This article describes the keynote speech on INODE presented at Fourth International Workshop on Systems and Network Telemetry and Analytics (SNTA) which is collocated with International ACM Symposium on High -Perform...
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ISBN:
(纸本)9781450383868
This article describes the keynote speech on INODE presented at Fourth International Workshop on Systems and Network Telemetry and Analytics (SNTA) which is collocated with International ACM Symposium on High -Performance Parallel and Distributed Computing (HPDC) on June 21 in Stockholm, Sweden.
knowledgegraphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the r...
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ISBN:
(纸本)9781450395656
knowledgegraphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology. However, the automatic generation of such ontology is demanding and existing solutions are often not satisfactory. An important challenge is a trade-off between two principles of ontology engineering: knowledge-orientation and data-orientation. The former one prescribes that an ontology should model the general knowledge of a domain, while the latter one emphasises on reflecting the data specificities to ensure good usability. We address this challenge by our method of ontology reshaping, which automates the process of converting a given domain ontology to a smaller ontology that serves as the KG schema. The domain ontology can be designed to be knowledge-oriented and the KG schema covers the data specificities. In addition, our approach allows the option of including user preferences in the loop. We demonstrate our on-going research on ontology reshaping and present an evaluation using real industrial data, with promising results.
knowledgegraphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the r...
详细信息
ISBN:
(纸本)9781450395656
knowledgegraphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology. However, the automatic generation of such ontology is demanding and existing solutions are often not satisfactory. An important challenge is a trade-off between two principles of ontology engineering: knowledge-orientation and data-orientation. The former one prescribes that an ontology should model the general knowledge of a domain, while the latter one emphasises on reflecting the data specificities to ensure good usability. We address this challenge by our method of ontology reshaping, which automates the process of converting a given domain ontology to a smaller ontology that serves as the KG schema. The domain ontology can be designed to be knowledge-oriented and the KG schema covers the data specificities. In addition, our approach allows the option of including user preferences in the loop. We demonstrate our on-going research on ontology reshaping and present an evaluation using real industrial data, with promising results.
Existing academic search systems like Google Scholar usually return a long list of scientific articles for a given research domain or topic (e.g. "document summarization", "information extraction")...
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ISBN:
(纸本)9783642411540;9783642411533
Existing academic search systems like Google Scholar usually return a long list of scientific articles for a given research domain or topic (e.g. "document summarization", "information extraction"), and users need to read volumes of articles to get some ideas of the research progress for a domain, which is very tedious and time-consuming. In this paper, we propose a novel system called AKMiner (Academic knowledge Miner) to automatically mine useful knowledge from the articles in a specific domain, and then visually present the knowledgegraph to users. Our system consists of two major components: a) the extraction module which extracts academic concepts and relations jointly based on Markov Logic Network, and b) the visualization module which generates knowledgegraphs, including concept-cloud graphs and concept relation graphs. Experimental results demonstrate the effectiveness of each component of our proposed system.
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