This paper shows how faceted search on biographical data can be utilized as a flexible basis for filtering target groups of people and, in particular, how generic data analysis and visualizations tools can then be applied for solving prosopographical research questions based on the filtered data. This idea is demonstrated and evaluated in practice by presenting two application case studies: 1) linked data extracted from a printed registry of over 10 000 alumni (1867-1992) of the prominent Finnish high school Norssi, and 2) a knowledge graph extracted from 13 000 short biographies of significant Finnish people (from 3rd century to present times) in the National Biography of Finland. In both cases, the data is enriched by linking their entities with several other external datasets.
Biographies describe life stories of particular people of
significance, with the aim of getting a better understanding of
their personality and actions, e.g., to understand their
motives
In our earlier paper
https://www.w3.org/TR/sparql11-query/
Riddle, 2005) and visualizations
The main contribution of this paper is to test and demonstrate the prosopographical method in practice by presenting how various data visualization tools using Google Charts and Google Maps can be integrated with the SPARQL endpoint allowing the end user to filter out target groups of people and biographies, and then to study them. In addition to providing statistical analyses of person groups, an interesting use case identified here is to compare analyses and visualizations based on different subgroups, e.g., people with same profession during different eras. The paper is organized as follows. First, prosopographical analyses and visualizations are presented and discussed for the two linked datasets and applications using the approach outlined above: the Norssi high school alumni on the Semantic Web and the Semantic National Biography of Finland. After this contributions of the work in relation to related research are summarized and directions for further research are outlined.
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The Norssi alumni data service is available as linked open data at the Linked Data Finland platform2, including some 892,000 triples about 131,000 resources. The digitization, ”lodification”, and the Vanhat Norssit Portal3 is described in more detail in Hyvo¨nen et al. (2017). The datasets consist of 10 137 person resources, enriched with graphs of relating career events and family relations, and vocabularies of titles, schools, companies, medals, and hobbies. These additional data were extracted automatically from the short biographical descriptions of a printed book using OCR and text extraction and cleaning tools based on regular expressions.
The ontology model representing people and their
biographical information in the Norssit alumni knowledge
2http://www.ldf.fi/dataset/norssit
3http://www.norssit.fi/semweb
graph is based on the Bio CRM data model4
The Vanhat Norssit Portal contains two search interfaces,
person pages, and two pages for statistical
visualizations. The search interface (Fig. 1) is based on SPARQL
Faceter
For analyzing and visualizing data statistics of a filtered target group of people, we created two views based on Google Chart6 diagrams. On the first visualization page7, the popularity of the most common educations (Fig. 2), universities and colleges, professions, and employers after the graduation of the alumni are shown as four pie charts. By making filtering selections on the facets, the graphics are updated accordingly. For example, by selecting ”professor” on the profession facet the employers of the 258 professors in the data can be seen on the employer pie chart. On the same page, there is also a Sankey diagram depicted in Fig. 3 that shows a list of universities on the left side and the corre4http://seco.cs.aalto.fi/projects/biographies/ 5http://cidoc-crm.org 6https://developers.google.com/chart/ 7http://www.norssit.fi/semweb/#!/visualisointi sponding educational titles (e.g., MSc in Technology, Doctor of Medicine, etc.) on the right. From this visualization one can see which titles were obtained from which universities regarding the filtered target group. The highlighted path in Fig. 3 shows, e.g., the connection from the University of Helsinki to Bachelor of Arts when no filtering choices have been made.
On the second visualization page8, there are first two histograms showing years of enrollment and matriculation of the target group. Below these, three multi-column charts show the most popular universities and colleges, employers, and occupations of the filtered people on a decade by decade basis. For example, from the histogram representing the years of enrollment (Fig. 4) one can see that when education in Norssi was started, a lot of pupils from other schools moved to Norssi (first high bar on the left). Also the changes made in the Finnish school system in the 1970’s are clearly visible as very low enrollment rates. Fig. 5 depicts the most popular employers. It shows a great and interesting variation of companies and organizations at different times: in the late 1800’s the Finnish State Railways (Valtion Rautatiet, blue columns) was the most popular employer, but declined soon probably because the main railway connections in Finland were built in 1850–1900.9 The Finnish Defense Forces (Puolustusvoimat, green columns), on the other hand, has its highest peek during the Second World War. After this the banking industry and the city of Helsinki became major employers for Norssi alumni.
The facet for links to external datasets provides also an interesting option for selecting target groups. For example, a student in the school may ask herself/himself the question: where should I work if I want to become famous and get an entry in the National Biography? By making the selection ”National Biography” on the facet and then looking at the employer multi-column chart one can get an idea of where to work in order to be included in the National Biography. The official motto of the Norssi high school is Non scholae sed vitae (not for school, but for life). Data analytics based on the linked data service now provides new insights on what actually happened to the school alumni in life after graduation in a prosopographical sense.
9https://en.wikipedia.org/wiki/History of rail transport in Finland
The National Biography of Finland10 consists of biographies of notable Finnish people throughout history (200– 2018). The biographies describe the lives and achievements of these historical and contemporary figures, containing vast amounts of references to notable Finnish and foreign figures, including internal links to other biographies of the National Biography of Finland. In addition, the text contains references to historical events, notable works (such as paintings, books, music, and acting), places (such as place of birth and death), organizations, and dates.
In this case, the texts and data were available in a database in a semi-structured form. As in the Norssi case above, the texts were transformed into RDF form by extracting entities from the semi-structured texts, and the result was uploaded into a SPARQL endpoint of the Linked Data Finland service.
The underlying ontology model represents people and their
biographical information. A natural choice for modeling
life stories is the event-based approach where a person’s
life is seen as a sequence of spatio-temporal, possibly
interlinked events from birth to death (and beyond). The events
are modeled according to the Bio CRM model
The source data consists (at the moment) of fields extracted
from the original database dump in CSV format. In the
simplest cases, the value of a data field is directly indicated by
the value of a property, e.g., date or place of birth. However,
most of the structured knowledge was extracted from short
snippets of text in the end of each biography describing
major life events of the protagonist, such as graduation from
a university, designing a building, publishing a book,
getting a honorary medal, etc. The resulting knowledge graph
includes 13 144 people with a biographical description in
the National Biography, 51 243 relating people mentioned
in the biographies, and 977 authors of the biographies. At
the moment, the data includes 37 730 births, 25 552 deaths,
and 102 300 other biographical events. In addition to that
there are 51 937 family relations, 4953 places, 3101
occupational titles, and 2938 companies extracted from the
source data.
When an event is hovered on the event list or on the
timeline, the corresponding marker on the map gets highlighted.
The size of the marker depends on the number of events
related to that specific location, so the most important places
for a person’s career are emphasized. In the example case,
the visualization is based on the biography of architect
Eliel Saarinen, and Helsinki and Michigan (where he lived
his later years) are emphasized. Data about the places in
Finland was extracted from the Finnish Gazetteer of
Historical Places and Maps (Hipla) databases and data
service13
As for prosopographical research, there are two different views available using Angular Google Maps16. The target group can be filtered by using a time span slider17 that is included as a facet for the user to specify a desired range of years in interest. Other filtering facets include choosing person’s profession, gender, dataset, related companies, related place, and linkage to external databases. The visualizations depicted in Fig. 8, show the results of a SPARQL query corresponding to the facet selections on Angular Google Maps. The markers on the map show places of birth in blue and places of death in red color. The size of the marker corresponds to the number of events that has taken place in that particular location. Clicking on a marker opens a modal window containing a list of people who were born or died at the location.
15https://www.britannica.com/topic/Hanseatic-League 16http://angular-ui.github.io/angular-google-maps/ 17https://github.com/angular-slider/angularjs-slider The first selection (Fig. 8a) shows the places of birth and death of Finnish clergy 1554–1721. According to the resulting rendering, the most active areas locate along the coastal Finland with main focus on the town of Turku, which during that era was the capital of Finland, and some are scattered around Sweden. The second selection (Fig. 8b) shows the data of Finnish clergy in 1800–1920. The data does not clearly concentrate on the largest towns of Helsinki and Turku, but seem to scatter evenly around Southern Finland. During that era Finland was a part of the Russian Empire but there are only a few markers on the Russian side except at the city of St. Petersburg. (a) Lifespan of people lived in 1700–1800. (a) The places of birth and death of Finnish clergy 1554–1721. (b) The places of birth and death of Finnish clergy 1800–1920. The Semantic National Biography demonstrator also includes a visualization page showing statistics as in the Norssit alumni case. The column charts in this case show (at the moment) five demographic histograms (with the mean value and standard deviation) of the target group: distribution of ages among the group, ages of marriage, ages of having the first child, the number of children, and the number of spouses.
Two examples of histograms are shown in Fig. 9. The upper (a) one shows the lifespan of people who lived in 18th century, and the lower one (b) people living in 1900–1950. The two figures can be compared, e.g., how the amount of deaths among young children has decreased and how the average age has increased between the two time periods.
This paper demonstrated how Linked Data can be used as a basis for representing biographical registries and for filtering out target groups of persons of interest. Our particular goal was to show by a series of examples, how a SPARQL (b) Lifespan of people lived in 1900–1950. endpoint can be used for data analysis and visualizations in biographical and prosopographical research. According to our practical experiences, the technology is very useful and handy to use for this after learning the basics of Linked Data standard publishing principles.
Previous works of applying Linked Data technologies to
biographical data include, e.g., Larson (2010),
Biographynet.nl18
The presented research is part of the Severi project19, funded mainly by Business Finland. Developing the National Biography of Finland is also part of the Open Science and Research Programme20, funded by the Ministry of Education and Culture of Finland.
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