Accessing LookinLabs

LookinLabs for HAL-archives is more than a search tool. It allows you to find, among teams/individuals/publications, those best matching your query.

LookinLabs4HAL is deployed for the laboratories partners of CominLab who have accepted a minimal set of conditions:   

  • Willingness to provide the list of lab members and teams;
  • To have a person responsible for interacting with LookinLabs development team.

The links to the services deployed for the different labs are :

What is LookinLabs ?

The tool exploits, as data, HAL-archives.
No ontology is used. No data need to be manually entered (besides the users’ queries).
The tool uses Elasticsearch as its core algorithm. This means that the matching is based on a distance between the query and the set of data attached, in HAL, to each team/individual/publication. Ranking is performed accordingly.

Explanations are given for each returned item. Correlation graphs are given, allowing to navigate through teams or individuals that share common interests (they may or may not be co-authors).

How to use LookinLabs ?

As a simplest use, just enter your query. For example, if you enter network security, the tool will look for best matching with respect to `network’, `security’, and the digram `network-security’, with a bias in favor of the latter.

If you really mean the digram only, you can specify this by adding quotes: “network security”. Then, the tool will look for the digram only and stop in case of the matching succeeds. In case of failure, quotes are removed and the tool operates in its basic mode.

The tool claims to perform semantic matching. If, however, by network security, you have in mind the whole domain with its bench of associated keywords, this implicit context is not known by LookinLabs since LookinLabs makes no use of any kind of ontology in its current release. You can still benefit from sort of an adaptive ontology by using the tool stepwise as follows:

1. Enter your query;
2. Read the abstracts of the very first publications returned by the tool and select the one(s) you find best for describing your topic;
3. Copy this abstract and enter it as your refined query.

This yields a rich query that will act as an ontology by enhancing your topic with all the words of the abstract.


This work was launched and supported by the Labex CominLabs. It is now jointly funded by CominLabs (https://cominlabs.u-bretagneloire.fr/) and Inria (https://www.inria.fr/).

Bibliographical comments

The tool is described in the Inria report RR 9158. When profiling the teams and individuals, the Synthesized Research Topics are obtained via text mining. The first algorithm is custom; it is described in RR 9158. The second one is the so-called Lingo method.