=Paper=
{{Paper
|id=Vol-2526/short2
|storemode=property
|title=Challenging Knowledge Extraction to Support the Curation of Documentary Evidence in the Humanities (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2526/short2.pdf
|volume=Vol-2526
|authors=Enrico Daga,Enrico Motta
|dblpUrl=https://dblp.org/rec/conf/kcap/DagaM19a
}}
==Challenging Knowledge Extraction to Support the Curation of Documentary Evidence in the Humanities (short paper)==
https://ceur-ws.org/Vol-2526/short2.pdf
Challenging knowledge extraction to support
the curation of documentary evidence in the humanities
Enrico Daga Enrico Motta
enrico.daga@open.ac.uk enrico.motta@open.ac.uk
The Open University The Open University
Milton Keynes, United Kingdom Milton Keynes, United Kingdom
ABSTRACT These are catalogued through a sophisticated workflow but more
The identification and cataloguing of documentary evidence from importantly by means of a rich ontology covering a variety of as-
textual corpora is an important part of empirical research in the pects related to the experience, for example, the time and place it
humanities. In this position paper, we ponder the applicability of occurred, the source where the evidence has been retrieved, and
knowledge extraction techniques to support the data acquisition the entities involved, such as, a performer, a composer, or a creative
process. Initially, we characterise the task by analysing the end- work [1]. Another example is the UK Reading Experience Database
to-end process occurring in the data curation activity. After that, (RED). UK RED includes over 30,000 records of reading experiences
we examine general knowledge extraction tasks and discuss their sourced from the English literature. The curatorial effort required
relation to the problem at hand. Considering the case of the Listen- to populate these databases was significant and the size and quality
ing Experience Database (LED), we perform an empirical analysis of these databases is a major achievement of these projects.
focusing on two roles: the listener and the place. The results show, In this position paper we ponder the applicability of knowledge
among other things, how the entities are often mentioned many extraction techniques to support the data curation activity. Initially,
paragraphs away from the evidence text or are not in the source at we introduce the case study and analyse the data curation activity.
all. We discuss the challenges emerged from the point of view of After that, we examine general knowledge extraction tasks and
scientific knowledge acquisition. discuss their relation to the problem at hand. Considering the case
of the Listening Experience Database (LED), we perform an em-
CCS CONCEPTS pirical analysis of a portion of the database, focusing on the role
"listener" and "place". Specifically, we elaborate on the hypothesis
• Information systems → Information extraction; • Comput-
that the related entities can be automatically retrieved from the
ing methodologies → Information extraction; • Applied com-
source. Finally, we discuss a set of challenges for knowledge ex-
puting → Arts and humanities.
traction related to supporting the curation of this type of evidence
KEYWORDS databases.
documentary evidence, knowledge extraction, named entity recog-
2 DATA CURATION ACTIVITY
nition, DBpedia
In general, the discovery and selection of documentary evidence
1 INTRODUCTION is an activity that may not be conducted systematically. However,
in the context of enterprises such as the LED project, there is an
The identification and cataloguing of documentary evidence from
attempt to objectively select, extract, and curate documentary ev-
textual corpora is an important part of empirical research in the
idence from texts. From the curator’s perspective, it is not about
humanities. An increasing number of recent initiatives in the dig-
searching archives or repositories but exploring specific sources of
ital humanities have as primary objective the curation of a data-
value, for example, specific books. In [8] we developed an approach
base collecting text excerpts augmented with fine-grained meta-
for retrieving textual excerpts relevant for a certain theme of inter-
data, mentioned entities, and their relations, often in the form of
est in a book by combining language analysis, entity recognition,
knoweldge graphs developed adopting the linked data paradigm.
and a general purpose knowledge graph (DBpedia) and showed
These databases are developed following controlled processes, in
that many of those pieces of evidence are characterised by implicit
the spirit of digital library management, where the identification
information. In addition, once the text is found, populating all the
and onboarding of relevant information is substantially entrusted
metadata is a long and difficult task.
to research students, librarians, and similar domain experts. The
To illustrate the problem, let’s consider two examples from the
Listening Experience Database Project (LED)1 , for example, is an ini-
LED project:
tiative aimed at collecting accounts of people’s private experiences
E1 "Music is certainly a pleasure that may be reckoned intellectual,
of listening to music [4]. Since 2012, the LED community explored
and we shall never again have it in the perfection it is this year, because
a wide variety of sources, collecting over 10.000 unique experiences.
Mr. Handel will not compose any more! Oratorios begin next week,
1 https://led.kmi.open.ac.uk/
to my great joy, for they are the highest entertainment to me." 2 The
Copyright ©2019 for this paper by its authors. Use permitted under Creative Commons
2 Source: Mary Granville, and Augusta Hall (ed.), Autobiography and Correspondence
License Attribution 4.0 International (CC BY 4.0).
In: Proceedings of the 3rd International Workshop on Capturing Scientific Knowledge of Mary Granville, Mrs Delany: with interesting Reminiscences of King George the
(Sciknow), November 19th, 2019. Collocated with the tenth International Conference on Third and Queen Charlotte, volume 1 (London, 1861), p. 594. https://led.kmi.open.ac.
Knowledge Capture (K-CAP), Los Angeles, CA, USA.. uk/entity/lexp/1444424772006 accessed: 30 September, 2019.
Third International Workshop on Capturing Scientific Knowledge (Sciknow), November 19th, 2019. Collocated with the tenth International Conference on Knowledge Capture
(K-CAP), Los Angeles, CA, USA. Enrico Daga and Enrico Motta
excerpt refers to Mrs Delany’s report of a (series of) live perfor- output described using a knowledge representation formalism. En-
mances of Operas and Oratorios by George Frideric Handel, tity extraction and classification are two related tasks referring
happened in March, 1737. to the location of mentions of entities in an input text and their
E2 "I then went to Amsterdam to conduct Oedipus at the Concert- categorization, as in the following example: "We went to the re-
gebouw, which was celebrating its fortieth anniversary by a series of hearsal of JoshuaP er son last TuesdayT ime ". Entity Linking, in-
sumptuous musical productions. The fine Concertgebouw orchestra, stead, refers to finding mentions of entities from a database into
always at the same high level, the magnificent male choruses from a natural language resource or, similarly, to appropriately disam-
the Royal Apollo Society, soloists of the first rank - among them Mme biguate words by associating a knowledge base identifier. Often,
Hélène Sadoven as Jocasta, Louis van Tulder as Oedipus, and Paul the three tasks are performed together and labelled Named En-
Huf, an excellent reader - and the way in which my work was re- tity Recognition and Classification (NERC) [12]. Linked Data
ceived by the public, have left a particularly precious memory that and NER together have been extensively employed in a number
I recall with much enjoyment." 3 Stravinsky, in the beginning of of knowledge extraction and data mining tasks (e.g., the work of
1928, celebrates the high level of the Concertgebouw orchestra H. Paulheim [21]). Relation extraction refers to the identifica-
and singers performing his Oedipus Rex. All of them are listed as tion of n − ary relations (for n ≥ 2) within the source, usually
entities in the LED database. addressed with a combination of NLP and machine learning tech-
In both examples, several of the entities involved are not men- niques [22]. The relations Composer(Opedipus Rex,Starvinsky)
tioned in the excerpt and are derived from the curator’s knowledge and Performed(Opedipus Rex,Concertgebouw,1928) are two
of the source (for example, Mrs Delany is the author of the letter in examples. Event extraction is a special case of relation extrac-
E 1 ) and the domain (e.g. the full name of the work is Oedipus Rex tion where the focus is on identifying an event, usually an action
in E 2 ). being performed by an agent in a certain setting. This task is ex-
Here we focus on the challenge of automatically populating the tensively studied in domains such as Biomedicine [5], Finance and
record and support an expert in identifying, collecting and inputting Politics [15], and Science [26]. Approaches dedicated to the de-
the relevant information. In other words, we aim at automatically tection and extraction of historical and biographical events are
populating (as many as possible) roles of the ontology. For instance, designed in [25, 29]. The notion of event is generally considered as
a listening experience specification can be derived from the avail- something happening at a specific time and place, which constitutes
able graph on data.open.ac.uk [7]. The type ListeningExperience an incident of substantial relevance [14]. Therefore, the objective is
includes the following properties, among others (we omit names- to identify the action triggering the event (e.g. the verb perform) and
paces for readability): then the associated roles. Data-driven approaches usually involve
• agent (who is the listener) statistical reasoning or probabilistic methods like Machine Learning
• time (when the listening event occurred) techniques. In contrast, knowledge-based methods are generally
• place (where it occurred) top-down and based on pre-defined templates, for example, lexico-
• subject (what was listened) semantic patterns [15]. The two approaches can be combined and
• is_reported_in (a link to the source) machine learning methods used to learn such patterns [23]. How-
• has_environment (e.g. was it a public or a private place, ever, the notion of event is still ill-defined in NLP research and
indoor or outdoor) this makes it hard to develop methods which are portable, effec-
tively, to multiple domains [14]. Research in open domain event
A ListeningExperience is related to other relevant items, notably
extraction focuses essentially on social media data [24] where the
Performance, WrittenWork, MusicArtist, and Country. The knowl-
task is the extraction of statements for summarization purposes,
edge extraction system should be able to derive the requirements
similar to the one of key-phrases extraction [28]. Ontology-based
from the ontology specification, primarily the data values and roles
information extraction (OBIE) uses formal ontologies to guide the
involved. For example, it should derive the requirement to find
extraction process [17, 27]. Relevant work in the area is surveyed
the agent of the ListeningExperience, its place and time, and
in [9, 19]. In 2013, Gangemi provided an introduction and compari-
that there may be a specific musical work to be identified and,
son of fourteen tools for knowledge extraction over unstructured
eventually, the author of the musical work, filling the roles associ-
corpora, where the task is defined as general purpose machine
ated to the path subject -> ? a Performance -> performance of -> ? a
reading [10]. A machine reader transforms a natural language text
MusicExpression.
into formal knowledge, according to a shared semantics. State of art
methods include FRED [11] and PIKES [6]. These approaches are
3 KNOWLEDGE EXTRACTION
based on a frame-based semantics that is at the same time domain-
Knowledge extraction is a branch of artificial intelligence cover- and task-independent. Instead, a domain-oriented solution would
ing a variety of tasks related to the automatic or semi-automatic identify knowledge components of interest in the text, similarly to
derivation of formal symbolic knowledge from unstructured or what explored, for example, in the work of Alani [3]. This task is
semi-structured sources4 . also considered as an automatic ontology instantiation [2] or semi-
The area comprehends research in a variety of problems re- automatic creation of metadata [13]. A suitable approach should be
lated to lifting an unstructured or semi-structured source into an able to detect the requirements from a domain-specific ontology
3 Igor Stravinksy, Igor Stravinsky: An Autobiography (1936), p. 139. https://led.kmi. and, having as input the text excerpt, the source metadata, and
open.ac.uk/entity/lexp/1435674909834 accessed: 30 September, 2019. potentially other knowldge bases, generate suitable hypotheses of
4 For a general introduction, see [16]. values and entities on any relevant role.
Challenging
Third International
knowledge
Workshop
extraction
on to
Capturing
support Scientific Knowledge (Sciknow), November 19th, 2019. Collocated with the tenth International Conference on Knowledge Capture
the curation of documentary evidence in the humanities (K-CAP), Los Angeles, CA, USA.
Listing 1: Detect the location of an excerpt in a source.
excerpt , Source ;
b e s t [ t , b , e , s ] ; / / t e x t , b e g i n , end , s c o r e
words [ ] = t o k e n i z e ( e x c e r p t )
words [ ] = s o r t B y L e n g t h D e s c ( words [ ] ) / / L o n g e s t on t o p
F o r e a c h word i n words [ ] :
o c c u r r e n c e s [ ] [ b , e ] = f i n d ( word , S o u r c e )
p o s i t i o n [ b , e ] = f i n d ( word , e x c e r p t )
Foreach occurrence [ b , e ] in occurrences [ ] [ b , e ] :
Figure 1: Statistics on the LED database to illustrate the cov- begin = occurrence . b − p o s i t i o n . b
erage of DBpedia entities and the scope of our analysis. end = occurrence . e + len ( excerpt ) − position . e
p o s s i b l e = s u b s t r i n g ( S o u r c e , b e g i n , end )
score = levenshtein ( excerpt , possible )
i f ( score < best [ s ])
4 EMPIRICAL ANALYSIS b e s t [ t , b , e , s ] = [ p o s s i b l e , b e g i n , end , s c o r e ]
To discuss the feasibility and difficulty of the task, we relax the fi
End
problem and verify to what extent the entities that are part of the
End
curated metadata could potentially be automatically derived from return best
the sources. Specifically, we want to answer the questions: (Q1)
Could a system find the target entities in the excerpt? (Q2) Could a
system find the target entities in the text surrounding the excerpt?
(Q3) How far from the excerpt the entity is? (Q4) Could it be found
in the metadata of the source?
We consider the case of the LED database and focus on two rela-
tion and roles: the listener and the place of the listening event. The
LED curation workflow reuses entities from DBpedia, MusicBrainz,
and also defines new entities in the Linked Data. Our analysis is (a) Places. (b) Agents.
limited to books from archive.org annotated with a listener or place
from DBpedia. We use DBpedia Spotlight [20] as entity recognition Figure 2: Distance of entity mention, in paragraphs.
and linking system.
First, we need to find the position of the evidence text back in
the original source. Identifying the position of LED items in the these, 7.3% includes DBpedia entities as place or agent, 690 excerpts
original book is not an easy task. In fact, the process of reporting from 26 books. These are the objects in our analysis.
an excerpt from the book involves a number of modifications in Results are summarised in Figure 2. Charts display the distance
the format that makes it very rare the chance that a precise text of the entity mentions, measured in number of paragraphs5 . This
match would work. In addition, the reported text includes often analysis is partial as it only covers DBpedia entities being used as
omissis or rephrasing in order to include co-references derived places or agents (listeners) with relation to books which sources
from previous paragraphs. To solve the problem, we developed the we could retrieve from the Web. However, the answers to the re-
algorithm presented in Listing 1. The method is based on using the maining questions are quite interesting. (Q1) The DBpedia place
longest words as locators. The algorithm selects the occurrences of was mentioned in the textual excerpt only in 25.9% of the observed
the longest words and isolate the surrounding portion of text using cases (179). The listener was mentioned in the excerpt only in 13
the length of the excerpt as heuristic. The resulting candidates are cases, 13.4% of the observed population (97). (Q2) 10% of the times
then ranked according to their similarity against the excerpt using the place mention is less then 5 paragraphs from the evidence text.
the well-known Levenshtein distance [18]. The candidate with the The agent is mentioned within 5 paragraphs from the evidence
lowest score is elected as the original text. in 4% of the observed cases. (Q3) 83.2% of the times the DBpedia
Figure 1 illustrates the features of the corpus. Of the 9059 lis- place was explicitely mentioned at least once in the source (574). In
tening experiences in the database with a textual excerpt reported, 79 cases (11.4%) the place hasn’t been found either in the excerpt
7999 include a place (88.3%) and in 7222 of them the place points to or anywhere else in the source. A similar result is observable for
DBpedia (79.9%). The agent is specified in 8258 of them (91.2%) but agents. Finally, there is good chance the entity is somewhere away
only 2996 refer to a DBpedia entity (33.1%). In all other cases the from the evidence text.
listener is created as a novel entity.
64.8% of the listeners are also the authors of the text - 5874 cases. 5 CHALLENGES
This is not surprising as one of the most researched type of resources There are several aspects that make the task of automatically sup-
were memories, diaries, and collection of letters. In addition, this porting the acquisition of knowledge about documentary evidence
answers our Q4 and shows how important it could be to intelligently particularly interesting from the point of view of scientific knowl-
derive information from the source metadata. However, less than edge acquisition.
half of the agents exist in DBpedia (2130 times, 23.5% of the total). 5 Text segmentation is itself a difficult task. In our analysis, we measured distances
Finally, only 11.3% of the sources could be retrieved as open texts, in number of characters, considered one word to be 5 characters (the approximated
referring to 1026 of the documentary evidence in the database. Of average length in english) and one paragraph to amount to 200 words.
Third International Workshop on Capturing Scientific Knowledge (Sciknow), November 19th, 2019. Collocated with the tenth International Conference on Knowledge Capture
(K-CAP), Los Angeles, CA, USA. Enrico Daga and Enrico Motta
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