Summary:
This paper mainly talks about a new paper search system, and I think that it can be thought as a thought initiation system rather than a paper search system. The article first proposes that scientific discovery is usually promoted by finding analogies in distant fields, but as more and more papers are published now, it is difficult to find papers with relevant ideas in a field, even though in those cross-field. Therefore, in order to achieve this aim, the authors introduced a hybrid system. In this system, crowdsourcing workers are mainly responsible for reading and understanding an article. People need to analyze an article from four aspects, Background, Purpose, Mechanism, Findings. The computer then analyzes the article based on these semantic frameworks, such as through TFIDF, or a combination of different architectures, and then finds similar usages from research papers. And through verification, found that these annotations are more effective, and can help experts.
Reflection:
First of all, I agree with the goal of the thesis, helping more researchers to obtain new ideas by analogy from outside the field, and then use these ideas and innovative ideas to promote the development of science and technology. I also think that analogies can help technology anyway. The article also cited quite a few examples to show the effectiveness of analogy and the breakthrough of research after the analogy. And in my reading in the past few weeks, I often feel that the article uses analogy. Since then, I have been thinking about how to help people more effectively through analogy and learn from other subject areas. Secondly, I think it is a very effective method to introduce people to assist in the completion. This is also the biggest gain in the course of my word. When a problem is encountered, the consideration is to use human power to solve it. And in the article, let the crowd source workers to annotate the article from four directions, I think it can greatly decompose an article, so that the computer can better understand this article. It is still difficult for a computer to directly understand an article and find out from it, but based on these architectures, finding the connection between articles is indeed a relatively simple task. But at the same time, I have some doubts about the effectiveness of the system proposed in the article. The article also spent a considerable amount of space to describe the limitations of this system. And my doubts are mainly focused on the third point, the usefulness in the real world. I think there are many aspects that will affect this practicality. For example, when the data increases, there will be more similar analogies, and the quality of these analogies is difficult to control. As we know, not all the lessons are useful, some ideas may bring Other problems, such as reduced efficiency, wasted resources, etc. The final point is that I think it takes a lot to get a good idea. We also need to control the quality of the work done by the workers and whether the algorithm can be so enlightening. In my opinion, a good innovation is usually electro-optical flint. Although this may be relatively easy to achieve on the basis of analogy, it still needs a good collaboration between human and machine to complete.
Question:
- Do you think finding analogy by analyze papers based on their framework is a useful way?
- Is there any other factors might influence this system, such as the increase of similar articles or different understanding between workers?
- Except the methods mentioned in the article, as computer scientists, what can we do to inspire people?
Good reflection. I think this paper made a good point that inspires researchers by finding an analogy. I think this would be very useful when we select the research topic or decide the research directions. However, I never thought about the quality control for this paper. Maybe we should consider this after the system could have a better performance. I believe similar articles probably influence system performance. Still, I think the significant influence comes from the amount of research paper required to find the matching paper and the annotation made by the workers. These features would most directly influence the system performance when finding the analogy.
I think finding analogies is really helpful to get ideas for research. But this paper focuses on finding analogies based on a new idea, which I am not sure is as useful. It definitely works for enhancing the understanding of the new idea by being able to compare it to existing simpler ideas, but not sure if its currently being utilized to its full potential.