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IIOP demo

The identifier interoperability (IIOP) of 2 datasets is the ratio of the real-world concepts identified with the same identifier across the 2 datasets over the total of real-world objects identified within the two datasets that overlap. The relevance of the interoperability is the number of facts where real-world objects are mentioned.

In this projects we will demonstrate a workflow to calculate the IIOP and the relevance on a couple of datasets hosted at

Step 0: what do you think?

In order to evaluate the score, we will ask you to first take a look at the datasets at and compare them to Give your own score to it for both the interoperability itself, as a relevance number for the interoperability.

You can use this questionnaire:

The results of the questionnaire can be found here: TBA

Step 1: Transforming the data towards triples

Tools such as open refine can be used to transform the datasets into triples. Each time, every id is concatenated with the URI of the dataset (e.g., for the identifier of the first row or as the identifier of the concept of longitude).

Tools like Open Refine, RML or DataLift can be used. These tools map data towards triples.

Caveat lector: even when we would have a dataset in RDF, we would still concatenate each URI with the dataset URI: we need a unique identifier for each identifier used in each dataset.

In this demo we have used Open Refine. The result of the mapping are put in the respective dataset folders in this repository:

  • reference/reference.ttl
  • ghent1/ghent1.ttl
  • newyork/newyork.ttl
  • ghent2/ghent2.ttl
  • antwerp/antwerp.ttl

Using a command-line tool which can be installed on debian/ubuntu systems using apt-get install raptor2-utils, we have converted the datasets into n-triples.

For each dataset we did:

rapper -i turtle -o ntriples antwerp.ttl > antwerp.nt

This generated this amount of triples:

Dataset number of triples
reference 21
ghent1 48
newyork 16
antwerp 45
ghent2 24

Step 2: Generating the list of identifiers

For each dataset, we generated a file containing all the IDs defined in a dataset. Using the n-triples file, we can generate this quickly using this bash command for each dataset:

cut -d" " newyork.nt -f1,2,3 --output-delimiter="
" | grep "^<" | sort | uniq > ids.txt
Dataset number of IDs
reference 12
ghent1 27
newyork 9
antwerp 18
ghent2 11
Total 77

Step 3: Getting the real world input

There are various ways to get to the real-world input: you can crowd-source it, you can reason over already existing data or you can fill it out yourself. The result of this step should be an n-triples file which contains for each identifier whether it does or does not mean the same in the real world. The predicates that we are going to use for this are defined at ; iiop:sameAs and iiop:notSameAs. For this dataset this means we need 5852 (77*76) statements. As these statements are unknown at this moment, we can generate a list of 5852 questions.

During the process of solving these questions yourself, you can use a reasoner to assist you. Each time a statement is added, the reasoner will then check whether more questions can be derived. In this case, we will use the reasoner by Ruben Verborgh which uses the EYE software. We will use this query:

@prefix iiop: <> .
{ ?a iiop:sameAs ?b . } => { ?b iiop:sameAs ?a . } .
{ ?a iiop:notSameAs ?b . } => { ?b iiop:notSameAs ?a . } .
{ ?a iiop:sameAs ?b . ?b iiop:sameAs ?c . }
{ ?a iiop:sameAs ?c . } .
{ ?a iiop:notSameAs ?b . ?b iiop:sameAs ?c . }
{ ?a iiop:notSameAs ?c . } .

Step 3a: Generating the list of questions

An iiop question is a triple with 1 unknown predicate. The predicate can be iiop:sameAs or iiop:notSameAs:

<id1> ?unknown <id2> .

In order to generate this list, we first generate a list of all identifiers in our system. This is easy, as we already have made a list of all identifiers in each dataset.

cat reference/ids.txt ghent1/ids.txt newyork/ids.txt antwerp/ids.txt ghent2/ids.txt > ids.txt

Now, this list will be used to generate a list questions.nt. This is solved using 2 while loops:

while read id1 ; do {
  while read id2 ; do {
    [[ $id1 != $id2 ]] && echo "$id1 ?unknown $id2 . " ; 
  } done < ids.txt ;
} done < ids.txt > questions.nt

Step 3b: Answering the questions

Answering the question can be done in various ways. Each kind of set of data might be able to use its own methods: if we're talking about geospatial data, the identifiers can be compared on a map, if we're talking about tabular data, a mapping GUI could be made. We are going to use a more rudimental approach: we're going to copy the questions.nt towards iiopstatements.nt and we're going to use a text editor to find-replace-all ?unknown with <>. Each time we find an identifier which is not the same, we're going to hit "yes, replace". If it is the same in the real world, we're going to hit "no, don't replace". Afterwards, all remaining ?unknown will be replace by <>.

After uploading the files on our server, we can use the EYE Reasoner to help us a bit in the process (note that this can actually be implemented by the GUI):

curl "" >temp.ttl
rapper -i turtle -o ntriples temp.ttl > temp.nt
curl> temp2.nt
cat temp.nt temp2.nt questions.nt | sort -t " " -k 1,1b -k 3,3b | sed 's/[ ]*$//' | uniq > answers.nt
rm temp.nt temp.ttl temp2.nt 

You can then reopen answers.nt in your editor, remove the duplicates, and carry on with your work.

Remove duplicates using Emacs regex (C-M %)

\(<.*?>\) \(<[sS]ameAs>\) \(<.*?>\) .
\1 \?unknown \3 . 


\1 \2 \3 .

And you can carry on.

Step 4: Making the calculations

Identifier ratio

grep -E 'reference/(.*?)> <> <http://.*?/\1>' iiopstatements.nt | cut -d" " -f3 | sort | uniq | while read a ; do { grep ${a#<} reference/reference.nt -o ; } done | sort | uniq | wc -l
grep -E 'reference/(.*?)> <> <http://.*?/\1>' iiopstatements.nt | cut -d" " -f3 | sort | uniq |  grep ghent1 | wc -l

Divide these 2 numbers, and you have the Identifier ratio

Reference and ... Identifier Ratio
ghent1 8/14 = 57%
newyork 1/1 = 100%
antwerp 5/5 = 100%
ghent2 3/4 = 75%

Identifiers time their use in triples

Relevance of string matching identifiers

The current relevance is the number of triples that would be returned if the 2 datasets would be joined together, only matching the true positives.

For instance for Ghent1 do this:

alltriples=$( cat reference/reference.nt $datasetname/$datasetname.nt | sort | uniq ; );
grep -E "reference/(.*?)> <> <http://.*?/$datasetname/\1>" iiopstatements.nt | cut -d" " -f1,3 | { while read id1 id2 ; do
    alltriples=${alltriples//$id1/$joined} ;
    alltriples=${alltriples//$id2/$joined} ;
    #echo "$alltriples" | grep joined; 
done ;
echo "$alltriples" | sort | uniq > $datasetname/joined_reference.nt ;
cut -d" " -f1,2,3 --output-delimiter="
" $datasetname/joined_reference.nt | grep '/joined' | wc -l

Maximum relevance possible

This is the count of the usage of matching identifier when we are making the iiop:sameAs links the same.

alltriples=$( cat reference/reference.nt $datasetname/$datasetname.nt | sort | uniq ; );
grep -E "reference/.*?> <> <http://.*?/$datasetname/.*?>" iiopstatements.nt | cut -d" " -f1,3 | { while read id1 id2 ; do
    alltriples=${alltriples//$id1/$joined} ;
    alltriples=${alltriples//$id2/$joined} ;
done ;
echo "$alltriples" | sort | uniq > $datasetname/joined_max_reference.nt ;
cut -d" " -f1,2,3 --output-delimiter="
" $datasetname/joined_max_reference.nt | grep '/joined' | wc -l

Relevance ratio

The relevance ratio is the ratio of the relevance over the sum of both triple counts.

The end results is this table:

Reference and ... identifier ratio Relevance of stringmatching IDs Relevance of real-world concepts relevance ratio questionnaire rank
ghent1 57% 64 104 62% high
antwerp 100% 40 40 100% high
ghent2 75% 18 24 75% medium
newyork 100% 0 8 0% low

Step 5: Processing feedback

The list of IIOP statements is a great opportunity to process feedback. Both the iiop:notSameAsas the iiop:sameAs statements are interesting for feedback towards the dataset maintainer. Each iiop:sameAs link can be considered as an opportunity to use the same identifiers across datasets.

The iiop:notSameAs is also a great opportunity to find conflicts between datasets. If you count the number of conflicts: string matches in the IDs, but there is a iiop:notSameAs statement. To calculate this, you can perform this small trick:

grep -E 'newyork/(.*?)> <> <http://.*?/\1>' iiopstatements.nt | wc -l

No datasets conflict with the reference dataset. When we however take newyork as the reference dataset, we get 1 conflict:

<> <> <> .

This is because newyork/location is an identifier for a relation between an entity and an address in a string. ghent1/location on the other hand is a description of the location, and not an address.

In order to reduce conflicts, we can start making use of URIs and point to global definitions of certain predicates or other concepts. If no URI is defined for what you want to put in the semantics of the identifier, then you can create your own identifier.


The demo of how to calculate a IIOP score






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