none of them...
In this paper, we represent a dynamic context-dependent weighting method for vector space model. A meaning is relatively decided by a context dynamically. A vector space model, including latent semantic indexing (LSI), etc. relatively measures correlations of each target thing that represents in each vector. However, the vectors of each target thing in almost method of the vector space models are...
Twitter-based messages have presented challenges in the identification of features as applied to classification. This paper explores filtering techniques for improved trend detection and information extraction. Starting with a pre-filtered source (Twitter), we will examine the application of both information theory and Natural Language Processing (NLP) based techniques as a means of preprocessing...
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