An entity conceptualize method combing topic based models and knowledge bases : Given a query, we first use a dictionary constructed from the knowledge bases to detect the possible entities and their associated categories. Then, we use a topic based method to derive semantic information from the text. By comparing the topical similarity between various candidate phrases, we get the most likely entities and their related categories.
An entity linking method based on Markov Logic Network: This Method links the entity mentions in scientific text to appropriate concepts in the domain ontology by Markov Logic Network. Combining the first-order logic and probability model, the framework enables us to integrate many domain specific features such as the domain naming rules as well as common context features and entity coreference features, thus provides an effective way to deal with the challenges of the name diversification, abbreviation and other complicated naming rules of entities in specific domains.
- 2016.01-2020.12 Research on Key Technologies of Large-Scale Linked Data Management Granted by the Natural Science Foundation of China(NSFC) under grant number 61532010
- 2016.07-2019.06 Large-scale Knowledge Graph Research on Microorganism Area Granted by the National Key Research and Development Program of China ‘Scientific Big Data Management System’ under grant number 2016YFB1000603
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