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- The missing link from Abstract Wikipedia to Lexicographic data in Wikidata.
In 2018, Wikidata launched a project to collect lexicographical knowledge. Several hundred thousand Lexemes have been created since then, and this year the tools will be further developed by Wikimedia Deutschland to make the creation and maintenance of the lexicographic knowledge in Wikidata easier.
The lexicographic extension to Wikidata was developed with the goal that became Abstract Wikipedia in mind, but a recent discussion within the community showed me that I have not made the possible connection between these two parts clear yet. Today, I would like to sketch out a few ideas on how Abstract Wikipedia and the lexicographic data in Wikidata could work together.
There are two principal ways to organize a dictionary: either you organize the entries by ‘lexemes’ or ‘words’ and describe their senses (this is called the semasiological approach), or you organize the entries by their ‘senses’ or ‘meanings’ (this is called the onomasiological approach). Wikidata has intentionally chosen the semasiological approach: the entries in Wikidata are called Lexemes, and contributors can add Senses and Forms to the Lexemes. Senses stand for the different meanings that a Lexeme may regularly invoke, and the Forms are the different ways the Lexeme may be expressed in a natural language text, e.g. in order to be in agreement with the right grammatical number, case, tense, etc. The Lexeme “mouse” (L1119) thus has two senses, one for the small rodent, one for the computer input device, and two forms, “mouse” and “mice”. For an example of a multilingual onomasiological collaborative dictionary, one can take a look at OmegaWiki, which is primarily organized around (currently 51,000+) Defined Meanings and how these are expressed in different languages.
The reason why Wikidata chose the semasiological approach is based on the observation that it is much simpler for a crowd-sourced collaborative project, and has much less potential to be contentious. It is much easier to gather a list of words used in a corpus than to gather a list of all the meanings referred to in the same corpus. And whereas it is 'simpler', it is still not trivial. We still want to collect a list of Senses for each Lexeme, and we want to describe the connections between these Senses: whether two Lexemes in a language have the same Sense, how the Senses relate to the large catalog of items in Wikidata, and how Senses of different languages relate to each other. These are all very difficult questions that the Wikidata community is still grappling with (see also the essay on Making sense).
Let’s look at an example.
“Stubbs was probably one of the youngest mayors in the history of the world. He became mayor of Talkeetna, Alaska, at the age of three months and six days, and retained that position until his death almost four years ago. Also, Stubbs was a cat.”
If we want to express that last sentence —“Stubbs was a cat”— we will have to be able to express the meaning cat (given in small-capitals semantics; here, we will focus entirely on the lexical level, and will not discuss grammatical and idiomatic issues; we will leave those for another day). How do we refer to the idea for cat in the abstract content? How do we end up, in English, eventually with the word form “cat” (L7-F4)? In French with the word form “chat” (L511-F4)? And in German with the form “Kater” (L303326-F1)?
Note that these three words commonly do not have the same meaning. The English word cat refers to both male or female cats equally; and whereas the French word could refer to a cat generically, for example if we wouldn’t know Stubbs’ gender, the word is male, but a female cat would usually be referred to using the word “chatte”. The German word, on the other hand, may only refer to a male cat. If we wouldn’t know whether Stubbs is male or female, we would need to use the word “Katze” in German instead, whereas in French, as said, we still would use “chat”. And English also has words for male cats, e.g. “tom” or “tomcat”, but these are much less frequently used. Searching the Web for “Stubbs is a cat” returns more than 10,000 hits, but not a single one for “Stubbs is a tom” nor “Stubbs is a tomcat”.
In comparison, for Félicette, the first and so far only cat in space, the articles indeed use the words “chatte” in French and “Katze” in German.
Here we are talking about three rather closely related languages, we are talking about a rather simple noun. This should have been a very simple case, and yet it is not. When we talk about verbs, adjectives, or nouns about more complex concepts (for example different kinds of human settlements or the different ways human body parts are conceptualized in different languages, e.g. arms and hands, terms for colors), it gets much more complicated very quickly. If we were to require that all words we want to use in Abstract Wikipedia first must align their meanings, then that would put a very difficult task in our critical path. So whereas it would indeed have been helpful to Abstract Wikipedia to have followed an onomasiological approach (how wonderful would it be to have a comprehensive catalog of meanings!), that approach was deemed too difficult and a semasiological approach was chosen instead.
Fortunately, a catalog of meanings is not necessary. The way we can avoid that is because Abstract Wikipedia only needs to generate text, and neither parse nor understand it. This allows us to get by using a Constructor that, for each language, uses a Renderer to select the correct word (or other lexical representation). For example, we could have a Constructor that may take several optional further pieces of information: the kind of animal, the breed, the color, whether it is an adult, whether it is neutered, the gender, the number of them, etc. For each of these pieces of information, we could mark whether that information must be expressed in the Rendering, or whether this information is optional and can be ignored, and thus what is available for those Renderers to choose the most appropriate word. Note, this is not telling the community how to do it, merely sketching out one possible approach that would avoid to rely on a catalog of meanings.
Each language Renderer could then use the information it needs to select the right word. If a language has a preference to express the gender (such as German) it can do so, whereas a language that prefers not to (such as English) can do so. If for a language the age of the cat matters for the selection of the word, it can look it up. If the color of the animal matters (as it does for horses in German), the respective Renderer can use the information. If a required information is missing, we could add this to a maintenance queue so that contributors can fill it out. If a language should happen not to have a word, a different noun phrase can be chosen, e.g. a less specific word such as ”animal” or “pet”, or a phrase such as “male kitten”, or “black horse” for the German word “Rappen”.
But the important design feature here is that we do not need to ensure and agree on the alignment of meanings of words across different languages. We do not need a catalog of meanings to achieve what we want.
Now, there are plenty of other use cases for having such a catalog of meanings. It would be a tremendously valuable resource. And even without such a catalog, the statements connecting Senses and Items in Wikidata can be very helpful for the creation and maintenance of Renderers, but these do not need to be used when the natural text for Wikipedia is created.
This suggestion is not meant to be prescriptive, as said. It will be up to the community to decide on how to implement the Renderers and what information to use. In this, I am sketching out an architecture that allows us to avoid blocking on the availability of a (valuable but very difficult to create) resource, a comprehensive catalog of meanings aligning words across many different languages.