Articial Intelligence/Machine Learning Solution. 4. Case study 2: Topics of prominence The idea of modelling science using computers and citation linkages is not a new idea. Eugene Gareld and Henry Small took this approach back in the 1980s, and their pioneer work played a role in the development of what is now known as Topic Prominence. Back in 1985, it took eight weeks to analyze 2% of the citation base in order to come up with a list of hot research topics namely, those in the Top 1%. Now, we use the entire corpus of literature to create a comprehensive view of the roughly one hundred thousand global research topics currently extant. 325 326

A. Gabriel / Articial intelligence in scholarly communications: An elsevier case study When we think about modeling research, there are a few key design principles to consider: full coverage, the right level of topic granularity, accuracy of topics that contain the right papers, and the stability of topics over time. Based on researcher needs and aimed at portfolio analysis, we chose to identify roughly one hundred thousand topics in science using direct citation on citation linkages (including those to cited non-indexed items) in the full Scopus database.

As I mentioned previously, it is important to use the full corpus of literature when modeling. Elseviers Topics of Prominence (TOP) tool does this. I wont go into too much detail here, but the following gure should give you an idea of the level of calculation required for such an analysis calculation of over half a billion cited-citing pairs to yield around ninety-seven thousand specic research topics (topic clusters) across every eld of research, from basic science to highly applied elds related to manufacturing and commercial technologies. A. Gabriel / Articial intelligence in scholarly communications: An elsevier case study To determine the right level of granularity, prior research and expert interviews were used to look at

Perhaps one of the most important elements in any model is how accurately that model captures scientic activity. Using direct citation analysis, we have a higher accuracy at 105 scale than most of the current classication schema have using around one hundred to three hundred categories. No pre-existing categories are assumed, unlike journal-based classication schema, and the clusters are calculated from the bottom up. These clusters are small enough so that we can see manufacturing-oriented research in specic geographies, and multiple topics around a single larger specialty or discipline. This also captures interdisciplinary research in a fundamentally dierent way. 327 328