26th International Conference on Inductive Logic Programming

4th - 6th September 2016, London


2016

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Call for Papers

Scope

Typical, but not exclusive, topics of interest for submissions include:

  • Theoretical aspects: logical-foundations of learning; computational/statistical learning theory; specialisation and generalisation; probabilistic logic-based learning; graph and tree mining.
  • Representation and languages for learning: logic programming; Datalog; first-order logic; description logic and ontologies; higher-order logic; Answer Set Programming; probabilistic logic languages; constraint logic programming; knowledge graphs.
  • Algorithms and systems: learning with (semi-)structured data; (semi-)supervised and unsupervised relational learning; relational reinforcement learning; predicate invention; propositionalisation approaches; multi-instance learning; learning in the presence of uncertainty; meta-level learning.
  • Applications of learning: art; bioinformatics; systems biology; games; medical informatics; robotics; natural language processing; web-mining; software engineering; modelling and adaptation of control systems; socio-technical systems.

In addition to the above topics, ILP 2016 is also encouraging contributions in the areas of cognitive technologies, knowledge acquisition from big data, the cloud and crowd sourced data, deep relational learning, as well as contributions on the application of any of these solutions to real world problems.

Submission

We solicit three types of submissions:

  1. Long papers describing original mature work containing appropriate experimental evaluation and/or representing a self-contained theoretical contribution. Accepted long paper submissions will be assigned a standard time slot for presentation and will appear in the Springer LNAI post-conference proceedings. If a long paper submission is not accepted as a long paper, it may be accepted as a "short paper" (see next paragraph), in which case it will be assigned a reduced time slot for presentation, and the authors may be given the opportunity to submit a revised version that will be reviewed after the conference for possible inclusion in the Springer LNAI post-conference proceedings. Long papers must not exceed 12 pages (including references).
  2. Short papers describing original work in progress, brief accounts of original ideas without conclusive evaluation, and other relevant work of potentially high scientific interest but not yet qualifying for the long paper category. They will be accepted/rejected on the grounds of their relevance. Accepted short papers will be assigned a reduced time slot for presentation. Authors of selected papers will be invited to submit a long version that will be reviewed after the conference for possible inclusion in the Springer LNAI post-conference proceedings. Short papers must not exceed 6 pages (not including references).
  3. Papers relevant to the conference topics and recently published or accepted for publication by a first-class conference such as ECML/ PKDD, ICML, KDD, ICDM, AAAI, IJCAI, etc. or journal such as MLJ, DMKD, JMLR etc. These will be accepted/rejected on the grounds of relevance and quality of the original publication venue. Authors of accepted papers will be assigned a reduced time slot for presentation. These papers will not appear in the Springer LNAI post-conference proceedings.

Awards

There will be up to three best student paper awards, sponsored by the Machine Learning Journal, and a best paper award sponsored by Springer.

Publication

The post-proceedings from the Conference will be published by LNAI Springer. Submissions must be in Springer LNAI format, according to the Springer LNCS author instructions. Long papers must not exceed 12 pages including references; short papers must not exceed 6 pages not including references. Papers in category 3 should be submitted in their original format and the authors should indicate the original publication venue. All paper submissions will be electronic through the ILP 2016 Easychair site.

We expect there will be a special issue of the Machine Learning Journal following the conference, which will be open for everyone. This special issue will welcome conference submissions from all three categories, which should be significantly revised and extended, to meet the MLJ criteria, and will be re-reviewed by PC members.

Papers under review elsewhere must NOT be submitted to ILP.