|Review Comment: |
This paper provides a survey of approaches to knowledge graph refinement and correction. After a general introduction to the field and an overview of existing knowledge graphs, the author separately discusses approaches to the completion and correction of knowledge graphs. In doing this, the authors distinguish the dimensions: i) completion vs. correction, ii) target of refinement (or correction), and iii) methods using the knowledge graph itself only or methods relying also on external data. Different evaluation regimes together with their pro and cons are also discussed. The review is certainly timely, well motivated and self-contained. It will make the topic accessible to people seeking guidance in getting familiar with knowledge graph refinement techniques.
As far as I can judge, all the comments from the reviews in the first round have been successfully addressed.
I have spotted a few errors that need to be corrected for the final version of the article to be published.
Likewise, we do not consider WordNet  as a knowledge graph, since it mainly concerned with common nouns and words
⇒ since „it“ is mainly concerned ???
a larger number of ontoloyg reasoners [19,20,61]. -> misspelling of „ontology“
Section 7, beginning
“From the survey in the last two sections, we can observe that there are quite a few works proposed for
knowledge graph refinement.”
What is meant here with „quite a few“? That there are many or indeed very few approaches? Please make this clearer as this statement is a bit vague.
“A first interesting observation is that our distinguishing into completion and error detection is a strict one.”
⇒ odd, I suggest rephrasing as follows:
„A first interesting observation is that our distinction between completion and error detection is...“
„The major knowledge graph used in the evaluations is DBpedia.“
⇒ "major" here unclear/ambiguous. I assume that here it does not refer to the size of DBpedia.
So I would rephrase as:
„DBpedia is the most frequently used knowledge graph for evaluation purposes“
As discussed in section 2, knowledge graphs differ heavily in their characteristic. -> „characteristics“