=Paper=
{{Paper
|id=Vol-2695/paper1
|storemode=property
|title=Modeling and Publishing Finnish Person Names as a Linked Open Data Ontology
|pdfUrl=https://ceur-ws.org/Vol-2695/paper1.pdf
|volume=Vol-2695
|authors=Minna Tamper,Petri Leskinen,Jouni Tuominen,Eero Hyvönen
|dblpUrl=https://dblp.org/rec/conf/esws/TamperLTH20
}}
==Modeling and Publishing Finnish Person Names as a Linked Open Data Ontology==
https://ceur-ws.org/Vol-2695/paper1.pdf
Modeling and Publishing Finnish Person Names
as a Linked Open Data Ontology
Minna Tamper1,2[0000−0003−1695−5840] , Petri Leskinen1[0000−0003−2327−6942] ,
Jouni Tuominen1,2[0000−0003−4789−5676] , and
Eero Hyvönen1,2[0000−0003−1695−5840]
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
Abstract. This paper presents an ontology and a Linked Open Data
service of tens of thousands of Finnish person names, extracted from
contemporary and historical name registries. The repository, first of its
kind available, is intended for Named Entity Recognition and Linking
in automatic annotation and data anonymization tasks, as well as for
enriching data in, e.g., genealogical research.3
1 Introduction
Actor ontologies of people, groups, and organizations (e.g., Getty ULAN4 ), also
called authority files [11] in Library Sciences, are a key ingredient needed in pub-
lishing and using Cultural Heritage (CH) Linked Data on the Semantic Web. For
representing actor ontologies, there exists several vocabularies, such as FOAF5 ,
REL6 , BIO7 , and Schema.org [6]. Actor ontologies make a distinction between
language-neutral concepts (resources identified by IRIs) and their literal names.
In Resource Description Framework (RDF)8 -based modeling in use on the Se-
mantic Web, only resources can have properties while literal names are consid-
ered only atomic data that do not have properties, except a possible datatype
and language tag attached. However, in many cases also literal words can have
qualifiers and properties: names of things change in time and in context, e.g.,
female names due to marriage, or the language version form of the name in differ-
ent countries and cultures (e.g., “Gabriela” vs. “Gabriele”). In linguistic Linked
Data repositories [22], modeling phenomena related to the properties of words
instead of real world things is actually the main reason for the research. For
modeling phenomena like this, the SKOS recommendation has been extended to
3
Copyright ©2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
4
http://www.getty.edu/research/tools/vocabularies/ulan/about.html
5
http://xmlns.com/foaf/spec/
6
http://vocab.org/relationship/
7
http://vocab.org/bio/
8
https://www.w3.org/RDF/
4 M. Tamper, P. Leskinen, J. Tuominen, E. Hyvönen
SKOS-XL9 , allowing specifying properties for literal SKOS labels, and various
linguistic ontology models such as Lemon10 and OntoLex-Lemon11 have been
devised for representing linguistic Linked Data repositories.
A person name individualizes and identifies an individual. A person name on-
tology is a collection of contemporary and historical person names in a machine-
understandable way. It is a knowledge graph describing names, their features,
and usage in different datasets. In actor ontologies of people, names are often
represented as literals. The features of the name are often ignored when describ-
ing people in actor ontologies although the name can carry information about
its bearer such as socioeconomic status or gender.
This paper introduces a data model for representing person names as an on-
tology, based on tens of thousands of person names from contemporary Finnish
name registries, including also historical names extracted from various CH linked
data sources. The new Finnish Linked Open Data name ontology HENKO12 has
been used as a basis for named entity recognition (NER) and linking tasks [7]
in automatic content annotation [29] and data anonymization services [25], as
well as enriching linked data for applications, such as genealogical network anal-
ysis [16,20]. To foster the reuse of the data, this repository of Finnish person
name data, first of its kind available, is published as a Linked Open Data service
for application developers to use under the open CC BY 4.0 license.
2 Datasets
The data for the person name ontology HENKO was collected from multiple
registries. It consists of given and family names and the number of users per
name. The amount of users for the given names was calculated by gender. In
addition, the given names data included the sum of users who have it as a first
and as other given name. The collected datasets, the total number of names, and
number of unique names in the data are shown in Table 1.
The first dataset in the table is from the Finnish Digital Agency13 (FDA),
a governmental agency that promotes digitalization of society, secures the avail-
ability of data, and provides services for the life events of its customers. The
agency publishes Finnish name data as open data in the governmental publi-
cation portal avoindata.fi14 . This dataset contains given names that are used
by a minimum of five persons, and family names for the minimum of 20 per-
sons. There are in total 23 018 family names, 9507 male given names, and 12 304
female given names (cf. Table 1). According to the product manager of FDA,
the dataset contains only a fraction of Finnish person names. The full registry
9
https://www.w3.org/TR/skos-reference/skos-xl.html
10
https://lemon-model.net
11
https://www.w3.org/2019/09/lexicog/
12
The name comes from the Finnish name Henkilönimiontologia (Person name ontol-
ogy); Henko is also a diminutive form of the name Henrik.
13
https://dvv.fi/en/individuals
14
https://www.avoindata.fi/data/en_GB/dataset/none
Modeling and Publishing Finnish Person Names as a LOD Ontology 5
contains a total of 293 367 family names and 126 119 given names. According to
FDA, the names used by less than the given amounts, are not publicly available
because rare names can single out individual persons violating their privacy.
Most of these unique names come from foreigners, and the rarer Finnish given
names are often compound or coined names. FDA publishes the data twice a
year; our the data has been collected starting from August 2018.
Family names Given names
Dataset Total
unique total unique female male
The Finnish Digital Agency 16 931 23 018 18 206 11 093 8299 42 410
BiographySampo 1205 5535 805 1705 1761 9001
Norssi High School Alumni 1002 4598 233 509 1039 6146
AcademySampo 6721 11 016 946 1389 1423 13 828
Table 1. Amount of names by dataset
In addition to using the FDA data, our ontology has names extracted from
the datasets Norssi High School Alumni on the Semantic Web [9], Biogra-
phySampo [10], and AcademySampo [17]15 . AcademySampo contains names of
university students from 1640 to 1899, and it contains plenty of historical, of-
ten Latin-based, names. BiographySampo data is based on 13 100 biographies of
significant Finns throughout the history from the 3rd century to present time,
and it has many Swedish names used by nobility and upper class because until
1809 Finland was an integral part of Sweden. The Norssi Alumni dataset records
students in a Finnish school from 1867 to 1992 and the unique names in it are
mostly rare Finnish names. Altogether these datasets provided 15 975 distinct
family names, 2791 male and 2500 female names.
In order to have more features for the names in the ontology, the name
datasets were processed and enriched using natural language processing (NLP)
methods. Family names, for example, can contain nobiliary particles or suffixes.
In Finnish family names [26] nobiliary particles are not used, but the names
have suffixes that have indicated once if a person came from a place (e.g., suf-
fixes -la, -lä), or person’s socioeconomic status (e.g., scholars, soldiers, clergy
with suffixes -er , -ius). To make this information explicit, the particles and suf-
fixes were extracted from the names. For the particle extraction, the corpus
of particles (in other languages) was compiled from the website of the Insti-
tute for the Languages of Finland (Kotus)16 to identify names that contain
particles. The extraction of suffixes was done using Lexical Analysis Service’s
(LAS)17 [18,19] language recognition service, hyphenation service, and a manu-
ally compiled stopword list of words in Finnish and Swedish compound names
15
https://seco.cs.aalto.fi/projects/yo-matrikkelit/en/
16
http://www.kielitoimistonohjepankki.fi/ohje/65
17
http://demo.seco.tkk.fi/las/
6 M. Tamper, P. Leskinen, J. Tuominen, E. Hyvönen
(e.g., fi. Mansikkamaa eng. strawberry field ). The process first filters out names
ending with a stopword, then detects the language, and lastly hyphenates the
name. The last syllable is recorded as the suffix. Short names with only two
syllables were ignored because they rarely end with a suffix.
In addition the NLP methods were used in identifying patronymics (e.g.,
Jaakonpoika, eng. son of Jaakko) and matronymics (e.g., Liisantytär, eng. daugh-
ter of Liisa). The matronymics and patronymics are identified in Finnish, Swedish,
and Russian. In Finnish and Swedish they end with a word that indicates if its
owner is a female (sv. -dotter, fi. -tytär ) or a male (sv. -son, fi. -poika) whereas
the Russian counterparts have a gendered suffix (e.g., -ov, -ova). The preceding
part of the word is a person name typically in the genitive case and it can belong
to an ancestor of the person. The ancestor’s name wass extracted from the pre-
ceding part and baseformed with the LAS lemmatization tool. Afterwards, the
ancestor’s name is used to find names with the same string form. If the name
exists, the application identifies the gender by using the existing data. The name
instance is typed as matronymic or patronymic depending on the result.
Lastly, the names of HENKO data were linked to their counterparts in
DBpedia and Wikidata to enrich the data with etymological information and re-
lations to other names. The names were also linked to the bearers of the names
in the source datasets (AcademySampo, BiographySampo, and Norssi Alumni).
In addition, the family names can reference place and vocation names [26]. To
identify names that refer to places and vocations, the names were linked to the
YSO places ontology18 (Finnish and Swedish place names) and to the Finnish
historical occupations ontology AMMO [15]. This information is not only inter-
esting topical information but can be used in tasks such as linking based NER
to identify names that can be place or vocation names.
3 A Data Model for Person Names
The data model for person names in HENKO has been created based on the
enriched name data. The data model is depicted in Fig. 1. The model has a class
for the written representations of the name, the WrittenNameForm, that includes
the string presentation of the name. Its instances are also instances of the CIDOC
CRM’s class E41 Appellation in order to enable the modeling of names and
their alternative forms. This is needed, for example, if a name is translated from
Russian to Finnish, as was the case with the Russian tsars Alexandr I-III, that
were called in Finnish Aleksanteri I-III. The WrittenNameForm class connects
to the GivenName and FamilyName classes via isGivenName and isFamilyName
properties accordingly.
The GivenName and FamilyName classes are subclasses of the Name class.
The Name class describes the basic features of the names, such as properties
for linking both names to their equivalent representations in other ontologies,
to people in other actor ontologies with the same names, in case of compound
18
https://finto.fi/yso-paikat/en/
Modeling and Publishing Finnish Person Names as a LOD Ontology 7
Fig. 1. The datamodel for Finnish person name ontology HENKO.
names by linking it to the parts (e.g., the name Henna-Maria can be linked to
Henna and Maria), and to linguistic information, such as name suffixes. Like
in the Wikidata [4] model, the GivenName class has information about the
gender (male, female, unisex) that is inferred for each instance based on the
name usage data. The FamilyName class instances contain information about
the nobiliary particle, such as von, or de la. Initially, the OntoLex-Lemon [21]
and MMoOn [14,13] ontologies were considered for modeling the particles and
affixes, but the models did not fit the needs of HENKO because of being too
complex or lacking in features to represent them. In addition, the references
to places and vocations have been recorded using their own properties. Middle
names19 are not common in Finland and are ignored currently in the processing.
The GivenName and FamilyName classes are connected to the GivenName-
Usage and the FamilyNameUsage classes through the isUsed property. These
classes describe the calculated usage of the name. They are the subclasses of
NameUsage class. The NameUsage class describes the general characteristics
of its subclasses, such as count (how often a name is used) and source (data
source for the information). The GivenNameUsage class also separates whether
the name has been used as a first name or other name (second, third) in ad-
19
https://en.wikipedia.org/wiki/Middle_name
8 M. Tamper, P. Leskinen, J. Tuominen, E. Hyvönen
dition to having the gender attribute. The DataSource class, that connects to
the NameUsage superclass, describes the used sources in more detail. It includes
attributes such as date (creation time of the data), URL (where the data was
retrieved), temporal information about the dataset, its publisher, and name. The
DataSource class is also connected directly to the WrittenNameForm class.
Finally, the MatronymicForm and PatronymicForm classes are subclasses of
the class WrittenNameForm. If the instances of the WrittenNameForm class
have been identified as patronymics or matronymics, the WrittenNameForm in-
stances are complemented with information about the origin (Finnish, Swedish,
Russian) and are linked to the given name of the ancestor (GivenName class in-
stance) using using Wikidata’s property “patronym or matronym for this name”.
The suffixes from the Russian origin names are recorded using the Name class
property suffix.
4 Use Cases
This section presents the applications of HENKO in automatic annotation tasks.
The applications are available as part of the SeCo Text Annotation Service20 .
Gender Identification Service The code behind the service21 has been devel-
oped in the projects Norssi Alumni, BiographySampo, and AcademySampo to
determine the gender by person’s name. The service uses HENKO vocabularies
of given names containing the frequencies of how often each name appears as a
male or a female name.
The decision is based on the standard Bayesian approach described in equa-
tions 1 and 2. Equation 1 defines the probability ρ(γ|n) that a person with
a single given name n has gender γ ∈ {”Female”, ”Male”}. DF (name) and
DM (name) are the frequencies of the name in the vocabularies of female DF
and male DM names. The smoothing variable α prevents the probabilities from
getting near-zero values in ambiguous cases. In this way, e.g., names with only a
few samples do not affect the final result too much. Likewise, if a name does not
appear in either vocabulary, the estimate reduces to 50%—a natural choice for
a prior probability when estimating an unknown gender. Equation 2 defines the
probability that a given sequence of names N = (name1 , name2 , . . .) relates to
gender γ. To simplify the calculations, the correlation between the names in the
sequence was theorized to be statistically independent, e.g., having name1 would
not correlate with having name2 . Besides, the used vocabularies do not include
information about the co-occurrences of given names. Therefore the probability
of a sequence could be calculated as a product of the probabilities for each name.
ρ(name|γ) · ρ(γ) Dγ (name) + α
ρ(γ|name) = ≈ (1)
ρ(name) DF (name) + DM (name) + 2α
20
https://nlp.ldf.fi
21
http://nlp.ldf.fi/gender-identification
Modeling and Publishing Finnish Person Names as a LOD Ontology 9
Q
ρ(γ|n)
n∈N
ρ(γ|N = (name1 , name2 , . . .)) = Q Q (2)
ρ(”Female”|n) + ρ(”Male”|n)
n∈N n∈N
For the final decision making, a threshold value τ (e.g., τ = 0.75) is used.
For example, if ρ(”Female”|N ) > τ , then the person is classified as a female, or
as a male in case ρ(”Male”|N ) > τ . Moreover, no inference is made in the range
ρ ∈ [1.0−τ, τ ] where the gender remains undefined. For example, when analyzing
a unisex name like Dominique, the result remains undefined, but adding another
name Gaston, the application interprets the sequence Dominique Gaston as a
male name, or as a female in the case Gabrielle Dominique.
Person Name Finder Service The Person Name Finder is an API service
for identifying references to people and collecting context around them from
texts. It utilizes the HENKO ontology to identify person names from texts as
a NER task. The Person Name Finder uses the linkage of the family names to
places and vocations to differentiate between them and person names. In case
the application finds from a text a reference to a single family name and there
are no full names with the same family name in the text, it checks if the name
is linked to either a place or vocation. If the family name has been linked to a
place name, the application returns the place reference to indicate that the name
can also be a place. The same procedure is applied to vocations; if a sentence
starts with a name that is linked to a vocation written with a capital letter in a
beginning of a sentence, the application returns the vocation link. Otherwise, the
application returns only person names with links to the person name ontology.
In addition, the service can identify information around the name such as times
of birth and death, and the gender by utilizing the Gender Identification Service.
The service identifies person names and returns the result set in JSON format.
It has been designed to aid in the extraction of personal information from registry
entries and natural language texts. The result set contains full names and offers
information related to the name such as location in text, links to HENKO,
and optionally contextual information, such as gender, dates within brackets,
etc. The API and its description22 are available at the SeCo Text Annotation
Service. Currently, the application is being developed and used as a part of
named entity recognition and linking to identify person names from the legal
and biographical texts. It has been able to identify most names and even some
older names, and to enrich them with information such as years within brackets,
and gender.
5 Evaluation
This section evaluates the enriching methods for the initial data in Section 2 and
the Gender Identification Service from Section 4.
22
http://nlp.ldf.fi/api-documentation/#api-NameFinder
10 M. Tamper, P. Leskinen, J. Tuominen, E. Hyvönen
The use of NLP methods for data enrichment provided satisfactory results.
The identification of matronymics and patronymics was calculated for 1000 ran-
dom samples. The F1-score for identification of matronymics was 87.27% and for
patronymics 94.42%. Most frequently encountered issue with identification was
the lack of Swedish or Russian given names from which the form is derived from.
The extraction of suffixes and particles worked well. The F1-score for a sample
of 1000 names was 92.78% for suffixes and 100% for particles. The suffix extrac-
tion failed for rarer non-Finnish names because they could not be hyphenated
correctly due to language identification or lack of hyphenation support.
The linking of names succeeded with varying results. Roughly 23 600 names
are linked to Wikidata, and 2500 to DBpedia. The rest of the names could not
be linked because either the database did not include the name or there were
errors in the data. Often older or less popular names could not be found in
either target ontology. Also, some Asian names were linked to several entities
in Wikidata with the same label, e.g. Jin was linked to two Chinese and one
Korean name. The linking of names to topics matched to 785 places and 30
vocations. The success of the linking depended on the quality and coverage of
the target ontology. Names from pre-Christian era could not be linked to places
or vocations because the target ontologies do not contain a historical vocabulary
for the entities.
The Gender Identification Service was evaluated using the names of the rela-
tives extracted from BiographySampo data. It recognized 97.70% of the unique
names leaving out only very rare or foreign names. In the test set, all recognized
genders were inferred correctly [16]. In addition to using given names, the gender
can be concluded e.g. by occupation, by known family relations, or by external
contextual information. For example, in the case of AcademySampo all students
starting earlier than in 1870 are male [17] since female students were not allowed.
6 Data Service
The person name ontology is published as Linked Open Data on the Linked Data
Finland (LDF.fi) platform [8], adhering to the FAIR principles23 . The platform
provides a public SPARQL endpoint24 , IRI dereferencing capabilities, including
a generic RDF browsing user interface, and a dataset homepage25 with general
documentation based on the SPARQL Service Description26 , containing a Vo-
cabulary of Interlinked Datasets (VoID) description27 of the dataset. For human-
readable data model documentation28 , we use LODE [27]: when dereferencing
IRIs of the name ontology’s schema, the user is redirected to a page listing the
classes and properties used. The ontology is also published in the ONKI Light
23
https://www.go-fair.org/fair-principles/
24
http://ldf.fi/henko/sparql
25
http://ldf.fi/dataset/henko
26
https://www.w3.org/TR/sparql11-service-description/
27
https://www.w3.org/TR/void/
28
http://ldf.fi/schema/henko/
Modeling and Publishing Finnish Person Names as a LOD Ontology 11
service29 , where it is searchable and browsable using SKOSMOS30 , a web-based
SKOS browser. The data is served on the Apache Jena Fuseki triplestore. The
Fuseki runtime and the person name ontology data are built into a Docker im-
age31 which can be easily rebuilt when there is a need to publish a new version
of the data, by simply updating the data in a Git repository.
7 Conclusions
This paper presents the person name ontology HENKO that consists of Finnish
person names from the 3rd century to present time. Unlike actor ontologies
and vocabularies such as ULAN and BIO, HENKO concentrates on describing
person names and their features. The ontology is published as linked open data
that connects to AcademySampo, BiographySampo, Norssi Alumni datasets and
semantic portals, Wikidata, DBpedia, YSO places, and AMMO ontologies. Its
unique data model was influenced by largely used ontologies and vocabularies
such as Wikidata, Schema.org, and DBpedia. Out of these ontologies, Wikidata
has the most extensive model thus far for names; it divides names by gender,
includes etymological information, and has pronunciation instructions. In addi-
tion, the Wikidata ontology differentiates patronymic and matronymic names. In
contrast, HENKO consists of a large set of Finnish names of which nearly 45%
could be linked to Wikidata. In addition, the HENKO has more information
about the names such as their usage statistics, linguistic information (suffixes,
particles), and provenance information. HENKO model can be used as is for
simple patterns consisting of given and family names. In addition, by adding the
modelling for middle names, it can be used for wider range of naming conven-
tions. Hence, the ontology is a novel resource for different applications. It can
also be used as training material for deep learning based NLP applications alike.
The accuracy of extracting particles and suffixes was satisfactory. The mi-
nor issues of suffix extraction could be solved by identifying and splitting fam-
ily names that are compound words with tools such as the Turku dependency
parser [12] or LAS’s morphological analyzer. In addition to family names, also
given names can contain suffixes that have so far been ignored. They can, e.g.,
indicate the bearer’s gender, like in the female Wilhelmiina based on the male
name Wilhelm. The identification and extraction of suffixes enables data analy-
sis for the names. For example, in the history of Finnish last names [26], there
have been periods when it has been popular to change Swedish or Russian names
to Finnish names with suffixes such as -la or -nen. When analyzing the Acade-
mySampo data, we found out that family names with suffix -nen start to appear
only after 1830. To analyze the temporal characters of family names with other
suffices remain as future work. Given names [28] have also been modified but by
the clergy keeping the parish registries according to the guidelines of different
central governments; for example the name Gregorius has been changed to the
29
http://light.onki.fi/henko/en/
30
http://skosmos.org
31
https://hub.docker.com/r/secoresearch/fuseki/
12 M. Tamper, P. Leskinen, J. Tuominen, E. Hyvönen
Finnish name Reijo32 . One future research direction for enriching the data could
be to represent there changes of names based on genealogical data and track the
changes and suffixes in different linked source datasets. This would also aid in
named entity linking (NEL), as the name changes in historical documents could
be understood and references to people could be disambiguated better if indi-
cated that the person used different changed names. Modeling of the changes of
names has been researched earlier, e.g., in the context of biological taxa [30,3].
The linking of family names to places and vocations enriched the ontology
and added context to names. The Person Name Finder utilizes the added context
to identify possibly ambiguous nouns when it is used to identify names from text.
Unlike typical NEL tools [23,24,5] that concentrate on simply linking entities to
knowledge bases, the application can be utilized to extract names from texts and
enrich them with contextual information. The Person Name Finder application
is still under work, and will be further developed to ease linking to related
actor ontologies. In addition to topical linking, in the future, place name linking
can be used similarly to, e.g., Tuomas Salste’s work33 by locating the origin of
names and visualizing them on a map to aid in genealogical research. By using
the extracted suffixes, the linking of names to places could be improved and
expanded to names that refer to places but contain a suffix that prevents linking
(e.g., Savola referes to Savo without the -la suffix).
The usage statistics of the names enables the usage of the ontology in the
Gender Identification Service. Although the functionality of the service is straight-
forward and based on relative trivial statistics, e.g., it does not consider the co-
occurrence of the names and it does not return an estimate for names missing
in the ontology, the results have been feasible in our use cases. Related to our
service, there are commercial projects such as genderize34 and gender-api35 that
also use name vocabularies for decision making. Attempt to infer the gender by
the ending of the name [1] is problematic with Finnish names where, e.g., Jari
and Kari are male names but Sari and Mari female ones. A blog post [2] by
Ellis Brown introduces a project where the gender is inferred from character
sequences in names using a recurrent neural network. Due to the feasible results
for our use cases, we have not implemented similar algorithms for inferring the
gender for names missing from our vocabulary.
Acknowledgments This work is part of the Anoppi project36 funded by
the Ministry of Justice in Finland. Thanks to Aki Hietanen, Saara Packalén,
Tiina Husso, and Oili Salminen of the Ministry of Justice, and Risto Talo, Jari
Linhala, and Arttu Oksanen of Edita Publishing Ltd. for collaboration. Thanks
also to Aleksandra Konovalova from University of Helsinki and Esko Kirjalainen
from The Finnish Digital Agency for insightful discussions. CSC – IT Center for
Science, Finland, provided us with computational resources.
32
https://www.genealogia.fi/nimet/nimi15s.htm
33
https://www.tuomas.salste.net/suku/nimi/
34
https://genderize.io
35
https://gender-api.com
36
https://seco.cs.aalto.fi/projects/anoppi/en/
Modeling and Publishing Finnish Person Names as a LOD Ontology 13
References
1. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. Analyzing
Text with the Natural Language Toolkit, chap. 6.1.1. O’Reilly Media, Inc. (2009)
2. Brown, E.: Gender Inference from Character Sequences in
Multinational First Names. https://towardsdatascience.com/
name2gender-introduction-626d89378fb0, accessed: 2020 Mar 3
3. Chawuthai, R., Takeda, H., Wuwongse, V., Jinbo, U.: Presenting and Preserving
the Change in Taxonomic Knowledge for Linked Data. Semantic Web 7(6), 589–616
(2016)
4. Erxleben, F., Günther, M., Krötzsch, M., Mendez, J., Vrandečić, D.: Introducing
Wikidata to the linked data web. In: International Semantic Web Conference. pp.
50–65. Springer (2014)
5. Francis-Landau, M., Durrett, G., Klein, D.: Capturing Semantic Similarity for En-
tity Linking with Convolutional Neural Networks. arXiv preprint arXiv:1604.00734
(2016)
6. Guha, R.V., Brickley, D., Macbeth, S.: Schema.org: Evolution of Structured Data
on the Web. Communications of the ACM 59(2), 44–51 (2016)
7. Hachey, B., Radford, W., Nothman, J., Honnibal, M., Curran, J.R.: Eval-
uating entity linking with Wikipedia. Artificial Intelligence 194, 130–150
(Jan 2013). https://doi.org/10.1016/j.artint.2012.04.005, http://dx.doi.org/10.
1016/j.artint.2012.04.005
8. Hyvönen, E., Tuominen, J., Alonen, M., Mäkelä, E.: Linked Data Finland: A 7-
star Model and Platform for Publishing and Re-using Linked Datasets. In: The
Semantic Web: ESWC 2014 Satellite Events, Revised Selected Papers. pp. 226–
230. Springer-Verlag (May 2014)
9. 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: Proceedings, Language, Technology and Knowl-
edge (LDK 2017). pp. 113–119. Springer-Verlag (June 2017)
10. Hyvönen, E., Leskinen, P., Tamper, M., Rantala, H., Ikkala, E., Tuominen, J.,
Keravuori, K.: Linked Data – A Paradigm Change for Publishing and Using Biog-
raphy Collections on the Semantic Web. In: Proceedings of the Third Conference
on Biographical Data in a Digital World (BD 2019) (September 2019)
11. Joudrey, D., Taylor, A., Miller, D.: Introduction to Cataloging and Classification.
Libraries Unlimited, 11 edn. (2015)
12. Kanerva, J., Ginter, F., Miekka, N., Leino, A., Salakoski, T.: Turku Neural Parser
Pipeline: An End-to-End System for the CoNLL 2018 Shared Task. In: Proceedings
of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal
Dependencies. Association for Computational Linguistics (2018)
13. Klimek, B.: Proposing an OntoLex-MMoOn Alignment: Towards an Interconnec-
tion of two Linguistic Domain Models. In: LDK Workshops. pp. 68–73 (2017)
14. Klimek, B., Arndt, N., Krause, S., Arndt, T.: Creating Linked Data Morpholog-
ical Language Resources with MMoOn – The Hebrew Morpheme Inventory. In:
Proceedings of the Tenth International Conference on Language Resources and
Evaluation (LREC’16). pp. 892–899 (2016)
15. Koho, M., Gasbarra, L., Tuominen, J., Rantala, H., Jokipii, I., Hyvönen, E.:
AMMO Ontology of Finnish Historical Occupations. In: Proceedings of the First
International Workshop on Open Data and Ontologies for Cultural Heritage
(ODOCH’19). vol. 2375, pp. 91–96. CEUR Workshop Proceedings (June 2019),
vol 2375
14 M. Tamper, P. Leskinen, J. Tuominen, E. Hyvönen
16. Leskinen, P., Hyvönen, E.: Extracting Genealogical Networks of Linked Data from
Biographical Texts. In: Proceedings of the 16th Extended Semantic Web Confer-
ence (ESWC 2019), Portoroz, Posters & Demonstrations (June 2019)
17. Leskinen, P., Hyvönen, E.: Linked Open Data Service about Historical Finnish
Academic People in 1640–1899. In: Proceedings of Digital Humanities in Nordic
Countries (DHN 2020), Riga. CEUR Workshop Proceedings (March 2020)
18. Mäkelä, E.: Combining a REST lexical analysis web service with SPARQL for
mashup semantic annotation from text. In: European Semantic Web Conference.
pp. 424–428. Springer (2014)
19. Mäkelä, E.: LAS: an integrated language analysis tool for multiple languages. The
Journal of Open Source Software 1(6) (October 2016)
20. Malmi, E., Rasa, M., Gionis, A.: AncestryAI: A Tool for Exploring Computation-
ally Inferred Family Trees. In: Proceedings of the 26th International Conference
on World Wide Web Companion. pp. 257–261. International World Wide Web
Conferences Steering Committee (2017)
21. McCrae, J.P., Bosque-Gil, J., Gracia, J., Buitelaar, P., Cimiano, P.: The OntoLex-
Lemon model: development and applications. In: Proceedings of eLex 2017 confer-
ence. pp. 19–21 (2017)
22. McCrae, J.P., Chiarcos, C., Bond, F., Cimiano, P., Declerck, T., De Melo, G.,
Gracia, J., Hellmann, S., Klimek, B., Moran, S., et al.: The Open Linguistics
Working Group: Developing the Linguistic Linked Open Data Cloud. In: Proceed-
ings of the Tenth International Conference on Language Resources and Evaluation
(LREC’16). pp. 2435–2441 (2016)
23. 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 systems. pp. 1–8. ACM (2011)
24. Nguyen, D.B., Hoffart, J., Theobald, M., Weikum, G.: AIDA-light: High-
throughput named-entity disambiguation. In: Proceedings of the Workshop on
Linked Data on the Web (LDOW 2014), co-located with the 23rd International
World Wide Web Conference (WWW 2014). vol. 1184. CEUR Workshop Proceed-
ings (April 2014)
25. Oksanen, A., Tamper, M., Tuominen, J., Hietanen, A., Hyvönen, E.: Anoppi:
A pseudonymization service for Finnish court documents. In: Araszkiewicz, M.,
Rodrı́guez-Doncel, V. (eds.) Legal Knowledge and Information Systems. JURIX
2019: The Thirty-second Annual Conference. pp. 251–254. IOS Press (December
2019)
26. Paikkala, S.: Sukunimet sukututkimuksessa. Sukutieto: Sukutietotekniikka ry:n
jäsenlehti 14(4) (1997)
27. Peroni, S., Shotton, D., Vitali, F.: Tools for the Automatic Generation of Ontology
Documentation: A Task-Based Evaluation. International journal on Semantic Web
and information systems 9(1), 21–44 (2013)
28. Rajasuu, R.: Kuopiossa, Oulussa ja Turussa vuosina 1725–1744 ja 1825–1844 syn-
tyneiden kastenimet. Ph.D. thesis, University of Eastern Finland (2013)
29. Tamper, M., Hyvönen, E., Leskinen, P.: Visualizing and Analyzing Networks of
Named Entities in Biographical Dictionaries for Digital Humanities Research. In:
Proceedings of the 20th International Conference on Computational Linguistics
and Intelligent Text Processing (CICling 2019). Springer-Verlag (April 2019)
30. Tuominen, J., Laurenne, N., Hyvönen, E.: Biological Names and Taxonomies on the
Semantic Web – Managing the Change in Scientific Conception. In: Proceedings of
the 8th Extended Semantic Web Conference (ESWC 2011). pp. 255–269. Springer-
Verlag (June 2011)