=Paper=
{{Paper
|id=Vol-2084/short12
|storemode=property
|title=Semantic National Biography of Finland
|pdfUrl=https://ceur-ws.org/Vol-2084/short12.pdf
|volume=Vol-2084
|authors=Eero Hyvönen,Petri Leskinen,Minna Tamper,Jouni Tuominen,Kirsi Keravuori
|dblpUrl=https://dblp.org/rec/conf/dhn/HyvonenLTTK18
}}
==Semantic National Biography of Finland==
https://ceur-ws.org/Vol-2084/short12.pdf
Semantic National Biography of Finland
Eero Hyvönen1,2 , Petri Leskinen1 , Minna Tamper1,2 ,
Jouni Tuominen1,2 , and Kirsi Keravuori3
1
Semantic Computing Research Group (SeCo), Aalto University, Finland and
2
HELDIG – Helsinki Centre for Digital Humanities, University of Helsinki, Finland
http://seco.cs.aalto.fi, http://heldig.fi
firstname.lastname@aalto.fi
3
Finnish Literature Society (SKS)
firstname.lastname@finlit.fi
Abstract. This paper presents the vision of publishing and utilizing textual bi-
ographies as Linked (Open) Data on the Semantic Web. As a case study, we pub-
lish the live stories of the National Biography of Finland, created by the Finnish
Literature Society, as semantic, i.e., machine “understandable” metadata in a
SPARQL endpoint using the Linked Data Finland (LDF.fi) service. On top of the
data service various Digital Humanities applications are built. The applications
include searching and studying individual personal histories as well as historical
research of groups of persons using methods of prosopography. The biographical
data is enriched by extracting events from unstructured and semi-structured texts,
and by linking entities internally and to external data sources. A faceted semantic
search engine is provided for filtering groups of people from the data for proso-
pographical research. An extension of the event-based CIDOC CRM ontology is
used as the underlying data model, where lives are seen as chains of interlinked
events populated from the data of the biographies and additional data sources,
such as museum collections, library databases, and archives.
1 The Vision: Biographies as Linked Data
Biographical dictionaries, a historical genre dating back to antiquity, are scholarly re-
sources used by the public and by the academic community alike. Most national bio-
graphical dictionaries follow the traditional form of combining a lengthy non-structured
text, often written with authorial individuality and personal insight, with a structure
supplement of basic biographical facts, such as family, education, works, and so on.
Biographies are an invaluable information source for researchers across the disciplines
with an interest in the past. [21]
Biographical on-line collections may contain tens of thousands of short biographies
of historical persons of national importance whose contents are interlinked by histor-
ical events, places, acquaintances, family relations, times, objects, traditions, etc. The
Oxford Dictionary of National Biography [7], with more than 60 000 lives, was first
published on-line in 2004, and since then major biographical dictionaries have opened
their editions on the Web. Other on-line national biographical collections include USA’s
American National Biography [1], Germany’s Neue Deutsche Biographie [5], France’s
Nouvelle Biographie générale [6], Biography Portal of the Netherlands [2], Biogra-
phyNet [3], and Dictionary of Swedish National Biography [4]. While the biographi-
cal dictionaries had an unconcealed nationalist agenda well into the 20th century, the
contemporary on-line national biographies have all made an effort to include groups
previously ignored by national history. Pioneering women in all fields are included, as
well as many marginal and minority groups.
The National Biography of Finland started out as an on-line publication in 1997, a
decade before its publication as a 10-volume book series was completed. A selection of
its 6 000 lives was published on-line in Swedish as Biografiskt lexikon för Finland. In
addition to the national Biography of Finland, the Biographical Centre of the Finnish
Literature Society has published several other peer-reviewed biographical collections
on-line, such as the Finnish Business Leaders, the Finnish Clergy (1552–1920), and the
Finnish Generals and Admirals in the Russian Armed Forces (1809–1917).
Even if lots of biographical information is available online for humans to read and
interpret, the information is seldom available as machine readable data for 1) Digital
Humanities research and 2) to be used in Cultural Heritage (CH) portals, such as Eu-
ropeana4 and Digital Public Library of America5 , or in CH applications for the public.
Furthermore, the information is distributed in different national data silos using het-
erogeneous formats and is written in different languages. This makes aggregation and
reuse of biographical data challenging.
A biographical data source can be used to address various research questions from
perspectives of different disciplines and nations, such as:
1. What kind of persons and institutions are actually included in the various national
biographical dictionaries?
2. How do countries portray their “heroes”; is there a place for people that belong
to minorities or opposition groups? Are women portrayed in a different way than
men? What about race and ethnicity? What qualifications are used for selecting the
portrayed individuals?
3. Which disciplines are the scholars representing? Are there national differences?
What disciplines are considered to be of international value?
4. What kind of persons are included in the corpora at different times? What topics
are considered to be breakthroughs on the long term?
5. What can we learn about the historical groups and institutions — social, religious,
political, etc. — by analyzing the biographical details of their members? What is
the nature of the networks that existed among them?
To address questions like these, biographies as data are needed, in many cases linked
across nations and languages. This can only be done in multi-disciplinary collabora-
tion between humanists, computer scientists, and linguists. There is a need for methods
to transform semi-structured biography entries and unstructured texts into structured
forms. We need methods to represent knowledge in an interoperable way across lan-
guage barriers, and tooling for data analysis, visualization, and knowledge discovery.
4
http://www.europeana.eu/portal/en
5
http://dp.la
This paper focuses on Finnish biographies of persons of national importance, se-
lected and edited by the scholarly editorial board of the National Biography of Finland
and four additional collections: Business Leaders, the Finnish Clergy (1554–1721) and
(1800–1920), and the Finnish Generals and Admirals in the Russian Armed Forces
(1809–1917). In our earlier work [16] on the Semantic National Biography of Finland
(SNBF), we addressed the research question: How can the reading experience of bi-
ographies be enhanced using web technologies? As a solution approach, the idea of
data linking and a spatiotemporal visualization based on an interconnected timeline and
a geographical map view were presented. To continue and complement this work, this
paper presents:
1. An extended aggregated collection of biographies from different databases.
2. A new datamodel Bio CRM for representing the biographical data.
3. A new knowledge extraction pipeline for mining entity references and events from
unstructured and semi-structured texts.
4. A faceted search engine for searching the biographies/persons.
5. New data analysis and visualization tools for research on groups of persons.
We first present the datasets and data model underlying the new version of SNBF.
Then the process of transforming biographies into data is discussed. As end-user per-
spectives to the data, a faceted search and browsing application is presented, with ad-
ditional data analysis and visualization tools for biographical research based on filtered
datasets. In conclusion, contributions of our work are summarized, related work dis-
cussed, and directions for further research are outlined.
2 Data Model and Datasets
To enrich and link biographical data with related datasets, the data must be made se-
mantically interoperable, either by data alignments (using, e.g., Dublin Core and the
dumb down principle) or by data transformations into a harmonized form [14]. Since
biographies are based on life events we selected the data harmonization approach and
the event-centric CIDOC CRM6 [10] ISO standard as the ontological basis in our case
study. To adapt CIDOC CRM for biographical data it was first extended to a model
we call “Bio CRM”. This model was then populated by instance data from different
biographical databases.
Bio CRM7 [33] is a semantic data model for harmonizing and interlinking hetero-
geneous biographical information from different data sources. It is a domain specific
extension of CIDOC CRM, effectively providing compatibility with other cultural her-
itage information, too. A natural choice for modeling life stories is the event-based
approach where a person’s life is seen as a sequence of spatiotemporal, possibly inter-
linked events from birth to death. The data model includes structures for basic data of
people, personal relations, professions, and events with participants in different roles.
6
http://www.cidoc-crm.org
7
http://ldf.fi/schema/bioc/
Bio CRM makes a distinction between enduring unary roles of actors, their endur-
ing binary relationships, and perduring events, where the participants can take different
roles modeled as a role concept hierarchy. Bio CRM provides the general data model for
biographical datasets. The individual datasets may concern different cultures, time pe-
riods, or are collected by different researchers that may introduce extensions for defin-
ing additional event and role types. For representing the roles of actors, we chose an
VIVO/BFO-inspired8 [32], intuitive, and simple approach where specific roles are in-
stantiated from the role classes, and connected to actors with the property ”inheres in”.
The Bio CRM model can be used as a basis for semantic data validation and enrich-
ment by reasoning. The data model aims to support principal prosopographical query
types, and is designed to be intuitive in terms of knowledge representation and writ-
ing SPARQL queries in flexible ways. Use cases for data represented using Bio CRM
include prosopographical information retrieval, network analysis, knowledge discov-
ery, and dynamic analysis. The development of Bio CRM was started in the EU COST
project Reassembling the Republic of Letters9 and was first piloted in the case of en-
riching and publishing the printed register of over 10 000 alumni of the Finnish Norssi
high school as Linked Data [24].
Datasets The National Biography of the Finland10 consists of biographies of no-
table Finnish people throughout history. The biographies describe the lives and achieve-
ments of these historical figures, containing vast amounts of references to notable Finnish
and foreign figures, including internal links to other biographies of the National Biog-
raphy 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.
The data consists of several person registry databases listed in Table 1. These source
datasets ware made available as CSV tables, which were converted into RDF format11 ,
the foundation of the Semantic Web standard stack. In addition to actors, the resulting
data includes (at the moment) 13 144 biographies, 51 937 family relations, 4953 places,
3101 occupational titles, and 2938 companies extracted from the source data.
Dataset name # of People
National Biographies 6478
Business Leaders 2235
Finnish Generals and Admirals 1809–1917 481
Finnish Clergy 1554–1721 2716
Finnish Clergy 1800–1920 1234
Table 1: The datasets provided by the Finnish Literature Society.
8
http://vivoweb.org
9
http://www.republicofletters.net
10
http://kansallisbiografia.fi
11
http://www.w3.org/RDF/
To earn the 5th star in the Linked Data 5-star model12 , the data was linked not only
internally but also enriched with owl:sameAs links to the external data sources of
Table 2. This facilitates data aggregation of a person described in several data sources.
Data Source # of Links Description
Wikipedia 5760 http://fi.wikipedia.org
Wikidata 5749 http://www.wikidata.org
BLF 972 Biografiskt Lexikon för Finland
BookSampo 715 Finnish fiction literature on the Semantic Web service
WarSampo 243 Second World War LOD service and portal
ULAN 171 Union List of Artist Names Online
VIAF 2272 Virtual International Authority Files
Geni 4935 Family research and family tree data
Homepages 43 Personal web sites
Parliament of Finland 628 Web pages of Parliament of Finland
Table 2: External data sources linked to the Semantic National Biography.
3 Content Creation: Entity and Event Extraction
This section discusses the process of entity linking and knowledge extraction from
semi-structured and unstructured biographies.
Semi-structured Data Extraction A simple custom event extractor was created for
transforming biographies into the Bio CRM model represented in RDF. The extractor
analyzes the major parts of a biography: a textual story followed by systematically ti-
tled sections listing major achievements of the person, such as “works”, “awards”, and
“memberships” as snippets. A snippet represents an event and typically contains men-
tions of years and places. For example, the biography of architect Eliel Saarinen tells
“WORKS: ...; Suomen Kansallismuseo (National Museum of Finland, 1902–1911;...”
indicating an artistic creation event. Also known family relations were extracted from
the textual descriptions, and for each mentioned relative also a resource was added into
the person ontology of the system.
The system has its own place ontology, and the ARPA linker [25] was used for find-
ing and linking place names mentioned in the event snippets. Places in Finland were ex-
tracted from the Finnish Gazetteer of Historical Places and Maps (Hipla) databases and
data service13 [19,17]. Foreign placenames were linked using the Google Maps APIs14 .
For example, the locations of medieval universities in Europe, towns of the Hanseatic
12
http://5stardata.info/en/
13
http://hipla.fi
14
http://developers.google.com/maps/
League, Finnish mansions, churches, and other well-known buildings in Finland were
added to the place ontology by using the Google services.
In order to create temporal links for the events, textual expressions of dates and their
intervals were analyzed and recognized using a set of regular expressions. The actor of
the event snipped was easily determined as the subject person of the biography.
The result of processing a biography was a list of spatio-temporal events with short
titles (snippet texts) related to the corresponding person. Altogether, first version of the
extracted knowledge graph of the Semantic National Biography of Finland had 37 730
births, 25 552 deaths, 102 300 other biographical events, and 52 000 family relations.
At the moment, the extractor uses only the snippets for event creation—more generic
event extraction from the free biography narrative remains a topic of further research.
From a data linking viewpoint, the birthday and full name of the persons were
known at this point, which could be used to enrich the data from several external
datasets listed in Table 2. Links were created to Wikipedia, Wikidata, Biografiskt lexikon
för Finland BLF15 , BookSampo16 Linked Data, WarSampo17 portal, ULAN18 authority
register of The J. Paul Getty Trust, VIAF19 , and the genealogical data service Geni20 .
Furthermore, some special links, like personal web pages or a person entry at the web
sites of the Finnish Parliament, where extracted from corresponding Wikidata resources.
At the moment no additional information is extracted from the external databases, but
the plan is utilize them in this way in the future, too.
For entity linking to external databases offering a SPARQL endpoint, the tool SPARQL
ARPA21 was used. In cases where the database provides a REST API, like Wikipedia or
Geni.com, a special Python script was created and used. A database specific script was
used also in the case of BLF, where the data was available as a CSV formatted table.
A Pipeline for Text Analysis In order to identify entities and events from unstruc-
tured texts, a tool is being constructed to extract knowledge from them. The application
uses multiple different linguistic tools to do morphological analysis, part of speech tag-
ging, and lemmatization. In addition, the tool’s purpose is to transform all the data into
NLP Interchange Format (NIF)22 and to enrich it with linguistic information gathered
from the linguistic tools. This linguistic information can be then used in decision mak-
ing in named entity recognition [27,11] by providing context to the entities, so that
they can be linked more correctly and efficiently into the corresponding ontologies and
datasets. In addition, the tool’s results can be changed into a format where it is possible
to disambiguate entities also manually if the results are not satisfactory to the user.
Our pipeline model is illustrated in the Fig. 1. In this model, the application can
retrieve the texts from a CSV file or from a SPARQL endpoint. In order to read a CSV
file, the application needs to know the columns for the text and possible document
15
http://www.sls.fi/sv/projekt/blf-biografiskt-lexikon-finland
16
http://www.kirjasampo.fi
17
http://sotasampo.fi/en/
18
http://www.getty.edu/research/tools/vocabularies/ulan/
19
http://www.viaf.org
20
http://www.geni.com
21
http://seco.cs.aalto.fi/projects/dcert/
22
http://persistence.uni-leipzig.org/nlp2rdf/specification/api.html
Fig. 1: Model of the pipeline application.
identifiers. SPARQL endpoint usage requires that the text is in a SPARQL endpoint and
that it is split into ordered text paragraphs. To query the text, the application needs to
be given a query and an endpoint to retrieve the text.
After the application has acquired the text, it transforms (in the Prepare data module)
the document structure (i.e. the document, its paragraphs and titles) into NIF format.
Each document is represented as an instance of Structure class that has a property that
refers to the document identifier. The document identifier can be read from the CSV
file or by querying the SPARQL endpoint along with the text. In addition, the text is
divided into paragraphs and titles that are divided into multiple different text files for
the pipeline to process one paragraph at a time. The division into paragraphs is done
because each sentence and word needs to be connected to a particular paragraph later
on in the process.
The process that the paragraphs go through is represented in the figure within the
curly brackets. It starts by taking a single text paragraph, transforms it into CoNLL
format using the Finnish dependency parser23 . After this, the CoNLL file is transformed
into NIF format by the CoNLL2NIF [9] application. This application constructs from
each word and sentence their corresponding classes (Sentence and Word) and adds the
CoNLL information for each word instance. The last part of the process is a reasoner
that infers missing information for each instance. Currently, the reasoner infers only
information about the order of the words and sentences from the existing information.
After processing a paragraph of text, the application serializes paragraph data into RDF
format.
Once the application has processed all the texts, there is a possibility for a user to
upload all the RDF files into a specific SPARQL server automatically. The output of
this application is the given text document in NIF format. This data can be later on used
to identify named entities and mentions of events in the texts. For future work we are
23
http://turkunlp.github.io/Finnish-dep-parser/
planning to implement an application that can use this data to identify named entities
and events, based on semantically interlinked texts and contexts. We envision that this
will help semantic disambiguation of the entities as well as extraction of complex events
significantly. An application of this is, for example, to use a contextual reader [26] for
giving the readers of the biographies more contextual information about the entities and
events mentioned in the texts.
4 Faceted Search Engine and Prosopography
Fig. 2: Faceted search in Semantic National Biography
Based on the RDF data, a faceted search and browsing application24 depicted in
Fig. 2 was created using the SPARQL Faceter tool [22] and AngularJS25 framefork. On
the left, the first column contains a free text search 1) and facets 2a-i) for searching or
filtering person entries included in external databases 2a), by time period 2b), by family
name 2c), by gender 2d), etc. For example, by selecting WarSampo or Wikipedia on the
external dataset facet 2a), people with a history in the WarSampo Second World War
history portal or people having a Wikipedia page can be filtered, and corresponding
personal homepages on these external services can be found. In the result list each
person is presented by an image (if there is one), her/his lifespan 3), and his/her linkage
24
http://semanticcomputing.github.io/nbf
25
http://angularjs.org
to external databases 4). The last column 5) contains original introduction text from
the biographical description. Specially interesting from a data linking perspective is the
facet and column for links to other external data sources. Using the links, the reading
experience of an end user can be extended substantially beyond the biography text in
SNBF.
After clicking on a person’s image or name fields, a personal “homepage” depicted
in Fig. 3 is opened. This page consists of the person’s basic information 2), links to
external databases 3), his relatives 4), and full biographical descriptions 5) – a bio-
graphical description can be much longer than what is shown in the figure. At the top
of the page 1), the user can switch between the person page with textual descriptions or
a timeline page, depicted in Fig. 4. On the timeline page, a list of events relating to the
person is shown on the left 1), events with known locations are shown on the map 2),
and below there is a timeline 3) showing the timespans of each event. The timeline has
four horizontal lines for showing family events, career events, achievements, and men-
tions of honour. When an event is hovered on the list or timeline by the mouse cursor,
the corresponding marker on the map is highlighted.
Fig. 3: A person’s page in Semantic National Biography
Fig. 4: A person’s timeline in Semantic National Biography
The faceted search engine provides the end user with a means for filtering and study-
ing subgroups of historical people in the data service for prosopographical research. The
criterion for filtering the group can be specified flexibly by using the facets. For exam-
ple, one can study people having a Wikipedia page, born in the same area during a time
period, having the same education or profession, etc.
A simple tool for such analysis is business graphics, but also other methods and
tools, such as network analysis or knowledge discovery could be applied here to support
Digital Humanities research. To start with, pie charts and histograms, based on Google
graphics, will be added to the system in a similar manner as with our earlier project on
Norssi high school alumni on the Semantic Web [18]. To study the behavior of groups
and other phenomena, a map and timeline application similar to the person pages will
be implemented.
5 Discussion, Related Work, and Future Research
Our case study suggests that biography publication is a promising application case for
Linked Data. The event-based modeling approach was deemed useful and handy, after
learning basics of the fairly complex CIDOC CRM model.
For the current RDF version of the biographies, only their semistructured parts have
been considered and linked by using the ARPA tool and custom linkers. The snippet
events could be extracted and aligned with related places, times, and actors fairly ac-
curately using simple string-based techniques without deeper semantic disambiguation.
However, the precision and recall of event extraction and entity linking have not been
evaluated formally. It is obvious that problems grow with larger datasets and when an-
alyzing free texts. These issues remain topics of future research.
Biographies have been studied by genealogists (e.g., (Event) GEDCOM26 ), CH or-
ganizations (e.g., the Getty ULAN27 ), and semantic web researchers (e.g., BIO ontol-
ogy28 ). Semantic web event models include, e.g., Event Ontology [30], LODE ontol-
ogy29 , SEM [12], and Event-Model-F30 [31]. A history ontology with map visualiza-
tions is presented in [28], and an ontology of historical events in [15]. Visualization
using historical timelines is discussed, e.g., in [20], and event extraction in [13].
Previous works of applying Linked Data technologies to biographical data include,
e.g., [23], Biography.net31 [29], and our own earlier work [16]. The conference pro-
ceedings [8] includes several papers on bringing biographical data online, on analyzing
biographies with computational methods, on group portraits and networks, and on vi-
sualizations. Complementing these works, the study of this paper focuses on extracting
linked data from semi-structured biographies. Our work also emphasizes the idea of
enriching the texts with external links to other biographical datasets. As for applica-
tions, faceted search and browsing of biographical data for prosopographical studies
was considered as well as spatiotemporal visualization of life stories.
Our work continues, e.g., on developing new models of biographical data for proso-
pographical research, on semantic disambiguation, on finalizing and evaluating the data
extraction and linking process (precision and recall), and on extending the demonstra-
tor with new tools for solving the DH research questions discussed in section 1. In a
project such as this it is of critical importance to assess the outcome also from the van-
tage point of historical and biographical scholarship and to evaluate relevance of the
new knowledge that these methods enable us to find and analyze.
Acknowledgements Our work is part of the Severi project32 , funded mainly by
Tekes. Our work is also part of the Open Science and Research Programme33 , funded
by the Ministry of Education and Culture of Finland.
References
1. American National Biography (2017), http://www.anb.org/aboutanb.html
2. Biography Portal of the Netherlands (2017), http://www.biografischportaal.nl/en
3. BiographyNet (2017), http://www.biographynet.nl
4. Dictionary of Swedish National Biography (2017),
https://sok.riksarkivet.se/Sbl/Start.aspx?lang=en
5. Neue Deutsche Biographie (2017), http://www.ndb.badw-muenchen.de/ndb aufgaben e.htm
6. Nouvelle Biographie générale (2017),
https://fr.wikipedia.org/wiki/Nouvelle Biographie g%C3%A9n%C3%A9rale
7. Oxford Dictionary of National Biography (2017), http://global.oup.com/oxforddnb/info/
26
http://en.wikipedia.org/wiki/GEDCOM
27
http://www.getty.edu/research/tools/vocabularies/ulan/
28
http://vocab.org/bio/0.1/.html
29
http://linkedevents.org/ontology/
30
http://www.uni-koblenz-landau.de/koblenz/fb4/AGStaab/Research/ontologies/events
31
http://www.biographynet.nl
32
http://seco.cs.aalto.fi/projects/severi
33
https://openscience.fi
8. ter Braake, S., Anstke Fokkens, R.S., Declerck, T., Wandl-Vogt, E. (eds.): BD2015 Bio-
graphical Data in a Digital World 2015. CEUR Workshop Proceedings (2015), http://ceur-
ws.org/Vol-1272/
9. Chiarcos, C., Fäth, C.: CoNLL-RDF: Linked corpora done in an NLP-friendly way. In:
Language, Data, and Knowledge - First International Conference LDK. pp. 74–88 (2017),
https://doi.org/10.1007/978-3-319-59888-8 6
10. Doerr, M.: The CIDOC CRM—an ontological approach to semantic interoperability of meta-
data. AI Magazine 24(3), 75–92 (2003), https://doi.org/10.1609/aimag.v24i3.1720
11. Hachey, B., Radford, W., Nothman, J., Honnibal, M., Curran, J.R.: Evaluating en-
tity linking with Wikipedia. Artificial Intelligence 194, 130–150 (January 2013),
http://dx.doi.org/10.1016/j.artint.2012.04.005
12. van Hage, W.R., Malaisé, V., Segers, R., Hollink, L., Schreiber, G.: Design and use of the
simple event model (SEM). Web Semantics: Science, Services and Agents on the World
Wide Web 9(2), 128–136 (2011)
13. Hogenboom, F., Frasincar, F., Kaymak, U., de Jong, F.: An overview of event extraction
from text. In: DeRiVE 2011, Detection, Representation, and Exploitation of Events in the
Semantic Web (2011), http://ceur-ws.org/Vol-779/
14. Hyvönen, E.: Publishing and using cultural heritage linked data on the semantic web. Morgan
& Claypool, Palo Alto, CA (2012)
15. Hyvönen, E., Alm, O., Kuittinen, H.: Using an ontology of historical events in seman-
tic portals for cultural heritage. In: Proceedings of the Cultural Heritage on the Semantic
Web Workshop at the 6th International Semantic Web Conference (ISWC 2007) (2007),
http://www.cs.vu.nl/ laroyo/CH-SW.html
16. Hyvönen, E., Alonen, M., Ikkala, E., Mäkelä, E.: Life stories as event-based linked data:
Case semantic national biography. In: Proceedings of ISWC 2014 Posters & Demonstrations
Track. CEUR Workshop Proceedings (October 2014), http://ceur-ws.org/Vol-1272/
17. Hyvönen, E., Ikkala, E., Tuominen, J.: Linked data brokering service for historical places and
maps. In: Proceedings of the 1st Workshop on Humanities in the Semantic Web (WHiSe).
pp. 39–52. CEUR Workshop Proc. (2016), http://ceur-ws.org/Vol-1608/#paper-06, vol 1608
18. Hyvönen, E., Leskinen, P., Heino, E., Tuominen, J., Sirola, L.: Reassembling and enriching
the life stories in printed biographical registers: Norssi high school alumni on the semantic
web. In: Language, Technology and Knowledge. pp. 113–119. Springer–Verlag (2017)
19. Ikkala, E., Tuominen, J., Hyvönen, E.: Contextualizing historical places in a gazetteer by
using historical maps and linked data. In: Proceedings of Digital Humanities 2016, short
papers. pp. 573–577 (2016)
20. Jensen, M.: Vizualising complex semantic timelines. NewsBlip Research Papers, Report
NBTR2003-001 (2003), http://www.newsblip.com/tr/
21. Keith:, T.: Changing conceptions of National Biography. Cambridge University Press (2004)
22. Koho, M., Heino, E., Hyvönen, E.: SPARQL Faceter—Client-side Faceted Search Based
on SPARQL. In: Troncy, R., Verborgh, R., Nixon, L., Kurz, T., Schlegel, K., Vander
Sande, M. (eds.) Joint Proc. of the 4th International Workshop on Linked Media and the
3rd Developers Hackshop. CEUR Workshop Proceedings (2016), http://ceur-ws.org/Vol-
1615/semdevPaper5.pdf
23. Larson, R.: Bringing lives to light: Biography in context (2010),
http://metadata.berkeley.edu/Biography Final Report.pdf, Final Project Report, University
of Berkeley
24. Leskinen, P., Tuominen, J., Heino, E., Hyvönen, E.: An ontology and data infrastructure for
publishing and using biographical linked data. In: Proceedings of the Workshop on Human-
ities in the Semantic Web (WHiSe II). CEUR Workshop Proceedings (October 2017)
25. Mäkelä, E.: Combining a REST lexical analysis web service with SPARQL for mashup
semantic annotation from text. In: Proceedings of the ESWC 2014 demonstration track,
Springer-Verlag (May 2014)
26. Mäkelä, E., Lindquist, T., Hyvönen, E.: CORE – a contextual reader based on linked data.
In: Proceedings of Digital Humanities 2016, long papers. pp. 267–269 (July 2016)
27. Mendes, P.N., Jakob, M., Garcı́a-Silva, A., Bizer, C.: DBpedia Spotlight: shedding light on
the web of documents. In: Proceedings of the 7th international conference on semantic sys-
tems. pp. 1–8. ACM (2011)
28. Nagypal, G., Deswarte, R., Oosthoek, J.: Applying the semantic web: The VICODI experi-
ence in creating visual contextualization for history. Lit Linguist Computing 20(3), 327–349
(2005), http://doi.org/10.1093/llc/fqi037
29. Ockeloen, N., Fokkens, A., ter Braake, S., Vossen, P., De Boer, V., Schreiber, G., Legêne, S.:
BiographyNet: Managing provenance at multiple levels and from different perspectives. In:
Proceedings of the 3rd International Conference on Linked Science (LISC’13). pp. 59–71.
CEUR-WS.org (2013), http://ceur-ws.org/Vol-1116/paper7.pdf
30. Raimond, Y., Abdallah, S.: The event ontology (2007),
http://motools.sourceforge.net/event/event.html
31. Scherp, A., Saathoff, C., Franz, T.: Event-Model-F (2010),
http://www.uni-koblenz-landau.de/koblenz/fb4/AGStaab/Research/ontologies/events
32. Smith, B., Almeida, M., Bona, J., Brochhausen, M., Ceusters, W., Courtot, M., Dipert, R.,
Goldfain, A., Grenon, P., Hastings, J., Hogan, W., Jacuzzo, L., Johansson, I., Mungall,
C., Natale, D., Neuhaus, F., Overton, J., Petosa, A., Rovetto, R., Ruttenberg, A., Ressler,
M., Rudniki, R., Seppälä, S., Schulz, S., Zheng, J.: Basic formal ontology 2.0 – specifica-
tion and user’s guide (2015), https://github.com/BFO-ontology/BFO/raw/master/docs/bfo2-
reference/BFO2-Reference.pdf, June 26
33. Tuominen, J., Hyvönen, E., Leskinen, P.: Bio CRM: A data model for representing biograph-
ical data for prosopographical research. In: Biographical Data in a Digital World (BD2017)
(November 2017), https://doi.org/10.5281/zenodo.1040712