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Patterns allow a complex system to be modularised and self-explaining, hence more re usable. This is true in architecture and in software engineering , where patterns provide solutions to recurrent problems and form a language for supporting communication among designers. Knowledge graphs are complex systems and ontologies are a means to govern them as far as their organisation, semantics, and interoperability are concerned.
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We are used to hear that knowledge graphs are valuable key assets for building intelligent systems. This is true under the assumption that they are interoperable, but when using them in real-world cases, they show their wild nature. Ontology design patterns play a crucial role for enabling such interoperability, however there is still lack of proper tools for widely spreading their adoption.
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Research-wise, this area offers a broad range of interesting problems, e. What are the main benefits of engineering ontologies with ontology patterns? Could you boil this down to a list of 5?
"Extending ontological semantics for the domain of tourism: The case of" by Dina H Mohamed
You are an expert on social robotics as well. What are the core concepts and drivers behind this idea? Social robotics can be described from different perspectives. The main idea behind it is to develop robots that feature socially plausible behaviour as opposed to service robots that are meant to perform specific tasks e.
From a technological perspective social robots can be life-changers, especially for people with disabilities and considering that our life expectation is growing, eventually all of us may benefit from them. Scientifically, what's really interesting in social robotics is that by implementing and testing computational versions of psychological, cognitive and social theories we understand their limits, possibly prove their soundness, and ultimately better understand ourselves.
You also are a profound expert on common sense knowledge and personalized knowledge graphs. What are these concepts about and how do they connect with the other areas you research in? You want the robot, for example, to know about you, to remind you about your past experiences, your friends and family, and keep track of the main events of your day, in case you need to refresh them.
Common sense knowledge is the knowledge we all share and use for every communication and task we perform. This knowledge is never explicit, however if we interact with another person, even if we never met her before, we make a number of assumptions on such a shared knowledge.
Similarly we assume that we are interacting with an intelligent agent, able to perform reasoning that relies on such shared knowledge. Sharing common sense knowledge between humans and machines, and for machines being able to use common sense reasoning are two main challenges in AI. Common sense is of course very relevant for social robots. MTSR is an annual international inter-disciplinary conference, which brings together academics, researchers and practitioners in the specialized fields of metadata, ontologies and semantics research.
OASIS: Ontology as Structured by the Interfaces with Semantics
Submit your proposal to EasyChair. The conference welcomes the organization of special tracks focusing on particular domains or topics. Proposals for special tracks are welcome. Recently, such emerging semantic models have achieved state-of-the-art results in several predictive applications e.
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The second, more traditional, approach conveys meaning in a structured form through embedded data markup using Schema. Ontological notions of semantics play a central role in the Semantic Web community, permitting publishers and consumers of data to interact using a well-defined ontology.
Such models give practitioners the powerful ability to reason about, and represent, instances in a well-defined manner. HSSUES is a half-day workshop that will explore the synergy, from perspectives of theory, application, experiments including negative results and vision, between both approaches, and how such synergies can be exploited to advance the state-of-the-art. We are interested in mechanisms, both theoretical and experimental, that range the spectrum of possible strategies and provide novel functionalities through hybrid approaches to statistical and symbolic understanding.
The broader goal is to foster a discussion that will lead to cross-cutting ideas and collaborations at a timely moment when Semantic Web research has significantly started intersecting with the natural language processing and knowledge discovery communities. We are looking forward to a highly interactive workshop with participants from a wide range of backgrounds, from researchers to application builders, and from academia to industry. Researchers with an interdisciplinary interest in exploring synergies between declarative semantics such as ontologies and machine learning and knowledge discovery methods, especially graph including knowledge graph embeddings and deep neural networks, are especially invited to participate and submit papers.
In addition to oral presentations selected from submitted papers, we will have poster sessions, and an invited talk from industry. We will also accept a small number of vision, opinion and position papers that provide discussions on challenges and roadmaps for hybrid systems, and emerging semantic models.