Currently, I'm developing an ontology related to diseases using the software Protege. When I save the file, it's saved in OWL file displaying in XML. Now, I would like to know the method to call the OWL file in my website. I am interested to do a website which allows user to ask questions related to diseases and the answer comes from the ontology that I've created. Is there anybody who can enlighten me regarding this matter?
Essentially you're looking for a tool that will let your users query your ontology. SPARQL is an RDF query language that can achieve what you desire but I cannot recommend any of the available SPARQL query builders as I have no experience with them.
I am developing something similar -- right now I am looking at using java and OWLAPI -- (http://owlapi.sourceforge.net/index.html).
There is a PHP tool that I will look into as well -- (http://arc.semsol.org/).
You could always publish your ontology on freebase.com, and use their SPARQL endpoint.
A good place to get started on testing SPARQL out is http://dbpedia.org/snorql/
Related
So here's my question. Supposing that one is about to create an online web appliation that takes as user input a current location and a location for destination, and displays as a result one of the 5-6 available routes that are stored in a database that is most suitable in terms of distance,and Open Street Map data and Open Layers are used which would be the best way to make this happen?
What I am asking for is what would I need for:
1.Storing the data in database
2.Do the routing calculations. If I would like to change a bit the algorithms for academic reasons and have more control of my final result how would I do that? Do I need any programming language? Any good tutorials?
3.What is the difference between using pgRouting and using any custom solution(like mentioned above)? Doing the all the coding again by myself would be like reinventing the wheel?
4.What would be best for a commercial website, where fast calculations would be needed?
UPDATE: What I need is a way to connect 1.user input(as geometry points) 2.Routing algorithm I have written 3.Road Network and return a result in terms of best way to go to a point
Please see the list of online routers and offline routers for OSM as well as the general wiki page about routing with OSM.
If that still doesn't answer your questions, ask a more specific one.
I am basically working on nlp, collecting interest based data from web pages.
I came across this source http://schema.org/ as being helpful in nlp stuff.
I go through the documentation, from which I can see it adds additional tag properties to identify html tag content.
It may help search engine to get specific data as per user query.
it says : Schema.org provides a collection of shared vocabularies webmasters can use to mark up their pages in ways that can be understood by the major search engines: Google, Microsoft, Yandex and Yahoo!
But I don't understand how it can help me being nlp guy? Generally I parse web page content to process and extract data from it. schema.org may help there, but don't know how to utilize it.
Any example or guidance would be appreciable.
Schema.org uses microdata format for representation. People use microdata for text analytics and extracting curated contents. There can be numerous application.
Suppose you want to create news summarization system. So you can use hNews microformats to extract most relevant content and perform summrization onit
Suppose if you have review based search engine, where you want to list products with most positive review. You can use hReview microfomrat to extract the reviews, now perform sentiment analysis on it to identify product has -ve or +ve review
If you want to create skill based resume classifier then extract content with hResume microformat. Which can give you various details like contact (uses the hCard microformat), experience, achievements , related to this work, education , skills/qualifications, affiliations
, publications , performance/skills for performance etc. You can perform classifier on it to classify CVs with particular skillsets
Thought schema.org does not helps directly to nlp guys, it provides platform to perform text processing in better way.
Check out this http://en.wikipedia.org/wiki/Microformat#Specific_microformats to see various mircorformat, same page will give you more details.
Schema.org is something like a vocabulary or ontology to annotate data and here specifically Web pages.
It's a good idea to extract microdata from Web pages but is it really used by Web developper ? I don't think so and I think that the majority of microdata are used by company such as Google or Yahoo.
Finally, you can find data but not a lot and mainly used by a specific type of website.
What do you want to extract and for what type of application ? Because you can probably use another type of data such as DBpedia or Freebase for example.
GoodRelations also supports schema.org. You can annotate your content on the fly from the front-end based on the various domain contexts defined. So, schema.org is very useful for NLP extraction. One can even use it for HATEOS services for hypermedia link relations. Metadata (data about data) for any context is good for content and data in general. Alternatives, include microformats, RDFa, RDFa Lite, etc. The more context you have the better as it will turn your data into smart content and help crawler bots to understand the data. It also leads further into web of data and in helping global queries over resource domains. In long run such approaches will help towards domain adaptation of agents for transfer learning on the web. Pretty much making the web of pages an externalized unit of a massive commonsense knowledge base. They also help advertising agencies understand publisher sites and to better contextualize ad retargeting.
This is my first time dabbling in NLP so please excuse my ignorance. I'm looking for a method to extract interests/likes/hobbies from users' social profiles. Here is an example where all the interests/likes/hobbies are in bold:
"I consider myself a pretty diverse character... I'm a professional
wrestler, but I'd take a bullet for Wall•E. I train like a one-man genocide machine in the gym, but I cried at
"Armageddon." I'll head bang to AC/DC, and I'm seriously
considering getting a Legend of Zelda tattoo. I'm 420-friendly. I
like to party it up with the frat crowd one night, hang out with
my Burning Man friends the next, play Halo and World of
Warcraft the next, and jam with friends that aren't any younger than
40 the next. My youngest friend is 16, my oldest friend is 66. I'll
sing karaoke at the bars, and I'm my friends' collective
psychiatrist/shoulder."
The profiles are plain text. There are no meta tags or ids associated with any of it, it's just a paragraph of text.
My naiive idea was to take each noun and match it against Freebase to see if it's an activity/artist/movie/book etc. The problem is that although most entities mentioned will be things the user likes, she will also mention things she doesn't like and I have no means of distinguishing the 2.
I have 2 questions:
What sub field of NLP should I be looking at? Some googleable algorithms/techniques/authors would be greatly appreciated.
How hard is this problem?
Thanks!
First, unless using NLP to do this is a particular objective for you, check your problem domain to see if you can avoid it completely.
For instance:
do these profiles have tags (supplied either by the Site or by the
user)?
what does the Site's API make available (assuming that's how you are accessing this data; if you are scraping it, then this doesn't of course apply)? A good example, Facebook. if you read a user's posts, you'll see words like "wrestler", "karaoke", etc. but if you look at what fields are exposed via the Graph API, you'll see that these activities nearly always have an associated FB ID.
I am not a specialist in this field, but I can recommend a couple of resources directed to NLP and which are accessible to the non-specialist or novice. The first is a text processing API. This simple web service uses REST and JSON IO. It is free and seems to have a fairly large rate limit.
This API appears to rely heavily on the excellent Natural Language Tooolkit (NLTK) which is a mature stable library in python, that includes modules directed to the problem in your Question, e.g., Sentiment Analysis, Tagging and Chunk Extraction, etc.
Which particular sub-domain is most relevant to solving the Question in the OP? I don't know, but I suspect there's a module somewhere in the NLTK that does what you need. Finding that module is hopefully just a matter of skimming the API Documentation (which is organized by module); reading the Getting Started section which contains an excellent survey of NLTK's modules as well as demos for all of each of them.
Don't know where to start on this one so hopefully you guys can clear up my question. I have project where email will be searched for specific words/patterns and stored in a structured manner. Something that is done with Trip it.
The article states that they developed a DataMapper
The DataMapper is responsible for taking inbound email messages
addressed to plans [at] tripit.com and transforming them from the
semi-structured format you see in your mail reader into a highly
structured XML document.
There is a comment that also states
If you're looking to build this yourself, reading a little bit about
Wrappers and Wrapper Induction might be helpful
I Googled and read about wrapper induction but it was just too broad of a definition and didn't help me understand how one would go about solving such problem.
Is there some open source project out there that does similar things?
There are a couple of different ways and things you can do to accomplish this.
The first part, which involves getting access to the email content I'll not answer here. Basically, I'll assume that you have access to the text of emails, and if you don't there are some libraries that allow you to connect java to an email box like camel (http://camel.apache.org/mail.html).
So now you've got the email so then what?
A handy thing that could help is that lingpipe (http://alias-i.com/lingpipe/) has an entity recognizer that you can populate with your own terms. Specifically, look at some of their extraction tutorials and their dictionary extractor (http://alias-i.com/lingpipe/demos/tutorial/ne/read-me.html) So inside of the lingpipe dictionary extractor (http://alias-i.com/lingpipe/docs/api/com/aliasi/dict/ExactDictionaryChunker.html) you'd simply import the terms you're interested in and use that to associate labels with an email.
You might also find the following question helpful: Dictionary-Based Named Entity Recognition with zero edit distance: LingPipe, Lucene or what?
Really a very broad question, but I can try to give you some general ideas, which might be enough to get started. Basically, it sounds like you're talking about an elaborate parsing problem - scanning through the text and looking to apply meaning to specific chunks. Depending on what exactly you're looking for, you might get some good mileage out of a few regular expressions to start - things like phone numbers, email addresses, and dates have fairly standard structures that should be matchable. Other data points might benefit from some indicator words - the phrase "departing from" might indicate that what follows is an address. The natural language processing community also has a large tool set available for text processing - check out things like parts of speech taggers and semantic analyzers if they're appropriate to what you're trying to do.
Armed with those techniques, you can follow a basic iterative development process: For each data point in your expected output structure, define some simple rules for how to capture it. Then, run the application over a batch of test data and see which samples didn't capture that datum. Look at the samples and revise your rules to catch those samples. Repeat until the extractor reaches an acceptable level of accuracy.
Depending on the specifics of your problem, there may be machine learning techniques that can automate much of that process for you.
I am a "rookie" in Semantic Web. So a lot things confuse me right now. I am going to make a semantic web search in website. But I am not sure what should be the workflow of that?
I just have basic opinion.
Please correct me
use a webspider to get web resources, and put thoses reources in files.
parse those resource files (lexical ananlysis) and use RDF format to describe those resources (now, the RDF contains the ontologies,
which are about resources).
parse the RDF files (contain resources), use OWL (combine inference mechanism) to describe the ontologies in RDF files.
semantic analyze the user input (from search text box), match it in OWL files, and then match in the RDF reources files, then provide
the related results.
Please give me suggestions and correct me.
See this resource for your engine.
You should learn to search and use existing resources (ontologies and more generally APIs), that allow to reuse semantic annotations on data. (Linked Data, see here). Anyway, if you get web resources, don't put them in files and reference the origin, because the copy changes the links semantic. Knowledge evolve over time...
Regards the semantic analysis, could be a difficult task. Before you start to implement yourself, search if there is some API out there, that fits your bill.