Digital Libraries containing metadata of diverse cultural heritage objects are meant to be accessible not only to domain experts but also to the general population. This calls for information services that can provide ease and eficiency to search, retrieval and exploration. Knowledge graphs (KGs) are essential for representation, organization, integration, and analysis of hierarchical and heterogeneous information. However, most KGs sufer from incompleteness and inaccuracies. This work intends to address various challenges arising from construction and refinement of a KG populated with historical objects, by defining domain- and application-appropriate ontologies and leveraging approaches in information extraction (IE) for improving metadata quality.
CEUR ceur-ws.org
described by Tan et al.[
To address these challenges necessitates a combination of solutions in knowledge representation, knowledge refinement, and information extraction. Therefore, this thesis proposes i) an ontology that enables interoperability across diferent types of CHOs while maintaining domain-specific semantics as discussed in Section 5.1; ii) a KG refinement approach leveraging NLP teachniques to improve metadata quality of historical objects; and iii) an Entity Linking approach for entities in historical objects.
This work will benefit not only the general population, but also the domain experts such as librarians, curators, and archivists. Proposed solutions will empower users from diverse backgrounds to seamlessly and eficiently search, retrieve, and explore Germany’s rich and voluminous collection.
Recent developments in AI can be leveraged to address the technical challenges facing the DDB. This work is relevant to the researchers working at the intersection of Semantic Web (SW), Digital Humanities (DH), and Natural Language Processing (NLP).
There have been several notable data models or ontologies proposed for cultural heritage
representation. Liu et al.[
A pioneer in the application of of SW technologies, theSampo series of semantic portals
showcase the national heritage of Finland. These systems make use of the modular FinnONTO
ontology infrastructure5[]. However, FinnONTO is not a full-featured ontology, but a taxonomy
of CHOs encoded as Simple Knowledge Organization System (SKOS) concepts. Following the
modular modeling approach is Italy’s ArC3O[
The core design principles of EDM, and by extension DDB-EDM, lead to definitions of general classes that require the bare minimum of metadata properties and controlled vocabularies. Thus, all CHOs, regardless of their sources, media types and object types, are instances of the classedm:ProvidedCHO, while their digitized representations on the Web are instances of the classedm:WebResource. This flexibility however results in imprecise representations and loss
of semantics inherent in the original objects 7[]. In particular, it is not possible to model the concepts and level of abstractions widely-accepted in the bibliographical domain.
The International Federation of Library Associations and Institutions (IFLA) developed the Functional Requirements for Bibliographic Records (FRBR8)][, where a book can be represented as several entities and the relationships that exist among these entities. A copy of a book (frbr:Item) is a specimen or exemplification of a specific publication ( frbr:Manifestation), which is an embodiment of an expression frbr:Expression that realizes the ideas of a creative work (frbr:Work).
Most Europeana users are less likely to search for specific items (11.3%) and are more inclined
to search by category (47.1%) and by subject (24.6%) [
The challenges of filling missing information and identifying erroneous information in a knowledge graph fall under the umbrella of Knowledge Graph Refinement. In particular, Knowledge Graph Completion (KGC) deals with the former challenge, whilEerror Detection deals with the latter.
By definition, internal methods for KGC use the content of the current KG either to determine class membership or to predict relations between entities. These methods require the current KG to at least possess reasonable quality in order for large scale evaluation to be feasib10le].[ On the other hand, external methods leverage other sources of knowledge for refinement, such as other knowledge graphs or text corpora.
With the rapid development in the area of Natural Language Processing (NLP), text corpora have become an excellent source of external knowledge. The subfield of Information Extraction (IE), an intermediate step to knowledge graph construction, can be defined as the process of gleaning structured information from unstructured tex1t1[]. A concrete example of this task would be to extract distinct properties and attributes identifying a literary work from the title.
An IE pipeline starts with Named Entity Recognition (NER), or the detection and classification of named entities mentioned in the text. Types of entities can be coarse-grained such asPERSON, WORK_OF_ART, DATE, et cetera or fine-grained such as AUTHOR, PUBLISHER, ARTWORK, PUBLISHER, LITERARY_WORK, PUBLICATION DATE, et cetera.
Specific entity types (fine-grained) are often found in domain-specific texts, or even
timespecific texts where concept drift is quite common. In the field of digial humanities, there are
a number of studies on NER with historical text 1[
Once the entities have been detected and classified, they are linked to specific entries in
reference knowledge bases or KGs. Entity Linking is particularly challenging due to the surface
form variations. In particular, names in historical texts can be multilingual, refer to aliases or
contain initials, include honorifics and designations. The names of geop-olitical entities are
also known to change through time. Pontes et al[.14] proposed an end-to-end multilingual
NER and EL (NERL) approach to address some of these challenges using some of the datasets
mentioned in Ehrmann et al. [
This section formulates the research questions (RQs) to address the challenges and limitations of existing approaches described in Section3.
RQ1: How can existing ontologies be adapted and extended to suit the domain and application profile of digital libraries, such as the DDB? Cultural heritage practitioners have been developing ontologies for specifc domains and applications. As of this writing, only EDM is used to represent metadata from several cultural institutions. In order to prevent data model silo1s5][ and to promote reusability, it is beneficial to consider existing ontologies that are applicable and appropriate for the use case of the DDB. Preliminary results are discussed in Sectio5n.1.
RQ2: How can we leverage state-of-the-art NLP models to improve metadata quality of historical objects? Non-contemporary titles in the DDB <(dc:title>) encode details that can be used to fillout missing properties, such as the title itself, author, publisher, editor, subject headings, and dates. Hence, this calls for extractive NLP approaches. Section5.2 presents some preliminary results.
RQ2.1: How can we automatically construct an evaluation dataset from the DDB? In order to address the succeeding RQs, an evaluation dataset for IE is required.
Section 5.2 briefly describes what has been done so far.
RQ2.2: How can we efectively extract fine-grained bibliographic entities from historical texts? The goal here is to address open challenges in the area of historical NER, such as how to properly handle the dynamics of an evolving language, where spelling and naming conventions change through time, and noise resulting from OCR engine. Dataset construction, design of experiments, and model development will be accomplished. RQ3: How can we link entities to records in the reference KG? The goal here is to accurately disambiguate named entities and link them to entries in external KGs, while addressing the challenges associated with historical texts. Moreover, entities that do not exist in the reference KG can used as further contribution to increase the coverage of authority files.
The entirety of this work is envisioned to guide the construction and refinement of a knowledge graph representing DDB’s cultural heritage objects. In addition, some open questions have yet to be addressed concerning NERL in historical texts.
The section describes preliminary work conducted to address the open questions presented in Section 4.
Extensive quantitative and qualitative analysis of the entire DDB metadata collection have been
conducted in order to ascertain the applicability of existing CH ontologies. Initially, objects were
logically classified according to their originating institution, whether from libraries, archives,
museums, media libraries, or historical preservation. In addition, the media type of an object
was also taken into account. Taking up a large proportion of the entire collection, the alignment
of textual bibliographic resources to an extension of FRBR5 have been presented [
FRBR, as the upper ontology, requires that each object is looked up against a list of creative
works, such as the German Authority File oGremeinsame Normdatei (GND7). This ensures that
the relationship between diferent objects resulting from the same creative work is represented
in the KG [
The alignment of bibliographic items to their corresponding literary works proved to be a
challenging task due to incomplete object descriptions1[
As described in [
A more forgiving evaluation measureP(recision@n) described in Section 6.2 was defined to
take into account the various naming conventions found in the text. An NER model (FLERT) 1[
The research questions enumerated in Section4 require diferent evaluation procedures, dataset, and metrics. These are described in the succeeding subsections. 5Functional Requirements for Bibliographic Records8][ 6DDB-O , https://ise-fizkarlsruhe.github.io/ddbkg/ddbo 7GND, https://www.dnb.de/DE/Professionell/Standardisierung/GND/gnd_node.html
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There several ways in evaluating ontologies. One of which is using is using competency questions (CQs). A collection of CQs published in GitHu8bare included in the partial ontological definitions and alignment activities. In addition, SPARQL query processing time for CQs that can be answered with DDB-EDM will be compared with queries using the proposed ontology.
Name matching for historical documents is non-trivial due to various naming conventions and
spelling variations. In a QA task, the most forgiving measure iAs ccuracy@1, which returns 1 if
there is a single token overlap between the ground truth and the answePrr.ecision@n measure is
a combination of 2 matching criteria: an exact match of the DDB object ID and an approximate
match for names using the Levenshtein edit distance [
The evaluation measures for RQ3 will not be any diferent from those associated with EL. A large proportion of the agents in the DDB are already linked to GND Persons. And there already exist links between GND and Wikidata entities. This means that it is trivial to combine naming variations and multilingual names for the evaluation dataset. Evaluating geopolitical entities will require prior knowledge of the age of the object in question.
As discussed in Section 5.2, the lack of a gold standard evaluation dataset brings a level of uncertainty to the experimental results. This will be addressed with the creation of a manually annotated dataset with fine-grained entities. Consequently, this dataset will be used to address RQ2.2. In addition, the work conducted to address RQ1 need to be finalized. Finally, entities that already exist in GND will be linked, while non-existing ones can be used to further increase the coverage of GND and Wikidata.
I would like to thank my supervisors Prof. Dr. Harald Sack and Dr. Shufan Jiang for their invaluable mentoring and support.
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