Semantic change and concept drift are studied in many different academic elds. Di erent domains have di erent understandings of what a concept and, thus, concept drift is making it harder for researchers to build upon work in other disciplines. In this paper, we aim to address this challenge and propose de nitions for these phenomena which apply across elds. We provide formal de nitions and illustrate how concept drift and related phenomena can be modeled in RDF through the use of context. We explain and support the de nitions through an example from historical research and argue that a formal modeling of semantic change in RDF can help to better interpret data.
Semantic change and concept drift are important elds of study for many academic elds, including history, linguistics, philosophy, political science and computer science. However, not every discipline uses these terms in the same way. This is not a problem in itself, but does create confusion when these elds communicate or even work together, like in digital humanities projects. In this paper, we aim to tackle this challenge with particular focus on how the relation between concept drift in Semantic Web research and other disciplines.
We de ne concept drift as a change in the intension (the de nitions and
associations) of a concept, while the (rigid) core of the concept remains stable
[
The rest of this paper is structured as follows. Section 2 provides an overview of work on concept drift in various disciplines. We introduce our de nitions and formal models in Section 3. In Section 4, we illustrate the complex interaction between concept drift and related phenomena through an example from history. We conclude with a discussion in Section 5. 2
Historians have been studying how concepts and their associations change over
time for decades, to understand historical events [
In sociology, some make a distinction between normal concepts and contested
and contestable concepts, like `democracy' and `freedom', which leave room for
lots of debate [
Within the humanities and social sciences a clear de nition of `concept' is
often lacking [11, p. 363]. Recently the philosophers Betti and Van den Berg have
argued that to study the history of thoughts (and concepts), researchers should
make use of more explicit models, frameworks, to avoid the scent of arbitrariness
in what they are doing [
Cognitive science has shown that people link many associations with concepts, some being more important than others [11, p. 354]. For example, the concept of `pig' has the associations of `mammal', `four-legged' and `farm animal'. While a three legged pig living in a castle still is a pig, few would say that a creature that looks like a pig but lays eggs actually is a pig. Any de nition of a
concept has a sense of arbitrariness and is based on choices of what associations to include [14, p. 35-37]. It therefore is desirable to study concepts with as much exibility as possible.
Kuukkanen makes a distinction between the core of a concept, determined by a historian, and its margins. By making this distinction, it is easier to develop a vocabulary to talk about semantic changes. When the core of a concept changes he speaks of conceptual replacement [11, p.367-370]. Wang et al. speak of the `rigid core' of a concept in this respect, and when that rigid core changes, the concept is not the same concept anymore. What belongs to the rigid core should ideally be determined by human domain experts (`oracles') [16, p. 8]. We adapt Kuukkanen's and Wang's view that it is useful to distinguish between core and margin associations and that domain experts are the ones to decide which properties are part of the concept's core. We de ne concept drift as changes in the concept's associations while the core stays in tact. This means we limit our de nition of concept drift to what Wang et al. call changes in intension. In the next section we will elaborate on this de nition and other forms of semantic change that have been treated as concept drift in the Semantic Web literature. 3
Wang et al 's research forms a central piece in work on concept drift within the
Semantic Web community. They provide de nitions of three forms of concept
drift as well as formulas to calculate changes. We rst address their de nitions
and explain how they relate to the ones we propose. We then introduce formal
ways of modeling concept drift, which is not covered by Wang et al. [
Wang et al., state that concept drift can involve changes in the concept's
intension (its associations), in its extension (its reference) or in its label (words used
to refer to it). We follow Frege's [
The concept of `pig' has an extension that changes with every pig that is born or slaughtered. Changes in this concept's extension are not relevant for investigating semantic change or ontological representation.5 Other concepts have extensions that inherently change (without the sense of the concept changing), where changes are relevant for formal modeling. The concept of `government' is an example: a change in government does not mean the concept of `government' 5 The approximate number of living pigs may be relevant for certain use cases, but it is di cult to nd a study where the exact extension is important to know. changed, but representing what government is in place is relevant ontological information. Finally, there are concepts where the extension is closely connected to the intension of the concept. This is the case for Wang et al.'s example of the European Union, which is partially de ned by its member states.
Semantic changes that concern the relation between labels (words) and the
concepts they refer to are also often related to concept drift. When a new or
di erent label is used for a concept, this often re ects an intensional change. For
instance, when someone chooses to use the word pig to refer to a `police o cer',
this invokes connotations that re ect the way they look at the police. We also
observe changes where the same label ends up referring to a di erent concept. A
well-known example of such change is the English word cute (based on Frermann
and Lapata (2016) [
We propose to model the phenomena outlined above through the use of contexts
and Lemon (lexicon model for ontologies) [
Changes in the intension or extension of the concept, on the other hand,
are not captured in the lexicalSense (since the label of the concept may remain
stable). We follow Bouquet et al. [
7 https://github.com/cltl/GRaSP/blob/master/examples/conceptdrift/cuteLemon.png illustrates cute's conceptual replacement. to a gender and income, tting the context of many European countries in the late 19th century. We can imagine other variations that capture how democracy was seen in (e.g.) the Soviet Union, Ancient Rome or Athens. Note that we can also use these contexts to model extensional changes (e.g. to represent which countries were part of the European Union in a speci c time). 3.3
Conceptual changes can be detected by comparing changing in an ontology [
DBpedia contains de nitions of several forms of democracy, including Soviet Democracy, Direct Democracy, Representative Democracy and Pseudo-Democracy. They are de ned as dct:subject of dbpedia:Democracy. They can be linked to speci c societies through the dct:subject predicate as well (e.g. Soviet Democracy is linked to the Soviet Union and Direct Democracy to Ancient Greek society). DBpedia does however not make explicit that the concept of `Direct Democracy' is a form of democracy that was practiced in Athens during a speci c time and that thus `Democracy' in the context of this society refers to a direct democracy. In our model these relations could be made explicit by creating a skos:sameAs link between `Democracy' and `Direct Democracy' for the context of Athens in the Ancient Greek society. A scholar interpreting Plato may use this information to deduct that the negative view of democracy expressed in Plato's view is not necessarily aimed at modern day democracy.
GeoNames provides information about locations and geo-political entities. One of GeoNames' challenges is that such entities constantly change: countries and cities appear and vanish and some areas have a controversial political status. GeoNames indicates that countries that no longer exist such as Czechoslovakia are `historical political entities', but it currently does not o er any information about the country's context. Though this may be easy to add for a country from the past that was relatively stable throughout its existence, this becomes more challenging for countries such as Poland, which are current countries, but have changed drastically throughout history (from being one of the largest empires in Europe to completely disappearing as a political entity twice to moving West after World War II). If GeoNames wants to accurately represent this information, time-dependent contexts are essential.
The examples above are still relatively straight-forward cases of concept drift and semantic change. In the next section, we will illustrate that the interaction between various phenomena can become quite complex and that for accurate historical interpretation, a wide range of concepts needs to be taken into account. 4
In 1541, Catharina de Chasseur ([3, for her biography]) was arrested on the suspicion of forgery and distribution of false coins. Many historians have described and interpreted this episode in di erent ways over the past two centuries. One contemporary had testi ed that Catharina, the huysfrou of Lord Van Assendelft, was quaet ende boos. To modern Dutch these two expressions would literally translate to `housekeeper' and `angry and angry'. In the sixteenth century, however, these words translated to `wife', `evil' and `bad'. Trained historians are aware of such semantic changes and will use the necessary dictionaries to interpret their source material well.
Both boos and quaet refer to a di erent concept in modern Dutch (though they may occur with their old meaning in some xed expressions). The changes related to huysfrou are slightly more complex. Conceptual replacement has taken place, because the modern word huisvrouw no longer refers to spouse. However, it still typically refers to a woman taking care of the household for her own family. At the same time, its original meaning `wife' has had a stable core meaning (a person of the female gender who is legally married to another person), but outside its core the intension heavily depends on context (time, place and culture). Even though a wife still is a female person married to another person, this other person could only be a person of the male gender in the sixteenth century. The sixteenth century `wife' would furthermore be more associated with `family ties', `status' and `bearer of children' than with `romantic love' and `equal' or `other/better half'. This concept drift, the concept of `wife' with a di erent intension, is imperative to identify for the historian, because they can lead to di erent interpretations of the past.
Catharina de Chasseur was the wife of the Lord van Assendelft. The Van Assendelfts opposed this marriage because Catharina was not of nobility. To them the concept of `wife' did not t her for their son, because she was socially mismatched. While modern observers could judge the Van Assendelft family's behavior as cruel and uncaring, they behaved mostly in line with the customs of their time. As such, the correct interpretation of sources is even more dependent on concept drift than on words changing meaning (conceptual replacement).
The example of huysfrou and `wife' illustrates the sometimes complex interaction between a shift in label and concept drift. The current word huisvrouw may have lost the core meaning of `spouse', but it still shares many associations with its original lexicalSense.8 It may be an interesting topic of investigation how the concept drift of `wife' relates to the lexical semantic change of huysfrou: did the drifting concept induce the lexical semantic change, or the other way around? The RDF representation proposed in this paper allows researchers to make both forms of change explicit supporting this kind of investigation. 5
The main goal of this paper was to show how various disciplines look at
concept drift in order to increase understanding and hence facilitate collaboration
between disciplines. We argued that out of the three forms of concept drift
addressed by Wang et al. [
We showed how various forms of semantic change can be modeled using lemon
to link lexical entries to concepts in ontologies and using contexts to de ne how
a speci c concept was seen or what its extension was in a speci c time and
place. Making this information explicit can allow researchers to better interpret
data [
Obtaining this information, however, is challenging. Recent advances in
distributional semantics have been shown to be highly e ective in picking up
semantic change from text [
This work was supported by the Amsterdam Academic Alliance Data Science (AAA-DS) Program Award to the UvA and VU Universities and NWO VENI grant 275-89-029. We would like to thank anonymous reviewers and the workshop's organizers for their feedback. All remaining errors are our own.