=Paper=
{{Paper
|id=None
|storemode=property
|title=Linked Data and Facets to Explore Text Corpora in the Humanities: A Case Study
|pdfUrl=https://ceur-ws.org/Vol-1272/paper_125.pdf
|volume=Vol-1272
|dblpUrl=https://dblp.org/rec/conf/semweb/Morbidoni14
}}
==Linked Data and Facets to Explore Text Corpora in the Humanities: A Case Study==
https://ceur-ws.org/Vol-1272/paper_125.pdf
Linked Data and facets to explore text corpora
in the Humanities: a case study
Christian Morbidoni
Semedia, Department of Information Engineering (DII), Università Politecnica delle
Marche, Ancona, Italy
Abstract. Faceted search and browsing is an intuitive and powerful
way of traversing a structured knowledge base and has been applied
with success in many contexts. The GramsciSource project is currently
investigating how faceted navigation and Linked Data can be combined
to help Humanities scholars in working with digital text corpora. In
this short paper we focus on the ”Quaderni dal carcere” by Antonio
Gramsci, one of the most popular Italian philosophers and politicians,
we present our ongoing work and we discuss our approach. This consists
of first building a RDF graph to encode different ”levels” of knowledge
about the texts and then extracting relevant graph paths to be used
as navigation facets. We then built a first prototype exploration tool
with a two-fold objective: a) allow non experts to make sense of the
extremely fragmented and multidisciplinary text corpus, and b) allow
Gramsci scholars to easily select a subset of the corpus of interest as well
as possibly discovering new insights or answer research questions.
Keywords: Faceted browsing, Digital Humanities, Entity Extraction
1 The data
Gramsci’s ”Quaderni dal carcere” is an extremely fragmented corpus composed
of more that 4,000 ”notes’ organized in 29 books (quaderni). Notes vary in
length from single lines to several pages and span different domains, from soci-
ology and politics to literature. They are available in the GramsciSource digital
library1 (created in the frame of the project) with stable URLs. We built a Linked
Data graph by merging structured knowledge coming from different sources: the
Linked Data Gramsci Dictionary, data coming from DBpedia Italia2 and seman-
tic annotations made with Pundit [1]3 .
The Linked Data Gramsci Dictionary, is a dataset extracted from the
Gramsci Dictionary [2], a recognized scholarly contribution within the inter-
national Gramsci scholarly community, which includes all the most important
topics in the Gramsci’s thought. Each topic in the dictionary is documented by a
1
The DL is currently officially offline due to maintenance, but can be reached at the
following address: http://89.31.77.216
2
http://it.dbpedia.org
3
http://thepund.it
2 C. Morbidoni
Fig. 1. A screenshot of the prototype
text with references to specific Gramsci’s Notes from the Quaderni dal Carcere.
We automatically processed such citations using regular expressions and pro-
ducing RDF triples representing such connections, expressing relations among
single notes and a number of dictionary topics they are relevant to.
Entity extraction and linking. Several approaches and tools for extract-
ing and disambiguating relevant entities mentioned in a text appeared in the
last years. Among them, DataTXT4 is, to our knowledge, one of the best tools
supporting Italian language. DataTXT derives from previous academic research
[3], makes use of Wikipedia to disambiguate matched entities and to link them
to the Italian DBPedia and proved to be highly performant even on very short
texts. Running such a entity extraction tool on all the notes resulted in over
2,038 notes (50%of the total number) annotated with at least one entity and a
total of 43,000 entities matched. After a manual revision of the results we re-
moved around 30 entities that were clearly wrong matches. We then inspected
80 random notes (2% of the total number of notes) and measured an accuracy
of around 85%. Extracted entities span 144 different entity rdf:types and 5,876
distinct dc:types (which can be considered as entities categories). A more accu-
rate evaluation of the results a well as a better tuning of the tool are goals for
the next stage of the project.
Scholars annotations. Pundit5 is a semantic web tool that enables users
to produce machine readable data in the form of RDF by annotating web pages.
Annotations from a single scholar are collected in so called ”notebooks”, which
can be private or public. For the purpose of our proof of concept we created a
set of sample annotations by manually linking texts to DBpedia and Freebase
entities. At data representation level, such annotations are equivalent to those
produced by DataTXT, once imported they are naturally captured by the facet
queries (discussed in the next section).
4
https://dandelion.eu/products/datatxt/
5
http://thepund.it
Linked Data and facets to explore text corpora in the Humanities 3
2 Faceted search prototype
Existing approaches to identify relevant facets to browse a RDF graph based on
quantitative measures such as predicate frequency, balance and objects cardi-
nality [4]. This kind of approaches do not account for informative content of a
facet and only consider facets derived from a set of triples with the same pred-
icate. In the general case, however, relevant facets could be derived from more
complex paths in the graph. Approaches to automatic facets extraction in such
a general case have been recently proposed [6] and we plan to investigate their
applicability in the near future.
Our simple approach is to derive facets from SPARQL queries of the form:
select distinct ?url ?facet ?value where { CUSTOM_QUERY }
Where ?url is a resource of interest (notes in our case), ?facet is a facet name
and ?value is a possible value of such a facet. Such a simple approach is also
quite flexible and allows, for example, to easily turn all the datatype properties
of a resource to facets, e.g with the following query:
select distinct ?uri ?facet ?value where {
?uri rdf:type gramsci:Note. ?uri ?facet ?facet.}
Deriving facets from SPARQL queries is an approach already explored in litera-
ture [5]. For the purpose of our proof of concept we chose candidate graph paths
by inspecting the data and accordingly to scholars preferences. The facets we
implemented in our prototype are:
– Gramsci Dictionary topics. This facet lists all the dictionary topics where a
note is referenced;
– DBpedia entities. A set of facets where entities mentioned in a note are
grouped according to their rdf:type. Relevant rdf:types individuated are Per-
sons, Books, Languages, Places and Events, but they could be more specific
(e.g. Politicians, Artists, Magazines, etc.);
– Categories. A facet listing all the dc:types associated to entities mentioned
in a note;
– Scholars Notebooks. This facet lists all the scholars (Pundit users) who man-
ually annotated a note.
To enable navigation of the corpus along the different ”dimensions”, we im-
plemented a faceted browser based on Apache Solr6 . Solr, along with its Ajax-
Solr7 frontend provides a relatively easy way to build a performant faceted
browser on top of Lucene. We built the solr index by running the SPARQL
queries (described in the previous section) and using results associated to the
?uri variable as document ID, ?facet as index field and ?value as field values.
The prototype is available at http://purl.org/gramscisource/quaderni.
6
http://lucene.apache.org/solr/
7
http://github.com/evolvingweb/ajax-solr/wiki
4 C. Morbidoni
Some usage patterns have been individuated by scholars involved in the
project: a) Using the Dictionary facet to intersect two or more topics from the
vocabulary. This is a simple but useful ”advanced search” feature; b) Choose one
or more Dictionary topics (e.g. Storia), then use the facets on the right (DBpedia
entities) to provide additional context (e.g. Hegel, Croce and Plechanov are the
main persons related to History, ”Teoria e storia della storiografia” and ”Misre
de la philosophie” are two related books, etc.); c) Start from a full text search or
from a DBPedia entity (e.g. ”Conte di Montecristo”) and discover related topics.
3 Conclusions and Acknowledgements
In this short paper we discussed preliminary results in leveraging Linked Data in
the GramsciSource project and we presented a proof of concept prototype. Feed-
back from Humanities scholars involved in the project (and in related projects,
such as DM2E8 ) was positive and encouraged us to move further. End user eval-
uation will be run in the next months. We are currently evaluating automatic
methods to derive entities and facets (e.g. based on language analysis tool such
as [7]), with the aim of making the approach easily applicable to different texts
corpora.
This work is supported by the GramsciSource project funded by the Italian
Ministry of Education under the FIRB action.
References
1. Marco Grassi, Christian Morbidoni, Michele Nucci, Simone Fonda and Francesco
Piazza. Pundit: Augmenting Web Contents with Semantics. Literary & Linguisting
Computing, 2013
2. Dizionario gramsciano 1926-1937, Curated by Guido Liguori, Pasquale Voza, Roma,
Carocci Editore, 2009, pp. 918.
3. Paolo Ferragina, Ugo Scaiella, TAGME: on-the-fly annotation of short text frag-
ments (by wikipedia entities), Proceedings of the 19th ACM international conference
on Information and knowledge management, New York, 2010
4. Eyal Oren, Renaud Delbru, Stefan Decker, Extending Faceted Navigation for RDF
Data, The Semantic Web - ISWC 2006, Lecture Notes in Computer Science Volume
4273, 2006, pp 559-572.
5. Philipp Heim, Jrgen Ziegler, Faceted Visual Exploration of Semantic Data, Human
Aspects of Visualization, Lecture Notes in Computer Science Volume 6431, 2011,
pp 58-75.
6. Bei Xu, Hai Zhuge, Automatic Faceted Navigation, Future Generation Computer
Systems archive, Volume 32, March, 2014, Pages 187-197
7. Dell’Orletta F., Venturi G., Cimino A., Montemagni S. (2014) T2K: a System for
Automatically Extracting and Organizing Knowledge from Texts. In Proceedings
of 9th Edition of International Conference on Language Resources and Evaluation
(LREC 2014), 26-31 May, Reykjavik, Iceland.
8
http://dm2e.eu