Semantic Web - Science topic

Great!

Check here if you can find some ontologies here. https://lov.linkeddata.es/dataset/lov/

Rey Segundo Guerrero-Proenza respondiendo a la pregunta que le hace a sobre el rol de las ontologías en la Web Semántica a Yaimel... estas permiten darle una organización y acceso más eficiente al conocimiento porque las ontologías estructuran conceptos y relaciones entre ellos, por lo que se puede utilizar en la creación de materiales educativos personalizados; por ejemplo, en el campo de la Psicología Empresarial, se puede desarrollar una ontología que conecte diferentes teorías psicológicas con casos prácticos de liderazgo y gestión organizacional. Otra aplicación pudiera ser con el uso de axiomas y correspondencias entre ontologías permite descubrir conexiones previamente desconocidas entre conceptos. Por ejemplo, al conectar ontologías sobre "estrés laboral" y "productividad", se pueden identificar patrones útiles para desarrollar estrategias empresariales, necesarias al realizar diagnósticos organizacionales.

Then I suggest starting a discussion. For example, your headline is a combination of semantic networks and network analysis. Both are distinct areas. Semantic networks have vertices and edges with assigned meaning, wheras network analysis deals with mathematical objects within a network, such as hubs, shortest paths, betweenness, cliques, clusters, percolation, random walks.

Regards,

Joachim

hi,

The W3C Web Ontology Language (OWL) is a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things.

OWL Full allows free mixing of OWL with RDF Schema and, like RDF Schema, does not enforce a strict separation of classes, properties, individuals and data values. OWL DL puts constraints on the mixing with RDF and requires disjointness of classes, properties, individuals and data values.

Kindly refer this link:

Best wishes..

@ Thanks for your reply. While "Python is just another language" it has from my experience some advantages which make it favorable in a scientific context:

- possibility of interactive usage (e.g. via Jupyter-Notebooks)

- plenty of science-relevant libraries (numpy, sympy, pytorch, ...)

- comparatively low entrance barrier to getting started (e.g. for undergraduate-students from subjects other than computer science)

To prevent misunderstandings: I do not claim that Python is the perfect language. It definitely has weaknesses (such as execution speed).

My point is: If there were more (and better supported) python-tools for semantic-related tasks available and the existing ones were better known, this would significantly foster the development and the applicability of semantic technologies such as ontologies, reasoners and rule-processors.

I expect the scientific world would benefit from such a development because suitable management of available knowledge is one of its core-challenges and having more and easier tools available to tackle that challenge would consequently result in more and better research results.

Even if we discount for "hype-factor": Machine Learning (or numerical AI) techniques have had tremendous success (both in quality and quantity) in many research disciplines and Python-based libraries and interfaces take a large part of the credit for that. I think a similar development could happen with semantic technologies (aka symbolic AI).

" In computing, an ontology is then a concrete, formal representation — a convention—on what terms mean within the scope in which they are used (e.g., a given domain). Like all conventions, the usefulness of an ontology depends on how broadly and consistently it is adopted and how detailed it is. Knowledge graphs that use a shared ontology will be more interoperable. Given that ontologies are formal representations, they can further be used to automate entailment."

Extracted from

Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D’amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann. 2021. Knowledge Graphs. ACM Comput. Surv. 54, 4, Article 71 (May 2022), 37 pages. DOI:https://doi.org/10.1145/3447772

Ontology Rules can be used for reasoning in order to deduce implicity knowledge based on existing KG data.

But an ontology itself can contain data (called individuals or instances or concrete objects). So what is the difference between an ontology and an ontology-based KG? Maybe data size.

Andre Valdestilhas Thank you for the answer. the properties that you mentioned are not always available.

An example to my question, If I need to know the related or similar movies to the initial movie <http://dbpedia.org/resource/The_Terminator>

there are direct links by WikiPageWikiLink property to other movies and other indirect links via a middle resource. so maybe give importance to existing of such direct links over indirect links better than depending on both links equally.

thanks.

regards.

This scientific contribution will be useful in your research.

Abstract

This paper examines the factors that influence the waste generation and willingness to minimise solid waste in Dhaka city, Bangladesh. Information on waste generation, willingness to minimise, socio-economic characteristics, and behaviour of the households towards solid waste management were obtained from interviews with 402 households in Dhaka city. Of these, 103 households regularly practised recycling activities. Ordinary least square (OLS) regression and logistic regression analysis were used to determine the dominant factors that might influence the waste generation and households’ willingness to minimise solid waste, respectively. The results found that the waste generation of the households in Dhaka city was significantly affected by environmental consciousness, income groups, particularly the middle-income earners, and willingness to separate. The significant factors for willingness to minimise solid waste were environmental consciousness, income groups particularly the middle-income earners, young adults mainly those aged between 25 to 35 years and storage facility. Establishment of a solid waste management programme could be an effective strategy for implementing sustainable waste management in Bangladesh. For this strategy to succeed, however, active partnership between the respondents and waste management service department is required. The respondents’ behaviour toward solid waste management practices should be taken into consideration, as should the results of this study, which are important indicators of respondents’ positive attitudes toward sustainable waste management in Dhaka city.

Always first hand data collection is preferred as the researcher has the options to get more information through questions which are appropriate but are not though of before. also, you are sure to get response.

In the digital form, a questionnaire must be well thought, well prepared and exhaustive. Of course even then it is difficult to get response from all the addresses.

The first one is time consuming, requires a lot of effort, but is more scientific and secured than the second one. Although the second approach is preferred now a days because of time constraint and ease of acess.

Prasath Sivasubramanian Thank you for your answer, it seems like all Javascript tools listed on w3.org aren't maintained anymore though. Like Hercules last released was in 2009, OAT is not reachable anymore...

The Eye (https://github.com/josd/eye) engine looks quite interesting I wonder if it's comparable to https://github.com/ucbl/HyL ?

This link maybe useful for you:

http://ceur-ws.org/Vol-576/paper1.pdf

Dear, Deepjyoti Kalita from these reflections that you propose, it would also be convenient to ask about the role of philosophy in relation to heritage in current times and in the post-pandemic era

Thanks for sharing!

Just heard in a podcast about a database that maps words in their semantic context between different languages: CLICS, Cross-Linguistic Colexifications. I think, with such mappings it could be made possible to combine networks from different companies.

Regards,

Joachim

Apache Jena also supports quads.

Greetings professor and great question!

I do hope to see interesting answers here. I will contribute based on my brief experience in the area.

I think that we can see several classical knowledge representation forms in modern applications, for example semantic networks is really popular. In the other hand, vector representation seems the most widespread representation form for machine learning. Vectorial knowledge representation is a different (bottom-up) approach compared to semantic networks. Fussing the top-down approach of semantic networks with the conventional bottom-up approach of machine learning seems to be a current-to-future trend in the area.

No matter what, I think that is more important to discuss the limitations of

vectorial data representation despite its widespread. Maybe that would be a good opportunity to explain how this is one of the core issues in pattern recognition and the role of the sub discipline of learning representations from data (and the incredible hypo that deep learning have had in this area due to its ability to learn several layers of representation from raw sensorial data). The current trend is clear when enough data is available: skip manually handcrafted features and learn the representation automatically.

I always concerns why we need deep learning on RDF or OWL-based data. As the natural of the semantic web, the graph data with specific relations actually presents the outcome of learning. Thus, the query language or rule query language should enable directly answer the questions.

Thanks a lot for the insight. I hope the provided link will

help me more. Otherwise, i have happened to find a claim's dataset on kaggle which i might start working on. For a young Mathematician like me, the information in the link will open my eye to what happens in the insurance in as far as policy lapsing is concerned. Thanks Dr. Gomathy.

That is a very interesting question. In my opinion ontologies have a wide variety of applications, not only in virtual learning. Although, i have not seen a major change in the way that ontologies have been used in the literature, on the other hand, it is a very promising research line, in the sense of what other technoloy or techniques (e.g. from AI) you use side by side with ontologies. For example the topic of context-aware knowledge acquisition for automated planning and execution applications, in that sense using ontologies is very promising for the direction of goal driven autonomy (GDA) and to provide robots that are executing a plan with a higher level of autonomy (bolstered autonomy) to dynamically generate goals and extend the robot domain model with relevant new objects of new types that the robot does not know (was not included in its design). This has two implications, i. if the robot task is a cognitive task / a non physical task basically this would mean that the robot can learn no functionalities (brand new action schemas). On the other hand, ii. if the task of the robot is physical and heavily depends on its physical operating capabilities, the interest would be is to extend its domain with only relevant and operable new objects of brand new types that are strictly manageable by the agent/robot capabilities. I would like to refer you to this paper: with respect to bolstering agents autonomy respecting physical capabilities, and also, this paper: with respect to bolstering autonomy of agents of non specialised physical task.

Hi,

if it's a research question you can see see how to use RFD and NoSQL, this is an interested work :

If about the most used existing tools you can see : https://www.xenonstack.com/blog/tags/semantic-analysis/

verify your threshold pleas

From a production automation point of view:

As already explained in the article published on OPC foundation, the mere vocabulary of the OPC-UA alone are not sufficient to automate the orchestration of different methods offered by the various OPC-UA servers of manufacturing assets in the factory shop-floor. The orchestration engine needs to take care of pre- and post-conditions of the method execution.

Apart from ensuring data consistency, the reasoning engine is also required to operate on the data in real time to understand the context of the manufacturing scenario. We need semantic technologies here to model the axioms and custom rulesets that needs to hold good to determine the next course of action.

I would be willing to collaborate/help/share if this work benefits both of us.

Cheers, Badari

Get this error there are 2 reasons

1. getresultVar function is missing in code

2. As dataset include triples so when query pass in will converted into triples but when match with dataset didnot find words( like to, the, is etc..)which are omitted during converstion of triples .

As i put all the words into txt file which is added into source code also but didn’t get why it is not working...

if you understand my comment and know about some thing please guide me.

thank u a lot Gilles-Antoine Nys.

But these material aren't useful for me as it interested with the breast cancer itself .

while I want make website about breast cancer that get the information from others website that support semantic .

can u help me ?

Apart from the LOD Cloud, which primarily consists of datasets published in Linked Data format, by contributors to the Linking Open Data community project via individuals or organisations, there exists other sources/use-cases of Linked Data which are private.

Many industries fuelled by scenarios including, finance, blockchain, automation and wireless sensor network are adopting semantic technologies for smart applications.

There are various private and government funded projects such as: https://www.researchgate.net/project/OpenBudgetseu-Financial-Transparency-Platform-for-the-Public-Sector , https://www.researchgate.net/project/Industrial-Data-Space , https://www.researchgate.net/project/Semantic-Data-Integration-for-Industry-40-Standards that focus on the consumption of Linked Data.

@Ivan: There is more on this earth (or better said web) than just Google ;-)

Of course, Google makes visual use of its knowledge graph, but I wouldn't call it "semantic web", since it does not follow the principles of the semantic web, i.e. to use (defacto) standards and URIs. You can easily inspect the box you mentioned and what you see there appears to be proprietary. Thus, in order to make this content machine-processable you have to understand it and hack your own interpreter.

However, sites which make use of the semantic web, often do it under the hood without any visual hint for human users. Inspect for example the source code of the pages:

Hence, the semantic web is actually imperceptible and that's probably the problem, why it has difficulties to be adopted more broadly: Its use is not directly visible. Therefore I found the statistic about the use of RDF and microdata, I mentioned above, very helpful.

Of course you are right, transforming HTML into RDF/microformats isn't the problem, the problem is - as you pointed out - understanding the content. But I am pretty sure, that sites which use the semantic web under the hood today, do not interpret their content. With high probability they have integrated the semantic web markup already into their CMS or HTML generation algorithms.

I would be happy to receive any confirmation about my last assumption.

The term data harvesting, or web scraping, has always been a concern for website operators and data publishers. Data harvesting is a process where a small script, also known as a malicious bot, is used to automatically extract large amount of data from websites and use it for other purposes. As a cheap and easy way to collect online data, the technique is often used without permission to steal website information such as text, photos, email addresses, and contact lists.

One method of data harvesting targets databases in particular. The script finds a way to cycle through the records of a database and then download each and every record in the database.

Aside from obvious consequence of data loss, data harvesting can also be detrimental to businesses in other ways:

There are also a lot of other Data Mining techniques but these seven are considered more frequently used by peoples.

Data never sleep. Data are everywhere. You ask about AI-based engines . Just take a look over twitter. Data can help to advance humanity by solving the world’s hardest problems. So, data are power. By harnessing technology to address core human needs, data aim to drive a bottom-up redistribution of power, capital and opportunity. Answers made by AI-based engines can help to create, store, modify, exchange, and exploit information. Of course, that increases the capacity to process data. But, also may lead to data disruptions.

https://www.pyramidanalytics.com/analytics-os?utm_source=twitter&utm_medium=social&utm_content=profile-007909kd57wanut&utm_campaign=EUROPE%3A+2017-11+General+campaign

Note: Just change the Lucene analyser variables to target any language used in SIREn source code (Lucene contains stemmer and tokenizer for English, Arabic and other languages )

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 .

Hi,

There is another issue here: the code that you are using is retrieving only the explicitly asserted subclassOf relations (that's why when you use Protegé to assert them, you can retrieve them). If you want to retrieve the inferred taxonomy, you should use a reasoner, and work with the OWLReasoner instead (or along with) of the OWLOntology. The DL reasoner infers the actual hierarchy that the ontology is modeling, and allows you to navigate through it . 

There are several services that can analyze text to and get RDF from content for concepts, entities, keywords, categories, sentiment, emotion, relations, and semantic roles, based on natural language understanding. Some of those services are free, and others are commercial, but you can use it for free for a small amount of calls.

FRED: Machine Reading for the Semantic Web, it is a service that can to parse natural language text in 48 different languages and transform it into linked data. http://wit.istc.cnr.it/stlab-tools/fred. And you can read the paper about FRED here: http://semantic-web-journal.org/system/files/swj1379.pdf

Hi Nassima,

I don't think that Apriori algorithm is useful for your task. You probably need to work with "cluster" technique.

HTH.

Samer

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

See These :

Good luck

So, since the problem is with regard to searching Arabic text, one way to determine whether your framework improves on the current situation is to examine whether, by using semantic annotations, the results are more accurate or formulating a search query is easier.

I believe this would depend on why and how the tattoos are being stored as archived objects. Is it meant to be stored as it is on the skin, or as a surrogate photograph (or something else)? Is the body viewed as a custodian or as a canvas? Is the tattooist viewed as a artist or a technician? Is the tattoo viewed as a cultural, historical or art object or replication/representation of another object? There are many questions to be answered before the core question can be answered.

I am sure you have already seen this:

There is also this Tattoo schema:

Maybe you will find this project interesting: 

Hi,

Have a look at this link:

HTH.

Samer

i believe  it can.   Some of my  works are related to TAM. You can try searching for  my name or my sister's .. Penjira Kanthawongs, Ph.D

The problem can be handled by building a cluster of computers and using it (queries are independent so parallel processing is useful). Such a cluster can be viewed as a private cloud. It also may be easier to use a public cloud services, get consulting how to do it and move your software to the cloud (Amazon, Google, Microsoft provide such services, for example).

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.

not that i know... worth double checking though

Hello, 

my answer would be Linked Open Data Project started in 2008 by the developer of  WWW in 2008, Here you can  find details of all..

 Here is the pdf, may be it is useful to you..

@Goran Pavlov, 

The idea is not only to create such site, my research requires this as an initial set-up. Traffic has nothing to do with it. 

You can find the function in package pps (see ftp://cran.r-project.org/pub/R/web/packages/pps/pps.pdf , http://www.inside-r.org/packages/cran/pps/docs/ppswr)    I guess this is what you need.

See http://www.semantic-web-journal.net/system/files/swj672.pdf

Regards,

Emmanuel 

Dear Marouen,

As you said, Semantic Data Lake is a new notion. It has zero h-index in Google Scholar. FYI, Semantic Networks has h-index > 50 and Data Lake has h-index=39.

In my opinion, the definition of Semantic Data Lake equals to the definition of Semantic Data, because the definition of Semantic Data doesn't concern with the size of the data.

Best regards,

Nazmul,

You can use an ontology tool like Protege to build a custom finance ontology, or find one online (example: http://www.fadyart.com/ontologies/data/Finance.owl). You can then download the ontology in RDF or OWL. These will be XML files with tags you can parse to extract the terms you want.

You can then add ontology terms to the query to expand it or narrow your results; or to validate the search terms prior to submitting the query. Since the ontology is typically a hierarchy, you can move up or down the hierarchy tree to expand or narrow the the results respectively as necessary.

You can also scan the returned pages for financial terms using the ontology terms by comparing the text in the search engine results pages with the ontology to validate your search results.

The simplest way to start would be to just extract and match the terms.

E.g. from the Finance ontology:

<!-- http://www.fadyart.com/Finance.owl#T-GovernmentTreasury -->

<CFIGuarantee rdf:about="#T-GovernmentTreasury">

<rdfs:comment rdf:datatype="&xsd;string"

>Secured by the governement treasury.

A government treasury is the office issuing Treasury bills, notes and bonds for account of a government.</rdfs:comment>

</CFIGuarantee>

You can then extract and add the rules (axioms).

Eg. from the Finance ontology:

<!--

///////////////////////////////////////////////////////////////////////////////////////

//

// General axioms

//

///////////////////////////////////////////////////////////////////////////////////////

-->

<rdf:Description>

<rdf:type rdf:resource="&owl;AllDifferent"/>

<owl:distinctMembers rdf:parseType="Collection">

<rdf:Description rdf:about="#J-Junior"/>

<rdf:Description rdf:about="#V-GovernmentAgency"/>

<rdf:Description rdf:about="#B-Subordinated"/>

<rdf:Description rdf:about="#T-GovernmentTreasury"/>

<rdf:Description rdf:about="#E-Senior"/>

<rdf:Description rdf:about="#S-Secured"/>

<rdf:Description rdf:about="#G-Guaranteed"/>

</owl:distinctMembers>

</rdf:Description>

Look at the URL file example and at some resources explaining RDF and OWL to figure out the things you want to extract from the files.

Thanks Nazmul, I will check this suite. Best regards.

Hi there, Semantic Web technologies have many applications in e-learning. Feel free to take a look at the following papers of ours:

Feel free to contact me if you need further details.

Best,

Stratos

when is the next conference, would have liked to submit a paper

Hi there,

what you are looking for is called "ontology learning" and it's the process of (semi)automatically building an ontology from textual resources. There are several tools that analyse natural language text and try to extract an ontology (plus instances) from that, although this is still an active research field and there are still many open research issues left.

If you are working with DBpedia or Wikipedia, you can use word2vec algorithm (https://code.google.com/p/word2vec/). It gives good results for finding semantic similarity between words (concepts).

The Object Management Group maintains datasets that formalise the "Semantics Of Business Vocabulary And Rules" (SBVR). You can find them here: http://www.omg.org/spec/SBVR/

Hello Jakub,

I have the same problem with OWL-S. I found a single API to OWL-S, she is old, because she uses depreciated Java APIs, and don't has native WADL or WSDL2 Grounding. I found one solution in iServe. iServe is one Service Semantic description repository. iServe implement matchmaking for semantic service description languagens, include OWL-S. http://iserve.kmi.open.ac.uk/.

I'm begginer in iServe, but It looks promising use and read the publications that they did about SWS. For example, they have this publication, " An Integrated Semantic Web Service Discovery and Composition Framework".

If you find something interesting about composition, share with me.

Best regards

Hi João,

Do you know for a custom semantic application developed and deployed in Sofia 2 Cloud, is any way that the user can set / configure the number of nodes that are allocated for that applications? For example, if I want to test how my app performs in a 5 nodes cluster vs. a 20 nodes, is this possible? How is done the resource allocations on Sofia 2? 

Thanks

Sorin

Hi Haneef, 

Hadoop can definitely help you out in this.

Please go through this link - This article talks about possible solution.

Have a look at http://www.quora.com/What-is-the-difference-between-Wikidata-and-DBpedia

sorry I do not have the expertise to answer this question.

Regards

ApacheBench Version 2.0.40- dev <$Revision: 1.146 $>Apache-2.0

Yes, more information would be required in order to answer your question effectively. However, my intuition tells me that you're probably looking for the Simple Knowledge Organisation System (SKOS):

http://www.w3.org/2004/02/skos/SKOS allows you to define things like broader and narrower relations between various concepts.

However, WordNet RDF/OWL representation (as referenced by Samir above), is a more linguistic approach.

Happy to keep the dialogue going! Let me know if that helps!

Daniel Lewis

Random walk. Variants are possible. For example

this one: start at a certain vertex multiple

times. Choose a random neighbor, but when a

neighbor is chosen that has been visited

before in the same run, start again.

The logarithm of the count of visits

can be used to measure the semantic distance.

Attached is a sample random walk on

cancer-diagnosis from the semantic net

Regards,

Joachim

Hello Simon,

Although ontologies are in essence OWA, they are described as a set of restrictions. IMO the concrete language ( OWL ) may perfectly describe CWA. The question is an interpretation. Obviously reasoners are based on OWA and because this is the only correct way to work with ontologies, getting CWA is a hard task.

In my OWL2XS tool I had to make a lot of logical querying based on negation to get CWA. The simplest example is: Monkeys have a tail and they are mammals. Do humans have a tail? In the OWA they "might". Any OWA reasoner will return an existential quantification hasTail for both mammals and humans unless you specifically negate it. To check CWA one should explicitly try to negate it for the class and this is very computationally intensive.

Probably more interesting would be to create a CWA reasoner - it should be very close to an OWL parser!

So my answer yes you can. Nothing prevents to see your ontology as a closed world.

The question is in which scenario would we need it? In my case I was interested in Web services generation (XML).

Kind regards,

Dmitry

how to implement this paper??

What tool should use for this?

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).

You better first get in touch with the people from the Europeana project who have lots of experience with describing data in an ontological framework

Median,

First of all, Protege does not show by default all the inferences that you may be interested in seeing, but you can easily activate different inference types through the Reasoner->Configure... menu (in your case you want to check the "Domains" and "Ranges" checkboxes under the "Displayed Object Property Inferences" section).

Second, although I am not a logics expert I think, that the answer to your first and second question is in fact "No", that is why I believe Protege does not show them, even if you activate the domain and range inferences in the configuration. Here is my reasoning, in case of your first question:

Although you correctly infer that 

whatever x is, if hasCar(x, y) => driver (x) => human(x),

x will still belong to the subset of those members of the human class that are also members of the driver class (i.e. they will never be member of the complement of the driver class, such as the non-driver humans - which BTW, I think is a inappropriate modeling in your example, since we know that non-driver persons can own a car in reality), and as such the the domain of the hasCar property will remain the driver class and cannot be extended to the entire human class. If you would say, for the sake of the argument, that there is no human that cannot drive (i.e. the human class is equivalent  with the driver class), than protege would show that the domain of the hasCar property is not just driver but also human.

Same reasoning goes for the range in your second question.

The answer to your third question is "True", but possibly not for the reasons that you would think of. The key statement that produce that inference are the range/domain statements, not the subclass relationship between audiCar and car. 

You may wonder why this inferable statement is not shown in Protege, even if you check all the possible reasoner configuration options. Simply, because there are infinite number of statements that can be entailed from an ontology, and Protege shows only a certain subset of those, which are commonly of interest to people who are building or using an ontology.  And this particular type of inference did not fall in that category.

However, if you write this statement into the DL Query plugin, and you check the "Equivalent classes" checkbox, you will see that driver class is returned as an equivalent class to the "driver hasAudiCar min 1 car" expression, and you can even get a nice explanation of why this is the case, if you click on the question mark on the right of the driver class in the query results.

I hope this helps.

Dear Abhishek,

you can start by trying to understand the factors that yield to rumors.

Some works that can interest you are

Best Regards,

Andreas

Dear Kazem, please check this link below, it might be helpful

Good luck.

Sumo is available in KIF here:

You can use this link to obtain the corresponding OWL file:

Hi Mona,

Linked Open Vocabularies (LOV) is great initiative when you are looking for existing ontologies that describe certain concept. You could simply search for a certain class or property and get links to the matched ontologies as a result .

The links usually contain descriptions of the classes and properties of an ontology as well as file in OWL, RDF or other Semantic Web/LOD standard format of the ontology that you could easily convert to OWL (if necessary) using online converters for instance.

Hope you'll find this useful,

Bojan

Thanks for your comments 

Hi, Jolly!

Look that link...

Link to Ontology API:

and nice tutorial: