RDF - Science topic

In your case, the FUE for 50% RDF (Recommended Dose of Fertilizer) was highest at 115 kg/kg NPK, and it decreased as the RDF increased. This suggests that at lower fertilizer levels, the plants were able to utilize a greater proportion of the applied nutrients, resulting in higher FUE. Conversely, at higher fertilizer levels, a larger proportion of the applied nutrients were likely lost or remained unabsorbed, resulting in lower FUE.

It’s important to note that while reducing fertilizer levels can increase FUE, it’s crucial to ensure that the plants are still receiving adequate nutrition for optimal growth. Balancing these factors is key to sustainable and efficient agricultural practices.

Simulating a chemical reaction in COMSOL Multiphysics involves using the Reaction Engineering interface. This interface allows you to set up and solve reaction systems involving multiple species and reactions. Here's a general guide on how to simulate a chemical reaction in COMSOL:

Remember that the specific steps and settings required for simulating a chemical reaction may vary depending on the complexity of the reaction system and the specific physics involved. Make sure to refer to the COMSOL documentation and tutorials related to reaction engineering for more detailed information on setting up your specific chemical reaction simulation.

Probably completion in a real world application is a strongly domain depending task. And starting from the fact that I am not aware of the specific knowledge represented here, I would speculate that OWL should be a good option, considering that its deductive capabilities can help in completion, at least by obtaining new items, not explicitly defined, and preserving the consistence, by regularly checking.

Can be argued that complexity will be a concern when rising the graph order/size, but you always can easily move from and OWL to other representation, keeping those nodes/edges obtained by deduction. On the other hand, you could consider apply "light weight ontology languages", but it's always depending on domain.

I suggest to be a bit more descriptive, and, BTW, include other keywords, like graph completion, in order to attract the attention the related to the problem specialists.

Hi,

You can try MD analysis or mdtraj RDF methods for your calculations.

Anelia Kurteva Thank you for your advice. Thanks?(?ω?)?

But I just want to visualize RDF triples, and I don't want to use neo4j to store RDF triples.

My data is stored in RDF Databases and queried by SPARQL Endpoint.

We in USW Ltd work on creating smart systems, which is obviously related to a semantics processing.

We work with graph organized data since 1997 year. This experience helped us to define the so called by us Semantic Network Based Architecture -https://www.researchgate.net/publication/334680146_Semantic_Network_Based_Architecture. Using this architecture we developed our Unified Platform for Innovations(UPI). Till now we built two UPI-based workflow management systems- one for administration (Smart DOCMAN), and a second for the industry. A general presentation can be found on www.usw.bg.

Currently we work on tools and technologies in UPI-environment, ensuring us to make our systems “smart”. Clearing our understanding about semantics, concepts, data and so on, we have reached to our definition for KG and the idea, that the KG has to be unified- Unified Knowledge Graph (uKG)- https://www.researchgate.net/publication/361532200_Unified_Knowledge_Graph.

Our next step was to clear our understanding about semantic constructions, which led us to the concept for The Ontology as a Unified Knowledge Graph construction- https://www.researchgate.net/publication/361813986_The_Ontology_as_a_Unified_Knowledge_Graph_construction.

We think, that smart systems have not to be learned, they have to be educated, as is described in Root Language and Machine Education- https://www.researchgate.net/publication/361814428_Root_Language_and_Machine_Education.

You can't bind results of property paths to triples with standard SPARQL.

Pobably you could try something like this:

SELECT ?since ?t {

?f1 ns:FRIENDS_OF+ ?f2 .

ns:p3 ns:FRIENDS_OF* ?f1 .

?f2 ns:FRIENDS_OF* ns:p2 .

BIND(<<?f1 ns:FRIENDS_OF ?f2>> AS ?t) .

?t ns:date_of_start ?since . FILTER (?since > 2016) .

}

First it generates all possible combinations of friends ?f1 and ?f2. Then it only keeps those that are related to ns:p3 and n2:p2.

Afterwards the RDF-star triple is created via a binding and used for testing the start date.

Hello

I think this article will be helpful

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..

My favourite is Blazegraph (not maintained anymore though)

Download the jar file and from command line launch it with one line

https://github.com/blazegraph/database/wiki/Quick_Start

NB. It requires Java to be installed!

@ 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).

Your idea sounds very interesting and I would really like to play around with those reasoners (ideally based on some well documented examples). Maybe a good start would be to setup a repo and create a dockerfile for each reasoner which should be supported. Once those backends behave well-defined it should not be too hard to develop a frontend providing the REST-API.

Some time ago I did something loosly related: https://github.com/ackrep-org/ackrep_deployment.

Sales Aribe Jr. thank you for the reply.

My question is specific about RDF* ou RDF-Star. It is a "an extension of RDF's conceptual data model and concrete syntaxes, which provides a compact alternative to standard RDF reification. This model and its syntaxes enable the creation of concise triples that reference other triples as subject and object resources."

See more at https://w3c.github.io/rdf-star/cg-spec/editors_draft.html

Without knowing how the system is built, what potential is used, there's no way of providing any useful comments.

Dear Ehsan

If you carefully study the paper on hydrogen bonds, usually three classes are distinguished: weak, moderate, and strong bonds, with energetic boundaries at about 2 and 15 kcal/mol.

The weak hydrogen bonds involve less polar X-H groups in proton donors. The weakest hydrogen bonds considered in the literature are about 0.5 kcal/mol. It has distance of upto 4.5 Armstrong.

Moderate occurs in in range of 2.0 to 3.5 Armstrong. While Strong hydrogen bonds do occur in range of 1.3 to 2.0 Armstrong. Strong hydrogen bond generally are involved in systems having high electronegative atoms.

Since you are using gromacs, which is generally based on molecular mechanics, it would be better to use quantum based techniques like DFT for better understanding of behaviour of hydrogen atoms around Oxygen atom of DMF.

Since, DMF has partial double bond character for the C-N and C-O bonds.

There is possibility that highly electronegative oxygen atom may form hydrogen strong bond with hydrogen atom of water.

Hope it will help.

regards

Tanuj

While building a knowledge graph from text, it is important to make sure our machine understand natural language. This is where Natural Language Processing (NLP) comes into the picture. The knowledge graph from an unstructured text can be built by using NLP techniques such as sentence segmentation, dependency parsing, parts of speech tagging, and entity recognition.

Hi

It is recommended to calculate the RDF by VMD software rather than LAMMPS. It has an explicit GUI with controllable parameters.

Bilal Ben Mahria check out this article

Hi.,

Kindly check this link:

Best Wishes..

Well, lets be clear about the definition of the term "ontology" first. Follwing Tom Gruber "An ontology is a specification of a conceptualization." (drawn from http://www-ksl.stanford.edu/kst/what-is-an-ontology.html) "That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents." Borst defined an ontology as a “formal specification of a shared conceptualization” (from https://iaoa.org/isc2012/docs/Guarino2009_What_is_an_Ontology.pdf), in order to be precise about the meaning that the community of agents should agree on the conceptualisation. Studer, Benjamins & Fensel extended this by the term "formal", but we can ignore this aspect for the moment.

Anyway, a knowledge graph - put aside how it is defined - should represent knoweldge about the world. Without any ontological definition of concepts, the nodes in the graph belong to, and the relations beween them, a knowledge graph is nothing more than a semantic network for which the criticism of Woods "What's in a link?" applies, i.e. he criticed that the links in a semantic network lack a proper meaning.

This can be seen more or less as the starting point of research on description logics, which by the way build the foundation of OWL. An important contribution of this research was the distinction of T-Box and A-Box, i.E. the terminological and the assertional component of knowledge representation. In that sense the T-Box represents an ontology following all definitions above. The point now is that the T-Box specifies our conceptualisation and hence at the same time fixes the terminology we use to describe a domain of discourse. The A-Box contains the contingent knowledge of the domain of discourse. In other words, if our domain of discourse is information about the world then a knowledge graph to large parts belongs to the assertional component and is described in terms of the terminology of the T-Box.

That are the reasons why I would say: if a knowledge graph should express knowledge (and not only information) about the world an ontology is an essential precondition for sharing the knowledge in the KG between agents.

I think it is possible to calculate but the result will be the same as the ion concentration profile. For more info you can see this book:

Karniadakis G, Beskok A, Aluru N. Microflows and nanoflows: fundamentals and simulation. Springer Science & Business Media; 2006 Feb 9.

As an answer to the question, I am sending pdf files for two publications.

Dear João Guimarães

This article using SKOS tool might be helpful, have a look:

Hope this helps.

Kind Regards

Qamar Ul Islam

You can follow the following paper, maybe can get ideas

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 ?

Thank You Luis Ramos I will definitely read this article.

This might be due to high multiplication of micor organism that need nitrogen for building block of body and also need to lowering the C:N ratio in crop residue using for making of vermicompost.

VMD is a freely available software to calculate RDF and many more properties.

you have missed 'out' in this command，this command should have been 'radial out rdf.xmgr'.

Dear

Hi,

Thank you very much for your reply.

I compared my RDF with a famous article results with a same system. I figured out my problem. Actually, the plot is true but I didn't know how I can extract results from the plot.

Thank you again for your reply

Mohammad

Frank Haferkorn Thanks, I will read it and try it.

Thanks for your reply.

Yes, you are right these are the plots of the anions. The protein is insulin which has only 51 amino acids and I have calculated the RDF for a specific N-terminal residue (Phenylalanine). I have also calculated the RMSD, RMSF etc.

Would like to know the reason for the discrepancy with respect to RDF and concentration of the anions.

Hello Inna Ermilova

I have tried adding the index file, but it says I have multiple groups called MEO and URE. This might indicate that my tpr/pdb files already know that there are groups called MEO and URE, no?

So I tried the following script and it worked:

gmx rdf -s tpr.tpr -ref MEO -sel URE -f xtc.xtc -surf mol -bin 0.03

However I got the same result- an unnormalized radial distribution.

What am I still doing wrong? Maybe I still don't understand the groups you suggested.

Thanks again for your help,

Ilan

You may use code generation functionality to generate code. It will generate at least a skeleton of the code which can be converted to a working code by inserting instances.

this paper help you

Good Answer Debasis Dhak

There are two ways to have ReaxFF force field parameters:

1. Develop this force field for your system through quantum mechanical calculations.

2. Finding articles that they already studied a structures similar to your structure.

(In terms of temperature, pressure, Phase and almost similar species). In this case, you can use the force field parameters of those articles in the Supporting information section.

Like this:

hi.,

refer this link:

hello!

It's a good question.

you can look to my book: " ontologies et web sémantique".

and I'm here for any question.

thank you.

Thanks to all who agreed with me. We should look into this and I feel it's the right time to guide future agriculture / crop production.

based on the economics in the US, about 1,000 tons/day plant would be the minimum for sustained operations

Alright, thank you guys!

Dear Shaid,

The output of this command is the com-com RDF and I try to calculated surf-com RDF. In GMX when I calculate RDF respect to surface the g(r) values arestrangely high.

since you have change across time I would try a longitudinal model. I would see this as somewhat like a simple clinical trial of one experimental drug versus standard treatments.A really good introduction to longitudinal models is Prof Marie Davidian's class notes available at this link.

https://www.stat.ncsu.edu/people/davidian/courses/st790/notes.html

Good luck, D. Booth

Dear Candy, Try to make an index of the CNT and ionic liquid atoms, then do the RDF between the two groups you want. Use the gmx make_ndx tool to do the index and gmx rdf to do the RDF.

How did you remove that vacuum layer? My rdf doesn't go to 1. stuck at 1.1

What is the purpose of your MD simulations? What questions do you have that you want to answer by running and analyzing MD simulations? Ask these questions to yourself, and then think what kinds of measurements could give you the answers.

(Complement the structural analysis with energetical analysis. The energies will help you with the interpretation of the structures, and vice versa.)

Dear Omar,

In addition to PCA and ICA, you may also try to utilize Nonnegative matrix factorization (NMF) technique, which is another method for projecting a data from a higher dimensional space into a lower dimensional space. Thus, reducing the dimension of the original dataset.

I hope you would find this tips helpful.

Best wishes.

Thanks Dr. Kranz and Dr. Sadeghi for such detailed explanations. The RDF plots make a lot more sense to me now. Earlier, I did not consider the effects of concentration while analyzing the plots. Through my simulations, I was expecting a double layer with the bulkier cations as the first layer.

There are just few more queries which I would like to clarify.

1. Could you please explain more on " your peaks are already pretty strong"? Can the plot be inferred as strong if the g (r) is approximately 1?

2. Does the plot mean that the both the cations and anions intercalate the protein backbone and are able to penetrate into the core of the protein?

3. Attached is the RDF of my second set of anions (acetate) with the same cation. Is it right to conclude that the acetate anions are interacting with the protein's surface much more than that of the anion 1? This combination of acetate and cation has indeed led to an increased RMSD.

I remember there were some papers in the Semantic Web conferences in the past, but I don't recall specifically what these were about and which conference that was. A quick search leads me to a couple of potential starting points for further search:

Nguyen, D., Kopena, J., Loo, B., & Regli, W., Ontologies for Distributed Command and Control Messaging, Formal Ontology in Information Systems, Proceedings of the 6th International Conference, May 2010, doi: 10.3233/978-1-60750-534-1-373

Simas, F., Barros, R., Salvador, L., Weber, M. and Amorim, S., 2017, November. A Data Exchange Tool Based on Ontology for Emergency Response Systems. In Research Conference on Metadata and Semantics Research (pp. 74-79). Springer, Cham.

Steel, J., Iannella, R. and Lam, H.P., 2008, May. Using ontologies for decision support in resource messaging. In Proceedings of the 5th International ISCRAM Conference. F. Fiedrich and B. Van de Walle, eds. Washington, USA (pp. 276-284).

Kantorovitch, J., Giakoumaki, A., Korakis, A., Papadopoulos, H., Milis, G., Kolios, P. and Staykova, T., 2015, November. Knowledge modelling framework. In Information and Communication Technologies for Disaster Management (ICT-DM), 2015 (pp. 145-151). IEEE.

Cheers

Markus

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.

Hi there,

RDF documents are named directed graphs: an RDF document is a series of subject,predicate,object statements that you can think of as a set of graph edges, named after the predicate and running from the subject to the object.

XML is a way to serialize RDF so that it can be stored or transferred, and it is not the only possible serialization for RDF. TTL, for instance, can be used instead of XML.

If your RDF data happens to be serialized as XML, it is possible to use XPath to query it, although it's not very advisable. If you use XPath you will spend too much effort and energy worrying about the syntax of the serialization instead of worrying about what it is you want to retrieve from the document.

SPARQL is a SQL-like language that is a much more intuitive way to query RDF documents, regardless of how they are stored. With SPARQL you will query the data using patterns over the edges of the graph, so that getting the names of X's grandchildren will be as straightforward as

?X ex:child ?Y .

?Y ex:child ?Z .

?Z foaf:name ?Name .

Now about your concrete problem, and if haven't been persuaded yet to use SPARQL instead of XPath, you should post the minimal data and the query needed to reproduce the problem, so people can help.

Best,

Stasinos

Unfortunately, this RDF for a given crop , when you replicate it on the same field , after 10 years , doesnt hold good, this is my first take . And , my another take on the issue is , how does this RDF cater to the needs of different soil types and other associated agro-pedological conditions using the same crop. How does the magnitude of crop response vary ..?? In that case , do we need to strengthen the soil -crop response models more stringently , especially with respect to interpretation of soil-crop relationship , since in field , it is multiple nutrients ( deficient or optimum levels) that a given crop has to respond...

@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.

Dear Mengist,

Please follow the reference papers given below:

1. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008, June). Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data (pp. 1247-1250). AcM.

2. Angles, R., & Gutierrez, C. (2008). Survey of graph database models. ACM Computing Surveys (CSUR), 40(1), 1.

3. Vicknair, C., Macias, M., Zhao, Z., Nan, X., Chen, Y., & Wilkins, D. (2010, April). A comparison of a graph database and a relational database: a data provenance perspective. In Proceedings of the 48th annual Southeast regional conference(p. 42). ACM.

4. Angles, R. (2012, April). A comparison of current graph database models. In Data Engineering Workshops (ICDEW), 2012 IEEE 28th International Conference on (pp. 171-177). IEEE.

5. Patil, N. S., Kiran, P., Kiran, N. P., & KM, N. P. (2018). A Survey on Graph Database Management Techniques for Huge Unstructured Data. International Journal of Electrical and Computer Engineering (IJECE), 8(2).

Thanks,

Sobhan

Dear Neil Movold,

Look the link, maybe useful.

Regards, Shafagat

You can use named graphs.

In particular, Nanopublications (http://nanopub.org/guidelines/working_draft/) allow you to specify both provenance of triples and provenance of provenance statements.

Hi Amany,

It seems to me that you are asking not just how the fundamentals of RDF work, but what RDF /is/. Is that right? I think that because the other answers, however correct, didn't seem to satisfy you.

RDF is the Resource Description Framework. The name is important because it gives a clue to the original thinking of the inventors. RDF is a framework (that is, a model) to describe resources. Which resources? Originally, and still often, they were resources on the Web. That is, Web pages. These days, the (very general) RDF data model is used to describe all sorts of things whether they are on the Web or not.

Here is an important, but subtle and underused, point: You cannot say anything in RDF less than one triple's worth of information. That is, a single triple is the least content possible in an RDF graph.

RDF is therefore an odd sort of data model in that you cannot just name a resource explicitly. You can only name it implicitly by assigning it an IRI used in a triple. The triples that refer to a resource can collectively provide enough information about the resource that everyone (should) agree on what the resource is.

A triple has three components, as Keven and Harsh described. The easiest way to think about an RDF triple is to note that triples are one of two kinds:

1) A triple may describe two /things/ and the relationship between them. Here is an example of a single RDF triple that names two things and links them together with a relationship:

<an IRI representing me> <an IRI representing the verb "authored"> <an IRI representing this answer>

(the first thing, me) (the relationship, in this case "authored") (the second thing, this answer)

You can read that triple as "David Hyland-Wood authored this answer".

In this case, IRIs are used to name the two things and also to name the relationship. That's almost always the way that RDF triples are written.

2) A triple might also provide a piece of data about a resource. In this case, the second thing has some form of data type, such as a string or a number or a date. That ability allows the RDF model to contain measurements, human-readable descriptions, and such. It is very useful when you want to describe your resources in great detail. For example:

<an IRI representing me> <an IRI representing the concept "has birthday"> <a date representing my birthday>

(the first thing, me) (the relationship, in this case "has eye colour") (the date "1963-08-28")

You can read that triple as "David Hyland-Wood was born on 28 August 1963".

So to get back to your original question, we use IRIs to represent things (nouns) and relationships/concepts (verbs). We use other non-IRI data types such as strings, numbers, and dates to represent data.

If we have a lot of RDF triples describing resources, they form a conceptual graph because we presume the IRIs are unique in the world. If there are two RDF triples that both start with the same IRI, then we say they are giving two pieces of information about the same thing.

The edges of RDF graph are the data, because the RDF model doesn't allow you to make any further statements about a string, date, number, etc. used in a data field. You can make all the statements you want about things that are named with IRIs.

Does that help?

Regards,

Dave

i guess you can use ovito to calculate radial distribution by CNA (commmon neighbour analysis), It gives you table and graphical representation

Hi Paul,

Even as an "RDF person", I understand your comment that some find it difficult to accept that RDF is not universal. There are certainly both philosophical stances in design and trade-offs in implementation and semantics.

There have been many, many papers published at the International Semantic Web Conference (ISWC, but not the wearable computing ISWC!) and the International World Wide Web Conference (WWW) over the last 10-12 years on logical extensions and different types of logics. 

The most advanced logic query engine for alternative logics that I know is Stardog (http://www.stardog.com).

making an index file and showing it in calculation of RDF is much easiest way to do it. indicating whole protein while you are creating an index file leads to take COM automatically  in RDF calculations.

 Dear Marzieh

Knowledge is always for expansion. Pl follow it in life. When one learns the things from scratch it gives more enjoyment.

You can open the FASTA file in DnaSP and generate haplotype data file in .rdf format for further analysis in NETWORK. 

Hi Rafid,

I work with AI methods. They excellently apply on function generation and data prognosys. I am not familiar with any application based on this methods for Enterprise Business Application testing. My suggestion is to make one for youreself, based on AI methods (Fuzzy, c-slusters, neuron network, ANFIS).

good luck

Carlo, I would also consider graph databases such as Titan or Neo4J. Briefly speaking, triple stores are suitable for reasoning, graph databases are suitable for graph operations and queries (find shortest path, etc.)...

In the near past I've used Jena to integrate a set of SWRL rules (pre-authored in Protege 3.4.4) for artificial intelligent and automated reasoning mechanisms. The programming language was -once more- Java and the only prerequisite was to include the Pellet reasoner. Both Apahe Framework and Pellet (now on GitHub) have extensive tutorials and examples, so give it a try instead of using PHP which has limited, or not at all, support in Semantic Web area...