Relatedness and TBox-Driven Rule Learning in Large Knowledge Bases
نویسندگان
چکیده
منابع مشابه
Rule-Based Inference in Large Knowledge Bases
Rule-Based Inference In Large Knowledge Bases *
متن کاملLearning Soft Inference Rules in Large and Uncertain Knowledge Bases
Recent progress in information extraction has enabled us to create large semantic knowledge bases with millions of RDF facts extracted from the Web. Nevertheless, the resulting knowledge bases are still incomplete or might contain inconsistencies, either because of the heuristic nature of the extraction process, or due to the varying reliability of the Web sources from which they were collected...
متن کاملLarge heterogeneous knowledge bases
This paper discusses large knowledge bases as software development tools which support the creativity of programming in the large. User requirements, architecture and internal knowledge representation language of large knowledge bases are considered. Higher order constraint networks are proposed for representing knowledge about computability. 1 Software reusability An important characteristic o...
متن کاملLarge engineering knowledge bases
We did not touch problems like maintenance of large knowledge-bases, because this requires more experience which can be obtained only from practice of using large knowledge-bases. Finally, it seems that a realistic way to get a universal engineering knowledge-base is by evolutionary development. This reminds us again how people are being taught. The crucial question in this case will be the abi...
متن کاملInstance-driven TBox Revision in DL-Lite
The development and maintenance of large and complex ontologies are often time-consuming and error-prone. Thus, automated ontology learning and revision have attracted intensive research interest. In data-centric applications where ontologies are designed or automatically learnt from the data, when new data instances are added that contradict to the ontology, it is often desirable to incrementa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i03.5690