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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...
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...
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...
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...
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,...
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 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...
The rate at which biomedical literature is being published is quickly outpacing our ability to effectively leverage this information for evidence-based medicine. While papers are readily searchable through databases such as Pub Med, clinicians are often left with the time-consuming task of finding, assessing, interpreting, and applying this information. Tools that structure evidence from published...
Connecting ontological representations and data models is a crucial need in enterprise knowledge management, above all in the case of federated enterprises where corporate ontologies are used to share information coming from different databases. OWL to ERD transformations are a challenging research field in this scenario, due to the loss of expressiveness arising when OWL axioms have to be represented...
Cultural heritage resources are huge and heterogeneous. They include highly structured, very unstructured, and semi-structured data or information obtained from both authorized and unauthorized sources and involving multimedia data including text, audio and video data. With the rapid development of the web, more and more cultural heritage organizations use digital methods to record, store and represent...
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...
To cope with the increasing amount of cyber threats, cyber security information must be shared beyond organization borders. Assorted organizations have already started to provide publicly-available repositories that store XML-based cyber security information on the Internet, but users are unaware of all of them. Cyber security information must be identified and located across such repositories by...
We present a method for the automatic classification of text documents into a dynamically defined set of topics of interest. The proposed approach requires only a domain ontology and a set of user-defined classification topics, specified as contexts in the ontology. Our method is based on measuring the semantic similarity of the thematic graph created from a text document and the ontology sub-graphs...
The ability to identify, process, and comprehend the essential elements of information associated with a given operational environment can be used to reason about how the actors within the environment can best respond. This is often referred to as "situation assessment," the end state of which is "situation awareness," which can be simply defined as "knowing what is going...
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...
Innovative analysis methods applied to data extracted by off-the-shelf peripherals can provide useful results in activity recognition without requiring large computational resources. In this paper a framework is proposed for automated posture and gesture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as a semantic-based resource discovery...
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