ILP 2017 : Inductive Logic Programming conference
Conference Series : Inductive Logic Programming
Call For Papers
Authors are invited to submit papers presenting original results on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, connections with other learning paradigms, and learning in other logic-based knowledge representation frameworks.
Typical, but not exclusive, topics of interest for submissions include:
- Theoretical aspects: logical foundations, probabilistic logic-based learning, computational and/or statistical learning theories, theories on abduction, data/model representation, …
- Representations and languages for logic-based learning: subsets of first-order logic (datalog, description logics for instance), higher-order logic, probabilistic logical representations, links between alternative representations.
- Algorithms and systems:
- supervised/unsupervised/semi-supervised relational learning, relational reinforcement learning, abductive learning, inductive databases
- learning from structured data (e.g., labeled graphs, tree patterns) and semi-structured data (e.g., XML documents)
- logical, probabilistic and statistical approaches, distance and kernel-based methods, propositionalization, predicate invention
- systems that implement inductive logic programming algorithms with special emphasis on issues like efficiency and scalability.
- Applications of ILP:
- bioinformatics, chemoinformatics, medical informatics, robotics, engineering
- natural language processing: computational linguistics, text/web mining
- social science, art and humanities, games, social networks
Three kinds of papers can be submitted:
1) Regular papers describing original mature work representing a self-contained theoretical contribution and/or containing appropriate experimental evaluation. These papers will be reviewed by at least 3 members of the program committee. Each accepted paper will be given a standard time slot for presentation and will be invited to submit a final version to the Springer LNAI post-conference proceedings without further reviews.
If not accepted as a long paper, it may be accepted as a work-in-progress paper and assigned a reduced time slot for presentation. The authors have the opportunity to submit a revised version, which will be reviewed after the conference for possible inclusion in the Springer LNAI post-conference proceedings.
Regular papers must not exceed 12 pages (including references).
2) Late-breaking papers describing original work in progress, brief accounts of original ideas without conclusive experimental evaluation, and other relevant work of potentially high scientific interest but not yet qualifying for the regular paper category. Late-breaking papers will be accepted/rejected by the PC chairs on the grounds of relevance. Authors of accepted late-breaking papers will be assigned a reduced time slot for presentation.
Each late-breaking paper will then be reviewed by at least 3 members of the program committee taking also into account the oral presentation. Based on these reviews, the authors may be given the opportunity to submit a long version for the Springer LNAI post-conference proceedings. In this case, the long paper will be reviewed again by the assigned PC members of the short paper and be finally accepted if satisfactorily addressing the reviewer's requirements.
Late-breaking papers must not exceed 6 pages (including references).
3) Recently published papers: 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. The PC chairs will accept/reject such papers 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.
Following the conference, a special issue of Machine Learning Journal is planned. It will be opened to everyone. This special issue welcomes conference submissions from all the three categories above, which should be significantly revised and extended to meet the journal criteria. They will be reviewed by the Program Committee.