Inductive Logic Programming: Theory, Algorithms and Applications

Dear Colleagues,

We are delighted to announce an open call for an Algorithms journal Special Issue on Inductive Logic Programming (ILP). ILP is a subfield of machine learning that uses logic programming as a uniform representation technique for examples, background knowledge, and hypotheses. Due to its strong representation formalism based on first-order logic, ILP provides excellent means for multi-relational learning and data mining. 

We invite you to submit high-quality papers to this Special Issue on ILP with subjects covering the whole range of topics from theory, algorithms, and implementation to applications. Typical, but not exclusive, topics of interest for submissions include: 

• theoretical aspects (logical foundations, computational and/or statistical learning theory, specialization and generalization operators, etc.) of learning in logic (logic programs, constraint logic programs, Datalog, first-order logic, description logics, higher-order logic, etc.) or from relational or graph databases;
• algorithmic and implementation aspects of learning in logic including the design of algorithms along with theoretical and/or empirical analysis, probabilistic and statistical approaches, distance and kernel-based methods, relational reinforcement learning, learning from multi-relational databases, scalability issues, inductive databases, link discovery, multi-instance learning, meta-interpretive learning, etc.; and
• applications including, but not restricted to, multi-relational learning from structured (e.g., labeled graphs, tree patterns) and semi-structured data (e.g., XML documents) in areas of science (systems biology and bioinformatics, ecology, cheminformatics, medical informatics, etc.), natural language processing (computational linguistics, relational text and web mining, etc.), robotics, engineering, or the arts. 

Submissions on theoretical and applied work bridging into areas such as cognitive technologies, human-like computing (HLC), neural-symbolic and deep relational learning, and relational learning from big data, the cloud, and data streams are also encouraged.

Dr. Alireza Tamaddoni-Nezhad
Prof. Stephen Muggleton
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• inductive logic programming (ILP)
• relational learning (RL)
• logic-based machine learning
• statistical relational learning (SRL)
• probabilistic logic programming
• relational data mining
• relational reinforcement learning
• relational deep learning
• constraint logic programming
• propositionalisation approaches
• subsumption and refinement
• evolutionary logic programming
• description logic and ontologies
• answer set programming
• unsupervised relational learning
• predicate invention
• meta-interpretive learning
• neuro-symbolic learning
• inductive programming