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지식의 힘 !! 그리고 Linked Open Data Knowledge Extraction from Text

amaya-hunter avatar
amaya-hunter
17 diapositives · 295 Views · Nov 12, 2014

지식의 힘 !! 그리고 Linked Open Data Knowledge Extraction from Text. 2014.1.24 김평 (kimpyung@gmail.com). 지식을 어떻게 추출할 것인가 ?. 배경 Text understanding is an old yet-unsolved AI problem consisting of a number of nontrivial steps.

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  1. 지식의 힘!! 그리고 Linked Open DataKnowledge Extraction from Text 2014.1.24 김평 (kimpyung@gmail.com)

  2. 지식을 어떻게 추출할 것인가? • 배경 • Text understanding is an old yet-unsolved AI problem consisting of a number of nontrivial steps. • The critical step in solving the problem is knowledge acquisition from text, i.e. a transition from a non-formalized text into a formalized actionable language (i.e. capable of reasoning). • Other steps in the text understanding pipeline include linguistic processing, reasoning, text generation, search, question answering etc. which are more or less solved to the degree which allows composition of a text understanding service. • On the other hand, we know that knowledge acquisition, as the key bottleneck, can be done by humans, while automating of the process is still out of reach in its full breadth.

  3. 지식의 추출과 서비스 - 1 • Carnegie Mellon University • Never-Ending Language Learning: http://rtw.ml.cmu.edu/rtw/ • Cycorp • Semantic Construction Grammar: http://www.cyc.com/ • IBM Research • Watson project: http://www.ibm.com/watson • IDIAP Research Institute • Deep Learning for NLP: http://publications.idiap.ch/index.php/authors/show/336 • JozefStefan Institute • Cross-Lingual Knowledge-Extraction: http://xlike.org • KU Leuven • Spatial Role Labelling via Machine Learning for SEMEVAL

  4. 지식의 추출과 서비스 - 2 • Max Planck Institute • YAGO project: http://www.mpi-inf.mpg.de/yago-naga/yago/ • MIT Media Lab • ConceptNet: http://conceptnet5.media.mit.edu/ • University Washington • Open Information Extraction: http://openie.cs.washington.edu/ • Vulcan Inc. • Semantic Inferencing on Large Knowledge: http://silk.semwebcentral.org/

  5. NELL: Never-Ending Language Learning • Read the Web

  6. OpenCyc • Semantic Construction Grammar • How can NIPS help with deep reading

  7. Watson • Watson understands natural language, breaking down the barrier between people and machines.

  8. Deep Learning • moving beyond shallow machine learning since 2006!

  9. XLike • Cross-lingual Knowledge Extraction

  10. Spatial Role Labeling • Spatial relationships between objects

  11. YAGO2s • A High-Quality Knowledge Base

  12. ConceptNet • ConceptNet is a semantic network containing lots of things computers should know about the world, especially when understanding text written by people.

  13. Open Information Extraction • Get answers to natural-language questions!

  14. SILK • Semantic Inferencing on Large Knowledge

  15. Wolfram|Alpha • Make all systematic knowledge immediately computable and accessible to everyone.

  16. Knowledge Extraction • the creation of knowledge from structured and unstructured sources

  17. 결론 • LOD가 확산되기 위한 절차 • 그 걸림돌은? • 누가, 무엇을, 어떻게???? • 어떻게 구축하고, 확산할 것인가? • 지식이 자동화되기 위한 어렵고도 먼 길…..