Ontology essentially won't interfere with the extendibility and flexibility of RDF graph.

[candlecao]

We may compare the schema of a relational database(RDB) and the ontology of RDF; also, we may face a question like how to map the schema of RDB to Ontology of RDF. Regarding these, there are usually some misunderstanding among people.

For example, as far as I am concerned, in our initial ideas of LinkedMusic, we put forward ideas like "avoiding complex schema which may hinder the extendibility and flexibility..." I think this would be further clarified.

• In terms of ontology, the most obvious element is the rdfs:domain or rdfs:range, hereby we ask ChatGpt this way:

In ontology of RDF, if we assert, the rdfs:domain of some property_A is some Class_A, it doesn’t mean instance of other class(for example, Class_B) can not use such property_A as its predicate; actually, it means, if some instance use property_A as its predicate, that instance must be of Class_A. (Being of Class_A and of Class_B is not contradictory) So an ontology is essentially not a restriction on the data structure but the augmented semantic for reasoning, then it’s quite different from a schema of a relational database–Is my understanding right?

Then Gpt may reply this way:

Your understanding is largely correct. Let’s break it down explicitly:

RDFS Domain and Class Membership:

When you assert that the rdfs:domain of a property (let’s call it property_A) is some class (let’s call it Class_A), you are essentially stating that if any instance uses property_A as its predicate, then that instance must be an instance of Class_A.
This does not mean that instances of other classes (e.g., Class_B) cannot use property_A. Rather, if an instance of Class_B uses property_A, that instance is inferred to also be an instance of Class_A. This allows for multiple class memberships.
Ontology vs. Relational Database Schema:

An ontology in RDF is not a strict restriction on data structure but rather an augmentation of semantics for reasoning. It provides a framework for understanding the relationships and properties of data.
In contrast, a relational database schema is a strict blueprint that defines the structure of the database, including tables, columns, data types, and constraints. It enforces rules on how data can be stored and related.
Semantic Reasoning:

Ontologies enable semantic reasoning, allowing systems to infer new knowledge based on the defined relationships and properties. For example, if an instance uses property_A, the system can infer that the instance belongs to Class_A.
This reasoning capability is a key difference from relational databases, which do not inherently support such inferencing based on the schema alone.

In summary, RDF ontologies provide a flexible and semantically rich framework for data representation and reasoning, whereas relational database schemas provide a rigid structure for data storage and integrity. Your understanding that ontologies are not strict restrictions but rather semantic augmentations is accurate.

It's my recent understanding.
Thanks for any criticism and corrections. I would appreciate it.

• On September 1st, 2024, I went on prompting ChatGPT with a further question (in context of aforementioned):

Has this something to do with “Open world hypothesis” or “Close world hypothesis”?
Gpt replied:

Yes, your question touches on a fundamental aspect of the differences between RDF ontologies and relational databases, specifically the concepts of the “Open World Hypothesis” (OWH) and the “Closed World Hypothesis” (CWH). Let’s delve into these concepts explicitly:

Open World Hypothesis (OWH)

• Definition: The Open World Hypothesis assumes that the knowledge base is incomplete. Just because something is not known to be true, it does not mean it is false. There may be additional information outside the current knowledge base that could affect the truth value of statements.

• Application in RDF and Ontologies:

RDF and ontologies typically operate under the OWH. This means that if a certain piece of information is not present in the RDF data, it does not imply that the information is false; it simply means that the information is unknown or not yet included.

For example, if an instance does not explicitly state that it uses property_A, it does not mean that the instance cannot use property_A in the future. The system remains open to the possibility of new information being added.

Closed World Hypothesis (CWH)

• Definition: The Closed World Hypothesis assumes that the knowledge base is complete. If something is not known to be true, it is assumed to be false. The absence of information is taken as evidence of falsehood.

Application in Relational Databases:

Relational databases often operate under the CWH. This means that if a certain piece of information is not present in the database, it is assumed to be false or non-existent.

For example, if a relational database does not have a record of a certain relationship or attribute, it is assumed that the relationship or attribute does not exist.

Relevance to Your Understanding

RDFS Domain and Class Membership:

When you assert that the rdfs:domain of property_A is Class_A, under the OWH, it means that if an instance uses property_A, it must be inferred to be an instance of Class_A. However, the absence of such a statement does not imply that the instance cannot use property_A in the future.

This flexibility aligns with the OWH, where the knowledge base is open to new information and inferences.
Ontology vs. Relational Database Schema:

Ontologies, operating under the OWH, provide a framework for semantic reasoning and inference, allowing for the possibility of new information and relationships to be discovered and added.

Relational database schemas, operating under the CWH, enforce strict rules and constraints, assuming that the absence of information implies non-existence.

In summary, your understanding of RDF ontologies as not being strict restrictions but rather semantic augmentations for reasoning is indeed related to the Open World Hypothesis. This contrasts with the Closed World Hypothesis typically applied in relational databases, where the schema enforces strict data integrity and completeness.

[dchiller]

This seems right... I will comment on one point.

we put forward ideas like "avoiding complex schema which may hinder the extendibility and flexibility..."

Indeed, I think our reconciliation with wikidata is helpful here. If our concept of album from TheSession has some properties (date_recorded, location_recorded, etc.) but then we introduce the concept of album from MusicBrainz, where for all of a sudden the album concept can also have a property release_country, the property exists in Wikidata and we can "extend" our album ontology to include the property release_country. Obviously, we could do that without using Wikidata, but the flexibility of Wikidata in that regard keeps us honest :) Then the country that the album was released in, say, France is already an entity of a class country with all sorts of properties that we did not have to define ourselves.

[fujinaga]

I guess we can use place of publication (P291) for release_country and location (P276) for location_recorded.
I find the following always useful: https://www.wikidata.org/wiki/Wikidata:WikiProject_Music

[candlecao]

I guess we can use place of publication (P291) for release_country and location (P276) for location_recorded. I find the following always useful: https://www.wikidata.org/wiki/Wikidata:WikiProject_Music

Yes. We sometimes refer to this. Thanks.

[candlecao]

This seems right... I will comment on one point.

we put forward ideas like "avoiding complex schema which may hinder the extendibility and flexibility..."

Indeed, I think our reconciliation with wikidata is helpful here. If our concept of album from TheSession has some properties (date_recorded, location_recorded, etc.) but then we introduce the concept of album from MusicBrainz, where for all of a sudden the album concept can also have a property release_country, the property exists in Wikidata and we can "extend" our album ontology to include the property release_country. Obviously, we could do that without using Wikidata, but the flexibility of Wikidata in that regard keeps us honest :) Then the country that the album was released in, say, France is already an entity of a class country with all sorts of properties that we did not have to define ourselves.

I think I understood most of you meanings. I did't understood "the flexibility of Wikidata in that regard keeps us honest :)".

[dchiller]

Thanks! I just mean that because we have wikidata, some of the flexibility is built in rather than something we have to constantly remind ourselves about.

[candlecao]

I think I got your meaning. Then I agree.

[candlecao]

If you actually want to define a "schema" of an RDF graph, what language can you use?

• For example, In Natural Language Question to SPARQL, you want to clarify the frame(schema) of an RDF graph so that ChatGPT won't make mistake in its understanding and task of SPARQL generation .
  

• Such as "The subject of the property wdt:P2561 can only be an instance of the class wd:Q482994, not an instance of any other class."

• Here’s how you can write a SHACL shape to enforce that the subject of the property wdt:P2561 must exclusively be an instance of the class wd:Q482994:

@prefix sh: <http://www.w3.org/ns/shacl#> .
@prefix wdt: <http://www.wikidata.org/prop/direct/> .
@prefix wd: <http://www.wikidata.org/entity/> .
@prefix ex: <http://example.org/> .

# Define a SHACL shape to target subjects of wdt:P2561
ex:PropertyShape_P2561_Subject
    a sh:NodeShape ;
    sh:targetSubjectsOf wdt:P2561 ;
    sh:property [
        sh:path rdf:type ;
        sh:hasValue wd:Q482994 ;
    ] .

Based on above, on Sep 6, I asked ChatGPT:

...So, I believe that SHACL is more analogous to a schema in a relational database, whereas OWL is not typically used in the same capacity. Furthermore, OWL is generally associated with the Open World Hypothesis. If we want to impose constraints on the structure of an RDF database, it is preferable to use SHACL because it aligns more closely with the Closed World Hypothesis, making it better suited for defining strict structural rules than OWL.
–Is my understanding right?

[candlecao]

GPT replied:

[candlecao]

Then, I resume asking GPT:

So, if I want to use ChatGPT to convert a natural language question into SPARQL, I used to provide OWL as the context before asking my question. However, it seems that sometimes OWL alone is not sufficient, and we may need to use SHACL to clarify the schema. Is my understanding correct? If I am right, can you give me an specific example?

GPT replied:

[candlecao]

Then it's associated with a quasi-conclusion with Prompt Engineering:

How to write the context for prompt ChatGPT to convert Natural Language Question into SPARQL more accurately?

With respect to this, I asked GPT:

So, if I want to use ChatGPT to convert a natural language question into SPARQL, I used to provide OWL as the context before asking my question. However, it seems that sometimes OWL alone is not sufficient, and we may need to use SHACL to clarify the schema. Is my understanding correct? If I am right, can you give me an specific example?

It replied:

Conclusion

Using OWL alone provides a framework for understanding the structure and relationships in an RDF dataset. However, OWL does not enforce strict data constraints. By incorporating SHACL, you can define and enforce constraints, which helps ensure data integrity and provides additional clarity on the structure of the data. This combination is particularly useful for converting natural language questions into accurate and contextually relevant SPARQL queries.

Your understanding is correct, and using both OWL and SHACL can enhance the process of converting natural language questions into SPARQL queries by providing both relational context and structural constraints.