I am not too new to semantic web and have a complete set of DBPedia2014 English loaded into my RDF store running. Now I would like to add the YAGO class datasets (links, types) into the DBPedia2014 RDF space and could not find a single TTL download (just TSV). The only somehow updated and running YAGO downloads are on http://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/downloads/ only tsv files.
One line of such a file is for instance:
<id_svyx35_88c_avbfa5> <Kuldeep_Raval> rdf:type <wikicat_Delhi_Daredevils_cricketers>
Note that <id_svyx35_88c_avbfa5>
is not a (resource) url, reading in this line causes errors. Furthermore how can the RDF space figure out to what entity this is referring to in DBPedia (I do have an idea, but this seems to me not really compliant to an RDF Space).
How can these YAGO files be integrated into DBPedia in an RDF store accepting formats like xmlrdf,ttl,n3,nt,nq ?
Thanks
Use the yago links from dbpedia
http://data.dws.informatik.uni-mannheim.de/dbpedia/2014/links/
Related
I want to use Protégé as an ontology manager. The most basic feature would be to see all the ontology files I have in my repo together in a single opened instance of Protégé i.e. a unified view of all the schema I have in a specific folder. It will be like an Ontology library. Can I do this in Protégé?
I know I can merge all the files first and then see the output file in Protégé but I am looking for a better solution.
Thanks.
I am trying to limit the DICOM tags, which are retained, by using
for key in keys:
if key.upper() not in {'0028|0010','0028|0011'}:
image_slice.EraseMetaData(key)
in Python 3.6 where image_slice is of type SimpleITK.SimpleITK.Image
I then use
image_slice.GetMetaDataKeys()
to see what tags remain and they are the tags I selected. I then save the image with
writer.SetFileName(outputDir+os.path.basename(sliceFileNames[i]))
writer.Execute(image_slice)
where outputDir is the output directory name and os.path.basename(sliceFileNames[i]) is the DICOM image name. However, when I open the image, with Weasis or with MIPAV, I notice that there are a lot more tags than were in image_slice. For example, there is
(0002,0001) [OB] FileMetaInformationVersion: binary data
(0002,0002) [UI] MediaStorageSOPClassUID:
(0002,0003) [UI] MediaStorageSOPInstanceUID:
(0008,0020) [DA] StudyDate: (which is given the date that the file was created)
I was wondering how, and where these additional tags were added.
The group 2 tags you are seeing are meta data tags, that are always written while writing the dataset. Unless "regular" tags, which start with group 8, these group 2 tags do not belong to the dataset itself, but contain information about the encoding/writing of the dataset, like the transfer syntax - more information can be found in the DICOM standard, part 10. They will be recreated on saving a dataset to a file, otherwise, the DICOM file would not be valid.
About the rest of the tags I can only guess, but they are probably written by the software because they are mandatory DICOM tags and have been missing in the dataset. StudyDate is certainly a mandatory tag, so adding it if it is missing is correct, if the data is seen as derived data (which it usually is if you are manipulating it with ITK). I guess the other tags that you didn't mention are also mandatory tags.
Someone with more SimpleITK knowledge can probably add more specific information.
I am using example source code from the Lucene 4.2.0 demo API:
http://lucene.apache.org/core/4_2_0/demo/overview-summary.html
I run IndexFiles.java to create an index from a directory of rtf, pdf, doc, and docx files. I then run SearcFiles.java and notice that I encounter several instances where my searches fail i.e. it does not return a document that contains the word I searched for.
I suspect it has to do with Lucene 4.2.0 not being able to correctly index non .txt files without additional customization.
Question: Can the IndexFiles.java source code (Lucene 4.2.0) correctly index pdf, doc, docx files as it is written in the provided link? Does anyone have examples or references on how to code that functionality?
Thank You
No, it can't. IndexFiles is a demo, an example for you to learn from, but not really designed for production use. If you take a look at the code, you'll see it just uses a FileInputStream (wrapped with an InputStreamReader, wrapped with a BufferedReader). Generally, Lucene won't handle how to parse different file formats (except it's own index files, of course). How to parse a file to provide meaningful content to Lucene is up to you to define.
Apache Tika might be a good place to look for this functionality. Here is a simple example using Tika with Lucene.
You might also consider using Solr.
I am new to this topic, but my requirement is to parse documents of different types(Html, pdf,txt) using a crawlers. please suggest me what crawler to use for my requirement and provide me some tutorial s or some example how to parse the document using crawlers.
Thankyou.
This is a very broad question, so my answer is also very broad and only touches the surface.
It all comes down to two steps, (1) extracting the data from its source, and (2) matching and parsing the relevant data.
1a. Extracting data from the web
There are many ways to scrape data from the web. Different strategies can be used depending if the source is static or dynamic.
If the data is on static pages, you can download the HTML source for all the pages (automated, not manually) and then extract the data out of the HTML source. Downloading the HTML source can be done with many different tools (in many different languages), even a simple wget or curl will do.
If the data is on a dynamic page (for example, if the data is behind some forms that you need to do a database query to view it) then a good strategy is to use an automated web scraping or testing tool. There are many of these.
See this list of Automated Data Collection resources [1]. If you use such a tool, you can extract the data right away, you usually don't have the intermediate step of explicitly saving the HTML source to disk and then parsing it afterwards.
1b. Extracting data from PDF
Try Tabula first. It's an open source web application that lets you visually extract tabular data from PDFs.
If your PDF doesn't have its data neatly structured in simple tables or you have way too much data for Tabula to be feasible, then I recommend using the *NIX command-line tool pdftotext for converting Portable Document Format (PDF) files to plain text.
Use the command man pdftotext to see the manual page for the tool. One useful option is the -layout option which tries to preserve the original layout in the text output. The default option is to "undo" the physical layout of the document, and instead output the text in reading order.
1c. Extracting data from spreadsheet
Try xls2text for converting to text.
2. Parsing the (HTML/text) data
For parsing the data, there are also many options. For example, you can use a combination of grep and sed, or the BeautifulSoup Python library` if you're dealing with HTML source, but don't limit yourself to these options, you can use a language or tool that you're familiar with.
When you're parsing and extracting the data, you're essentially doing pattern matching.
Look for unique patterns that make it easy to isolate the data you're after.
One method of course is regular expressions. Say I want to extract email addresses from a text file named file.
egrep -io "\b[A-Z0-9._%+-]+#[A-Z0-9.-]+\.[A-Z]{2,4}\b" file
The above command will print the email addresses [2]. If you instead want to save them to a file, append > filename to the end of the command.
[1] Note that this list is not an exhaustive list. It's missing many options.
[2] This regular expression isn't bulletproof, there are some extreme cases it will not cover.
Alternatively, you can use a script that I've created which is much better for extracting email addresses from text files. It's more accurate at finding email addresses, easier to use, and you can pass it multiple files at once. You can access it here: https://gist.github.com/dideler/5219706
I've been reading this but I was just wondering, does Solr have the capability to search static files (i.e. outside of a content management system or a database)?
Some of my files are just straight up html...or server side code with html "blocks"...
SolR can index any text input. The important bit is that it indexes text. So if your static files are not text files, you may need to run them through a tool like Tika first. Then SolR should have no problem indexing the extracted textual data.
There is the ExternalFileField field type. But it's use looks limited.
http://lucene.apache.org/solr/api/org/apache/solr/schema/ExternalFileField.html