Building an Ontology for the Domain of Plant Science using Prot\'eg\'e

Due to the rapid development of technology, large amounts of heterogeneous data generated every day. Biological data is also growing in terms of the quantity and quality of data considerably. Despite the attempts for building a uniform platform to handle data management in Plant Science, researchers are facing the challenge of not only accessing and integrating data stored in heterogeneous data sources but also representing the implicit and explicit domain knowledge based on the available plant genomic and phenomic data. Ontologies provide a framework for describing the structures and vocabularies to support the semantics of information and facilitate automated reasoning and knowledge discovery. In this paper, we focus on building an ontology for Arabidopsis Thaliana in Plant Science domain. The aim of this study is to provide a conceptual model of Arabidopsis Thaliana as a reference plant for botany and other plant sciences, including concepts and their relationships

Semantic Web in Action: Ontology-driven Information Search, Integration and Analysis

Keynote at the Net Object Days and MATES, Erfurt, Germany, September 23, 2003

Towards Understanding and Answering Multi-Sentence Recommendation Questions on Tourism

We introduce the first system towards the novel task of answering complex multisentence recommendation questions in the tourism domain. Our solution uses a pipeline of two modules: question understanding and answering. For question understanding, we define an SQL-like query language that captures the semantic intent of a question; it supports operators like subset, negation, preference and similarity, which are often found in recommendation questions. We train and compare traditional CRFs as well as bidirectional LSTM-based models for converting a question to its semantic representation. We extend these models to a semisupervised setting with partially labeled sequences gathered through crowdsourcing. We find that our best model performs semi-supervised training of BiDiLSTM+CRF with hand-designed features and CCM(Chang et al., 2007) constraints. Finally, in an end to end QA system, our answering component converts our question representation into queries fired on underlying knowledge sources. Our experiments on two different answer corpora demonstrate that our system can significantly outperform baselines with up to 20 pt higher accuracy and 17 pt higher recall

KRC: KnowInG crowdsourcing platform supporting creativity and innovation

The deep financial and economic crisis, which still characterizes these years, requires searching for tools in order to enhance knowledge sharing, creativity and innovation. The Internet is one of these tools that represents a practically infinite source of resources. In this perspective, the KnowInG project, funded by the STC programme MED, is aimed at developing the KnowInG Resource Centre (KRC), a sociotechnical system that works as a multiplier of innovation. KRC was conceived as a crowdsourcing platform allowing people, universities, research centres, organizations and companies to be active actors of creative and innovation processes from a local to a transnational level.Comment: 15 page

On construction, performance, and diversification for structured queries on the semantic desktop

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Machine Learning with World Knowledge: The Position and Survey

Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn. Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly. It has become a key obstacle in making learning protocols realistic in applications. In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work. We start from the comparison of world knowledge with domain-specific knowledge, and then introduce three key problems in using world knowledge in learning processes, i.e., explicit and implicit feature representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision. Finally we discuss the future directions of this research topic

Ontology Assisted Query Reformulation Using Semantic and Assertion Capabilities of OWL-DL Ontologies

End users of recent biomedical information systems are often unaware of the storage structure and access mechanisms of the underlying data sources and can require simplified mechanisms for writing domain specific complex queries. This research aims to assist users and their applications in formulating queries without requiring complete knowledge of the information structure of underlying data sources. To achieve this, query reformulation techniques and algorithms have been developed that can interpret ontology-based search criteria and associated domain knowledge in order to reformulate a relational query. These query reformulation algorithms exploit the semantic relationships and assertion capabilities of OWL-DL based domain ontologies for query reformulation. In this paper, this approach is applied to the integrated database schema of the EU funded Health-e-Child (HeC) project with the aim of providing ontology assisted query reformulation techniques to simplify the global access that is needed to millions of medical records across the UK and Europe.Comment: 15 pages, 4 figures. Proceedings of the 12th International Database Engineering & Applications Symposium (Ideas2008

Multi-site software engineering ontology instantiations management using reputation based decision making

In this paper we explore the development of systems for software engineering ontology instantiations management in the methodology for multi-site distributed software development. Ultimately the systems facilitate collaboration of teams in multi-site distributed software development. In multi-site distributed environment, team members in the software engineering projects have naturally an interaction with each other and share lots of project data/agreement amongst themselves. Since theyare not always residing at the same place and face-to-face meetings hardly happen, there is a need for methodology and tools that facilitate effective communication for efficient collaboration. Whist multi-site distributed teams collaborate, there are a lot of shared project data updated or created. In a large volume of project data, systematic management is of importance. Software engineering knowledge is represented in the software engineering ontology whose instantiations, which are undergoing evolution, need a good management system. Software engineering ontology instantiations signify project information which is shared and has evolved to reflect project development, changes in the software requirements or in the design process, to incorporate additional functionality to systems or to allow incremental improvement, etc

Autonomic Approach based on Semantics and Checkpointing for IoT System Management

Le résumé en français n'a pas été communiqué par l'auteur.Le résumé en anglais n'a pas été communiqué par l'auteur