Matches in ESWC 2020 for { ?s ?p The paper presents a solution for supporting complex decision making processes in part through the use of ontologies and reasoning. The solution uses semantic technologies and has been used in applications in "building refurbishment" and "IT security risk management" by real users with very promising results. As a result, the paper first the In-Use Track criteria. I would like to see the paper published and presented at ESWC, but the paper's presentation can be improved to include more details and more clearly discuss the benefits and shortcomings of semantic technologies. It would help if you can use a single use case to more clearly describe each step involved in the process. I found Figure 2 very useful (as a side note, it has a strange black mark on some boxes so it's not fully readable), but that is only Step 3. For Steps 1-2, your examples are very high-level and not fully understandable, and for the remaining steps you have screenshots that are harder to understand and some are not English). Overall, what I would have liked to understand is: 1) How critical is the role of using an ontology and the use of a reasoner? 2) What would the solution look like without semantic technologies, but with some automation. In your use cases, you are comparing non-automated ways with your automated way and of course your solution is better. 3) What is the cost of setting up the solution for a new problem and how feasible it is to repeat the process for a new application. At least some estimates would be useful, e.g. did you spend a month to setup semergy.net or 5 years!? It would help if you can translate Figure 7 or add translations as notes on top of the figure. As per the usefulness of using a reasoner, in one or all of your use cases, can you bring examples where a basic matching of criteria would fail or would be much more difficult? How would your solution compare with other alternative decision support solutions that are not ontology-based but based on some other form of knowledge acquisition? For example the following work uses "Mind Maps" and transformation into an AI Planning model for decision support: http://www.cs.toronto.edu/~shirin/Sohrabi-AAAI-18.pdf". }
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- Paper.11_Review.0 hasContent "The paper presents a solution for supporting complex decision making processes in part through the use of ontologies and reasoning. The solution uses semantic technologies and has been used in applications in "building refurbishment" and "IT security risk management" by real users with very promising results. As a result, the paper first the In-Use Track criteria. I would like to see the paper published and presented at ESWC, but the paper's presentation can be improved to include more details and more clearly discuss the benefits and shortcomings of semantic technologies. It would help if you can use a single use case to more clearly describe each step involved in the process. I found Figure 2 very useful (as a side note, it has a strange black mark on some boxes so it's not fully readable), but that is only Step 3. For Steps 1-2, your examples are very high-level and not fully understandable, and for the remaining steps you have screenshots that are harder to understand and some are not English). Overall, what I would have liked to understand is: 1) How critical is the role of using an ontology and the use of a reasoner? 2) What would the solution look like without semantic technologies, but with some automation. In your use cases, you are comparing non-automated ways with your automated way and of course your solution is better. 3) What is the cost of setting up the solution for a new problem and how feasible it is to repeat the process for a new application. At least some estimates would be useful, e.g. did you spend a month to setup semergy.net or 5 years!? It would help if you can translate Figure 7 or add translations as notes on top of the figure. As per the usefulness of using a reasoner, in one or all of your use cases, can you bring examples where a basic matching of criteria would fail or would be much more difficult? How would your solution compare with other alternative decision support solutions that are not ontology-based but based on some other form of knowledge acquisition? For example the following work uses "Mind Maps" and transformation into an AI Planning model for decision support: http://www.cs.toronto.edu/~shirin/Sohrabi-AAAI-18.pdf"".