Summary
This paper introduced a hybrid-initiative system called SOLVENT, which aims to find analogies between research papers in different fields through combining the work of human annotations of critical features of research paper and computational model which construct a semantic vector from those annotations. The author conducted three studies to demonstrate the performance and efficiency. In the first study, the author let people with specialized domain knowledge to do the annotation works and proved the system able to find known analogies. In order to prove the effectiveness of using SOLVENT to find analogies with the real-word case, the author demonstrated a real-word scenario, explained the primary process, and let professional without domain knowledge people to do the annotation. The results indicate that the relevant match found by the system was judged to be both useful and novel. The third study proved that they could scale up the system by allowing crowd workers to annotate papers.
Reflection
I think this paper brought up a novel and needed the idea. One limitation of searching for related work online is that the scope of the searching result is too narrow if we search for the keyword or citation. The results usually only include the related paper, the paper which cited the related paper, or the same paper that published from a different place. However, you can always find some inspiration from paper that not relevant to what you are looking for, or it can be an irrelevant paper. Nevertheless, this situation is usually unattainable. Take our project as an example. Our project was inspired by one of the papers we read before, and we would like to improve the work in that paper further. It should be straightforward to find related work because we have previous work already. Disappointingly, we could not find many useful papers or even latent techniques. The only thing we found appears most frequently is the same paper which inspired us, but published from a different place. Thus, from my perspective, this system is designed for searching inspirations through finding analogies. If the system can achieve this, it would be significant.
On the other hand, this seems like a costly approach because it requires a large number of workers to do the annotation work of a large scale of paper to guarantee the system’s performance. Besides, based on the results provided in the paper, the system performance can only be described as “useful” instead of “efficiently.” If I urgently need inspiration, I may try such a system, but I would not count on this system.
Question
- What do you think of the original idea presented in the paper “Scientific discoveries are often driven by finding analogies in distant domains”? Do you think this theory applies for the majority of people? Do you think finding analogies would inspire your work?
- What do you think regarding the system “usefulness” and “efficiency”?
- Can you think about any other way to utilize the SOLVENT system?
- The user mentioned in the paper that crowd workers can do the annotation work with no domain knowledge or even with no experience of reading a paper. Do you think this will influence the system performance? What are your criteria for recruit workers?
Word Count: 538
For the first question, I think that analogy can be applied to most people, and it is also an effective method. At the same time, the idea of analogy has also been used in many previous papers we read. As for the second question, I think the usefulness of these analogies still needs to be considered, especially whether these ideas are contrary to common sense in physics or more effective than existing methods.