Hi , i’m working on a project to build knowledge graph , so that the relationships within the document will help us to query documents with those attributes from the entire corpus. Would like some help what methods can be used to mine relationships from the document.
I am looking to get a knowledge graph of an image using some pre-trained model.
What is the current SOTA work that has a reproducible implementation?
Can you share some pointers from your experience trying to extract KG from images?
Thank you
This is something I've thought about for a while, transformers are really powerful, and the larger models produce semi-coherent responses to most questions, as long as the question is somewhat straightforward:
> "Who was the main hero of the lightsaber movie?"
"Luke Skywalker"
But once you add more degrees of reference and abstraction, you see answers like this:
> "Who was the master of the main antagonist of the lightsaber movie?"
"The master" (intended: Palpatine)
or we've all seen questions like this: > "What happens if you leave milk out of a fridge for too long?"
"Milk is typically inside of a fridge"
While transformers do high dimensional anaysis of these queries, it would be great if you could quantize them, and find the nearest vertices/entities on a knowledge graph. Then try to get the knowledge graph and transformer to learn from each other. The knowledge graph behaves kind of like a discriminator in a GAN.
Has anyone done any work like this or thought of this? Poke some holes into my idea. Basically, I want to quantize some of the high dimensional aspects of text into a knowledge graph, to a transformer, and back, maybe repeat a few times. The benefit a knowledge graph brings is that it's interpretable, can hold "truth", and can be used to easily traverse the domain surrounding a transformer query.
I have a few ideas for how I would implement this, but I don't want to go down a rabbit hole if this is a dumb idea and or already tried and failed.
I've been seeing a lot of stuff on here about Wikipedia being devalued by Google, but can't find much evidence to suggest that, especially as Knowledge Graphs and the method entities score are massively driven by Wiki pages/other high value domains.
Can anyone provide evidence here?
My current view is grey hat SEO will continue to work (e.g. paying wikipedia editors)... Change my mind!
Cheers
We have built our first "smart" feature into our app. Now, we can show when "similar" content likely needs changes. Say, you have 20 files (invoices/contracts/job postings) that all include an address, a company description/name, an IBAN/routing number, legal clauses. You open one of them and edit that clause/change the address/IBAN/company description. Our engine will say "there are 4 files that probably require changes, too".
We use LSH & MinHashing for the file similarities and run our ML for dynamic knowledge graphs to determine which of those 20 "similar files" are still active (papers below)
A quick graphic:
https://preview.redd.it/imrsumx8xcu61.png?width=2856&format=png&auto=webp&s=926e1d90dbba93457917d6cbf09720e415069a86
Papers here:
https://dl.acm.org/doi/abs/10.1145/3038912.3052672
https://arxiv.org/abs/1905.05305
http://proceedings.mlr.press/v124/tabibian20a
https://www.pnas.org/content/116/10/3988.short
https://dl.acm.org/doi/abs/10.1145/3018661.3018685
https://dl.acm.org/doi/abs/10.1145/2939672.2939875
https://arxiv.org/abs/1805.09360
(for good measure: we're just building a little community here r/reasonal, or you can get to the survey to get access to our closed beta here reason.al)
As a knowledge seeker looking to expand capabilities in the clinical health field of endeavor
I want to help cultivate and refine a knowledge graph data set, focused on diagnosis, interventions and outcomes using the MIMIC III core data and any pertinent reference corpus, dictionaries, libraries in order to make this solution available through an interactive visual mechanism (e.g. web app, AR app)
so that a user, such as a clinician, can traverse the graph to assist in exploring and evaluating healthcare options for a patient
Acceptance criteria and considerations:
- Graph should be simple and follow the PAR form: Problem -> Action -> Result
- Problem for clinical health may follow the pattern: locus -> bodypart -> ailment (e.g. upper GI bleed)
- Action can be an individual intervention action or a sequence/regiment of intervention actions
- Result would be reduction or elimination of the ailment; likely expressed as a probability (may also specify risks/side effects and their probabilities)
- Solution can be a functional prototype; this can help focus research and design (need to consider how solution will be used)
- User should be able to select a body part or ailment (may be a whole body symptom such as fatigue); may need to use an anatomy hierarchy or image in order to help the user quickly narrow in on a body part
- Zooming in can allow narrower focus (e.g. initially select abdomen area, then isolate an organ, then section of organ such as upper GI tract); need to keep number of gestures/clicks low
- If multiple Action alternatives are available, need to come up with ranking mechanism (e.g. risk/benefit weighted)
- Can use ranking mechanism to order problems/ailments with most common coming up first
Contributors: Dan Rigoli
I didn't build it. But I found it interesting. What could be other alternative approach to solve this problem?
I'm new to the whole Crypto thing and want to learn more about it. I've created a Notebook in Hypernotes in which I filled in some basic information and connect topics to help me with that. The result of this lead to this knowledge graph which shows how the nodes are connected to each other. I know it is incomplete as it is still work in progress and the whole thing is so vast that it is nearly impossible to get all information inside this Notebook. Check it out if you want, would love to hear some feedback!
These should perhaps be pinned in the information of this subreddit. https://thegraphportal.com/ https://thegraph.academy/ https://thegraph.com/docs/introduction https://thegraph.com/docs/network Want to see GRT do well? Knowledge and sharing that knowledge is the key. Spread these links to anyone who inquires about The Graph or GRT. Read them and you will have a better understanding of the protocol and therefore your investment.
Wondering for my own project as a lot of users ask to visualise the connections between files and their activities and communications.
I'd love to see it myself but I wonder why. Is it only nice to look at or does it have some other value?
Very interesting Paper: "On The Role of Knowledge Graphs in Explainable AI"
by Freddy Lecue
"The current hype of Artificial Intelligence (AI) mostly refers to the success of machine learning and its sub-domain of deep learning. However, AI is also about other areas, such as Knowledge Representation and Reasoning, or Distributed AI, i.e., areas that need to be combined to reach the level of intelligence initially envisioned in the 1950s. Explainable AI (XAI) now refers to the core backup for industry to apply AI in products at scale, particularly for industries operating with critical systems. This paper reviews XAI not only from a Machine Learning perspective, but also from the other AI research areas, such as AI Planning or Constraint Satisfaction and Search. We expose the XAI challenges of AI fields, their existing approaches, limitations and opportunities for Knowledge Graphs and their underlying technologies."
link to paper: http://www.semantic-web-journal.net/system/files/swj2198.pdf
The original posts are from Neo4j, click the following links for details if interested:
- Graph Data Science 101: Understanding Graphs and Graph Data Science
- Graph Data Science 101: Using Graph Data Science in the Real World
- Graph Data Science 101: Evolving Your Application of GDS Technology
- Graph Data Science 101: Using Neo4j as a Graph Data Science Platform
- Graph Data Science 101: Detecting Fraud with Graph Data Science
- Graph Data Science 101: Ten Tips with Resources for Successful Graph Data Science
We have built our first "smart" feature into our app. Now, we can show when "similar" content likely needs changes. Say, you have 20 files (invoices/contracts/job postings) that all include an address, a company description/name, an IBAN/routing number, legal clauses. You open one of them and edit that clause/change the address/IBAN/company description. Our engine will say "there are 4 files that probably require changes, too".
We use LSH & MinHashing for the file similarities and run our ML for dynamic knowledge graphs to determine which of those 20 "similar files" are still active (papers below).
A quick graphic:
https://preview.redd.it/0feo7strt3w61.png?width=2856&format=png&auto=webp&s=8998bdb0e146a453a7fd3f7036bf7c2fe994dc30
Let me know what you think, and, for good measure: if you're interested in testing (and destroying) the beta, add yourself to the waitlist & fill in the short survey. If you are onboarded onto our closed-beta, we're happy to grandfather you (Lifelong Pro Membership for you + 4 other accounts)! Pls use this link for it so that we can connect you: Reasonal Reddit Link. Otherwise, we're just building a little community here r/reasonal.
____
Papers here:
https://dl.acm.org/doi/abs/10.1145/3038912.3052672
https://arxiv.org/abs/1905.05305
http://proceedings.mlr.press/v124/tabibian20a
https://www.pnas.org/content/116/10/3988.short
https://dl.acm.org/doi/abs/10.1145/3018661.3018685
We have built the first "smart" feature into our app. Now, we can show when "similar" content likely needs changes. Say, you have 20 files (invoices/contracts/job postings) that all include an address, a company description/name, an IBAN/routing number, legal clauses. You open one of them and edit that clause/change the address/IBAN/company description. Our engine will say "there are 4 files that probably require changes, too".
We use LSH & MinHashing for the file similarities and run our ML for dynamic knowledge graphs to determine which of those 20 "similar files" are still active (papers below).
A quick graphic:
https://preview.redd.it/zwf8pdehbvz61.png?width=2856&format=png&auto=webp&s=c1d51293960dc5c97fa1984cee42160139e62617
If you're interested in testing (and destroying) the beta, add yourself to the waitlist & fill in the short survey.
If you are onboarded onto our closed-beta, we're happy to grandfather you (Lifelong Pro Membership for you + 4 other accounts)! Pls use this link for it so that we can connect you: Reasonal Reddit Link.
____
Papers here:
https://dl.acm.org/doi/abs/10.1145/3038912.3052672
https://arxiv.org/abs/1905.05305
http://proceedings.mlr.press/v124/tabibian20a
https://www.pnas.org/content/116/10/3988.short
https://dl.acm.org/doi/abs/10.1145/3018661.3018685
I'm new to the whole Crypto thing and want to learn more about it. I've created a Notebook in Hypernotes in which I filled in some basic information and connect topics to help me with that. The result of this lead to this knowledge graph which shows how the nodes are connected to each other. I know it is incomplete as it is still work in progress and the whole thing is so vast that it is nearly impossible to get all information inside this Notebook. Check it out if you want, would love to hear some feedback!
I'm new to the whole Crypto thing and want to learn more about it. I've created a Notebook in Hypernotes in which I filled in some basic information and connect topics to help me with that. The result of this lead to this knowledge graph which shows how the nodes are connected to each other. I know it is incomplete as it is still work in progress and the whole thing is so vast that it is nearly impossible to get all information inside this Notebook. Check it out if you want, would love to hear some feedback!
