Semantic Web vs. semantic technology

Semantic Web Technologies course 
http://www.meteck.org/SWT.html

Foundations of Semantic Web Technologies (許多課程投影片)
http://www.semantic-web-book.org/page/Slides

• Knowledge Representation for the Semantic Web, course at the Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, winter quarter 2012. Covers RDF and OWL in depth.

Transformation of ZML schemas and XML data to RDF/OWL
http://topquadrantblog.blogspot.tw/2011/09/living-in-xml-and-owl-world.html


XML SchemaPlus Specification
This document presents a specification of XML SchemaPlus (XSP) for describing the layout of an XML document in such a way that RDF/OWL semantics can be retained.



Semantic University
http://www.cambridgesemantics.com/semantic-university/semantic-web-vs-semantic-technology#

Some examples of semantic technologies include natural language processing (NLP), data mining, artificial intelligence (AI), category tagging, and semantic search.

Semantic Web technologies include:

• a flexible data model (RDF),
• schema and ontology languages for describing concepts and relationships (RDFS and OWL),
• a query language (SPARQL),
• a rules language (RIF),
• a language for marking up data inside Web pages (RDFa),
• and more.

• Semantic Web Landscape
  • Getting Started: Understanding Semantic Technologies
  • Semantic Technologies Compared
  • Semantic Technologies Applied

• Technical Lesson Tracks
  • Learn RDF
  • Learn OWL and RDFS
  • Learn SPARQL
  • Semantic Web Design Patterns

• About Semantic University
  • Contact Semantic University
  • 
    

Deep Learning for Named Entity Recognition

some interesting recent (2015-2016) papers related to that:

http://deeplearningkit.org/2016/04/23/deep-learning-for-named-entity-recognition/

1. Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks – authors: M Francis
2. Entity Attribute Extraction from Unstructured Text with Deep Belief Network – authors: B Zhong, L Kong, J Liu
3. Learning Word Segmentation Representations to Improve Named Entity Recognition for Chinese Social Media – authors: N Peng, M Dredze
4. Biomedical Named Entity Recognition based on Deep Neutral Network – authors: L Yao, H Liu, Y Liu, X Li, MW Anwar
5. Shared tasks of the 2015 workshop on noisy user-generated text: Twitter lexical normalization and named entity recognition – authors: T Baldwin, MC de Marneffe, B Han, YB Kim, A Ritter…
6. Semi-Supervised Approach to Named Entity Recognition in Spanish Applied to a Real-World Conversational System – authors: SS Bojórquez, VM González
7. Boosting Named Entity Recognition with Neural Character Embeddings – authors: C dos Santos, V Guimaraes, RJ Niterói, R de Janeiro
8. Exploring Recurrent Neural Networks to Detect Named Entities from Biomedical Text – authors: L Li, L Jin, D Huang
9. Entity-centric search: querying by entities and for entities – authors: M Zhou
10. Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach – authors: X Ren, A El
11. Boosting Named Entity Recognition with Neural Character Embeddings – authors: CN Santos, V Guimarães
12. Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network. – authors: Y Wu, M Jiang, J Lei, H Xu
13. Context-aware Entity Morph Decoding – authors: B Zhang, H Huang, X Pan, S Li, CY Lin, H Ji, K Knight…
14. Training word embeddings for deep learning in biomedical text mining tasks – authors: Z Jiang, L Li, D Huang, L Jin
15. Entity Attribute Extraction from Unstructured Text with Deep Belief Network – authors: B Zhong, L Kong, J Liu
16. Building Text-mining Framework for Gene-Phenotype Relation Extraction using Deep Leaning – authors: D Jang, J Lee, K Kim, D Lee
17. Text Mining in Social Media for Security Threats – authors: D Inkpen
18. Text Understanding from Scratch – authors: X Zhang, Y LeCun
19. Syntax-based Deep Matching of Short Texts – authors: M Wang, Z Lu, H Li, Q Liu
20. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks – authors: J Tang, M Qu, Q Mei
21. Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach – authors: X Ren, A El
22. Domain-Specific Semantic Relatedness from Wikipedia Structure: A Case Study in Biomedical Text – authors: A Sajadi, EE Milios, V Kešelj, JCM Janssen
23. Deep Unordered Composition Rivals Syntactic Methods for Text Classification – authors: M Iyyer, V Manjunatha, J Boyd
24. Representing Text for Joint Embedding of Text and Knowledge Bases – authors: K Toutanova, D Chen, P Pantel, H Poon, P Choudhury…
25. In Defense of Word Embedding for Generic Text Representation – authors: G Lev, B Klein, L Wolf

Notes on Conversational Interfaces

https://quip.com/VjJFAFmzJ35P

Knowledge base systems

Knowledge-based Artificial Intelligence
http://www.mkbergman.com/1816/knowledge-based-artificial-intelligence/
A recent interview with a noted researcher, IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley, provided a downplayed view of recent AI hype. Jordan was particularly critical of AI metaphors to real brain function and took the air out of the balloon about algorithm advances, pointing out that most current methods have roots that are decades long [1]. In fact, the roots of knowledge-based artificial intelligence (KBAI), the subject of this article, also extend back decades.


一些和Knowledge base systems相關的資料整理網站
http://appleiphones.org/images/knowledge+base+systems