A heightened interest in the presence of the past has given rise to the new field of memory studies, but there is a lack of search and research tools to support studying how and why the past is evoked in diachronic discourses. Searching for temporal references is not straightforward. It entails bridging the gap between conceptuallybased information needs on one side, and term-based inverted indexes on the other. Our approach enables the search for references to (intersubjective) historical periods in diachronic corpora. It consists of a semantically-enhanced search engine that is able to find references to many entities at a time, which is combined with a novel interface that invites its user to actively sculpt the search result set. Until now we have been concerned mostly with user-friendly retrieval and selection of sources, but our tool can also contribute to existing eforts to create reusable linked data from and for research in the humanities.
There has been a monumental shift from the future to the past in
the cultural orientation of Western societies, starting in the 1980s.
In a sense, the past has increasingly gained in presence: in the
literary and artistic expressions of (traumatic) memories that cannot be
contained by the evidence that forms the basis of historical studies,
the proliferation of museums and archives, and the
commodification of the past as marked by docudramas, historically-themed
amusement parks, and memorabilia of pasts that never existed [
SEMANTiCS 2017 workshops proceedings: Drift-a-LOD September 11-14, 2017, Amsterdam, Netherlands other groups, such as journalists and politicians, have incorporated the past into their narratives.
Identifying and interpreting the presence of the past in the
discourse of non-historians is worthwhile, not because we expect to
establish new facts about past events, but rather because laypersons
and practitioners of other disciplines draw upon the past to make all
kinds of judgments and decisions in daily life [
In response to the “spatial turn” in Digital Humanities,
substantial efort has been put into the development of tools that
allow for spatial navigation through text collections. In the Pelagios
project, for example, the Pleiades gazetteer serves to anchor
locations mentioned in text to machine-readable representations of
these locations, which can be combined with linked data to form
rich map-based visualizations and allows for spatial access to the
texts through the Peripleo search interface [
In this paper, we propose an approach to support researchers who
aim to identify and interpret (indirect) references to historical
periods in a particular discourse. It consists of a semantically-enhanced
search engine that is able to find references to many entities at a
time in diachronic corpora, which is combined with an interface
that invites its user to actively sculpt the search result set. As several
pilot studies have indicated [
The search for references to historical periods entails bridging the
gap between conceptually-based information needs on one side,
and term-based inverted indexes on the other. When a researcher
is looking for the fragments of text that refer to, e.g., the French
Revolution in a specific collection, they are not served well by
providing only the documents that contain the literal phrase “French
Revolution.” This is the case because such periods do not pre-exist
in reality, waiting to be discovered and named, but rather come into
being when the disparate observable elements of a phenomenon
are “seen together” as a synthetic whole [
Periods are but one kind of colligatory concept that is commonly
constructed by historians; the others being characters, such as
‘Louis XVI’ and ‘the French people,’ and ideal types, for instance
‘capitalism’ and ‘revolution’ [
This model captures accurately the multivocal aspect of period
definitions, but is not suficient to retrieve references to these
periods in discourse that is about another subject. Here we are
confronted with the diference between colligatory concepts which are
necessary constructs in the writing of history, and the subjects that
are intended to group together multiple histories that exhibit
common “patterns of colligation” [7, p. 1098]. As a colligatory concept, a
period is a particular representation of the past that binds together
historical entities in a unique narrative. When this individual
representation leads to further discourse, the discourse as a whole is
not about the original colligation, but rather about a homonymous
subject that allows us to, e.g, ask a librarian for ‘novels set during
the French revolution.’ In the practice of information organization,
we establish common referents and shared structure between
colligations in order to group a multiplicity of perspectives under a
single label [
We have no way of searching directly for the period that the researcher has in mind (it exists only as a cognitive representation), but by starting from its associated subject we can make an educated guess about the elements of the period. Named entities—events, people, artifacts—are central in our search approach, because they serve as the points of consensus that enable the search for as of yet unknown perspectives on segments of the past. The aim is to collect (and represent) an intermittent discourse, consisting of sentences that make (indirect) reference to the target period of the search, with as much context as is needed to understand them. To be sure, we do not equate the colligatory concepts that are put forward by historical work with the scattered references to the past that are found in non-historical discourse. Rather, we expect the (re)searcher to have a particular period (i.e. colligatory concept) in mind, and aim for the system to find references to the entities that are bound together by this period. 3
The task that we aim to facilitate—selecting a research corpus of
text fragments that refer to a particular period—is not supported
well by either subject indexing [
Some groupings of entities that people might want to search for
already exist as Linked Open Data. The category network of Wikipedia
serves an analog function to that of the subject access systems of
librarians and archivists. It is a knowledge organization system
(KOS) that relates more specific subjects to more general ones by
broader-than relations [
In order to obtain possible mappings between periods of
interest and the entities that are bound together by such
periods, we extracted a subgraph of DBpedia, corresponding to
Wikipedia’s category network and its related entities, into a
property graph database (see [
Our proof-of-concept makes use of DBpedia, but any knowledge graph that conforms to the SKOS ontology can be loaded easily. 1For namespace prefixes, see https://dbpedia.org/sparql?nsdecl. Linked data that is structured diferently can also be used, as long as a grouping or categorization that is familiar to the intended users can be derived from it. It is important that the representations of entities in this data are identifiable by the same URIs as those used in the entity links in the corpus, to be able to connect periods, via colligated entities, to the text fragments that refer to these entities.
Finally, the system needs access to coarse temporal clues about entities. Because DBpedia does not provide this data reliably across entity types, we extract mentioned years from the rdfs:comment values of DBpedia resources with a simple regular expression, and add them to the graph. The same technique may be successful for other linked data sources that provide textual descriptions which often include temporal expressions. It would be preferable, however, to use representations that incorporate structured temporal relations in the form of RDF literals for all entities. 3.2
Our proof-of-concept, named WideNet [
The interface guides users through three research phases: (1) selection of root category, (2) assessment of the categories’ and entities’ relevance, and (3) close-reading. In the first step the user selects one or several root categories from a typeahead search box (see Figure 1), and demarcates the query by selecting a time period, which is used to prune the underlying entities of the selected categories. WideNet subsequently retrieves the network of narrower categories for each selected root category, and collects the contained entities as potentially relevant query components. Behind the scenes, each entity is compared with the target period, and is considered to be outright relevant to the period, or not, or a borderline case, or as lacking temporal clues altogether. In the current implementation this classification is achieved with simple rules, based on the features: ‘fraction of years within period,’ ‘fraction of intervals that overlap with the period,’ and ‘has at least one year in period.’ The system uses this information to deselect (sub)categories where more than half of the dated member entities are out-of-period, i.e., those categories are excluded from the query.
The next step for the WideNet user is to assess which of the retrieved subcategories actually contain entities that lead to relevant results. By doing this, researchers can operationalize their own definition of their target period, at least for the purpose of retrieval. The interface facilitates this task by showing, per subcategory, which entities are mentioned in the corpus, and how frequently, as well as which entities did not occur (see Figure 2). It also displays a list of preview results, showing limited context, to ofer quick clues about the relevance of the category. This preview is also useful to identify individual entities that are not relevant after all, which can be deselected by the user. At the end of this step, the researcher’s decisions amount to a motivated and organized representation of ‘relevant’ or ‘possibly present’ elements of the target period.
After inspecting and selecting relevant categories of entities— thereby sculpting the final query, the WideNet interface allows further scrutinizing of the sources by providing an environment in which the retrieved documents can be studied up-close, as shown in Figure 3. By situating the close reading activity within the same interface, the user is able to compile a corpus of relevant documents which may be saved and exported. Moreover, the user can examine the results in relation to the document metadata, e.g. to look for saliency by plotting the annotations over time, or to study bias by comparing how often diferent political parties refer to the entities of interest. The selected corpus, representing a fragmented discourse, may also be used to analyze changes in how and why the demarcated segment of the past was evoked, establishing contesting perspectives and trends over time, provided that enough references could be found.
As a product of the search, semantic annotations are created which
link the source documents to the entities that are referenced in the
corpus. These expert-approved annotations have much more value
than the automatically generated entity links. For one, the identity
of the referents is established with a greater confidence when a
user chooses to include a particular document fragment in his/her
research corpus. We cannot interpret this as a direct assessment of
the entity links, but when a user has confirmed that the document
fragment (indirectly) refers to the target period, it is tempting to
assume that the document was retrieved because the entity links
are correct, and not by some mistake. To model the provenance of
such relevance decisions, it is necessary to produce a representation
of the broader context of the search process to which individual
assertions can be explicitly related (see [
In the first screen that users encounter when starting a new search process in WideNet (depicted in Figure 1), we are able to capture the motivation and a rough demarcation of the search. This information forms the backbone of a representation to which subsequent assertions can refer. The selection of categories and entities (in Figure 2) provides assertions about their relevance for the search target, according to the researcher and dependent on the corpus. We currently store each decision that is made by the researcher, so when a previously made decision is revisited, we create representations of e.g. both the act of deselecting an entity, and reselecting it after more preview results had been inspected. Capturing this process data, rather than only the final selection of entities, benefits the richness of the provenance of the subsequent assertions about the relevance of text fragments.
The semantic annotations that are derived from relevance
assertions on text fragments can provide richly indexed access to
statements about the past. This notion is similar to Ryan Shaw’s
proposal for “deep gazetteers,” in which multiple descriptions of the
same named entity are linked to fragments of discourse in which
its name is used [
While we capture the described representations of search
processes and semantic annotations in our current proof-of-concept,
we do not yet publish this data. Our approach is related to
ongoing eforts to produce reusable data for research in the humanities
(e.g. [
In searching diachronic corpora for fragmented discourses about historical periods, we need to come to a fundamental understanding of conceptual diference before we can give any account of conceptual drift over time. Our approach for finding references to periods consists of a semantically-enhanced search engine that is able to find references to many entities at a time in diachronic corpora, which is combined with an interface that invites its user to actively sculpt the search result set. Besides yielding sources that are useful for the searcher, the search tool also produces an operationalization of the search target by the (re)searcher as well as semantic annotations that are much richer than those that we can generate algorithmically.
Although a pragmatic treatment of concepts is suficient to search for multifaceted subjects, we envision how the products of such search processes can collectively provide a shared source for more elaborate knowledge representations. In designing a tool that implements this approach, we were faced with some trade-ofs between usability of the tool and reusability of the data it captures. We prioritize supporting the present-day researcher well, and facilitate publishing the search process and its results as linked open data, so that subsequent refinement of the captured data may be a community efort.
We thank the anonymous reviewers for their suggestions and remarks. This work was supported by the Netherlands Organization for Scientific Research (ExPoSe project, NWO CI # 314.99.108).