*4.3.2. Visualization function of statistical information*

As in **Figure 7**, the analytic view can visualize the summary of bibliometric information of the nodes contained in the view. There are several widgets, such as for citation (Impact Factor, SJR, and CiteScore) metrics, publications by year, citations by year, and publications by each country. Moreover, the users can select the nodes in a rectangle area and see the statistical information of the selected nodes. The upper part of the publication by country shows an article count (AC) (https://www.natureindex.com/faq). The AC means the country-level participation in a study, where a country is counted if one or more authors of the article are from the country. For example, if countries of three authors' affiliations in a article are A, B, and B, A is counted as one and B is also counted as one. In contrast, the lower part of the publication by country shows a fractional count (FC) that means the contribution of each country. In the above example, A becomes 1/3, B becomes 2/3.

### *4.3.3. Summarization function of feature phrases*

As in **Figure 8**, the feature phrases of the selected nodes can be summarized in word clouds. At most 10 feature phrases of each node are extracted based on the BM25 method in advance. Then, if the users select the multiple nodes, the feature phrases with higher frequencies are displayed larger and placed closer to the center of the word cloud. This function is useful for understanding specific themes of the selected nodes in a cluster.

**Figure 7.** Statistical information.

**4.3. Analytical functions provided on the map**

**Figure 6.** Comparison of graph layout algorithms.

186 Scientometrics

*4.3.1. Abstract/description translation function*

In addition to the functions described in Section 4.1, the Mapping Science provides the following analytical functions: (1) translation of article abstracts and project descriptions, (2) visualization of statistical information, (3) summarization of feature phrases, (4) querying and exporting using SPARQL, (5) change of layout algorithms, and (6) generation of customized analytic views.

In the analytic views, users can see the detailed information, such as titles, article abstracts/ project descriptions, authors/project members, affiliations, and publication year/proposed

the node is highlighted and the viewpoint is automatically moved to the node. Moreover, the users can store their own SPARQL queries as macros. Therefore, users who are not familiar

Mapping Science Based on Research Content Similarity http://dx.doi.org/10.5772/intechopen.77067 189

In addition, since we received requests for downloading the information displayed on the map, the information of the selected nodes and all nodes in a cluster can be exported in comma-separated values (CSV) format. The result of SPARQL queries can be also exported in CSV format.

As described in the previous section, the layout of the analytic view was calculated by the OpenOrd (edge-cutting value: 0.91). In addition to that, the analytic views can be redrawn by the OpenOrd (edge-cutting value: 0.94 or 0.88), LGL, Fruchterman-Raingold, or Kamada-Kawai [26]. When the users select a layout, the layout algorithm is executed in the background, the resulting layout information is stored and the view is redrawn. If the layout information is stored in advance, the layout is redrawn immediately. The layout calculation time depends on the number of nodes, and the average time is a few seconds to a few minutes.

The analytic views were composed by the info map algorithm, but the users can create the customized Analytic views by keyword search. When the users enter keywords into the widget in the portfolio view, the nodes are extracted by the full-text search for all nodes in five research areas, and then the layout is calculated by the OpenOrd based on the cosine similarities of the extracted nodes. For example, an analytic view for studies related to neural networks and artificial intelligence across multiple research areas can be created by keywords such as "Artificial Intelligence [AND] Neural Network." This function could help find interdisciplinary studies. The calculation time depends on the number of nodes, and the average time is a few seconds to a few minutes. The information on the customized analytic views is stored in the background; the same view is immediately displayed for the second time. The customized analytic view can provide the same analytical functions, such as keyword search, visualization of statistical

information, visualization of the cumulative changes by year, and layout change.

In this map, we try to understand the formation processes of several research areas through chronological changes of network structure. This section describes two cases for the Internet

In **Figure 10** shows the analytic views for an IoT area from 2012 to 2016, which includes 574 nodes as of 2016. The last view is the analytic view in 2016 displaying >0.6 cosine similarities

In 2012, four islands (places, at which nodes are densely located) mainly for IoT frameworks and networks and for IoT system and security are barely found (labels of each island have

**5. Case study for the formation process of research areas**

been extracted by the summarization function of feature phrases).

of Things (IoT) and Brain-Computer Interface (BCI).

as edges.

with SPARQL can simply call the macros and obtain the query results.

*4.3.5. Layout change function*

*4.3.6. Custom analytic view function*

**Figure 8.** Feature phrases in the selected nodes.

#### *4.3.4. Query function and export function*

The background data in the Mapping Science have been converted to RDF data and stored in a graph database. Therefore, the analytic views provide a high-level search using a formal query language, SPARQL, as in **Figure 9**. For example, the users can search for articles, which have >0.8 similarities with articles cited 100+ times from journals with >10 impact factor (such articles might be obscure but important). When the users click a node ID in the result table,


**Figure 9.** SPARQL search widget.

the node is highlighted and the viewpoint is automatically moved to the node. Moreover, the users can store their own SPARQL queries as macros. Therefore, users who are not familiar with SPARQL can simply call the macros and obtain the query results.

In addition, since we received requests for downloading the information displayed on the map, the information of the selected nodes and all nodes in a cluster can be exported in comma-separated values (CSV) format. The result of SPARQL queries can be also exported in CSV format.
