Stephen Muggleton

12.4k citations

119 papers · 5.0k indexed · 2 hit papers · h-index 31

Impact in

Artificial Intelligence top 0.2%
- Logic, Reasoning, and Knowledge
- Machine Learning and Algorithms
- Semantic Web and Ontologies
- Natural Language Processing Techniques
- AI-based Problem Solving and Planning
Computational Theory and Mathematics top 0.2%
- Rough Sets and Fuzzy Logic
- Computational Drug Discovery Methods

Papers in ⓘ

Computational Theory and Mathematics 46
- Computational Drug Discovery Methods 18
Artificial Intelligence 71
- Logic, Reasoning, and Knowledge 35
- Machine Learning and Algorithms 21
- Semantic Web and Ontologies 13
- Natural Language Processing Techniques 12

Co-authors: Luc De Raedt (3 shared papers)Ross D. King (12 shared papers)Michael J.E. Sternberg (32 shared papers)Cheng Feng (1 shared paper)Ashwin Srinivasan (9 shared papers)Alireza Tamaddoni‐Nezhad (15 shared papers)Christopher H. Bryant (4 shared papers)Stephen G. Oliver (2 shared papers)
Journals: Machine Learning (19 papers)Journal of Molecular Biology (4 papers)Nature (3 papers)Communications of the ACM (3 papers)Journal of Proteome Research (2 papers)
Partner nations: United Kingdom United States Japan

In The Last Decade

Stephen Muggleton

116 papers receiving 4.5k citations

Hit Papers

align trajectories log scale

Inverse entailment and progol 1995 · 628 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

1995 New Generation Computing
1994 The Journal of Logic Programming

Peers

Countries citing papers authored by Stephen Muggleton

Since Specialization

Citations

This map shows the geographic impact of Stephen Muggleton's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Stephen Muggleton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen Muggleton more than expected).

Fields of papers citing papers by Stephen Muggleton

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stephen Muggleton. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Stephen Muggleton. The network helps show where Stephen Muggleton may publish in the future.

Co-authors

The 25 scholars most cited alongside Stephen Muggleton, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Stephen Muggleton Line = papers co-authored together Stephen Muggleton links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 119 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Inductive Logic Programming: Theory and methods The Journal of Logic Programming ·Stephen Muggleton, Luc De Raedt Hit paper breakdown →	1994	713
2	Inverse entailment and progol New Generation Computing ·Stephen Muggleton Hit paper breakdown →	1995	628
3	Inductive logic programming New Generation Computing ·Stephen Muggleton	1991	457
4	Functional genomic hypothesis generation and experimentation by a robot scientist Nature ·Ross D. King, Kenneth E. Whelan, Ffion Mair Jones, Philip G. K. Reiser, Christopher H. Bryant, Stephen Muggleton, Douglas B. Kell, Stephen G. Oliver	2004	389
5	Efficient Induction of Logic Programs Stephen Muggleton, Cheng Feng	1990	318
6	Protein secondary structure prediction using logic-based machine learning Protein Engineering Design and Selection ·Stephen Muggleton, Ross D. King, Michaël Stenberg	1992	140
7	Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proceedings of the National Academy of Sciences ·Ross D. King, Stephen Muggleton, Richard A. Lewis, Michael J.E. Sternberg	1992	137
8	Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proceedings of the National Academy of Sciences ·Ross D. King, Stephen Muggleton, Ashwin Srinivasan, Michael J.E. Sternberg	1996	130
9	Theories for mutagenicity: a study in first-order and feature-based induction Artificial Intelligence ·Ashwin Srinivasan, Stephen Muggleton, Michael J.E. Sternberg, Ross D. King	1996	128
10	Applications of inductive logic programming Communications of the ACM ·Ivan Bratko, Stephen Muggleton	1995	81
11	Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited Machine Learning ·Stephen Muggleton, Dianhuan Lin, Alireza Tamaddoni‐Nezhad	2015	77
12	Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL Machine Learning ·Paul W. Finn, Stephen Muggleton, David Page, Ashwin Srinivasan	1998	73
13	Inductive programming meets the real world Communications of the ACM ·Sumit Gulwani, José Hernández‐Orallo, Emanuel Kitzelmann, Stephen Muggleton, Ute Schmid, Benjamin G. Zorn	2015	71
14	Alan Turing and the development of Artificial Intelligence AI Communications ·Stephen Muggleton	2014	69
15	PAC-learnability of determinate logic programs Sašo Džeroski, Stephen Muggleton, Stuart Russell	1992	68
16	Duce, an oracle-based approach to constructive induction International Joint Conference on Artificial Intelligence ·Stephen Muggleton	1987	61
17	Structured machine learning: the next ten years Machine Learning ·Thomas G. Dietterich, Pedro Domingos, Lise Getoor, Stephen Muggleton, Prasad Tadepalli	2008	59
18	The predictive toxicology evaluation challenge International Journal of Biological Macromolecules ·Ashwin Srinivasan, Ross D. King, Stephen Muggleton, Michael J.E. Sternberg	1997	57
19	Meta-interpretive learning: application to grammatical inference Machine Learning ·Stephen Muggleton, Dianhuan Lin, Niels Pahlavi, Alireza Tamaddoni‐Nezhad	2013	55
20	ILP turns 20 Machine Learning ·Stephen Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter Flach, Katsumi Inoue, Ashwin Srinivasan	2011	54

About Stephen Muggleton

Stephen Muggleton is a scholar working on Computational Theory and Mathematics, Artificial Intelligence, Software, Molecular Biology and Information Systems and Management, having authored 119 papers that have together received 5.0k indexed citations. Recurring topics across this work include Logic, Reasoning, and Knowledge (35 papers), Machine Learning and Algorithms (21 papers), Computational Drug Discovery Methods (18 papers), Semantic Web and Ontologies (13 papers), Natural Language Processing Techniques (12 papers), Machine Learning in Bioinformatics (11 papers), Microbial Metabolic Engineering and Bioproduction (11 papers) and Protein Structure and Dynamics (10 papers). The work is most often cited by research in Artificial Intelligence (3.3k citations), Computational Theory and Mathematics (1.5k citations), Information Systems (1.1k citations), Software (155 citations) and Signal Processing (325 citations). Stephen Muggleton has collaborated with scholars based in United Kingdom, United States and Japan. Frequent co-authors include Luc De Raedt, Ross D. King, Michael J.E. Sternberg, Cheng Feng, Ashwin Srinivasan, Alireza Tamaddoni‐Nezhad, Christopher H. Bryant, Stephen G. Oliver, Douglas B. Kell and Philip G. K. Reiser. Their work appears in journals such as Machine Learning, Journal of Molecular Biology, Nature, Communications of the ACM and Journal of Proteome Research.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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