Ontology Engineering - Science topic

Dear Carsten Knoll. I developed ThingFO two y. ago, so I'm not aware so far that other researchers used it for abstract mathematical concepts like polynomial or complex number as you are looking for. My interest in ontologies started in early 2000. At that moment, we developed a process ontology, and an ontology for metrics and indicators. In the last years we have harmonized semantically those ontologies with ThingFO in the context of the four-layered ontological architecture. Obviously that for indirect metrics and elementary and derived indicators, we use mathematical constructs (formulas, aggregation models), but that is all. These ontologies are specified at the low-domain level.

For MetricsLDO

For IndicatorsLDO

Try to do real time application of tools in ontology and analyse the results.

Have a look to this paper

C. ROUSSEY,S. BERNARD, G. ANDRÉ, D. BOFFETY. Weather Data Publication on the LOD using SOSA/SSN Ontology.Semantic Web Journal, 2019 http://www.semantic-web-journal.net/content/weather-data-publication-lod-using-sosassn-ontology-0

Additional resources are on Youtube; enter barry smith ontology into the search field.

An ontology is a solution that clarifies the domain vocabulary and the dependecies between the different elements of the domain.

well basically if we are start it from the basics of catalog. They were standalone, not related, not connected. With semantic web technologies they are more interrelated, cross related with other domains and with appropriate meaningful search and discovery through associating more metadata for inferencing by machine. In-short there will be increase in precision and accuracy in searching for specific search item. But still a long way to go to achieve such precision.

I would love to participate but few knowledge about the hardwires. but I can try if suits you mate? Please let me know.

I wish you success in your project.

All the best!

Emre

Hi colleagues, I also suggest to have a look at "Semantic web for the working ontologist" in its second edition. It contains a pragmatic approach with very concrete examples how Semantic Web Technologies help to model information and how to proper model this information to obtain benefits, .e.g., from the reasoning process.

I have investigated this with the of the standard offices and this is the answer they gave me. 

In case a concept is reused, the source (URL) of each reproduced term need to be explicitly mentioned.  In addition, the acknowledgment & disclaimer should be written for each term. 

For example:

Copyright © Year Organization City, Country. Organization web page, e.g., www.organization.com

Yes I start the process of mapping the asserted facts ; classes and instances  and the rules( so the fact that m1 is ham is mapped  as the type of the individual already to the engine ) and then I run the Jess engine and then map the result back to the ontology . Ok I will share it with you . When  I searched I found that this is because of non monotonic reasoning of OWL and SWRL  and that the SWRl rule facts are asserted not inferred so I posted the question to check and find an alternative such that the asserted fact are inferred .

There are also possibilities to construct ontology of domain that is not derived from textual information, but from sensors, databases, visual observation etc. In such case purely artificial language can be generated to express ontology.

If you want to generate an ontology from a Database and have the ability to store semantics on your data stored in the Database, then ONTOP is your tool. I recommend it.

Best regards.

Here's an example of a concept that might illustrate problems I sometimes encounter with describing something structurally or functionally.  DUST-COVER is a part that covers the ejection port and prevents dirt infiltration.  For an AR-15 there is a dust cover that is purely structural and one has the one purpose.  But for the AK-47 the dust cover is actually just the selector lever.  When the AK-47 is in safe the lever covers the ejection port and functions as a dust cover.

There are two ways I can think to solve this.  I don't know which you'd suggest is better.  The first would be multiple-inheritance with concept AK47-SELECTOR having IS-A of both SELECTOR-SWITCH and DUST-COVER.  The second would be AK47-SELECTOR having a single parent, SELECTOR-SWITCH, but maybe being INSTRUMENT to an event like COVER-EJECTION-PORT.

Thank you very much madam for your valuable guidance.

Thanks a lot.

Hello, 

typically, you need to 

1) Load an existing ontology into the memory

2) Add some classes, relations or individuals (assuring uniqueness of URIs)

3) Save back the model into a file

Multiple examples are in Jena API documentation. You may also take a look at 

Best regards

I think that the OWL protege is more interesting for your request

Have you considered owl:unionOf to define a class extension that contains all individuals of the class descriptions in the list? For example:

Note that owl:unionOf is not part of OWL Lite.

Basically, the selection  depends on what you expect of a fuzzy ontological model and fuzzy reasoning. There are tools (I think referenced in https://www.researchgate.net/publication/260514203_Application_of_Fuzzy_Ontological_Reasoning_in_an_Implementation_of_Medical_Guidelines) that incorporate membership functions into the model and support a number of fuzzy operators.

However, another approach (presented in the mentioned paper) is possible. It consists in using plain OWL and adding datatype property (weight) supporting membership level. However, such approach has a number of consequences:

The paper shows that it is possible to encode Mamdani rules with SWRL and combine Jena and Pellet to implement a FIS. See also attached sildes: https://www.researchgate.net/publication/263083436_SLIDES_-_Application_of_Fuzzy_Ontological_Reasoning_in_an_Implementation_of_Medical_Guidelines

Best regards

We can as well use our day today examples about fuzzy ontology where exactness does not matter but outcome matters. 

Hello Farshad,

You can get OntoQA from https://code.google.com/archive/p/tartir-ontoqa/downloads

Plaese let me know if you need any help, or if you have any sugggestions to improve OntoQA.

Also, thank you Jasmin for your recommnedation

 Samir

Dear Santhosh John,

May be the attached dissertation (Semi-automatic Ontology Construction

based on Patterns) can help you.

Best regards,

Harry Achsan

yes, we have done research in this area, details can be found under this link:

Let me know, if you would like to be involved in the research

Conjecture:  Customers and developers must have “sufficiently equivalent” rich ontologies in a domain of discourse (e.g. flight control) to have meaningful discussions about quality attribute goals (e.g. safety goals, ease of use goals, understandability goals).

Conjecture:  If the domain is familiar and the parties are experienced, they are likely to share an ontology.

Questions:  What happens when the domain is unfamiliar to both parties (e.g. to determine compliance of prescription drug ads with FDA regulations)?  What happens when one or both of the parties are inexperienced?  Is meaningful consensus on quality goals possible in these scenarios and under what conditions?

Barry Boehm is working to develop an ontology for quality attributes.  Is a single ontology likely to be useful across the spectrum of software applications and organizations or will organization-tailored and project-tailored quality ontologies be more useful?  Will something else be more useful?

Hope this helps.

As you are primarily interested in matching on instance level, you might also be interested in record linkage or entity resolution approaches. See the following link for instance:

Dear Grangel

You can use any construct to evaluate what you want. Likert scale will allow you to do that. You can try various scales from 3 to 9 (but not dichotomous). And The most important is to see the cronbach alpha. I recomend those papers about the constructs. 

Regards

Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30, 199–218.

Scott B. MacKenzie, Philip M. Podsakoff, Nathan P. Podsakoff (2011). CONSTRUCT MEASUREMENT AND VALIDATION PROCEDURES IN MIS AND BEHAVIORAL RESEARCH: INTEGRATING NEW AND EXISTING TECHNIQUES

Hi Maizura - can you clarify your question for non nuclear physicists?  I am not sure what an 'authorization system' is.  My guess is that it is a kind of algorithm - is that right? and as such would fall under the ontological category of mathematical systems. 

I am not sure how a simulation tool - a software system - would simulate an ontology. 

Sorry if this is unhelpful.  Mike

You are right, Event-B is well-suited for event-based system modeling

Have a look at https://schema.org to start with some basic temrs

I presume you are using protege, rather than jena library/OWL API directly. Have you tried with the export (all inferred axioms) option? or u just saved the file after running reasoner?

Dear

hope this will help you

regards

If a mapping is based on lexical or syntactic similarity only, one should consider how the nature of the mapping adds to the ontological foundation of that mapping. In other words, do all the conclusions that you can draw from that particular class in O1 remain valid for all instances of the mapped items? This is a stronger restriction than the accuracy argument! If you cannot guarantee the consolidation of the validity throughout all logical deductions, I would consider the mapping illegal and hence advice against it.

My impression of the MM literature is that this would be an unusual approach. Gert Biesta has a chapter in the (giant) 2010 edition of the SAGE Handbook of Mixed Methods (Chapter 4, pp95-119) that talks a little about ontological mixing, and doesn't seem to rule it out - though I don't see him give any examples where it was done. You might find it useful one way or another:

There are quite some open onologies in the area of Biology and Biomedicine. Try http://www.obofoundry.org/ for a first overview and further links.

When it comes to medical terms and concepts you certainly want to look at SNOMED and UMLS (https://uts.nlm.nih.gov/home.html)

@Aniruddh I am particularly working on a framework for semantic search (attached) that can support both domain specific or federated searches. I am in process of drafting some detailed work and will post as soon as I am done with it. I have attached a paper (from some other authors) that seems closer to what you are looking for.

I hope it will help. Good Luck!

For me it worked setting 1GB in run.bat

jre\bin\java.exe -Xmx1G -Dlog4j.configuration=file:log4j.xml -DentityExpansionLimit=100000000 -Dfile.encoding=utf-8 -Dorg.protege.plugin.dir=plugins -classpath bin/felix.jar;bin/ProtegeLauncher.jar org.protege.osgi.framework.Launcher

then it loaded successfully the ontology.

However in Win32 systems the JVM can not use more than 1.4GB as documented in this note by Oracle

Ontop 

"-ontop- is a platform to query databases as Virtual RDF Graphs using SPARQL. It's extremely fast and is packed with features."

any application that needs to overcome the semantic heterogenity in particular:

-Integrating sources of information - seeking information - discovery and composition  of web service- the evolution of information systems - changing ontologies .....

Before a good answer can be given, try to decide your definition of similarity. How are two terms or words related? Exact similarity or synonymy is better defined and hence easier to computer. But what about similarity in one context and not in other? [Is DBPedia the right basis for context and hence disambiguation?] What about partial similarity or relatedness? What is the model in which you can consider matching and mapping?  To give you an analogy, see a relatively broader model of similarity called "semantic proximity" in http://dl.acm.org/citation.cfm?id=765855 . It requires you to consider the domain and context, the model of representation, whether you are limited to schema level information or are also looking at instances.

Now you may need to define the model for your need (which is not the database as in the reference above). There is a rich literature on semantic relationships...may want to check it.

On the other hand, current view on ontology alignment (and the benchmarks used for a popular evaluation referred to OAEI) offers a very narrow types of similarities/relationships, and hence has a limited value.

Thanks a lot 

First of all, there is a small syntax error in your SPARQL, remove the ";" at the end of the first line. Second, SUMO does not specify a proper URL as ontology identifier (look at SUMO.owl, it contains the following: <owl:Ontology rdf:about="SUMO">). In Jena, the proper prefix then is the file URL, e.g.:

prefix sumo:<file:///some/path/SUMO.owl#>

If that's not what you want, you could work around using Jena's prefix mapping capabilitites (use something like ParameterizedSparqlString).

Hi Rafael,

Thanks for pointing out the diff tools. I believe, the task I want to do can be accomplished using ecco. Once, I have the ontologies built, I will try them out on ecco web-based front end.

Thanks,

Javed

The connections refers to instantiations of some concepts related to other ontologies (ex: FOAF) in your ontology.

You can filter the classes defined in SUMO by just analysing the URIs prefixes. 

I hope that I was clear and helpful.

This thesis seems to contain some:

see also

Dear Shaeeqa,

I would suggest that the ontology of  "hospitals" would be a combination of three historical events in the region.

The first being India's peoples natural/instructional medical history. This would include the history of the peoples healing beliefs, traditional herbal treatments, medical education history, and the history of medical science.

The second epoch would be the colonial period when under the rule of the Crown the forced "centralization" of medical care due to profit concerns over human life.

Third would be the from India's liberation/independence from British until modern times.

I hope this helps,

Douglas

.  

BioPortal Biomedical Ontology - http://bioportal.bioontology.org/

Ontology for General Medical Science - https://code.google.com/p/ogms/

Open Clinical - http://www.openclinical.org/ontologies.html

I would say that for the beginner, Fluent Editor by Cognitum, would be well worth looking at. The reasons I like this are:

Good luck!

Link to Ontology API:

and nice tutorial:

Hi Saba,

I downloaded food RDF ontology from www.w3.org and I'm using Protege 3.4.5. However, I changed the file extension from food.rdf to food.owl so I can easily loaded it Protege as a new project.

All individuals of a specific class vin:Winery:

SELECT ?x

WHERE { ?x rdf:type vin:Winery}

Facts in the ontology - individuals, classes, relationships (i.e. TBox and ABox):

SELECT distinct ?ind ?class

WHERE { ?ind rdf:type ?class }

ORDER BY ?class

...or even better:

SELECT *

WHERE {

?ind rdf:type ?type .

OPTIONAL { ?type rdfs:subClassOf ?class }

}

ORDER BY ?class

I did not specify prefix. The SPARQL code here is direct copy-paste from Protege.

Remember that prefix is a logical unique identifier. All ontologies have to be successfully imported (loaded) in the same project and the prefix is only used to distinguish between them. It is not uncommon to use several separate ontologies at once and they share same labels for different things so prefixes become necessary. In this working environment (Protege 3.4.5 + SPARQL Query Panel) there is no "loading ontologies in run time" when you run a query. TBox and ABox have to be set up before querying.

If you have more questions about getting individuals of classes I suggest you take a look at semanticweb.org (http://answers.semanticweb.com/questions/2443/find-classes-with-sparql) and stackoverflow.com (http://stackoverflow.com/questions/18666574/sparql-query-to-retrieve-individuals-of-a-class). A short tutorial on Protege imports: http://pmc.ucsc.edu/~dmk/notes/Protege+OWL+Imports.html. 

Also, on the web there are several nice tutorials on SPARQL with examples such as:

I hope this helps,

Marko

There is a rather large body of work which looks at quality of ontologies, particularly in the biomedical domain but not exclusively. 

For a tool you can just use there is the OntoOops online system: slides and link to online tool in slides: http://www.eudat.eu/system/files/MariaPovedaVillalon.pdf  It looks for common issues with ontologies and flags them giving you an idea of quality.

Jeremy Rogers' early work on medical ontology quality: http://www.ncbi.nlm.nih.gov/pubmed/16685334

on concepulization quality: 

and on activity levels as a metric:

and many more...

dear Sourish

thank you so much.

can you introduce me some tutorial in the areas you mentioned ?

Enterprise Integration Patterns is a good start: http://www.enterpriseintegrationpatterns.com/toc.html

Typically you are looking at Remote invocation, Distributed Tracking, Discovery protocols and Message etc. 

Hi Saba, your question is a bit confusing, since these terms refer to different things: rule bases, knowledge bases, ontologies. What Matt suggests above is a repository of ontologies. Rule sets (and rule bases) are quite more difficult to find, but from your question I understand that you need to load an ontology in Protege and then run some rules on it. This is quite easy to do in Protege and you will use the SWRL rule language. Please let us know if you need any assistance in that.

Hi,

This depends greatly on the context, i.e. the problem domain.

What will you be retrieving and how is your data described? Do you already have a sufficiently expressive and decidable ontology? Does the vocabulary of your ontology describe your data well and covers all important details and finesses? The ontology must be decidable so the retrieval can actually finish. The best (and safest) bet is to use ontology languages such as OWL-DL, OWL Lite or RDF/RDFS (in that order). If all this is solved then you have your TBox. After this you must transfer all data that will be searched to ABox – that is, generate individuals from, say, some SQL database of yours. There’s no universal solution to this problem. Many write their own applications which transfer SQL rows to, for example, OWL-DL. This isn’t very difficult. Essentially you have to read SQL and parse it to XML (OWL-DL is based on XML). That can be done in many computer languages. When you have TBox and ABox you have created knowledge base suitable for retrieval with a reasoner.

Finally, you need ontology and a reasoner that support SPARQL (SPARQL Protocol and RDF Query Language). SPARQL is somewhat similar to SQL (at least superficially in syntax) and can be used to query a knowledge base with a reasoner. The output is essentially a retrieved set of data from ABox, described with vocabulary in TBox, that satisfies conditions defined in SPARQL query. Or in other words, that is your retrieved dataset.

To put it all to work I suggest you, for starters, download Stanford’s Protégé. Protégé distribution comes with Pellet reasoner and natively supports SPARQL tab. Open some sample ontology like family.owl and in Protégé 3.x go: Reasoning menu -> Open SPARQL Query panel. A new panel will open on the bottom of the screen where you can input SPARQL queries and see the retrieved individuals (i.e. data). You can also try SPARQL in DBPedia’s Virtuoso Query Editor interface.

Resources:

Hope this helps,

Marko

We have a paper in press where we used an exemplary glossary as the basis for extracting the ontology of a domain. (We were then able to establish the hierarchy of dependencies.)

You might want to take a look at the FIPA device ontology specification: http://www.fipa.org/specs/fipa00091/PC00091A.html#_Toc511707121

@Sowmya

Thank you very much for all your effort! :)

I have downloaded oBrowse and will deploy it on my local Tomcat in a while.

Meanwhile, I have also deployed Manchester's ontology browser here (http://kerveroc.gr:8888/browser/) - feel free to play around and experiment with browsing some ontologies (it has some pre-loaded ontologies, but you can also input the URL of your ontologies if you like).

Will get back to you with my feedback on the oBrowse tool.

I assume that you already know the Protégé Library at http://protegewiki.stanford.edu/wiki/Protege_Ontology_Library. There you can get the travel.owl.

When talking about word meaning, it always is connected with context where it is used. Semantic is part of semiotic, the study of signs. Ontology is a term with several meanings, first of them was the theory of being, developed by Aristotle more than 2300 years ago (but the word "Ontology", in that sense, was written for the first time in the XVIII Century). Currently, and since 1991 (more or less) ontology is used as a term to identify an Artificial Intelligence (AI) concept, a form of knowledge modeling / representation.

One of the most used definition of ontology in AI and related fields (included the ontology engineering and its applications) is: "explicit formal specification of a shared conceptualization".

Both concepts are linked in Semantic Web. Ontology is the formalism selected for representing the "human meaning", in computer understandable way, of resources in the Web, their semantic.

Thank you Samir Amir sir

If your input is textual documents, you need to do text processing techniques. For eg. to find all the classes for your ontology Do Named entity recognition and find out the concepts.

Ontology Maturing is a continuous process that starts with new emergence of idea (which) can be represented through a new tag or relation within the ontology to be then more discussed by the community and/or refined, then shared as a common vocabulary which then can be part of a light-weight formal ontology (Mature ontology).

in the MATURE project (http://mature-ip.eu/) you can find some articles for knowledge maturing and ontology maturing models such as: (Ontology Maturing:

a Collaborative Web 2.0 Approach to Ontology Engineering)

the research of this is still going on and we are in Learning layers project (http://learning-layers.eu/) taking this research further to tackle the problem of scaling up technology at the work place.

You can extract the specialist terms or glossary headwords (e.g., that do not appear in a standard dictionary) or even the concepts (e.g. <headword> <is a> <definition>) from the course corpus to form an ontology or concept map.

This specialist vocabulary/set of concepts has to be taught in the course. Expert students will already know the specialist vocabulary / set of concepts, and do not really need the course. Novices need to learn the specialist vocabulary / set of concepts. The process of learning is converting the student from a novice to an expert. The degree of mastering the specialist vocabulary / set of concepts is measured by the number of headwords / concepts mastered.

Here is some links to publicly available datasets, hope this will help you.

• UCI Machine Learning Repository: Data Sets

– http://archive.ics.uci.edu/ml/datasets.html

• Link to "Open Data" provided in the comments of ResearchGate.Net:

– http://web.ist.utl.pt/acardoso/datasets/

– http://webspam.lip6.fr/wiki/pmwiki.php

• SNAP - Stanford Network Analysis Project:

– http://snap.stanford.edu/data/

– http://snap.stanford.edu/data/links.html

– http://snap.stanford.edu/data/other.html

• The daily news cycle [9* 2.0 GB]:

– http://www.memetracker.org/data.html

• Public Data Sets on AWS:

– http://aws.amazon.com/publicdatasets/

• Wikipedia Revision History [314 millions of rows]:

– https://developers.google.com/bigquery/docs/dataset-wikipedia

– http://dumps.wikimedia.org/enwiki/

• Data for Data Scientists:

– http://www.kaggle.com/

Those already reported, plus LSCOM.

Will it be possible for you to share the findings.

Fact model is based on formal first order logic representation and has uniform solution that that can be reached by series of first order logic equations

On the other hand , domain ontology dos'nt provide a finite uniform solution but reaches to some range of solution domain

Thank you all for your helpful suggestions. I found ARC 2 - An Easy RDF and SPARQL for LAMP systems to expose the endpoints.

(https://github.com/semsol/arc2/wiki )

As the rest of our system was build using LAMP, PHP and MqSQL getting ARC2 working with the rest of the system was very easy. You can try our end point at

This is an agriculture knowledgebase for farmers.

Our Agriculture Ontology was developed using protege. We find it takes a very long time to populate it with NamedIndividual.

Does anyone know a better method to add large number of NamedIndividuals to an ontology developed with protege ?

These should be relevant:

http://www.w3.org/TR/hcls-swan/ (despite the title, it is as a non domain-specific discourse ontology)

check with Michael Vitevitch?

Are you talking about http://www.surrey.ac.uk/englishandlanguages/staff_list/complete_staff_list/margaret_rogers/

and

?

Hi,

when you establish necessary conditions on a class, what you're saying is that every instance that belongs to that class, satisfies them. When you give a definition, you give necessary and suficient conditions for an instance to belong to that class, so, every instance that hold the condition belongs to the class.

For example, I you say that Employee is a subconcept of (Person and has some Job), if you assert that 'John' is an Employee, a reasoner will tell you that John has a Job, because has some Job is a condition that all the instances of Employee must fulfil. However, John having a Job is not enough for the reasoner to tell you that he is an Employee (you are only giving necessary conditions). Then, you could add BadEmployee, establishing that it is equivalent to (Person and (has some Job) and perfoms hasValue "reallyBad"). With the definition, you're stating that every instance of Person, that has some Job and performs badly, belongs to BadEmployee, and the reasoner can use it to classify the instances.

In your question, despite you don't say explicitly that A meets the conditions, when you assert that A belongs to CheesyPizza, a reasoner can infer that A meets them, because you asserted that belongs to CheesyPizza. With a definition , a DL reasoner can infer that an instance belongs to the defined concept by looking whether it holds the conditions; while with a class, the "only" thing that it can infer is that, if you assert that the instance belongs to the class, then it meets all the conditions of the class.

Sorry for the long answer :)

For me, creating the ontology that will be seen within a particular domain or cross-domain as a standard one (standard i.e. widely recognized and used by the ontologists from that domain, who will be willing to reuse the model in their work) should have, among others:

1. Well researched, prepared and described knowledge sources (if, and probably they will, coming from very different sources and levels of abstraction) for a given domain/topic/area of research etc. Especially important when you have knowledge from different domain experts (they will often talk about the same thing but with use of different terminology and their specific point of view).

2. Well researched and specified the reason or i.e. the need for the solution you are going to develop - Questions that your ontology should support answering (mentioned in the posts above). This step is important if you want to provide the means of assessing whether or not your ontology is suitable for a given problem tackled by some other scientists/developers in the future.

3. Well researched and documented relations to already existing ontologies that can/should be used during your project.

Well, this is a difficult notion indeed. Having worked on semantic interoperability for over ten years, I will try to give a simple answer.

First of all you might want to distinguish between Ontology (big O) as a philosophical discipline and ontologies (small o) as engineering artifacts. From what I understand above, you are more interested in the latter.

Second, in general, an ontology tries to specify (formally describe) a view on a particular domain of discourse, by breaking down the central concepts using characteristic distinctions, e.g. the one between a persistent thing and a process (this one is very much discussed, by the way). The use of a logic or a functional formalism adds rules to the relations between the elements you are speaking about and thereby helps to disambiguate their intended interpretation. For example you might want to clarify that you speak about a bank in the financial sector and not about a piece of furniture.

Third, you can see several purposes behind the engineering and use of ontologies. You might want to (i) formalize your view in order to check for consistency; (ii) classify instances (talking about knowledge-bases then); (iii) calculate similarities between entities in your ontology; (iv) map to another world view); etc. The decisions on the used formalization mechanism and also overall modeling approach will depend on the intended purpose. This would also include an evaluation if existing ontologies might/should be re-used, which will anyway require some additional assessment.

Fourth, and before going into too many details, I would recommend the following two articles: Guarino's "Formal Ontology and Information Systems"(www.loa.istc.cnr.it/Papers/FOIS98.pdf), a real classical article; and Kuhn's "Geospatial Semantics: Why, of What, and How?" (http://ifgi.uni-muenster.de/~kuhn/research/publications/pdfs/refereed%20journals/JODS%202005.pdf), which is related to the geospatial sector, but help a lot to understand the general ideas.

I mean specifically support for ontologies into PostgreSQL... Thanks