The authors introduced a mixed-initiative system named SOLVENT in this paper. In the system, humans annotate aspects of research papers that denote their background, purpose, mechanism, and findings. A computational model is used to construct a semantic representation from these annotations which are valuable for finding analogies among papers. They tested their system through three experiments. The first one used annotation from domain expert researchers, the second one used annotation from experts outside the papers’ domain and the last one used crowdsourcing. From the experiments’ results, the authors found that the system was able to detect analogies among different domains and even crowd-sourcing workers, who have limited domain knowledge, are able to do the annotations. Additionally, the system has a better performance than similar systems.
Reflections
I used to only search papers within the computer science area when I did my projects or tried to solve a problem. Of course, sometimes we can get inspired by the ideas in papers from other domains, but it is quite difficult to find such a paper. There are countless papers in various domains, and there was not an efficient method to find needed papers from other domains. Additionally, even we found a paper that is valuable to the problem we are solving, the lack of background knowledge may make it difficult for us to understand the ideas behind the paper.
This system is a great help to the above situation. It can let people find related papers from other domains even they have limited knowledge about that domain. Even though the amount of papers is increasing sharply, we can still find the papers we need efficiently with this system. Before, we can only search for keywords related to the specific area. With this system, we can try to search for ideas instead of specific words. This is beneficial if some of the papers used abbreviations or analogies in the titles. If we only use keyword searching, we may miss these papers. But with idea searching, these papers will be marked as valuable. Also, the human annotation in the system can help us to understand the ideas of the papers easily, and we can exclude irrelated papers with high efficiency.
One more point is that cross-domains projects or researches are increasing significantly nowadays. For these studies, they needed to read a huge amount of papers in both domains and then they can have a novel idea to solve the cross-domain problem. If these researchers can have the system, they can find similarities in both domains easily and can have headlines about the background, purpose, mechanism, and findings in the papers. The efficiency of these researches can be improved, and the researchers can find more novel interdisciplinary studies with the help of the system.
Questions:
Will the performance of the system decrease when dealing with larger size database and more difficult papers?
Is it possible to update the system results regularly when newly published papers are added into the database?
Can this system be applied in the industries? For example, find the similarity of the mechanisms in the production of two products and use the findings to improve the mechanisms.
For the first question, I think this is inevitable. As the number of articles increases, the number of articles expressing similar topics will also increase. At the same time, with the uncertain ability of crowd source workers to analyze the article framework, its accuracy and efficiency will inevitably decline, but, more articles may produce better and more creative ideas.