Knowledge graphs often express static facts, but concepts and entities change over time. In this position paper, we propose challenges that arise from the perspective of combining NLP and KG evolution in the digital humanities domain based on preliminary experiments.4 Knowledge graphs (KGs) intend to represent what we consider true about (part of) the world. KGs are created at a certain point in time and can be considered static snapshots of the real world [8]. However, \Knowledge lives. It is not static, nor does it stand alone" [2]. Thus, concepts continuously change over time and can vary between social contexts and locations, i.e. we live in a world with in nite variation and variability. These concept changes may be a result of technological developments, changing social constructs, political decisions, globalization etc. For example, our current understanding of family as a concept has changed drastically over the years, e.g. with same-sex marriages being allowed in more and more states. Likewise, the concept of a country can change in terms of tangible properties such as borders, o cial language(s) and rulers, but also in latent ways such as its citizen's identities within the state and the perception of a country's culture by foreigners. These concepts are manifested not only in our cultures norms and values, but also documented through photographs, newspapers, books, music, lm, and ads. Digital humanities research often involves the understanding of cultural heritage data. Recently, novel methods involving Natural Language Processing (NLP) supported by KGs have entered the humanities research community [5]. Hence, the evolution of real-world concepts within a KG in combination with NLP is especially relevant. Therefore, evolution can be understood in two ways:
1. Natural language text can form the content of a KG through NLP. In this way, evolution refers to the text itself. Historical text as the mirror to societies in varying realities and contexts thereby de nes what is being modeled in a KG. Here, NLP is thus an essential part of the process of KG evolution. 2. It can also be assumed that a KG is created or evolves independently of automated NLP processes. In this case, evolution means that classes, instances and values are created or altered by a source outside of the reality a text was authored or analyzed in. In this case, NLP is not part of the initial process of evolution, but applies whatever reality is de ned in the KG to its source text.
There are a number of challenges in representing the uidity of a concept within KGs, especially respecting their cultural, temporal and geographical contexts. The goal of this position paper is to describe the challenges that arise from the perspective of combining NLP and KG evolution in the digital humanities domain based on preliminary experiments on the concept of apple pie. Furthermore, we present some strategies on how these challenges may be addressed.
The remainder of the paper is structured as follows. Section 2 presents related work on knowledge graph evolution. The use case of apple pie recipes is brie y described in section 3. In section 4, the problems of KG evolution in combination with NLP is discussed based on the use case of apple pie recipes and strategies on how to tackle these issues are presented. Section 5 concludes this paper. 2
Concept drift over time is studied in [
Once concept drift has been detected, maintaining KGs with respect to
changing entities is the next challenge. [
Knowledge graphs are dynamic and the facts related to an entity are added
or removed over time. Therefore, multiple versions of the knowledge graph
represent a snapshot of the graph at some point in time. Entities undergo evolution
when new facts are added or removed. The approaches to solve the problem
of automatically generating a summary out of di erent versions of a knowledge
graph are limited. The authors in [
In order to study the evolution of apple pie recipes over time, data from
Dutch and American newspapers was collected. As [
The concept of apple pie is seemingly simple: it should always contain apple, a kind of our, a sweetener and a fat { so what is there to evolve? The challenges arising from our preliminary analysis are presented in the following section.
During the preliminary analysis of the recipe data, we identi ed the following challenges: Spatio-temporal context When extracting knowledge from historical sources, several spatio-temporal contexts often have to be taken into account. For example, an article published in an American newspaper in 1995 that describes a typical Hungarian apple pie recipe from the 1950s entails multiple contexts. Here we can distinguish the spatio-temporal metadata of the concept itself (in this example the recipe) and the metadata of its source (i.e. newspaper article). These provenance information will enable to trace the evolution of the concept over time and geographic regions.
Cultural context What is considered as true in one cultural setting may not be in another. For instance, the traditional apple strudel could be considered as a type of apple pie in some cultures, however, in the area formerly belonging to the Austro-Hungarian empire, a clear distinction is made between both desserts, even though the ingredient list is rather similar. Contextual information such as cookbook indexes (or more generally, taxonomies) can help resolve this issue.
Units Extracting and understanding ingredient units presented in the texts was found to be a major challenge in this use case. In modern sources, this involves modern units and their conversion between e.g. the imperial and the metric system of units (kilogram, pound, litre, cups) and in historical sources, this also includes units (usually) not customary on this day, e.g. ell, zentner. Furthermore, less tangible units are sometimes used, e.g. \a load of butter" or \two deep plates of apples", which provides a greater challenge for the automated detection and interpretation of values and quantities. There are resources for historical measures which can be employed, but imprecise quantities will require human interpretation.
Language When attempting to generate a KG from historical text sources that
captures the uidity of concepts, a number of challenges for NLP arise. In
our use case, we found gures of speech, such as metaphors (likely to appear
in newspapers), that complicated the process of detecting apple pie recipes
correctly. For instance, the following recipe was found: \Take 1000 kilos
of bombs, a few hundred hand grenades, as many boxes of cartridges, go
to Vienna with them, make a coup there and wait until you get arrested.
Then the apple strudel will be ready [...] " 6. Without correctly detecting the
metaphor, bombs, grenades and cartridges would be added to the KG as
ingredients. Previous research has started to investigate the combination of
deep learning and KGs to detect metaphors [
Furthermore, the meaning of concept terms may change over time which
can be captured with the help of latent representation of the words and
6 http://anno.onb.ac.at/cgi-content/anno?aid=kik&datum=19190907&query=\%
22Apfelstrudel+Rezept\%22~10&ref=anno-search&seite=7
represented in the KG. One of the initial approaches construct time series of
word usage using word embeddings (where one embedding space is generated
for each point in time) [
Evaluation Finally, an evaluation of an ontology capturing concept change in a KG based on natural language text descriptions should provide measures on how well the model reacts to changes in the data. However, as described above, evaluating whether or not something is to be regarded as an apple pie (or any other concept) depends on many aspects, e.g. cultural background as well as the spatio-temporal setting. Hence, a concise ground truth is a challenge to create and possibly only tendencies can be given. One strategy on how to deal with this is to create a crowd-truth which states the viewpoints on a cultural heritage object by a greater amount of human evaluators from varying backgrounds as well as domain experts. In future work, a crowdsourcing campaign on apple pie (and further use cases) is envisioned. 5
The real world is constantly changing and knowledge that was considered true at one point in time in a speci c cultural and spatial setting may not be true in another context. That means contexts evolve. On the other hand, there are KGs, which are created and maintained to continuously compose knowledge. However, often KGs are static and only re ect one snippet of reality. This static representation of the real world is a problem when attempting to understand historical descriptions of concepts (e.g., in newspapers), because linking historical concepts to today's understanding of the same concept may distort its meaning.
In this paper, the problem is addressed on the foundation of preliminary experiments on the concept of apple pie. The take home message of this contribution is that modeling KG evolution even for simple and contained concepts like apple pie provide complex challenges for ongoing research in the Semantic Web community including the various contexts that need to be taken into account, ambiguities in language and used units as well as the granularity of the model and evaluation. Most of the challenges that were detected are generalizable to numerous concepts within the digital humanities domain.
In future work, more use cases (apart from apple pie) will be analyzed and methods on how to represent KG evolution will be evaluated on the foundation of the challenges and strategies as presented in section 4.
Acknowledgement. This work was made possible by the International Semantic Web Research Summer School 7 in Bertinoro, July 2019. The authors would like to thank the Summer School directors, tutors, the organizing team and the fellow students, especially Mortaza Alinam, Wouter van den Berg, Lientje Maas, Fabio Mariani and Eleonora Marzi.