Probabilistic Inductive Logic Programming (PILP) is gaining attention for its capability of modeling complex domains containing uncertain relationships among entities. Among PILP systems, cplint provides inference and learning algorithms competitive with the state of the art. Besides parameter learning, cplint provides one of the few structure learning algorithms for PLP, SLIPCOVER. Moreover, an online version was recently developed, cplint on SWISH, that allows users to experiment with the system using just a web browser. In this demo we illustrate cplint on SWISH concentrating on structure learning with SLIPCOVER. cplint on SWISH also includes many examples and a step-by-step tutorial.

Probabilistic inductive logic programming on the web / Riguzzi, Fabrizio; Zese, Riccardo; Cota, Giuseppe. - ELETTRONICO. - 10180:(2017), pp. 172-175. (Intervento presentato al convegno 20th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2016 tenutosi a ita nel 2016) [10.1007/978-3-319-58694-6_25].

Probabilistic inductive logic programming on the web

COTA, Giuseppe
2017-01-01

Abstract

Probabilistic Inductive Logic Programming (PILP) is gaining attention for its capability of modeling complex domains containing uncertain relationships among entities. Among PILP systems, cplint provides inference and learning algorithms competitive with the state of the art. Besides parameter learning, cplint provides one of the few structure learning algorithms for PLP, SLIPCOVER. Moreover, an online version was recently developed, cplint on SWISH, that allows users to experiment with the system using just a web browser. In this demo we illustrate cplint on SWISH concentrating on structure learning with SLIPCOVER. cplint on SWISH also includes many examples and a step-by-step tutorial.
2017
9783319586939
Probabilistic inductive logic programming on the web / Riguzzi, Fabrizio; Zese, Riccardo; Cota, Giuseppe. - ELETTRONICO. - 10180:(2017), pp. 172-175. (Intervento presentato al convegno 20th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2016 tenutosi a ita nel 2016) [10.1007/978-3-319-58694-6_25].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2870771
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