I think its worth noting that inconvenience aside, the high rate of theft and stock loss is correlated to item price increases overall -- even above inflation.
Watched a man walk out of Safeway with two 12-packs under his arms makes me understand why eggs are $8 a dozen.
Even if shoppifting tripled it would be minuscule in the overall scheme of things. Groceries pay 2 dollars per dozen eggs to farmers and have to toss out them out every few days, wastage-wise it’s basically a blip
more that execs are looking for an excuse to tell their board of directors why sales are falling that doesnt include online ordering. the movement to E-Commerce is a double hit to stores because not only do they directly lose sales but when something is ordered from their stores (on e.g -doordash) they have to put people on packaging those goods- which they see no income from and makes the brick-and-mortar store less staffed.
No doubt that there have been poor teaching experiences, but I would counter that my experience with EdTech teachers has been amazing and empowering. Really don't think we can tar all the teachers with the "EdTech sucks" brush.
Aside from the Knowledge Graph buzzword, isn't this exactly the same idea as Tim Berners-Lee and the Semantic Web back in 2001?
- web of resources, not pages
- ontology-based schema
- RDF based encoding
- URI (IRI) resource identifiers
- Automated agents and reasoning (DL) support
Considering the ensuing reception and general avoidance of the semantic web outside academic papers, I guess no one wants to talk about it.
And there are related standards like HATEOAS for state full behaviour right?
But this isn't about a new way of presenting information to rival sparql or something.
This is a technical report about a guy who wrote a slightly advanced crawler bot that discovers the behaviour of a modern Web application and can maybe be used to generate automated tests.
It has a lot in common with that post about a new yc company using llms to generate integrations for existing undocumented Web applications.
> Knowledge graph-based systems, such as Squirrel [8], have been proposed to crawl the semantic web and represent data in structured formats. However, these approaches are typically limited to RDF-based web data and do not capture the dynamic, user-driven interactions seen in modern web applications.
How is this dismissive? It's a fairly straightforward statement of fact. The only way I could possibly read it as dismissive is if you interpreted it like, "These approaches are typically limited to [old, boring ass,] RDF-based web data and do not capture the [rich, luscious] dynamic, user driven interactions seen in [beautiful] modern web applications"
Very interesting article and helpful to visualize the layers and perspectives in and more comprehensive architecture diagram. I would add that the layers of abstraction could also be represented in a standard Semantic Web OWL ontology which would capture a lot more context and sematic detail related to hierarchies, concepts, classes, and object property relationships. Picture are great, but it's always a struggle to get them complete.
I agree with you, but I read this as:
> No process and tools
> No / limited documentation
> No RACI or alignment (yay, kumbaya)
> No plan or managed dependencies
I'm not saying this is the outcome of an Agile approach, but sure seems common on the Scrum projects I work on.
I would venture the implied discussion is about more than shipbuilding. Resources, labor, regulation, and other factors raised in the article are endemic across lots of areas and seems symptomatic of market forces and government policy. As a counter argument, I have to ask why the U.S. needs to build ships faster, larger, cheaper than other countries? The U.S. certainly seems to be able to build infrastructure and housing reasonably competitively.
I hadn't heard the term "architecture astronaut porn" before. Seems highly accurate in this context and wondered if you could point me to other resources on this term?
Good article on the high level concepts of a knowledge graph, but some concerning mischaracterizations of core functions of ontologies supporting the class schema and continued disparaging of competing standards-based (RDF triple-store) solutions. That the author omits the updates for property annotations using RDF* is probably not an accident and glosses over the issues with their proprietary clunky query language.
While knowledge graphs are useful in many ways, personally I wouldn't use Neo4J to build a knowledge graph as it doesn't really play to any of their strengths.
Also, I would rather stab myself with a fork than try to use Cypher to query a concept graph when better standards-based options are available.
> While knowledge graphs are useful in many ways, personally I wouldn't use Neo4J to build a knowledge graph as it doesn't really play to any of their strengths.
I'd strongly disagree. The built-in Graph Data Science package has a lot of nice graph algos that are easy to reach for when you need things like community detection.
The ability to "land and expand" efficiently (my term for how I think about KG's in Neo4j) is quite nice with Cypher. Retrieval performance with "land and expand" is, however, highly dependent on your initial processing to build the graph and how well you've teased out the relationships in the dataset.
> I would rather stab myself with a fork than try to use Cypher to query a concept graph when better standards-based options are available.
Cypher is a variant of the GQL standard that was born from Cypher itself and subsequently the working group of openCypher: https://opencypher.org/
> That the author omits the updates for property annotations using RDF* is probably not an accident and glosses over the issues with their proprietary clunky query language.
Not just that, w.r.t. reification they gloss over the fact that neo4j has the opposite problem. Unlike RDF it is unable to cleanly represent multiple values for the same property and requires reification or clunky lists to fix it.
Not sure what the problem is here. The nodes and relationships are represented as JSON so it's fairly easy to work with them. They also come with a pretty extensive set of list functions[0] and operators[1].
Neo4j's UNWIND makes it relatively straightforward to manipulate the lists as well[2].
I'm not super familiar with RDF triplestores, but what's nice about Neo4j is that it's easy enough to use as a generalized database so you can store your knowledge graph right alongside of your entities and use it as the primary/only database.
It has been a while so maybe things have changed, but the main reasons I remember are 1) lists stored as a property must be a homogeneous list of simple builtin datatypes so no mixing of types, custom types, or language tagging like RDF has as first class concepts. 2) indexes on lists are much more limited ( exact match only iirc) so depending on the size of the data and the search parameters it could be a big performance issue. 3) cypher gets cumbersome if you have many multi-valued properties because every clause becomes any(elem in node.foo where <clause>). In Sparql it's just ?node schema:foo <clause>.
I don't think everybody should run away from property graphs for RDF or anything, in terms of the whole package they are probably the right technical call ninety-something percent of the time. I just find Neo4J's fairly consistent mischaracterization annoying and I have a soft spot for how amazingly flexible RDF is, especially with RDF-star.
GraphDB is the one I usually use. It has a web interface that eases the first steps.
Virtuoso (especially Virtuoso 7, which is open source) is also an option. [a bit more command line based].
In case you want to have a look a the SPARQL client I maintain, Datao.net, you can go to the website and drop me a mail. [i really need to update the video there as the tool has evolved a lot since that time]
The new kid on the block is very much QLever. Still lacking some features, especially wrt. real time update that make it unsuitable for replacing the Wikidata SPARQL endpoint altogether just yet, but it's clearly getting there.
If you just want to try some queries, there is a public sparql wikidata endpoint at https://query.wikidata.org . If you press on the file folder icon there are example queries, which let you get a feel for the query language.
While I'm all for standards-based options, I think the fetishization does a disservice to anyone dipping their toes into graph databases for the first time. For someone with no prior experience, Cypher is everywhere and implements a ton of common graph algorithms which are huge pain points. AuraDB provides an enterprise-level fully-managed offering which is table stakes for, say, relational databases. Obviously the author has a bias, but one of the overarching philosophical differences between Neo4J and a Triple Store solution is that the former is more flexible; that plays out in their downplaying of ontologies (which are important for keeping data manageable but are also hard to decide and iterate on).
I can attest to that, or at least to the inverse situation. We have a giant data pile that would fit well onto a knowledge graph, and we have a lot of potential use cases for graph queries. But whenever I try to get started, I end up with a bunch of different technologies that seem so foreign to everything else we’re using, it’s really tough to get into. I can’t seem to wrap my head around SPARQL, Gremlin/TinkerPop has lots of documentation that never quite answers my questions, and the whole Neo4J ecosystem seems mostly a sales funnel for their paid offerings.
I think neo4j is a perfectly good starting point. Yeah, I feel like they definitely push their enterprise offering pretty hard, but having a fully managed offering is totally worth it IMO.
I enjoy cypher, it's like you draw ASCII art to describe the path you want to match on and it gives you what you want. I was under the impression that with things like openCypher that cypher was becoming (if not was already) the main standard for interacting with a graph database (but I could be out of date). What are the better standards-based options you're referring to?
But then data validation everywhere; so for language-portable JSON-LD RDF validation there are many implementations of JSON Schema for fixed-shape JSON-LD messages, there's W3C SHACL Shapes and Constraints Language, and json-ld-schema is (JSON Schema + SHACL)
Paywalled, but not a huge surprise really. Sounds like part of the problem was the internal pharma team not having a plan for what to use CoPilot for and going for the "it's AI!" theme from Microsoft.
I am seeing great value in AI-based solutions related to knowledge assistants and for automation N(A). Neither of these seem like strong suites for CoPilot, but it's very early days.
That we're seeing much better results using self-hosted open source models (Llama 3.1, etc.) should be concerning for Microsoft as well.
>> The attitude this instills is that of having to work hard, while scheming against the taxman, to improve your life.
Sounds like a lot of politicians I know. Really, how is this not being "Smart" and gaming the system? If we're all upset about being "fair" then we would have changed the system.
