Re: Trip Reports on Dagstuhl Seminar on Knowledge Graphs

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> 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>
> 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> 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>
>>> 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>
>>>> 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
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