Re: Trip Reports on Dagstuhl Seminar on Knowledge Graphs

Paola,

I think you greatly misunderstand the notion of a Dagstuhl workshop.
The very concept of proceedings indeed runs counter to the very idea of Dagstuhl,
which is about discussing and creating new ideas that lead on to 
*future* research.

Some of these ideas already led to a new European Training Network
starting next month called „KnowGraph - Knowledge Graphs at Scale“.
The consortium will be able to hire 15 PhD students who will also
spend part of their time in industry research working on KGs and KG technologies.
To those attending this conversion and thinking about doing a PhD,
watch this space for formal announcements soon and/or drop me a personal message.

The report scratches the surface of what has been discussed and has been released
rather quickly following the event. The organizers did a great job of giving it 
a smooth look, but obviously could not turn it into a monographical piece in a short time span.

Under the leadership of Aidan Hogan, we are currently preparing a survey whose purpose is to tie together the different strands
of KG research - and indeed there are very many - and it is an undertaking that has been going on for a year
and will still need several months. It will also be a major outcome of this workshop.
 It targets the newbie to KGs and again will give an overview, but will
not be able to dive deep in all the manifold aspects.

For the manifold novel research aspects you better watch the space of usual conference (ISWC, Web Conf., AI conferences, etc.),
because there is no monolithic approach, progress is social and incremental all over the place,
just as Axel mentioned.

Overall, I do not get what you want to achieve with your critique points below and whom you want to help with this criticism;
because if criticism does not help anyone, it could as well be buried somewhere.
Maybe before criticising an event (or something else) you may first want to understand what the event (or something else) was about
in the first place. Towards this end, I have given the above explanation.

Steffen



> Am 29.08.2019 um 02:21 schrieb Paola Di Maio <paoladimaio10@gmail.com>:
> 
> Thank you all,
> 
> and Valentina for finding the sentence in the report were limitations were addressed-
> seems a bit notional tho?
> 
> Steffan S:  thanks for the questions. Do you see what I mean, the scope of the workshop
> based on the report, seemed limited, So many more questions beg to be asked.
> 
> Josh, as far as I am aware most KGs  in use are embedded, and due to various reasons
> which were NOT even mentioned in the report, the reliability for the purpose of reasoning
> is uncertain. Yes, you are right 
> with unreliable or incomplete data, while an inevitable fact of life, is not necessarily a problem one should attempt to solve at the KG level.  
> true- but that is not what my problem is here
>  see below for the summary my criticism
> l;dr plenty of things appear to have been said at the seminar which are more actionable than much of the established theory around KR and SW.  
> I did not see much of that, maybe need to read it again
> 
>  In this thread, I asked  about proceedings for this workshop, as it looked promising, but then forgot about it, as no pointers were provided to resources. During a recent search trying to answer certain questions the report came up, and I was surprised not to see even remotely the expected breadth of questions (ideally answers) relating to this important theme. 
> 
> Will -  there have been a few threads where people ask what is the fuss about KGs
> and are they just hype, well I concluded that its just a name for triples/ntuples, and yes they
> are a form of KR, trendy and useful but perhaps overinflated a bit. Without further qualification KG do not satisfy the full scale of requirements for  KRs, especially in large automated complex reasoners-
> 
> So, Alex  Valentina and all, if I am allowed, the main criticism for me remains":
> 
> 1. very limited publicly accessible proceedings for a publicly funded workshop (the report, which as you say is just a  short summary but no other more comprehensive resource is provided)
> 
> 2.  there is no novel contribution, the account of what KG are given in the report is limited (superficial)  Not much new came out of this workshop, how can this be?
> How can the best scholars in this field completely fail even just  to identify key open issues?
> 
> 3.  The workshop, based on the report, fails to raise the important questions pertaining to the challenges relating to KGs and does not even get near to pointing to work to be done
> 
> 4.  without capturing and addressing the limitations of KG as KR, and the work that needs to be done to overcome those limitations, the workshop/report falls short of its aims
> 
> Now, given that KGs are an important and interesting topic, and given the quality and quantity and brilliance of the participants, from my perspective, the outcome of the workshop reads comparatively trivial
> 
> I am perplexed what may be the cause that level of triviality, other than some hidden agenda
> 
> While I am sure you all had a great party, from a scholarly perspective based on the report
> sounds like not the best use public resources, but agree that much research  these days is like that- 
> 
> Thank you, and apologies for the lack of diplomacy in expressing my concerns
> 
> PDM
> 
> 
> 
> On Thu, Aug 29, 2019 at 12:08 AM Alexander Garcia Castro <alexgarciac@gmail.com <mailto:alexgarciac@gmail.com>> wrote:
> KGraphs are an umbrella term that brings together more than one single tech a practical implementation/path that exemplifies an application of AI (semantics, linked data, ontolgies, etc). KGraphs offer more flexibility and scale better than pure ontology based solutions -IMHO. in my experience modeling on a KGraph makes it easier when dealing with real data in enterprise enviroments, also, KGraphs scale as needed. There are issues with KGraphs, I should better say with commercial KGraphs solutions and there is a lot of room for improvement; this is all true. We use Kgraphs for exploring scientific literature at a scale that would otherwise be very difficult to manage. We get from a KGraph pretty much the same in terms of query formulation, and some times more, as we would get from a SPARQL endpoint. the Kgraph allows us to add more data and remodel as needed  considering only bussines constraints. 
> 
> On Wed, Aug 28, 2019 at 4:19 PM Joshua Shinavier <joshsh@uber.com <mailto:joshsh@uber.com>> wrote:
> Hi Paola,
> 
> OK; I look forward to a more detailed argument in your article. So far, I have only skimmed the paper you linked, but I can see that -- apart from the fact that it is a little dated and does not mention currently popular graph embedding techniques such as GraphSAGE (usual disclaimer: I am no expert in embeddings) -- the criticism applies at best to one relatively inessential and separable aspect of enterprise knowledge graphs. W.r.t. information extraction, I can tell you from experience that dealing with unreliable or incomplete data, while an inevitable fact of life, is not necessarily a problem one should attempt to solve at the KG level. At least 9 times out of 10, the problem is better addressed at the level of individual data sources, where the solutions are very domain-specific.
> 
> "Knowledge graph" may be a marketing term, but IMO it represents a shift away from pure research and toward technologies that scale well and which serve real-world needs, as Steffen mentioned. This is a good thing; it means that KR is succeeding, even if it is doing so in unanticipated ways. It is important to acknowledge the rise of lightweight KR (if I may use that term) in the developer community via data models such as property graphs which dispense with formal semantics altogether, and I think it is also telling that many of the large-scale corporate knowledge graphs, at their core, are not based on either RDF or property graphs, but on special-purpose data models which have been designed in-house. I will tell you about ours (Uber's) in a paper currently in internal review. Last week, I had a chance to ask Xiao Ling (Apple) and Scott Meyer (LinkedIn) about theirs. For Siri's knowledge base, Apple is using an RDF-like data model (supporting "triples" with "qualifiers" that enable reification), but not RDF proper. For the Economic Graph, LinkedIn is using a Datalog-based data model which again is based on triples, but not on RDF or PG. This tells me that the standards built for knowledge representation on the Web are being used not so much for their associated formal properties, but as a means of data interchange -- a point that was made, and which really stood out to me in Paul Groth's trip report.
> 
> tl;dr plenty of things appear to have been said at the seminar which are more actionable than much of the established theory around KR and SW. At the same time, I believe there is tendency now to look back at SW and earlier work and attempt to learn from it, adding more formality around ontologies, inference, and rules where it makes sense to do so.
> 
> Josh
> 
> 
> 
> On Wed, Aug 28, 2019 at 12:18 AM Paola Di Maio <paoladimaio10@gmail.com <mailto:paoladimaio10@gmail.com>> wrote:
> Joshua
> 
> thanks for the opportunity to clarify and apologies for the brashness
> of my remarks
> 
> I did not mean that they KGs are not a type of KR, which arguably they are
> 
> but they do not satisfy KR adequacy criteria in many ways (I ll address that more extensively
> in an article) and come with limitations, an example linked below
> 
> The  lack of acknowledgment of such limitations is startling for me,  and shows superficiality given that the workshop participants are leading researchers and colleagues, and include best of the sw researchers crop otherwise in many ways
> 
> 
> PDM
> 
> this article explains some of the issues with KG, and especially using
> KGs as sole KR methods
> 
> https://www.aclweb.org/anthology/D17-1184 <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.aclweb.org_anthology_D17-2D1184&d=DwMFaQ&c=r2dcLCtU9q6n0vrtnDw9vg&r=yHrezOOUvTAeD_KgsElyJw&m=aNjZ2E21bTW1NHEQwPsqbJsQlCISkjiFHveUp3Qsp-U&s=TeWvt9PiUMH_e7fu6xP8vySKoOGki8BZFCsQWbp95SI&e=>  
>   Unfortunately, information extraction approaches for KG construction must overcome complex, unreliable, and incomplete data. Many machine learning methods have been proposed to address the challenge of cleaning and completing KGs. One popular class of methods learn embeddings that translate entities and relationships into a latent subspace, then use this latent representation to derive additional, unobserved facts and score existing facts (Bordes et al., 2013; Wang et al., 2014; Lin et al., 2015)  
> 
> 
> 
> On Wed, Aug 28, 2019 at 2:26 PM Joshua Shinavier <joshsh@uber.com <mailto:joshsh@uber.com>> wrote:
> Maybe I need to read some of the past threads for context, but this dismissive statement took me by surprise. In what way are KGs not KR? If that were a true, it would deeply affect my own outlook and messaging. I ought to at least try to understand your point of view. Are you referring to some very limited and traditional definition of KR? Insofar as an RDF statement is a claim about the world <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.w3.org_TR_rdf11-2Dconcepts_&d=DwMFaQ&c=r2dcLCtU9q6n0vrtnDw9vg&r=yHrezOOUvTAeD_KgsElyJw&m=aNjZ2E21bTW1NHEQwPsqbJsQlCISkjiFHveUp3Qsp-U&s=1ijuTw-9KTkWBdXnIoz2Hfg4v4uthQl0MBbr6mMEePs&e=>, the humblest RDF graph is a representation of knowledge. So...
> 
> My $0.02 is that KG is a particular, typically simple and pragmatic form KR by a new name -- a pretty uncontroversial point of view, I would have thought. Not looking for a debate, just clarification.
> 
> FWIW, I was not involved in the Dagstuhl event, but really appreciated the trip reports
> 
> Josh
> 
> 
> 
> On Tue, Aug 27, 2019 at 11:07 PM Paola Di Maio <paola.dimaio@gmail.com <mailto:paola.dimaio@gmail.com>> wrote:
> Juan and all
> 
> I finally got hold of the report, courtesy of Alex P
> /aic.ai.wu.ac.at/~polleres/publications/bona-etal-DagstuhlReport18371.pdf <https://urldefense.proofpoint.com/v2/url?u=http-3A__aic.ai.wu.ac.at_-7Epolleres_publications_bona-2Detal-2DDagstuhlReport18371.pdf&d=DwMFaQ&c=r2dcLCtU9q6n0vrtnDw9vg&r=yHrezOOUvTAeD_KgsElyJw&m=gaA1u5UYZsI_ZXB4pczTes7Z4Y5XsNf17VTvGW4NoQA&s=kzwa3xf1kft82oywOFTmr3190FCOd5k-5puzviUCFy8&e=>
> 
> As a scholar in KR, I am concerned at the suggestion that KG are being proposed
> as KR,  and at the superficiality of the content of this report, and I am aggravated to note the complete lack of acknowledgement of  the limitations of this approach.  
> 
> Sounds like a good example of ineptitude, inadequacy and corruption  heavily influencing academic research and the field of AI KR
> 
> *two cents still allowed?
> 
> PDM
> 
> 
> 
> On Thu, Sep 20, 2018 at 6:41 AM Juan Sequeda <juanfederico@gmail.com <mailto:juanfederico@gmail.com>> wrote:
> Hi all,
> 
> Last week there was a Dagstuhl seminar on: Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web
> https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=18371 <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.dagstuhl.de_en_program_calendar_semhp_-3Fsemnr-3D18371&d=DwMFaQ&c=r2dcLCtU9q6n0vrtnDw9vg&r=yHrezOOUvTAeD_KgsElyJw&m=gaA1u5UYZsI_ZXB4pczTes7Z4Y5XsNf17VTvGW4NoQA&s=woJkjA7MzT9frcSHwr6o-5llrKuG9HDjHT-_mVaNkTQ&e=>
> 
> A formal report will be coming out soon. For the mean time, some folks have written their own reports. I'm sure folks in this community would be interest:
> 
> Eva Blomqvist: http://blog.liu.se/semanticweb/2018/09/15/dagstuhl-seminar-on-knowledge-graphs/ <https://urldefense.proofpoint.com/v2/url?u=http-3A__blog.liu.se_semanticweb_2018_09_15_dagstuhl-2Dseminar-2Don-2Dknowledge-2Dgraphs_&d=DwMFaQ&c=r2dcLCtU9q6n0vrtnDw9vg&r=yHrezOOUvTAeD_KgsElyJw&m=gaA1u5UYZsI_ZXB4pczTes7Z4Y5XsNf17VTvGW4NoQA&s=G69b8OTXXr2Zy497b6s0DYeIAvJdAhuromY8ZC7V8AY&e=>
> Paul Groth: https://thinklinks.wordpress.com/2018/09/18/trip-report-dagstuhl-seminar-on-knowledge-graphs/ <https://urldefense.proofpoint.com/v2/url?u=https-3A__thinklinks.wordpress.com_2018_09_18_trip-2Dreport-2Ddagstuhl-2Dseminar-2Don-2Dknowledge-2Dgraphs_&d=DwMFaQ&c=r2dcLCtU9q6n0vrtnDw9vg&r=yHrezOOUvTAeD_KgsElyJw&m=gaA1u5UYZsI_ZXB4pczTes7Z4Y5XsNf17VTvGW4NoQA&s=R8dpWgBXbjHVDqM2etP3BiTZPTPGcwsF-VmotEHrLUw&e=>
> Juan Sequeda: http://www.juansequeda.com/blog/2018/09/18/trip-report-on-knowledge-graph-dagstuhl-seminar/ <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.juansequeda.com_blog_2018_09_18_trip-2Dreport-2Don-2Dknowledge-2Dgraph-2Ddagstuhl-2Dseminar_&d=DwMFaQ&c=r2dcLCtU9q6n0vrtnDw9vg&r=yHrezOOUvTAeD_KgsElyJw&m=gaA1u5UYZsI_ZXB4pczTes7Z4Y5XsNf17VTvGW4NoQA&s=6A-VzuGsMu0_Ey3Mp-TSXjUM4-p3MK85sjcaJZEpXzo&e=>
> 
> Cheers
> 
> Juan
> 
> --
> Juan Sequeda, Ph.D
> www.juansequeda.com <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.juansequeda.com&d=DwMFaQ&c=r2dcLCtU9q6n0vrtnDw9vg&r=yHrezOOUvTAeD_KgsElyJw&m=gaA1u5UYZsI_ZXB4pczTes7Z4Y5XsNf17VTvGW4NoQA&s=S2dSQ7Xed01N86mt8fYTovscWTGH6x-VYNyYknz6abo&e=>
> 
> -- 
> Alexander Garcia
> https://www.researchgate.net/profile/Alexander_Garcia <https://www.researchgate.net/profile/Alexander_Garcia>
> http://www.usefilm.com/photographer/75943.html <http://www.usefilm.com/photographer/75943.html>
> http://www.linkedin.com/in/alexgarciac <http://www.linkedin.com/in/alexgarciac>
>