Research products

Duplicates among research products are identified among results of the same type (publications, datasets, software, other research products). If two duplicate research products are aggregated one as a dataset and one as a software, for example, they will never be compared and they will never be identified as duplicates. OpenAIRE supports different deduplication strategies based on the type of results.

The next sections describe how each stage of the deduplication workflow is faced for research products.

Candidate identification (clustering)

To match the requirements of limiting the number of comparisons, OpenAIRE clustering for research products works with two different strategies based on entity types:

Software

• Title extraction functions: two clustering functions are applied to the title (normalized, stemming, etc.)
  • stats and suffix prefix of words: the function generates a key that depends on (i) number of significant words in the title, (ii) module 10 of the number of characters of such words, and (iii) a string obtained as an alternation of the function prefix(3) and suffix(3) (and vice-versa) on the first 3 words (2 words if the title only has 2). For example, the title Search for the Standard Model Higgs Boson becomes the two keys 5-3-seaardmod and 5-3-rchstadel
  • n-grams: the function generates ngrams from the title. For example, the title Search for the Standard Model Higgs Boson becomes the keys tan, sta, ode, mod, ear, hig, igg, sea
• DOI extraction function: the function generates the DOI when this is provided as part of the record properties
• URL extraction function: the function generates the hostname part provided by the URL of the software, if any

Publication, Dataset and Other Research Product

• PID extraction function: the function generates the PIDs when at least one is provided as part of the pid record properties
• Author and Title extraction function: the function generates a key that depends on (i) the number of authors of the product, with a cap of 21 authors (ii) number of significant words in the title (normalized, stemming, etc.), divided by 10, and (iii) a string obtained as an alternation of the function prefix(3) and suffix(3) (and vice versa) on the first 3 words (2 words if the title only has 2).
  For example, a product composed by 197 authors and titled ``Search for the Standard Model Higgs Boson`` becomes the two keys ``21-0-seaardmod`` and ``21-0-rchstadel``

Duplicates identification (pair-wise comparisons)

Comparisons in a block are performed using a sliding window set to 50 records. The records are sorted lexicographically on the normalized version of their titles. The 1st record is compared against all the 50 following ones using the decision tree, then the second, etc. Local information about matching records is kept and possibly used to prune unneeded comparisons, for example once it is known that A equals to both B and C, B will not be compared against C because the A,B,C group will be anyway discovered by the global transitive closure step later.

A different decision tree is adopted depending on the type of the entity being processed. Similarity relations drawn in this stage will be consequently used to perform the duplicates grouping.

Publications

For each pair of publications in a cluster the following strategy (depicted in the figure below) is applied. The comparison goes through different stages:

1. trusted pids check: comparison of the trusted pid lists (in the pid field of the record). If at least 1 pid is equivalent, records match and the similarity relation is drawn.
2. instance type check: comparison of the instance types (indicating the subtype of the record, i.e. presentation, conference object, etc.). If the instance types are not compatible then the records does not match. Otherwise, the comparison proceeds to the next stage
3. untrusted pids check: comparison of all the available pids (in the pid and the alternateid fields of the record). In every case, no similarity relation is drawn in this stage. If at least one pid is equivalent, the next stage will be a soft check, otherwise the next stage is a strong check.
4. soft check: comparison of the record titles with the Levenshtein distance. If the distance measure is above 0.9 then the similarity relation is drawn.
5. strong check: comparison composed by three substages involving the (i) comparison of the author list sizes and the version of the record to determine if they are coherent, (ii) comparison of the record titles with the Levenshtein distance to determine if it is higher than 0.95, (iii) "smart" comparison of the author lists to check if common authors are more than 60% in case of titles whose length is greater than 30 chars or more than 90% otherwise.

Datasets and Other types of research products

For each pair of datasets or other types of research products in a cluster the strategy depicted in the figure below is applied. The decision tree is almost identical to the publication decision tree, with the only exception of the instance type check stage. Since such type of record does not have a relatable instance type, the check is not performed and the decision tree node is skipped.

Software

For each pair of software in a cluster the following strategy (depicted in the figure below) is applied. The comparison goes through different stages:

1. DOI pids and URLs check: comparison of the pids of type DOI and URLs in the records. If at least 1 DOI is equivalent or 1 URL is equivalent, then records match and the similarity relation is drawn
2. title check: comparison of the record titles with Levenshtein distance, excluding versioning information. If the distance is below 0.95 then the records does not match. Otherwise, the comparison proceeds to the next stage
3. untrusted DOI check: comparison of all the available DOIs (in the pid and the alternateid fields of the record). If at least 1 DOI is equivalent, records match and the similarity relation is drawn
4. authors check: "smart" comparison of the author lists to check if the two products share all authors

Duplicates grouping

The aim of the final stage is the creation of objects that group all the equivalent entities discovered by the previous step. This is done in two phases.

Transitive closure

As a final step of duplicate identification a transitive closure is run against similarity relations to find groups of duplicates not directly caught by the previous steps. If a group is larger than 200 elements only the first 200 elements will be included in the group, while the remaining will be kept ungrouped.

Creation of representative record (dedup record)

The general concept is that the field coming from the record with higher "trust" value is used as reference for the field of the representative record.

The IDs of the representative records are obtained by prepending the prefix dedup_ to the MD5 of the first ID (given their lexicographical ordering). If the group of merged records contains a trusted ID type (i.e. the DOI), also the type keyword (i.e. DOI) is added to the prefix.