Summary
Chan et al.’s paper “SOLVENT: A Mixed Initiative System for Finding Analogies Between Research Papers” explores the feasibility to leverage a mixed-initiative system to categorize research papers into their relational schemas by a collaborative Human-AI team, which can be utilized to identify analogical research papers potentially leading to innovative knowledge discoveries. The motivation of the researchers is the boom of research papers during recent decades, which makes searching for relevant papers in one domain or cross domains become more and more difficult. To facilitate the paper retrieval and interdisciplinary analogies search, the researchers develop a mixed-initiative system called SOLVENT in which humans mark the key aspects of research papers (their background, purpose, mechanism, and findings) with a computational machine learning model extracting semantic representations from these key aspects, which can facilitate the identifying analogies across different research domains.
Reflection
I think this paper conducted an innovative study on how the proposed system can actually support knowledge sharing and discovery in one domain and across different research communities. In the research explosion era, researchers would greatly benefit from using such a system for their own research and explore more interdisciplinary possibilities. That makes me think about why the system can achieve good performance via annotating the content of abstracts in the domains they conducted experiments. As we know, abstracts of the papers usually summarize the most important point of the research papers at a high-level. So it is intuitive and wise to utilize that part for annotating and further tasks. The researchers adopt the pre-trained word embedding models to generate semantic vector representations for each component, which performs pretty well in the tasks presented in the paper. I would imagine that the framework would probably work especially well for experimentation-driven domains, computer science, civil engineering, biology, etc., in which the research papers follow a specific writing structure. Can the proposed framework scale up to other less structured text materials, such as essays, novels, by extending it to full content instead of focusing on the abstracts? I think that would be an interesting future direction to explore.
In addition, one potential future work discussed in the paper is to extend the content-based approach with graph-based approaches like citation networks. I feel this is a novel idea and there is a lot of potential in this direction. Since the proposed system has the ability to find analogies across various research areas, I would be curious to see if it is possible to generate a knowledge graph based on the analogy pairs that can create something similar to a research road map, which indicates how the ideas from different papers in various research areas relate in a larger scope. I would imagine researchers would benefit from a systematized collection of research ideas.
Discussion
I think the following questions are worthy of further discussion.
- Would you use this system to support your own research? Why or why not?
- Do you think that the annotation categories capture the majority of the research papers? Can you think about other categories the paper did not mention?
- What do you think of the researchers’ approach to annotating the abstracts? Would it be helpful to expand on this work to annotate the full content of the papers?
- Do you think the domains involved in cross-domain research share the same purpose and mechanism? Can you think about some possible examples?
I would most definitely use it in my own research, because I think there is value in finding analogies, especially maybe mapping concepts in computer networks and distributed systems to existing systems in nature. Studying animal coordination patterns could potentially give insight on how to set up information networks because animals have had millions of years to optimize their algorithms, there’s no need to reinvent the wheel.