In this episode, we invite Sebastian Riedel to talk about knowledge base construction (KBC). Why is it an important research area? What are the tradeoffs between using an open vs. closed schema? What are popular methods currently used, and what challenges prevent the adoption of KBC methods? We also briefly discuss the AKBC workshop and its graduation into a conference in 2019. Sebastian Riedel's homepage: http://www.riedelcastro.org/ AKBC conference: http://www.akbc.ws/2019/
Hello and welcome to the NLP highlights podcast where we talk about interesting work in natural language processing.
This is Matt Gardner and Waleed Ammar, we are research scientists at Allen Institute for Artificial Intelligence.
Okay. Today our guest is Sebastian Riedel. He is a researcher at Facebook AI research and a professor at University College London. I guess I first met Sebastian at the first automatic knowledge base construction workshop, AKBC. I enjoyed seeing Sebastian talk there and it’s really great to have you on the program. Welcome.
Yeah, pleased to be here. Thanks for inviting me.
Today we’re going to be talking about knowledge based construction as a topic. It’s something that Sebastian has worked on a lot and I dabbled in at least a little bit. My thesis was related to this. I’ve not dealt with it a lot in the more recent years, but it’s an important topic, so I guess Sebastian, maybe you could start this by just giving us a brief description of what knowledge base construction is and why people care about this.
So yeah, knowledge base construction, automatic knowledge base construction. I guess it’s generally about taking text, but also more recently other kinds of modalities, and sort of representing the texts. Say document collection, in usually some form of knowledge graph where you have edges corresponding to relations and end notes corresponding to entities and these relations connect these entities in terms of how the entities are related. Then that’s supposedly good for all kinds of downstream applications such as question answering such as showing the semantic panels you see on Google today when you query for an entity you see a bit of sort of right hand side information. That’s actually information that comes from these knowledge graphs which can be automatically extracted or produced based on texts and then that’s called automatic knowledge construction or they could be manually annotated and then like for me it’s an interesting field because it sort of combines two angles.
One is that I think it’s one of the few what I call like naturally occurring semantic representations in the world where people have been building even before we thought about NLP or even without thinking of NLP at all, after building representations of meaning of a certain domain such that they can access it, in an effective way. And I contrast that with, I don’t know, let’s say first Autologic semantic parsing where you know, there aren’t necessarily naturally occurring big databases of first order object statements that, we use for any kind of downstream tasks. That’s usually an academic endeavor, right? Where we are thinking, you know, how could we represent language maybe using personal logic and so let’s build a dataset for that. Whereas in KVC they exist, these databases, they exist freebase back then Wikidata, they exist, the Google knowledge graph, they exist a lot of biomedical knowledge graphs, right?
That have been built even without any sort of like NLP in mind just because they seem to be reasonable and useful data structures for downstream users. So I think that’s kind of interesting and it’s, I think it has some interesting consequences in terms of the kind of work we do in that space as an this whole idea of distance supervision, which is really big within a knowledge based construction. I think that’s something we see there a lot and we see it a lot because they exist naturally occurring, you know, semantic representations of meaning. So I think that is really interesting from a very applied point of view and I guess my applied, you know, heart in me is sort of liking that. On the other hand, I think it’s really interesting AI problem in the sense that we have to build agents right, that go around and observe the world and somehow assemble what they observe and represent it in some kind of memory in order to later on access that knowledge again.
And so I think you can look at KBC or AKBC also as one hypothesis in terms of how agents can do this right? In the traditional AKBC world you sort of build these relational graphs? And the idea is then that agents have these relational graphs of the world, which they can use later on to answer questions about it. And I think that’s one way to go about it. I think a lot of other ways to go about it and like in recent months and years, I think that we’ve seen a lot of sort of other ideas in that space and I think could be interesting but, but still I think it’s an interesting fundamental question of how agents can go around the world, observe things represented somehow compactly and then make inferences on top of it or share it and all of that. So, so it has these two angles that I find really exciting.
Yeah, that’s a really good description. And I hadn’t thought about this like naturally occurring collections of facts perspective before. That’s nice. IMDb is another example that you didn’t mention but, but that people have used like just collections of facts about movies. People just naturally build these things. You’re totally right and WordNet. So I guess that’s a good way of thinking about like what a knowledge base is. It’s just a set of facts that someone might just write down about something. Then you start to think about how might this be used in a practical NLP setting. You could try to get super low level and think about, well what about finding sentence structure, parsing sentence structure. Like “I ate spaghetti with a fork.”
If I know facts about forks, maybe that will help me know that fork should attack to the eating and not to the spaghetti. Whereas “I ate a spaghetti with meatballs.” If I know about these things that will help me attach meatballs to spaghetti and not to eat right. But this is something that no one actually uses the knowledge base for. Right. This is something that typically, at least these days, something like Elmo or Bert or whatever, we’ll just pick up by seeing a whole bunch of text. So what’s different about these collections of facts that people write down? Does, does this question make sense?
Yeah, I think it makes it a sense and to me it points to one of sort of maybe disappointments. I I have with sort of AKBC in a sense that, well I think the kind of methods that we’ve developed and the pipelines that we have and the existing knowledge basis that we build with these, they’re kind of possibly useful for users somewhere within NLP I have seen very little evidence that they are useful for other things in NLP we could be doing for it. It just hasn’t really happened and so we’ve been sort of happily working on better distance supervision and better relation extraction with the hope that they are sort of a lot of maybe industry usages of it. But in terms of actual further AI uses of it that we directly see, I haven’t seen much and I think generally that’s a problem of recall.
Like we don’t have enough coverage of all the facts you need in a way. And the reason for that is that even if you give an AKBC system, the same knowledge or the same texts you’d give say Elmo or Bert or the AKBC system will need to check out so much of the information in there based on the kind of relational ontologies that uses based on the kind of pipelines and make mistakes. So you lose so much information there that it’s unlikely that for like the cases that you just mentioned about like forks and spaghetti and whatnot, that exactly the knowledge that you need will be in the knowledge base at enough times. And I’m generally skeptical about knowledge bases as a sort of form of downstream enrichment of NLP tasks. Like I think, uh, we have never seen anything like the impact of Elmo and Bert in terms of downstream performance increases in the AKBC world and I doubt that we’ll necessarily get there.
I think when you, when you think about AKBC from the perspective of building these graphs of facts, entities and relations, then to me like the main point of that is because there are users who want exactly that. Right. And they will use that in one way, and there is interface, if you want to do better X, Y, Zet on top of it, I don’t think it’s the best way. And I think other approaches are better and I sort of interested in the middle ground between these obviously. But um, I think that’s really a summary. I’d say no, you wouldn’t even use a knowledge based construction method in that case. I think you use it if you want interpretable representations of knowledge that you can give to the human or some kind of other agent. But the minute that’s machine learning agent you trained with some other downstream data, I think they’d be better representations for you to feed into.
Yeah, that’s, yeah, that’s a really good perspective. I would feel like to push it a little bit more on the distinction between spaghetti and meatballs and forks and say Barack Obama and Michelle Obama, a and U S presidents or former US presidents or for example, Sebastian Riedel and Facebook, AI research like there’s a relationship there that I’m, I’m quite certain that Elmo doesn’t know, but your web page knows or Wikipedia might know. I don’t, I haven’t checked if you have a Wikipedia page or what, but what is it that’s different about these things such that Elmo does know about spaghetti’s and forks but doesn’t know about the facts that you might see in a collection of facts?
That’s a good question. I like to point out at this point that we actually have been testing Elmo and Bert a little for that kind of relational knowledge. And it turns out, at least in sort of preliminary, uh, results that we have that that is actually not too bad, even with some of this relational knowledge. Right. It might be right for the wrong reasons. Right. And it might just be guessing that you know, based on various cues it might not remember that I was, you know, I’m part of Facebook AI research or something like that. But it’s somehow gets it compared to an off the shelf relation extraction system for example, not so much fewer. I don’t know that this is, this is weird English, but you know what I mean like a, it actually gets it a right relatively often in comparison to these systems. So I kind of challenge a bit the, even the assumption that there is a big difference between that and we actually also looked a bit in common-sense knowledge in Elmo and Bert. Yeah. Sort of well known that it does that but it’s sort of on a similar level, at least with some amount of types of relations that we’re looking at.
This is really interesting, I actually haven’t heard anyone talk about trying to explicitly check for facts and stuff. We have some people starting to look into this a little bit. When open-AI GPT-2 came out. The first thing I did, I read the articles for those who have been sitting under a box and don’t know about this open AI released this super huge language model that generated to me very surprisingly coherent long form text. Obviously not as coherent as a person, but much, much more coherent longterm than I’d ever seen before. And so the first thing that I did after realizing how good this looked was to look at the facts like it mentioned Cincinnati and it mentioned at like a train robbery in Cincinnati and then mentioned Covington station. It turns out Covington station is a train station, but it’s not in Cincinnati it mentioned the U S energy secretary. It turns out there’ve been like 14 or 16 of these in the history of the U S energy secretary position. And I think the name it associated with the U S energy secretary was Tom Hicks, which is not one of those 14 people that have, that have actually held to this position. So like it has some general concept of what kinds of facts are related, but it’s not remembering at all the specifics of those facts. Right?
Yes. That’s probably even similar to what we have observed in some sense. And I in the sense that there would be example where it gets a completely wrong, right. But there are actually quite a few examples where it gets it right. And if you look at the recall coverage of existing relation extraction systems for these kinds of relations, they actually miss a lot of things as well. So I’m maybe not saying like Bert or GPT-2 gets it perfectly right, but it’s not that far off. Right. And so relatively speaking, you know, that’s still quite impressive.
Yeah. Yeah, that definitely, and I, my intuition here, maybe you have other evidence, but my intuition is that the things that it can remember are things that had seen a lot because they’re like head entities, people in places that are talked about a whole lot. Whereas stuff that’s more rare which would be caught, like if it was mentioned once and your knowledge base construction method had a reliable extraction for that mention it would remember it and be in the knowledge base and be available for use in the model without having to have seen the thing a whole lot of times. Right. That feels to me like the distinction here that these more learning based store facts in my weight’s kinds of approaches work for things that I see a lot and that are mentioned frequently. But knowledge based construction is particularly useful for things that I don’t see a whole lot and I just want to remember. Does that make sense?
Yeah, I think that makes sense. I think that is probably true, its good thing to further test. I think you right that a relation extraction system. Like if the particular pattern in the text has been trained or we had sort of training data for that pattern, it usually gets it right. It’s just that when the thing only appears once and it happens to be the wrong pattern is also completely lost in the relation extraction system. So the output sometimes is relatively similar, but there is definitely a tale of things that the Ari system would get that like Bert or GPT-2 wouldn’t get, due to what you said. I think that’s, that’s fairly true.
Okay. Yeah. Great. This was interesting. Not exactly what I was intending on starting this discussion, but it was really interesting to talk about. Um, so I think we’ve gotten a good handle on like what a knowledge base is and why someone might care about it. So, um, I think we should move on to talking about knowledge base construction. How did this come up as a field? Like it’s, it feels to me like it grew out of some other related methods. You have a lot more perspective on this than I do. How do people build these knowledge bases?
I guess it’s usually some sort of pipeline that involves a couple of steps. First step is to figure out what are the entities and text using named entity recognition. Then you link these entities to existing entities in a knowledge base or you cluster them in co-reference or mentions that refer to the same entity are sort of really linking to the same entity cluster together and then you figure out relations between these mentions and texts. So I don’t know if you find a sentence, Barack Obama was born in Hawaii. Then you figure out Barack Obama is an entity. Hawaii is an entity that Barack Obama refers to the president, Barack Obama most likely in Hawaii first place. And then you look at the phrase was born in and you have some model that you know knows that that means the birthplace of that entity is the other entity.
I think that in a nutshell is a, at least the traditional way of extracting or building knowledge bases. There are different variants of that. In terms of how you define or work with the schema that your knowledge base should have. Is it an open schema or is it like closed schema? Different ways of dealing with different amounts of supervision.
Can I jump in here? Can you give an example of what you mean by open schema versus closed schema?
The closed schema is I have maybe an existing knowledge base and I decide I want a model four types of relations ahead of time. One being maybe born in, birth date, profession or employer and spouse, let’s say. And so you have these four relations and they are your schema and everything you are going to do with text with fit into that schema, any information in the text, that doesn’t fit into that schema would just be discarded.
And then later on you can make inferences using that knowledge base that you construct, but only in terms of these four relations. So that’s what I would call a closed schema information extraction. And then you have the sort of idea of open information extraction where on the other extreme, the kind of relationship you extract are essentially the phrases you see between the entities and texts. So if in the text you have the phrase was born in as a phrase between Barack Obama and Hawaii, then was born in becomes one of the relations of your schema and every time you see a new phrase between new entities that will become a new relation. With this approach just means like finding out what are the entities and then somewhat normalizing the phrase between the entities to make it a bit more like an actual predicates. These are the two options at the far ends of the spectrum I’d say. And then they are hybrids that combine the two of them. But I think like most importantly you have, I guess these two ends of the scale.
Yeah. Another interesting distinction between open and close I think has to do with what kind of entities you would like to represent in the knowledge base.
Yeah, that’s a good point.
Do you want to have only needed entities? Which types of entities would you be interested in? Um, and I feel like this has a lot of applications on what relations will exist between them and also like what, yeah, what does it, uh, what this knowledge base is going to be used for. Uh, I see things like WordNet, we can think of as a knowledge base. It doesn’t only have new entities or things like Wikipedia tends to, I guess also Wikipedia has, has entities that are not named. I think that knowledge based construction community focused much more so on the named entity part. You have any thoughts on why that was the case?
Yeah, I don’t know why that is the case that that’s definitely true. I am not 100% sure why. Maybe maybe it’s worthwhile to look a bit back into the history of it. I feel like a lot of KBC and information extraction comes from these early conferences. You know, the message, understanding conferences, the mock conferences, uh, that DARPA organized, I think late 80’s early 90’s they were all about, I guess passing the military reports for events that it would happen. And these events involve actors, and entities. That was mostly what they cared about. And somehow I thought maybe that would just maybe that has just stuck with the community. There must be a better reason, but I’m actually not. Not a hundred percent sure. It’s a good question. Why is that? It seems easier maybe and maybe that’s one reason, but I also don’t really know.
Yeah, that’s, that’s my intuition that this is because it’s a whole lot easier to find named entities and to know. It’s a very well scoped, well defined problem to see a sentence like Barack Obama is married to Michelle Obama to find the named entities run in any r-system that at least kind of works to pull out what things are named entities and then say, Oh, there must be some relationship between these two that I find in the sentence. Whereas if you take something like his first three speeches as president, how do you treat that as an entity? Like language is so complex when you get away from named entities, it’s not really clear. What is a mention of something that might go in a knowledge base? How do you detect this? What’s going on? There’s this whole Wikification line of work that tries to link text to a Wikipedia page, but there you even you, you get these problems like his first three speeches. Maybe you would link this phrase to a Wikipedia page for speech, but that’s not actually what this is referring to. Like it’s complex and so it’s a lot easier and well scoped to just talk about the named entities.
Yeah, I think you’re right it, and then I think what you said in the end makes a lot of sense as in I think the core problem with that isn’t maybe so much the recognition of these phrases. I mean these are kind of noun phrases in many cases, but it could also be other kinds of phrases, but it’s the linking of those are the co-reference of those that it’s just super hard. Like as you said, like speeches, like is there a speech event that is specific that we’d want to link to or the general notion of speeches that seems really hard. Yeah, you’re right. I think that’s really the hard part.
I think also from a utility perspective, since many of these knowledge bases were tracked or constructed because we think people want to use them. I feel like most of the useful knowledge bases are about name entities. Like if you think about IMDb most of the bioinformatics knowledge bases are, they’re all centered. People care much more about curating the information for the named entities than they care about curating for just regular entities.
Yeah, that’s true. And it might also be that they do care about that because also that’s easier to annotate and produce. Then actual sort of other kinds of concepts or events they would have to link. So the simplicity of that might even play in there. It’s just really hard even manually to build a coherent knowledge base of events and concepts in a way that, or compared to how you build a named entity knowledge base. But yeah, I agree. I think knowledge bases that exist in the wild, are usually haw entities are searched as well.
Yeah. Great. So going back to like how we construct these things, the general approach as we’ve been talking about you first detect what things you want to call entities and then you, you train some system to take the language surrounding those two entities and predict a relation. Maybe you aggregate this across lots of documents. There’s a long line of interesting work that you Sebastian a big part of on distance supervision. And I wonder if you could give an overview of what this, what this means. You mentioned it briefly as you were going through, but can you describe what this is for the listeners and what happened? Like the history of this, of this kind of approach?
Yes, I’m happy to. So generally again distance supervision is that, when you extract these relations in the traditional supervised setting, somebody goes over a lot of sentences and says, yes, that’s a sentence expressing the birthplace relation. No, that’s not a sentence expressing the birthplace relation and they annotate for some number of sentences. And then that gets fed into a supervised learning algorithm that then learns a predictor. But that’s expensive. And thankfully to some extent in the context of knowledge base population, not necessary because we have these, as I mentioned in the beginning, naturally occurring sets of of facts that we can use to in a way holistically annotate the sentences. And the simplest way of doing that is to say if I have a knowledge base that contains Barack Obama and Hawaii as a the birthplace of Barack Obama and I have a sentence that mentioned Barack Obama and Hawaii.
Then I’m just going to pretend that that sentence is expressing the relation birth place because I just assumed that when I mentioned Barack Obama and Hawaii, it must be because they are birth place because they are in a birth place position because that’s in my current knowledge base. Obviously that can be violated. Like Barrack Obama just flew to Hawaii, that doesn’t mean he was born there, and so we get wrong labels and there has been a lot of work in trying to reduce these sort of wrong labels and the noise that you produce by this type of weak supervision. But by and large, I think the idea is still roughly the same. So you assume that because things are related in your knowledge base, somehow sentences that mention these related entities are more likely to express that relation and somehow that’s a training signal for your relation extraction system. That’s, I guess specific case of relation extraction. You can generalize that notion to name entity recognition and all kinds of other tasks. You have some sort of free signal of data that you convert and then turn it into a direct signal and do that in one way or another.
Great. Um, there’s, there’s a related issue here of how much do I trust different sources of information. I don’t remember how much this actually got addressed in the literature. I know a few people were thinking about it, but like you’ve mentioned Barack Obama and Hawaii. There are also a lot of other documents on the web that, that talk about other birthplaces for Barack Obama. How do methods deal with this problem, in general? Distance supervision, at least the methods you were talking about, I don’t think really tried to do this other than like this expressed at least once assumption that hopefully at least one of the times that I saw Barack Obama in Hawaii, it was actually expressing the relationship that’s in my knowledge base. So I guess, yeah, this is less on the training side because Barack Obama and Kenya won’t have a relationship in my knowledge base, it’s in my actual prediction side when I’m constructing the knowledge base and I see conflicting evidence, how do I deal with this? Are you, are you familiar with work that does this?
Um, actually, no. I think it must exist. I haven’t really followed up on that, but I think it is something that always comes up actually every time I give presentations, that’s the first thing that they ask. Like, what have you extracted from a source that is wrong. Right. How do you integrate that with other kinds of conflicting information? I mean now and other systems had some ways of, I guess aggregating conflicting information in one way or another. At least that’s how I remember it. So there were different signals and you sort of use that I don’t think explicitly in terms of the sources that you would take from like you wouldn’t have a trust value associated with this particular source that helps you to down-weigh that fact or off-weigh that fact. So I am not very aware of work in that space but that doesn’t mean at all that it isn’t there. I mean it should be there and maybe you know more about that.
All I remember is a few papers. I think there were, there was a small team at Google on the, related to the knowledge vault team that was trying to build a knowledge base that was thinking about this stuff when extracting things from the web. There were some postdocs with Tom Mitchell on the NELL project that also briefly thought about this for a little bit and Ndapa was working on this I think at one point. So yeah, it just feels like a really hard problem. And maybe this is part of the reason that knowledge grafts haven’t seen as much practical application like constructing knowledge basis hasn’t seen as much practical application because it’s just so hard to control for the noise that you get in your input.
Yeah. I mean actually so you mentioned this and I’m interested in your view on this. So you mentioned knowledge grafts haven’t seen so much practical use, which I agree with. In the case of downstream NLP applications of it. What I’m really uncertain about is downstream users of that in the wild and applied and sort of in I guess data science or other kinds of areas, right. Where I think we always tell ourselves that there are people who use this downstream but I’m actually not so aware of this. I mean other than a few of the big players who are like Google actually relying on knowledge graphs as something to drive their algorithms. As far as I understand, I’m not so aware of these, but I’m curious about NELL for example, cause you have been a part of that. Like, what was the sort of downstream user of that, like where are the downstream users of that, then and how did they look like?
Um, NELL, the reason I said about what I did is I can’t really think of any knowledge-based construction projects that had practical applications. And actually I’m looking at Waleed and thinking yes there is a big one right there and we can talk about that in a minute. Cause yeah, that semantic scholar is one that I totally, skipped, glossed over as I was thinking about this. But NELL, and YAGO the KBC stuff. Maybe there are some like actual military applications of this that I, I, that’s all a black box once you submit stuff to DARPA. So I don’t know what they actually ended up doing with any of this work. Um, but I know that Google, for instance, canceled their knowledge base construction project because there wasn’t high enough precision to actually be useful in their product. Oh, I didn’t know that they use knowledge graphs, right? Like, I’m not saying that that knowledge graphs don’t have any applications, that the automatic construction of knowledge graphs has been too noisy to actually be useful for people who are, who are building these things. Uh, except for um, semantic scholars. So Waleed do you want to tell us about that.
Well, I think actually I would utter what you said. Uh, I’m not aware of any practical downstream use for automatically constructed knowledge bases including semantic scholar and semantic scholar. We have been primarily everything that you currently see on the website or knowledge that’s constructed that was important from existing databases or knowledge bases. And we currently are predicting new relationships and there’s another project which we’re trying to find new entities that were not originally in the knowledge base, but we have, we’re still doing some verification because we only want to expose them to the user after the pass a certain threshold of accuracy. I do think that’s kind of like the big question for automatic construction knowledge base community is can we put it in a state where it’s actually usable for downstream users.
I think part of the problem is going to be the general accuracy and then part of it we’ll have to do with how can we differentiate between things that are factual or things like where the author is hedging or there’s a negation and we’re extracting the relationship as if it’s, if it’s correct, it’s also there are also a long, like a long tail of situations where the fact is this relationship is true under certain circumstances. So if you do this prerequisite, then this relationship holds, should we add it to the knowledge base or not? I feel like these are questions that are nuanced and it has not been addressed and yeah, it’s kind of like disheartening that with all this work in knowledge base construction, we still have intrigued the benefits. But another related efforts which is also I think worth mentioning here is how can we put together multiple knowledge bases and there I think people have actually made use of multiple knowledge bases, because like when you a downstream application that’s relevant to multiple knowledge bases. Some of them may be automatic constructed but mostly no, we want to consolidate them somehow and there are many efforts mostly in the data mining community that tried to do this. But yeah, I think this is also very, very important.
Yeah. I’ll jump in just to say here quickly that a lot of Sebastian talked about open information extraction earlier in this conversation and a lot of the more recent open information extraction stuff does try to handle this scoping of facts that are extracted. So there, there is some work on trying to do this. You would think that like the closed information extraction stuff just trains a model to extract the fact and hopefully it would pick up on this kind of thing, but that’s not necessarily super accurate. But also I’ll call out a previous episode we did with Rachel Rudinger on Factuality, which is also very much related. So can you detect, given some event or a verb and its arguments that are expressed in text, can you detect whether the speaker was actually implying the truth of this statement or not? And that’s definitely a preconditioned to like accurate extraction here. Right. So yeah, this is, this is a hard problem with a lot of moving parts to it.
Yes. This is a really challenging, I’m also not a hundred percent sure whether the way to get this to build is very precise graphs that capture all of these conditions precisely and explicitly in a symbolic way or whether we can somehow improve our language models to sort of get more clever in terms of representing these things as well. Maybe that’s like too far in the future, but in my ideal world I’d rather see a better Elmo sort of taking care of that directly. Then us spending a lot of detailed work on labeling this correctly on a couple of instances and then getting that bit of precision or a bit of recall out of our supervised methods. So like a guess the jury’s still out on on that.
Yeah. I think the challenge here has to do with the scale. Like you mentioned before, in order to specify this information in a knowledge graph, you have to make the schema rich enough to represent it and the more complex your schema as you know, you need more training data for it and it becomes hard to manage.
Yes, And it becomes harder and harder to annotate even right. Like a, the more complex this kind of schema and conditioning is, the harder it is to explain that to people in the right way and the more noisy your annotation gets. So I think, uh, sometimes feels like an uphill battle.
Yeah. But one of the things that I’m actually very excited about in Semantic Scholar has to do with extracting. So as you know, like people, researchers introduced new terms all the time in their papers and it’s really hard to keep up with the new terminologies that are being generated every day. Right. Some of it like make it to everyone, like Bert and Elmo did a great job with this. Right? But many other names that are less popular don’t end up being well known. And one the things that we’re currently trying to do is learn from the terms. That’s I think if terms you already have seen and we see in papers, learn how people re introduce these new concepts and then we can construct, um, like it’s a much more targeted kind of extraction, for creating those knowledge bases or at least the entities in the knowledge bases and then the relations that relationships can come at a later time.
That sounds really good. Yeah. Maybe I just to sort of, not end but get to a more positive note because it sounded a bit like AKBC is just not working at all. I disagree with that, right? Like, I think it’s, it’s sort of working and I think it will progress. It is true that we haven’t seen as much downstream usage of it yet. On the same time though, and like this is where I want to talk about the AKBC conference a bit. There is a lot of particularly like industry interest in this, so there must be a lot of like use for that because we just see much inbound interest in that probably see much more inbound interest from industry then from researchers themselves, like researchers right now they’re all on Elmo and Bert, right? Or all the other sort of exciting things out there.
But when it comes to talking to companies in their needs, the first thing they talk to me about is, often just, Oh, it will be great to have these knowledge bases automatically extracted from it. I also like maybe, and that’s just hypothetically, I feel that some of the uses we’ll see of YAGO or Wikidata, right? I mean, I don’t know. Wikidata is also not automatic extractors as I know is relatively hand build I suppose. But let’s say JAGO I think it might be people out there who use that data, but they’re not gonna write papers about it and we’re not going to see citations coming in. But we see companies that are using either that knowledge base or they used the ways that or the methods that are proposed in his papers that built the knowledge bases. So right. Maybe now hasn’t necessarily seen a lot of downstream use of it’s facts that it extracted, but the kind of findings that we made there, you find them being used in big biomedical companies that extract these kinds of protein protein interaction networks from text. Right. So I feel very positive about that, but I don’t feel very positive about I guess academic reuse of knowledge bases.
Yeah. Great. Thank you for clarifying that. I totally agree with you. Yes. That made me think I should rephrase what I said previously. It’s more that we haven’t figured out how to make knowledge graphs useful in lower level NLP. As you said, there are a whole lot of naturally occurring knowledge bases, right? Yeah. People like these collection of facts. And so this is actually an end in itself. So yes, automatic knowledge base construction is totally useful. Yeah. If what you want is a knowledge base out and what you can get about facts about real people from the web might not be high precision enough for someone like Google. But it’s still useful enough for a lot of people in a lot of different specific domains. And there’s been a lot of great research helping this and that. That’s totally, it’s just we don’t see as much downstream uses of it in NLP because we don’t really know how to consume a knowledge base in language understanding models. And that’s, that’s an active area of research.
Yeah. I’m wondering also if we need to spend more time thinking about how can we help the curators of knowledge bases. So we know that some knowledge bases that are actively being maintained receive a lot of annotation and curation. So I think the interplay between AI and manual curation for these knowledge bases, I think maybe the easiest path to increase the adoption of the knowledge based construction methods, but it’s of course that requires actual product and that’s kind of part of the reason why we are doing it semantic scholar because there are actually users who can give us feedback on whether the things that we’re extracting are correct or not. Right. I, yeah, I hope there are other efforts somewhere that are also copying this.
Oh yeah, that sounds great. I’m looking forward to your talk at the AKBC, a conference on, on that.
Yeah. I guess there were a bunch of things that I had listed that we could have talked about, but we are running out of time. Is there anything that we missed that you particularly wanted to talk about or want to highlight before we finish?
I’m not from the top of my head right now. I think we covered quite a bit of ground.
One thing, we can conclude with this point, you mentioned before that there’s this knowledge construction conference. Now that I’ve mentioned at the beginning that I met you at the first automatic knowledge base construction workshop and now it’s a conference.
Do you have anything to say about that whole process of like is this, is this the coming of age of the fields? Like what, what is this?
Yeah, I think that it’s part of that there is generally a steady and increasing interest in this which we have seen over the past, but maybe even more so. I mean you mentioned sort of the KBC community and and all that, there isn’t a natural place for them in a way that there is a place for NLP researchers or vision researchers, right? Like so if you work in link prediction for example, which doesn’t necessarily need to talk about language, right? Like where do you submit your paper to like where do you go and talk to other people in this field? It could be like AAAI or IJCAI, maybe the machine learning conferences where you just don’t have the same type of domain specific conference like you’d have for NLP. And so one goal of this conference and this conference series is to become that place and get people from these different areas together and to give them a venue where they can publish their work and discuss it with like minded people. So that’s, I think, one of the main motivations behind it.
Yeah. Sounds great to me. Thanks for coming on. This has been a really interesting conversation. It’s been good to talk to you.
Yeah, yeah. Really enjoyable. Thanks so much for, for running this. I think that’s much appreciated. Generally.