By Honghan Wu https://orcid.org/0000-0002-0213-5668

Review Details

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Reviewer has chosen not to be Anonymous

Summary of paper in a few sentences (summary of changes and improvements for second round reviews):

This position paper presents an overview about the data science as a discipline with a focus on discussing the differences and associations of the two main methodologies in Artificial Intelligence - knowledge representation (symbolic representations and their computations) and statistical approaches (e.g., neural networks, deep learning and etc.). Specifically, the authors discussed 1) how explicitly presented knowledge can help data science / statistical methods? 2) how statistical approaches can help identify patterns / regularities from data, which can further be distilled as domain knowledge? 3) the future (grand) challenges in data science - challenges that might require the invention of disruptive methodologies that are able to abstract the unobservable, but fundamental laws of the world like Galileo’s principle of inertia.

Reasons to accept:

Most of my concerns have been addressed in this revision.

Reasons to reject:

No

Nanopublication comments:

Further comments:

By Agnieszka Ławrynowicz https://orcid.org/0000-0002-2442-345X

Review Details

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Reviewer has chosen not to be Anonymous

Summary of paper in a few sentences (summary of changes and improvements for second round reviews):

This is a position paper treating on the relationship between statistical (Data Science) and symbolic (AI) approaches.

Reasons to accept:

The topic is central to the scope of the journal.
The paper presents a well chosen set of problems arising from trying to combine statistical and symbolic approaches in Data Science.

Reasons to reject:

None

Nanopublication comments:

Further comments:

I thank the authors for their efforts to address my comments.
I am satisfied with the current version of the paper.

I have only a remark on Section 2 (including the title) which could probably be a bit restructured to treat Life Sciences as an illustrative example of a domain to illustrate the general concept rather than focusing explicitly on this domain. Of course this domain is a mature example where both data and knowledge are heavily exploited for data analysis.
Currently the fact that this chapter is explicitely focused on Life Sciences also comes as a surprise since nothing is mentioned of such a focus in the abstract.

Minor:
What is „NN” in the abstract?

Reviewer has chosen to be Anonymous

Summary of paper in a few sentences (summary of changes and improvements for second round reviews):

This paper describes the interrelationship between the areas of data science and symbolic AI

Reasons to accept:

As discussed in a previous review, the paper deals with a nice topic for the inaugural issue of this journal: the intersection between these two disciplines that so far seem to be largely disconnected, and which will clearly start converging in the coming years.

Reasons to reject:

The paper has not improved significantly (indeed, I would have expected a letter presenting the differences with respect to the previous version, according to the original comments, as it is usual practice in this type of reviewing process.

Some of the initial questions made in my initial review remain:
This paper does not bring in clearly a very clear landscape of where we are, but mostly a sequence of descriptions of existing efforts that are in between both areas, which do not flow very clearly and do not tell a clear story. Which are your main conclusions from the analysis that you have done? Which are the actual challenges and opportunities that you refer to in the abstract and title? You refer in section 5 to the limits of data science and the relationship to some theories in Science, but these are just examples, and it is not clear how symbolic AI can help in those specific cases, where it seems that mathematical formulations are potentially more useful. Only a paragraph has been added in this respect, and it is still difficult to see where the main contribution would be here.

Nanopublication comments:

Further comments:

I see that the paper has improved in its storytelling, and this is the reason why I have moved my decision to "undecided". However, a good essay on this intersection would present more clearly those intersection points, and other types of graphics that allow understanding the commonalities and differences between those approaches, and how to overcome them, and how to align initiatives in both respects.