Re: Wikidata vs DBpedia

[kumar rohit]

Hello Lorenz, if it can not be extracted like DBpedia then how can we use
it in our application. Several month ago, I asked a question, I think here,
that if some data is not available on DBpedia, what we should do and some
body respond that do use Wikidata.
How can we make use of Wikidata in our applications?

On Mon, Mar 20, 2017 at 10:32 AM, Lorenz B. <
buehm...@informatik.uni-leipzig.de> wrote:

> That question could be applied to any other thing in the world where two
> possible options exists...
>
> "why should I use A if I can do what I want already with B?"
>
> I'd suggest read more about Wikidata in your case, then it should be
> clear that Wikidata provides a totally different concept how the data is
> added. It does not extract data from Wikipedia, as DBpedia does, is a
> user curated source for for structured information which is included in
> Wikipedia.
>
> > I am sorry if it is slightly off topic.
> >
> > How Wikidata differs from DBpedia, in terms of building semantic web
> > applications. Wikidata, as I studied as, is a knowledge base which every
> > one can edit? How it differs then from Wikipedia?
> >
> > DBpedia extracts structured data from wikipedia infoboxes and publishes
> it
> > as rdf.
> >
> > If we need Berlin population, we get it from DBpedia via SPARQL. If we
> can
> > do it, why then we need Berlin resource in Wikidata?
> >
> > This question will look strange for some, but I want to understand the
> > concept.
> > Thank you
> >
> --
> Lorenz Bühmann
> AKSW group, University of Leipzig
> Group: http://aksw.org - semantic web research center
>
>

Re: [MASSMAIL]Re: about TDB JENA

[A. Soroka]

OWL 2 certainly features profiles [https://www.w3.org/TR/owl2-profiles] and OWL 
2 RL, as Lorenz indicated, is explicitly intended for implementation via rules:

> The OWL 2 RL profile is aimed at applications that require scalable reasoning 
> without sacrificing too much expressive power. It is designed to accommodate 
> both OWL 2 applications that can trade the full expressivity of the language 
> for efficiency, and RDF(S) applications that need some added expressivity 
> from OWL 2. This is achieved by defining a syntactic subset of OWL 2 which is 
> amenable to implementation using rule-based technologies (see Section 4.2), 
> and presenting a partial axiomatization of the OWL 2 RDF-Based Semantics in 
> the form of first-order implications that can be used as the basis for such 
> an implementation (see Section 4.3).

See https://www.w3.org/TR/owl2-profiles/#OWL_2_RL

But if you mean to say that your ontology is known not to fit any of the OWL 2 
profiles, then you can still use rules (and SPARQL) for some of the problems 
you may wish to solve, but not others. For example, as Lorenz (and I) remarked, 
if your classes are atomic you can use SPARQL property paths to solve 
subsumption problems.

You may not be able to throw all of your data and problems into a single 
"inference machine", but you may very well be able to solve most or all of your 
problems using different techniques.

---
A. Soroka
The University of Virginia Library

> On Mar 20, 2017, at 7:50 AM, Manuel Enrique Puebla Martinez  
> wrote:
> 
> 
> I'm not sure I completely understood your answer. Please confirm my 
> interpretation.
> 
> I think I have understood, that the solution is to write rule-based 
> materialization that substitute the reasoners. Apparently since TDB it is 
> possible to execute those rules of inferences on my big ontology, is it?
> 
> It seems that rule-based materialization is not applicable to OWL2 ontologies 
> (which do not fit into any of the profiles), is it?
> 
> Greetings and thank you very much for your time.
> 
> 
> - Mensaje original -
> De: "Lorenz B." 
> Para: users@jena.apache.org
> Enviados: Lunes, 20 de Marzo 2017 2:24:33
> Asunto: Re: [MASSMAIL]Re: about TDB JENA
> 
> 
> It totally depends on the reasoning that you want to apply. OWL 2 DL is
> not possible via simple rules, but for instance RDFS/OLW Horst and OWL
> RL can be doen via rule-based materialization.
>> I keep going into details, thank you for responding.
>> 
>> Of the 13 million property assertions, almost 80% are assertions of object 
>> properties, ie relationships between individuals. In the last ontology I 
>> generated automatically, only for one of the municipalities in Cuba, I had 
>> 27 763 887 of object properties assertions, 105 054 data property assertions 
>> and 8 158 individuals.
>> 
>> The inference I need is basically the following:
>> 
>> 1) To know all the individuals that belong to a class directly and 
>> indirectly, taking into consideration the equivalence between classes and 
>> between individuals.
> Depends on the reasoning profile and the ontology schema, but might be
> covered by SPARQL 1.1 as long as you need only RDFS/OWL RL reasoning.
>> 
>> 2) Given an individual (Ind) and an object property (OP), know all 
>> individuals related to "Ind", through OP. Considering the following 
>> characteristics of OP: symmetry, functional, transitivity, inverse, 
>> equivalence.
>> 
>> 3) Search the direct and indirect subclasses of a class.
> SPARQL 1.1 property paths as long as the classes are atomic classes and
> not complex class expressions.
>> 
>> 4) Identify all classes equivalent to a class, considering that the 
>> equivalence relation is transitive.
>> 
>> 5) Identify the set of superclasses of a class.
> SPARQL 1.1 property paths as long as the classes are atomic classes and
> not complex class expressions.
>> 
>> Could JENA and TDB afford that kind of inference on my big ontologies?
>> 
>> Excuse me, but I'm not a deep connoisseur of the SPARQL language. I have 
>> only used it to access data that is explicit on the ontology, similar to SQL 
>> in relational databases, I have never used it (nor do I know if it is 
>> possible to do so) to infer implicit knowledge.
> The approach that people do is either query rewriting w.r.t. the schema
> or forward-chaining, i.e. materialization based on a set of inference
> rules. For RDFS, OWL Horst and OWL RL this is possible. Materialization
> has to be done only once (given that the dataset does not change).
>> 
>> I put copy to Ignazio Palmisano, an excellent researcher and connoisseur of 
>> the framework OWLAPI. With which I have been exchanging on this subject.
>> 
>> Best regards.
>> 
>> 
>> - Mensaje original -
>> De: "Dave Reynolds" 
>> Para: users@jena.apache.org
>> Enviados: Domingo, 19 de Marzo 2017 13:45:48
>> Asunto: Re: [MASSMAIL]Re: about TDB JENA
>> 
>> 

Re: [MASSMAIL]Re: about TDB JENA

[Manuel Enrique Puebla Martinez]

 
 I'm not sure I completely understood your answer. Please confirm my 
interpretation.

I think I have understood, that the solution is to write rule-based 
materialization that substitute the reasoners. Apparently since TDB it is 
possible to execute those rules of inferences on my big ontology, is it?

It seems that rule-based materialization is not applicable to OWL2 ontologies 
(which do not fit into any of the profiles), is it?

Greetings and thank you very much for your time.


- Mensaje original -
De: "Lorenz B." 
Para: users@jena.apache.org
Enviados: Lunes, 20 de Marzo 2017 2:24:33
Asunto: Re: [MASSMAIL]Re: about TDB JENA


It totally depends on the reasoning that you want to apply. OWL 2 DL is
not possible via simple rules, but for instance RDFS/OLW Horst and OWL
RL can be doen via rule-based materialization.
>  I keep going into details, thank you for responding.
>
> Of the 13 million property assertions, almost 80% are assertions of object 
> properties, ie relationships between individuals. In the last ontology I 
> generated automatically, only for one of the municipalities in Cuba, I had 27 
> 763 887 of object properties assertions, 105 054 data property assertions and 
> 8 158 individuals.
>
> The inference I need is basically the following:
>
> 1) To know all the individuals that belong to a class directly and 
> indirectly, taking into consideration the equivalence between classes and 
> between individuals.
Depends on the reasoning profile and the ontology schema, but might be
covered by SPARQL 1.1 as long as you need only RDFS/OWL RL reasoning.
>
> 2) Given an individual (Ind) and an object property (OP), know all 
> individuals related to "Ind", through OP. Considering the following 
> characteristics of OP: symmetry, functional, transitivity, inverse, 
> equivalence.
>
> 3) Search the direct and indirect subclasses of a class.
SPARQL 1.1 property paths as long as the classes are atomic classes and
not complex class expressions.
>
> 4) Identify all classes equivalent to a class, considering that the 
> equivalence relation is transitive.
>
> 5) Identify the set of superclasses of a class.
SPARQL 1.1 property paths as long as the classes are atomic classes and
not complex class expressions.
>
> Could JENA and TDB afford that kind of inference on my big ontologies?
>
> Excuse me, but I'm not a deep connoisseur of the SPARQL language. I have only 
> used it to access data that is explicit on the ontology, similar to SQL in 
> relational databases, I have never used it (nor do I know if it is possible 
> to do so) to infer implicit knowledge.
The approach that people do is either query rewriting w.r.t. the schema
or forward-chaining, i.e. materialization based on a set of inference
rules. For RDFS, OWL Horst and OWL RL this is possible. Materialization
has to be done only once (given that the dataset does not change).
>
> I put copy to Ignazio Palmisano, an excellent researcher and connoisseur of 
> the framework OWLAPI. With which I have been exchanging on this subject.
>
> Best regards.
>
>
> - Mensaje original -
> De: "Dave Reynolds" 
> Para: users@jena.apache.org
> Enviados: Domingo, 19 de Marzo 2017 13:45:48
> Asunto: Re: [MASSMAIL]Re: about TDB JENA
>
> On 19/03/17 15:52, Manuel Enrique Puebla Martinez wrote:
>> I consider that I did not know how to explain correctly in my previous 
>> email, I repeat the two questions:
>>
>>
>> 1) I read the page https://jena.apache.org/documentation/tdb/assembler.html, 
>> I do not think it is what I need.
>>
>>I work with large OWL2 ontologies from the OWLAPI framework, generated 
>> automatically. With thousands of individuals and more than 13 million 
>> property assertions (data and objects). As one may assume, one of the 
>> limitations I have is that OWLAPI itself can not manage these large 
>> ontologies, that is, because OWLAPI loads the whole owl file into RAM. Not 
>> to dream that some classical reasoner (Pellet, Hermit, etc.) can infer new 
>> knowledge about these great ontologies.
>>
>> Once explained the problem I have, comes the question: Does JENA solve this 
>> test ?, ie with JENA and TDB I can generate my great ontologies in OWL2 ?, 
>> With JENA and TDB I can use a reasoner to infer new implicit knowledge 
>> (unstated) on my big ontologies?
>>
>> I do not think JENA will be able to solve this problem, it would be a 
>> pleasant surprise for me. Unfortunately so far I had not read about TDB and 
>> the potentialities of JENA in external memory.
> Indeed Jena does not offer fully scalable reasoning, all inference is 
> done in memory.
>
> That said 13 million assertions is not *that* enormous, the cost of 
> inference depends on the complexity of the ontology as much its scale. 
> So 13m triples with some simple domain/range inferences might work in 
> memory.
>
> TDB storage itself scales just fine and querying does not load all the 
> data 

Re: Wikidata vs DBpedia

[Lorenz B.]

That question could be applied to any other thing in the world where two
possible options exists...

"why should I use A if I can do what I want already with B?"

I'd suggest read more about Wikidata in your case, then it should be
clear that Wikidata provides a totally different concept how the data is
added. It does not extract data from Wikipedia, as DBpedia does, is a
user curated source for for structured information which is included in
Wikipedia.

> I am sorry if it is slightly off topic.
>
> How Wikidata differs from DBpedia, in terms of building semantic web
> applications. Wikidata, as I studied as, is a knowledge base which every
> one can edit? How it differs then from Wikipedia?
>
> DBpedia extracts structured data from wikipedia infoboxes and publishes it
> as rdf.
>
> If we need Berlin population, we get it from DBpedia via SPARQL. If we can
> do it, why then we need Berlin resource in Wikidata?
>
> This question will look strange for some, but I want to understand the
> concept.
> Thank you
>
-- 
Lorenz Bühmann
AKSW group, University of Leipzig
Group: http://aksw.org - semantic web research center

Re: [MASSMAIL]Re: about TDB JENA

[Lorenz B.]

It totally depends on the reasoning that you want to apply. OWL 2 DL is
not possible via simple rules, but for instance RDFS/OLW Horst and OWL
RL can be doen via rule-based materialization.
>  I keep going into details, thank you for responding.
>
> Of the 13 million property assertions, almost 80% are assertions of object 
> properties, ie relationships between individuals. In the last ontology I 
> generated automatically, only for one of the municipalities in Cuba, I had 27 
> 763 887 of object properties assertions, 105 054 data property assertions and 
> 8 158 individuals.
>
> The inference I need is basically the following:
>
> 1) To know all the individuals that belong to a class directly and 
> indirectly, taking into consideration the equivalence between classes and 
> between individuals.
Depends on the reasoning profile and the ontology schema, but might be
covered by SPARQL 1.1 as long as you need only RDFS/OWL RL reasoning.
>
> 2) Given an individual (Ind) and an object property (OP), know all 
> individuals related to "Ind", through OP. Considering the following 
> characteristics of OP: symmetry, functional, transitivity, inverse, 
> equivalence.
>
> 3) Search the direct and indirect subclasses of a class.
SPARQL 1.1 property paths as long as the classes are atomic classes and
not complex class expressions.
>
> 4) Identify all classes equivalent to a class, considering that the 
> equivalence relation is transitive.
>
> 5) Identify the set of superclasses of a class.
SPARQL 1.1 property paths as long as the classes are atomic classes and
not complex class expressions.
>
> Could JENA and TDB afford that kind of inference on my big ontologies?
>
> Excuse me, but I'm not a deep connoisseur of the SPARQL language. I have only 
> used it to access data that is explicit on the ontology, similar to SQL in 
> relational databases, I have never used it (nor do I know if it is possible 
> to do so) to infer implicit knowledge.
The approach that people do is either query rewriting w.r.t. the schema
or forward-chaining, i.e. materialization based on a set of inference
rules. For RDFS, OWL Horst and OWL RL this is possible. Materialization
has to be done only once (given that the dataset does not change).
>
> I put copy to Ignazio Palmisano, an excellent researcher and connoisseur of 
> the framework OWLAPI. With which I have been exchanging on this subject.
>
> Best regards.
>
>
> - Mensaje original -
> De: "Dave Reynolds" 
> Para: users@jena.apache.org
> Enviados: Domingo, 19 de Marzo 2017 13:45:48
> Asunto: Re: [MASSMAIL]Re: about TDB JENA
>
> On 19/03/17 15:52, Manuel Enrique Puebla Martinez wrote:
>> I consider that I did not know how to explain correctly in my previous 
>> email, I repeat the two questions:
>>
>>
>> 1) I read the page https://jena.apache.org/documentation/tdb/assembler.html, 
>> I do not think it is what I need.
>>
>>I work with large OWL2 ontologies from the OWLAPI framework, generated 
>> automatically. With thousands of individuals and more than 13 million 
>> property assertions (data and objects). As one may assume, one of the 
>> limitations I have is that OWLAPI itself can not manage these large 
>> ontologies, that is, because OWLAPI loads the whole owl file into RAM. Not 
>> to dream that some classical reasoner (Pellet, Hermit, etc.) can infer new 
>> knowledge about these great ontologies.
>>
>> Once explained the problem I have, comes the question: Does JENA solve this 
>> test ?, ie with JENA and TDB I can generate my great ontologies in OWL2 ?, 
>> With JENA and TDB I can use a reasoner to infer new implicit knowledge 
>> (unstated) on my big ontologies?
>>
>> I do not think JENA will be able to solve this problem, it would be a 
>> pleasant surprise for me. Unfortunately so far I had not read about TDB and 
>> the potentialities of JENA in external memory.
> Indeed Jena does not offer fully scalable reasoning, all inference is 
> done in memory.
>
> That said 13 million assertions is not *that* enormous, the cost of 
> inference depends on the complexity of the ontology as much its scale. 
> So 13m triples with some simple domain/range inferences might work in 
> memory.
>
> TDB storage itself scales just fine and querying does not load all the 
> data into memory. So if you don't actually need inference, or only need 
> simple inference that can be usefully expressed as part of the SPARQL 
> query then you are fine.
>
> Dave
>
> La @universidad_uci es Fidel. Los jóvenes no fallaremos.
> #HastaSiempreComandante
> #HastalaVictoriaSiempre
>
-- 
Lorenz Bühmann
AKSW group, University of Leipzig
Group: http://aksw.org - semantic web research center