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The integration of the classical Web (of documents) with the emerging Web of Data is a challenging vision. In this paper we focus on an integration approach during searching which aims at enriching the responses of non-semantic search systems (e.g. professional search systems, web search engines) with semantic information, i.e. Linked Open Data (LOD), and exploiting the outcome for providing an overview...
Healthcare datasets are increasingly characterized by large volume, high rate of generation and need for real time analysis (velocity), and variety. These datasets are often termed biomedical big data and include multi-modal electrophysiological signals and electronic health records. In this talk, we focus on the computational challenges associated with signal data management and the role of semantic...
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,...
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...
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...
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...
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...
Drug-drug interaction (DDI) study is an important aspect of therapy management and drug efficacy. DDI study investigates how drugs interact with each other and determine whether these interactions may lead to dire effects or nullify the therapeutic effects of each other. In this paper we model metabolic pathways of drugs that include the reaction effects between drugs and the related enzymes. By modeling...
This paper presents our experiment on the "Bade a" system. A system designed for the automated extraction of semantic relations from text using a seed ontology and a pattern based approach. We describe the experiment using a set of Arabic language corpora for extracting the antonym semantic relation. Antonyms from the seed ontology are used to extract patterns from the corpora, these patterns...
A well-known drawback in building machine learning semantic relation detectors for natural language is the lack of a large number of qualified training instances for the target relations in multiple languages. Even when good results are achieved, the datasets used by the state-of-the-art approaches are rarely published. In order to address these problems, this work presents an automatic approach to...
The elasticity characteristic of cloud computing enables clients to acquire and release resources on demand. This characteristic reduces clients' cost by making them pay for the resources they actually have used. On the other hand, clients are obligated to maintain Service Level Agreement (SLA) with their users. One approach to deal with this cost-performance trade-off is employing an auto-scaling...
To inform citizens when they can use government services, governments publish the services' opening hours on their website. When opening hours would be published in a machine interpretable manner, software agents would be able to answer queries about when it is possible to contact a certain service. We introduce an ontology for describing opening hours and use this ontology to create an input form...
Incorporating structured data in the Linked Data cloud is still complicated, despite the numerous existing tools. In particular, hierarchical structured data (e.g., JSON) are underrepresented, due to their processing complexity. A uniform mapping formalization for data in different formats, which would enable reuse and exchange between tools and applied data, is missing. This paper describes a novel...
Location-based Services (LBS) are one of the longest-standing value-added services in the mobile communications industry. The location of a user is the fundamental factor shaping such services and is usually computed solely in terms of the physical location relying on Reverse Geocoding APIs. It does not take into consideration the semantics of the location, but rather only the geographic spatial information,...
Structure-similarity method for attributed generalized trees is proposed. (Meta)data is expressed as a generalized tree, in which inner-vertex labels (as types) and edge labels (as attributes) embody semantic information, while edge weights express assessments regarding the (percentage-)relative importance of the attributes, a kind of pragmatic information added by domain experts. The generalized...
In this paper, we propose an image classification method that recognizes several poses of idol photographs. The proposed method takes unannotated idol photos as input, and classifies them according to their poses based on spatial layouts of the idol in the photos. Our method has two phases, the first one is to estimate the spatial layout of ten body parts (head, torso, upper and lower arms and legs)...
The paper addresses the problem of modeling the relationship between phrases in English using a similarity graph. The mathematical model stores data about the strength of the relationship between phrases expressed as a decimal number. Both structured data from Wikipedia, such as that the Wikipedia page with title "Dog" belongs to the Wikipedia category "Domesticated animals", and...
We propose a novel algorithm, QuIET, for binary classification of texts. The method automatically generates a set of span queries from a set of annotated documents and uses the query set to categorize unlabeled texts. QuIET generates models that are human understandable. We describe the method and evaluate it empirically against Support Vector Machines, demonstrating a comparable performance for a...
The Web has made possible many advanced text-mining applications, such as news summarization, essay grading, question answering, and semantic search. For many of such applications, statistical text-mining techniques are ineffective since they do not utilize the morphological structure of the text. Thus, many approaches use NLP-based techniques, that parse the text and use patterns to mine and analyze...
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