Denny,

This dataset from Google AI could massively reduce the complexity of your constructors. By matching the synthetic language and accompanying metadata from Wikidata with the incoming marked-up text, your rules of action and/or translation are simplified. You have 1500 relationships that can really amp up your context awareness. You are out of the grammar business, which I think is preferable. This still does not solve how you will handle the wilds of natural language out in the interwebs, but it makes what you are doing an active knowledge representation.

I know this is data generated by the reviled vector models, but it's human readable, thus much more approachable than even markup. The Google AI article does a great job of explaining the logic of generating the synthetic text. I think this shows us how machine learning can enhance knowledge sharing, while keeping the curations human readable. We are not out of the loop. We have to make the machine communication in our language, not theirs.  I'm personally more excited about this than GPT-3.

Doug

On Thu, May 13, 2021 at 5:08 PM Denny Vrandečić <dvrandecic@wikimedia.org> wrote:
Hi Douglas,

Thank you for your message.

Yes, you are right that if we were trying to understand a sentence such as "The cat is digging", we would need to resolve the ambiguity in that sentence. But, as I wrote in the newsletter, our trick is that we can avoid the necessity to parse and understand text. The abstract content will already be written by the contributors in a representation that disambiguates to the level that is needed to generate the text in the languages we support - no automatic disambiguation of natural language is thus needed.

Thank you for publishing the Wikipragmatica proposal. I have read it when you published it back in January, and I find it interesting and I certainly hope that you will try it out. It is a very different approach to what we are trying to achieve with Abstract Wikipedia, where we don't aim to annotate existing textual resources, but to create entirely new ones from scratch. Wikipragmatica is squarely aimed at the difficult and important task of natural language understanding. Abstract Wikipedia is, very intentionally, trying to circumvent that task. I have not reached out for a discussion because of these significant differences - I think we are aiming for very different goals using very different approaches. The goals of Wikipragmatica are to understand the content, and use that understanding for detecting misinformation, ascertaining truth, and discovering inconsistencies. These are extremely valuable goals, and very difficult, and I have tried to steer explicitly away from them. The same is true for machine learning and vector-based approaches. I cannot figure out how to incorporate these in a way that allows the community to truly own the system and the outputs, which I think is crucial for a Wikimedia project where the community owns and maintains the content. I think that is a very worthwhile question to explore, that still needs a crucial insight or two to make it work.

Yes, FrameNet and WordNet are much more related to our approach than GPT-3 or Bert. About a decade ago, Chuck Filmore, the creator of FrameNet, and I were teaching together in Berkeley, and back then I learned a lot about FrameNet from him, and how much effort is in it. Later, during my time at Google I had the particular luck that some of my colleagues were a few of Chuck's former collaborators on FrameNet and have discussed it with a number of them in detail. This made it clear that one of the biggest risks in the Abstract Wikipedia project is the absolute number of constructors that we will need, as this will ultimately decide how much effort it will be to make the content in Abstract Wikipedia available in a new language. Regarding WordNet, Christiane Fellbaum was one of the initial members of the advisory board for Wikidata, and her work and results were very influential in designing the data model for the lexicographic space in Wikidata (albeit, indirectly, as we settled on the Lemon model that came later and has learned from WordNet).

You are exactly right, we are going down a well worn path. I keep saying that in my talks: this is not a research project, we are applying well-known results from several fields such as natural language generation, crowd-sourcing, programming languages, etc. I still consider it a risky project, as there are a number of unknowns (e.g. the number of constructors, and how multilingual the constructors are) that will play a major role in how effective our approach will be, but I also think that we will certainly achieve something worthwhile - but we don't know yet exactly what and how far this architecture will carry us.

Thank you for your comment,
Denny






On Fri, May 7, 2021 at 9:28 AM Douglas Clark <clarkdd@gmail.com> wrote:
Gerard and Denny,

The problem with a lexeme approach is that the constructors and renderers will become so complex and convoluted as to be non-scalable. The use of lexemes is problematic due to a complete lack of context awareness. Just because you have a word, and know all of its senses, how do you know which sense to pick?

Using Denny's example, "cat" could actually refer to the American construction equipment maker Caterpillar. "That cat is digging!" works for both the animal and the machine. We humans are somewhat unpredictable in when we set context. Your constructors will have to walk up and down the text chain to try and find context for each verb and noun. With a word based approach, words are your granularity, so everything is a lookup for a word, even though your application is at the sentence level. GPT-3, the most powerful NLP tool yet created, has 175 billion parameters for its lexeme based dataset, yet it too loses context. Humans are great at rephrasing something to fit their complete communique. WordNet is the most complete and scientifically accurate lexeme database on the planet, yet very few NLP approaches use WordNet. The traversals of the WordNet thesaurus can be compute intensive, and would be sensitive to how your constructors' logic walks the tree. The rules alone would become massive. You have to at least move up to phrases, and I recommend sentences (paraphrases). As for phrases, the FrameNet folks can tell you how hard it is to build a dataset of phrases for NLP.

I've asked several times to discuss this with you and to save you and the team from going down this dead end path. The Wikipragmatica proposal directly addresses both context and semantics. If you used Wikipragmatica, translation logic would entail semantic disambiguation, paraphrase detection on nearest neighbors, node assignment, and then a lookup of node members for the appropriate language. If you decide to go down the lexeme path, I highly recommend you spend some noodle time on context brokering. I'm confident that in short order you will understand the magnitude of the context problem using lexemes. You are going down a well worn path.

Respectfully,

Doug

On Fri, May 7, 2021 at 8:53 AM Thad Guidry <thadguidry@gmail.com> wrote:
Denny,

Wait...
Your original posting mentions that Constructors would essentially hold the conditional logic, or "rules"?
But in your followup, I see you mention Renderers?

I'm curious where the delineation of rules will occur, and if the answer is "it depends"?

Have you given much thought to constraints on Constructors or Renderers themselves (Are there high level design docs available for each of those yet)?
Or do you think that will be something still being worked through in the long term with community use cases, and practices that evolve?



On Fri, May 7, 2021 at 10:07 AM Denny Vrandečić <dvrandecic@wikimedia.org> wrote:
Hi Gerard,

If the abstract content states (and I am further simplifying):

type: animal type phrase
- type of animal: cat
- sex: male

that might be represented e.g.

{
  Z1K1: Z14000,
  Z14000K1: Z14146,
  Z14000K2: Z14097
}

or it could be, if we are using QIDs for the values,

{
  Z1K1: Z14000,
  Z14000K1: Q146,
  Z14000K2: Q44148
}

so it wouldn't be based on English, it would be abstracted from the natural language.

Now there could be a Renderer in Dutch for 'animal type phrases' that would include:

if Z14000K1 = Q146/cat:
  if Z1400K2 = unknown or Z1400K2 = Q43445/female organism:
    return L208775/kat (Dutch, noun)
  if Z1400K2 = Q44148/male organism:
    return L.../kater (Dutch, noun)
...

etc.

This is just for selecting the right Lexeme. Further functions would now select the right form, depending on how the sentence looks like.

But nowhere do we need to refer to the Senses or to explicitly modeled meanings.

On the other hand, we *could* refer to the Senses and items. (And this is what I meant with not being prescriptive - I am just sketching out one possibility that does *not* refer to them). Because we could also write a multilingual Renderer (e.g. as a fallback Renderer?) that does for example the following:

Animal = Z1400K1  // which would be Q146/cat in our example
Senses = FollowBacklink(P5137/item for this sense)
Lexemes = GetLexemesFromSenses(Senses)
DutchLexemes = FilterByLanguage(Lexemes, Q7411/Dutch)
return ChooseOne(DutchLexemes)  // that would need to be some deterministic choice)

This probably would need some refinement to figure out how the sex would play into this, but it's a just the start of a sketch. You could also imagine to build something on Defined Meanings at this point.

I hope that makes sense - happy to answer more. And again, it is all just suggestions!

Also, Happy Birthday, Gerard!

Cheers,
Denny

On Thu, May 6, 2021 at 10:23 PM Gerard Meijssen <gerard.meijssen@gmail.com> wrote:
Hoi,
I fail to understand. You have the data in the prescribed manner for an article. The original is based on English. How can you generate from the data a text in Dutch or any other language, when you do have the Senses but not the meanings of the words.
Thanks,
      GerardM

On Thu, 6 May 2021 at 23:38, Denny Vrandečić <dvrandecic@wikimedia.org> wrote:
The on-wiki version of this newsletter can be found here:

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 the OmegaWiki project, 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” (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 could 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.


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