Tracing Organisation Evolution in W ikidata (2025)

Abstract: Entities change over time, and while information about entity change is contained in knowledge graphs (KGs), it is often not stated explicitly. This makes KGs less useful for investigating entities over time, or downstream tasks such as historical entity linking. In this paper, we present an approach and experiments that make explicit entity change in Wikidata. Our contributions are a mapping between an existing change ontology and Wikidata properties to identify types of change, and a dataset of entities with explicit evolution information and analytics on this dataset.

## introduction already in 500bc greek philosopher heraclitus said that everything is in motion and nothing stays fixed @xcite . knowledge graphs (kgs) aim to capture information about entities and relationships between them, often modelling information from an entity-centric perspective @xcite . however, information about entity change is often not stated explicitly in kgs @xcite . this makes them less useful for investigating entity change over time or for downstream tasks such as entity linking to historical sources @xcite . while there has been much work on entity evolution across different kgs (cf. @xcite , we are looking at representations of change within a single kg. to more usefully represent the evolution of entities, we have argued for 'unflattening' knowledge graphs @xcite . we define unflattening as representing the different aspects of an entity and how it changed over time, i.e. its evolution. in this paper, we present an approach and experiments to make explicit the evolution of organisation entities in wikidata @xcite ). 1 we do so by expanding on properties that indicate some type of change, such as an acquisition or a change in the legal structure of an organisation. we thus make explicit the evolution of an entity using existing information in the knowledge graph. our main contributions are: 1) a mapping between wikidata properties and a change ontology, 2) a set of organisation entities that exhibit change, and 3) analysis of the extracted entities. the remainder of this paper is organised as follows. in section 2, we discuss related work, followed by our framework and method in section 3. the evolution of entities that we extract from wikidata is described in section 4, followed by a mapping of change types of wikidata properties in section 5. we present our analysis of the data in section 6 and discussion in section 7. we conclude with a summary and directions for future work in section 8. our code and data are available at https://github.com/ trifecta-project/wikidata-change . ## related work our work relates to modelling change of entities represented in kgs. in this section, we discuss the different research perspectives: data models for kgs that capture temporal information, evolution of entities across kgs, and modelling changes on top of an existing dataset. various ontologies have been proposed to model change, cf. @xcite @xcite . these ontologies often propose to add temporal information to a triple or set of triples to timebound a statement or set of statements. @xcite proposed to model knowledge graphs from an event-centric instead of an entity-centric perspective, thus putting change at the forefront. the cidoc-crm @xcite is entity-centric but provides many building blocks to model things that happened to these entities. modeling the evolution of entities between different iterations of knowledge graphs (kgs) has been investigated in @xcite and @xcite . to go beyond a pairwise comparison of entities in two different snapshots of a kg, @xcite automatically generate summaries of entities over different snapshots and compare these via a matrix. they focus on person entities. in @xcite the evolution of temporal knowledge graphs is investigated through a representation learning framework that takes into account both changing relations between entities as well changes in the overall structure of the kg. in our work, we focus on the changes in entities that are expressed within the same version of the kg, i.e. any temporally bound properties that express a change, rather than the evolution of the kg. this is termed the 'temporal kg' or 'time as data' perspective in @xcite . modelling change has been researched in the geographical information science (gis) domain (cf. @xcite @xcite . due to the long history of maps and digitisation efforts, gis practitioners have been mapping changes in the scope of territories (e.g. the expansion and contraction of the prussian empire) and names (e.g. constantinople vs istanbul) for decades. changes can be recorded and connected in a pointwise manner but not qualified, as for example the historical atlas of the low countries 1350-1800 does @xcite . here, the focus is on creating and linking layers by points where each point has certain properties and points can be grouped into various clusters that designate an administrative area at any given time. similarly, @xcite developed a conceptual schema for connecting different manifestations of a settlement over time that also includes properties to express name changes. conceptually the closest model, and the one we adapt for expressing entity change more generally, is the tsn-change ontology @xcite which is linked data-ready @xcite . while there is certainly an aspect of concept drift or concept change @xcite associated with the problem we are investigating, that research avenue focuses on how users perceive or use a concept. the research gap we address is the manner in which a concept change is represented within a kg: how can we make this change explicit such that it can be analysed? ## modelling change we take inspiration from modelling change in the field of geographical information systems (gis) where @xcite defined change drivers for geographical entity as coming from structure changes and feature changes. a structure change denotes a change that impacts several features at the same time. in their case, a feature is a territory, in our case it could be an organisation or another entity type. a feature change denotes changes that only affect one feature (i.e. territory), such as a name change. these changes can be mapped to two of the three aspects of concept drift as defined by @xcite . they define a label as how the entity or concept is referred to, its intension are the properties, or characteristics, implied by it, and the extension the set of things that are covered by a concept. a name change corresponds to a shift on the label of a concept, whilst structural changes correspond to changes of the definition or intension of a concept. we consider the use or extension of a concept out of the scope of this work as we focus on properties inside a kg. we have also considered ontologies specific to the organisation domain such as boro (de cesare and partridge, 2016) and coot @xcite that model organisations and their transformations. however, for the modelling of organisational structures, we take the wikidata data model as a given and we prefer a more general description of changes rather than one very specific to the organisation domain to facilitate extensions to other domains in future work. not all feature changes have to be sequential, as entities can also hold different roles at the same time. as not all elements of the tsn change ontology presented in @xcite apply to non-geographical entities, we focus on those classes that are relevant to other types. structurechange change operations that impact the entity and several of its features simultaneously. an example of structure change in organisations is when one organisation acquires another one. merge two or more entities merge, e.g. two political parties merge; split an entity splits into two or more entities e.g. a band splits up and the band members go on to individual music careers. featurechange a change that affects one entity of a given type. an example is when an organisation changes its name or when a person starts a different role, for example from being a lawyer to a politician. appearance the entity comes into existence. ## extracting change from wikidata in this paper, we focus on organisations and specifically their subtypes businesses, companies and enterprises. figure 1 illustrates our workflow. to identify a set of change indicators for businesses, we first query for all organisations and properties associated with them. this results in a set of 3,211 properties which are manually assessed for whether they indicate a change and if so, what type of change. further details on this mapping are described in section 5. for this, we used the wikidata mirror hosted by university of freiburg at https://qlever.cs.uni-freiburg. de/wikidata/ as it has a longer time-out than the main wikidata endpoint. there is a trade-off here as its information is not always up to date as we found by querying for number of unique entities of type organisation on both endpoints (347,357 on the freiburg endpoint vs. 473,292 on the wikidata endpoint). 2 however, we deem the set of results on 2 query performed on 7 march 2025. the freiburg endpoint large enough to assess general properties and statistics on this entity type and the wikidata public endpoint resulted in a time-out. the results from this step were used to identify the most relevant change properties. all other queries from hereon are queried via the wikidata public endpoint. we do take a subset of organisations, namely businesses, companies, and organisations for these, to manage the query load. ## analysing change to characterise changes across business entities in wikidata, we chose to focus on 6 different aspects of businesses that may change over time: 1) name/acronym, 2) motto, 3) industry, 4) ownership (of other organisations), 5) ceo, and 6) number of employees (indicating expansion or contraction). for each of these dimensions, we collect entities involved and, if present, start and/or end times or points in time during which a statement was valid. to mitigate endpoint time-outs, we first query wikidata for all entities of type business, then we iteratively perform a query for each of the 232,605 entities to obtain change indicators. all queries were performed in february and early march 2025 on the public wikidata sparql endpoint. not every entity has the properties that we query for, thus our resulting dataset contains 109k entities that have at least one statement that expresses a change in its name, motto, industry, ownership, ceo, or number of employees. temporal data in wikidata is identified via qualifiers on statements. the most common temporal qualifiers in wikidata are p585 for point in time, p580 for start time, and p582 for end time. whilst other large, general purpose kgs such as dbpedia foot_1 or yago foot_2 were also considered, an exploratory analysis showed that wikidata contains the most timebound information suited to our purposes such as corporate acquisitions accompanied by dates. this is probably due to the fact that wikidata sources its information from a variety of sources. yago 4.5 has incorporated parts of wikidata to provide a cleaner and more consistent resource @xcite , we found that the type of information that expresses change in entities is less well represented in yago than in wikidata. however, as ## discussion the main limitation to our temporal information extraction experiments is the availability of data. the proportion of timebound statements in wikidata is limited: @xcite found that only 7.35% have a point in time associated with a statement, 4.96% have a start date, and 2.54% have an end time. in our experiments, we only used these, but wikidata has an additional 64 other very specific datetime-type properties, such as p9946: 'date of probate' or p574: 'year of publication of scientific name for taxon'. the yago consortium has already worked on consolidating the wikidata data model @xcite , using a cleaned up data model would be preferable to creating query templates that cover the (current) 67 temporal properties. event-centric databases such as bio2rdf @xcite , news-reader @xcite and the european olfactory knowledge graph @xcite provide more temporally bound information, but the trade-off is that they are more domain-specific and would thus be suited to more specific questions rather than general questions. furthermore, the coverage of entities is uneven. for example, for finlayson, a finnish textile manufacturer founded in 1825, the data regarding its number of employees has 20 statements but only up to 1925, whilst the company is still in business. the reason for this lies in the power of wikidata being a collaborative kg, as this particular information comes from a finnish history book published in 1932 that a user inserted, foot_5 but it does provide gaps and unpredictable coverage. when comparing wikidata to other large-scale general kgs such as dbpedia and yago, it is considered the most complete for information regarding people, music albums and films, but less so for organisations, places, and events @xcite . the richness of the wikidata data model and the size of the resource also provides a hurdle in collecting and analysing information. it has almost become imperative to run a local version of the resource to be able to query it effectively. the size of the wikidata taxonomy and its inconsisten use mostly affects our industries analysis. this is a known problem that has yet to be resolved @xcite . ## conclusion & future work in this paper, we have presented: 1) a mapping between over 3,000 wikidata properties and an adapted version of the tsn change ontology, 2) a set of organisation entities that exhibit change, and 3) an analysis of the extracted entities. we show that the tsn change ontology that was developed for the geographical domain can be applied more generally. we have extracted temporally bound information from wikidata and classified it according to the change ontology, illustrating that whilst more information could be temporally bound, the available data already indicates that organisations exhibit different types of change and this is captured in the data. the entities we have extracted, along with over statements that describe some change of that entity provide a starting point for exploring how businesses, companies and enterprises change, which we have done in our analyses of companies and their subsidiaries, company names and acronyms, company mottos, leadership changes and number of employees over time. in future work, we will extend our experiments to cover information about more different types of entities and change events that happen to them. as wikidata has good coverage of people and music albums, those would be obvious domains to start. furthermore, the historical domain provides additional entity types that exhibit change, such as ships that have changed roles and names. a 20th century example is mv wilhelm gustloff, a german cruise ship that was repurposed as a military ship or the 18th century french ship jason which was captured by the english and sold to the dutch who renamed it toevalligheid. 8 people can undergo changes too such as different roles and/or titles, see for example charles v. for this, additional information will need to be extracted from other sources and we intend to consult company histories and experts. we aim to feed this information back into wikidata, along with enrichments about change descriptors such as the mappings after we have tested them on these additional entity types. richer data will also enable more in-depth analysis of triggers of change, for example how does a change in leadership correlate with other changes in the company such as acquisitions or number of employees. across companies, one could trace whether companies 'copy' each other's behaviour or whether they maintain their own strategy. our ontology and experiments have opened up a new avenue of investigating temporal change in kgs showing that the 'time as data' @xcite perspective is there, and there is much more to explore. author contributions (by author initials) are listed according to the contributor roles taxonomy (credit). conceptualization: me; data curation: me, jz; formal analysis: me, jz, vp; funding acquisition: me; methodology: me, jz; project administration: me; software: me, jz; supervision: me; visualization: me, jz, vp; writing (original draft): me, vp; writing (review and editing): me, jz.