Paper:
Knowledge graph is a kind of valuable knowledge base which would benefit lots of AI-related applications. Up to now, lots of large-scale knowledge graphs have been built. However, most of them are non-Chinese and designed for general purpose. In this work, we introduce TechKG, a large scale Chinese knowledge graph that is technology-oriented. It is built automatically from massive technical papers that are published in Chinese academic journals of different research domains. Some carefully designed heuristic rules are used to extract high quality entities and relations. Totally, it comprises of over 260 million triplets that are built upon more than 52 million entities which come from 38 research domains. Our preliminary experiments indicate that TechKG has high adaptability and can be used as a dataset for many diverse AI-related applications.

Review:
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Original review:
This paper presents the extraction of a bibliographic database of Chinese technical papers.  This database could potentially be a valuable resource for the community.  However, the paper is mis-targeted to the ICLR conference, as it does not discuss learning representations or using deep learning (the 36-page appendix does include some material on knowledge graph embedding, but this is not covered in the main body of the paper).  Also, for important tasks like name deduplication, the paper does not discuss or compare against techniques from previous work, and instead proposes a small set of heuristics, which is a sensible approach for building a resource but will not be of interest to the ICLR audience.
The paper refers to their resource as a knowledge graph, but I would say it is more accurate to call it a bibliographic database (it consists primarily of paper titles, authors, and keywords).  This is very different from the broader KBs and KGs discussed in the related work.  YAGO, Freebase, Cyc, and so on capture a much wider variety of semantic relationships and entity types.  I think re-positioning this submission as being aimed at building a bibliographic database, rather than a Knowledge Graph, would help ensure it reaches the right audience.  Also, targeting a different venue like a digital libraries conference and making a strong case that this set of bibliographic data offers advantages in coverage or accuracy over other comparable resources, if any, would help.
Finally, one of the most valuable features of a bibliographic database is the citation graph, it would be exciting if TechKG could be extended to include citation information.
Minor:
Section 2, most of these citations should not be shortcites but should be regular full cites.  For example in the first paragraph, Miller (1995) should be (Miller, 1995).