지식의 힘 !! 그리고 linked open data knowledge extraction from text

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지식의 힘 !! 그리고 Linked Open Data

Knowledge Extraction from Text

2014.1.24

김평([email protected])


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 acqui-

sition from text, i.e. a transition from a non-formalized text into a formalized actionable language (i.e. capable of rea-soning).

Other steps in the text understanding pipeline include lin-guistic processing, reasoning, text generation, search, ques-tion answering etc. which are more or less solved to the de-gree which allows composition of a text understanding ser-vice.

On the other hand, we know that knowledge acquisition, as the key bottleneck, can be done by humans, while automat-ing of the process is still out of reach in its full breadth.

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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

Jozef Stefan Institute Cross-Lingual Knowledge-Extraction: http://xlike.org

KU Leuven Spatial Role Labelling via Machine Learning for SEMEVAL

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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.washing-

ton.edu/ Vulcan Inc.

Semantic Inferencing on Large Knowledge:

http://silk.semwebcentral.org/

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NELL: Never-Ending Language Learning

Read the Web

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OpenCyc

Semantic Construction Grammar How can NIPS help with deep reading

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Watson

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

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Deep Learning

moving beyond shallow machine learning since 2006!

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XLike

Cross-lingual Knowledge Extraction

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Spatial Role Labeling

Spatial relationships between objects

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YAGO2s

A High-Quality Knowledge Base

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ConceptNet

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

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Open Information Extraction

Get answers to natural-language questions!

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SILK

Semantic Inferencing on Large Knowledge

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Wolfram|Alpha

Make all systematic knowledge immediately com-putable and accessible to everyone.

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Knowledge Extraction

the creation of knowledge from structured and unstructured sources

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결론

LOD 가 확산되기 위한 절차 그 걸림돌은 ?

누가 , 무엇을 , 어떻게 ????

어떻게 구축하고 , 확산할 것인가 ?

지식이 자동화되기 위한 어렵고도 먼 길… ..

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지식의 힘 !! 그리고 linked open data knowledge extraction from text

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