| Nome |
# |
|
Distributed Parameter Learning for Probabilistic Ontologies, file e309ade0-d159-3969-e053-3a05fe0a2c94
|
189
|
|
Tableau reasoning for description logics and its extension to probabilities, file e309ade1-4985-3969-e053-3a05fe0a2c94
|
150
|
|
Abductive Logic Programming for Normative Reasoning and Ontologies, file e309ade0-e264-3969-e053-3a05fe0a2c94
|
149
|
|
Probabilistic logic programming on the web, file e309ade0-e445-3969-e053-3a05fe0a2c94
|
148
|
|
A web system for reasoning with probabilistic OWL, file e309ade0-d9a1-3969-e053-3a05fe0a2c94
|
138
|
|
Probabilistic inductive constraint logic, file e309ade4-8c8f-3969-e053-3a05fe0a2c94
|
127
|
|
Causal inference in cplint, file e309ade1-994b-3969-e053-3a05fe0a2c94
|
111
|
|
Probabilistic Description Logics under the distribution semantics, file e309ade0-86ee-3969-e053-3a05fe0a2c94
|
96
|
|
cplint on SWISH: Probabilistic Logical Inference with a Web Browser, file e309ade2-f13e-3969-e053-3a05fe0a2c94
|
84
|
|
Reasoning with Probabilistic Ontologies, file e309ade1-2ca8-3969-e053-3a05fe0a2c94
|
80
|
|
Summarizing significant subgraphs by probabilistic logic programming, file e309ade2-20e3-3969-e053-3a05fe0a2c94
|
77
|
|
Probabilistic inductive constraint logic, file e309ade2-d68d-3969-e053-3a05fe0a2c94
|
76
|
|
Economic and organizational impact of a clinical decision support system on laboratory test ordering, file e309ade1-a457-3969-e053-3a05fe0a2c94
|
74
|
|
Bandit-based Monte-Carlo structure learning of probabilistic logic programs, file e309ade0-8ed6-3969-e053-3a05fe0a2c94
|
60
|
|
Preface to special issue on Inductive Logic Programming, ILP 2017 and 2018, file e309ade2-20d6-3969-e053-3a05fe0a2c94
|
59
|
|
Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach, file e309ade2-0af0-3969-e053-3a05fe0a2c94
|
57
|
|
Probabilistic Declarative Process Mining, file e309ade1-b396-3969-e053-3a05fe0a2c94
|
52
|
|
An Abductive Framework for Datalog± Ontologies, file e309ade0-8be0-3969-e053-3a05fe0a2c94
|
49
|
|
Abductive logic programming for Datalog+/- ontologies, file e309ade0-9b21-3969-e053-3a05fe0a2c94
|
47
|
|
Modeling Bitcoin Lightning Network by Logic Programming, file e309ade2-c45f-3969-e053-3a05fe0a2c94
|
37
|
|
Probabilistic Constraint Logic Theories, file e309ade0-d153-3969-e053-3a05fe0a2c94
|
36
|
|
Tableau Reasoners for Probabilistic Ontologies Exploiting Logic Programming Techniques, file e309ade1-b3c3-3969-e053-3a05fe0a2c94
|
33
|
|
Integration of Logic and Probability in Terminological and Inductive Reasoning, file e309ade1-d5a7-3969-e053-3a05fe0a2c94
|
33
|
|
Structure Learning with Distributed Parameter Learning for Probabilistic Ontologies, file e309ade1-bb53-3969-e053-3a05fe0a2c94
|
31
|
|
Scaling Structure Learning of Probabilistic Logic Programs by MapReduce, file e309ade0-d1b8-3969-e053-3a05fe0a2c94
|
28
|
|
Statistical relational learning for workflow mining, file e309ade0-d633-3969-e053-3a05fe0a2c94
|
28
|
|
Logic programming techniques for reasoning with probabilistic ontologies, file e309ade0-e39a-3969-e053-3a05fe0a2c94
|
25
|
|
Scaling Structure Learning of Probabilistic Logic Programs by MapReduce, file e309ade2-37cb-3969-e053-3a05fe0a2c94
|
25
|
|
Reasoning on Datalog± Ontologies with Abductive Logic Programming, file e309ade2-e8ab-3969-e053-3a05fe0a2c94
|
25
|
|
Approximate Inference in Probabilistic Answer Set Programming for Statistical Probabilities, file 59b40089-722e-4dbc-af66-b185e05ec532
|
22
|
|
Learning Probabilistic Ontologies with Distributed Parameter Learning, file e309ade1-1255-3969-e053-3a05fe0a2c94
|
20
|
|
MAP Inference in Probabilistic Answer Set Programs, file 72d39ea7-5d62-4f1f-a96d-1c7ba2e1120d
|
17
|
|
A web system for reasoning with probabilistic OWL, file e309ade1-c4e0-3969-e053-3a05fe0a2c94
|
17
|
|
A survey of lifted inference approaches for probabilistic logic programming under the distribution semantics, file e309ade3-04dc-3969-e053-3a05fe0a2c94
|
17
|
|
Abduction with probabilistic logic programming under the distribution semantics, file e309ade5-1bac-3969-e053-3a05fe0a2c94
|
17
|
|
null, file e309ade0-4494-3969-e053-3a05fe0a2c94
|
16
|
|
MAP Inference for Probabilistic Logic Programming, file e309ade2-b69b-3969-e053-3a05fe0a2c94
|
16
|
|
A survey of lifted inference approaches for probabilistic logic programming under the distribution semantics, file e309ade1-2403-3969-e053-3a05fe0a2c94
|
15
|
|
Tableau reasoning for description logics and its extension to probabilities, file e309ade1-cd8a-3969-e053-3a05fe0a2c94
|
15
|
|
A Framework for Reasoning on Probabilistic Description Logics, file e309ade2-fcf9-3969-e053-3a05fe0a2c94
|
15
|
|
Probabilistic logic programming on the web, file e309ade1-45ea-3969-e053-3a05fe0a2c94
|
14
|
|
Abduction with probabilistic logic programming under the distribution semantics, file e309ade5-1bab-3969-e053-3a05fe0a2c94
|
13
|
|
Reasoning on Datalog± Ontologies with Abductive Logic Programming, file e309ade1-b3c5-3969-e053-3a05fe0a2c94
|
12
|
|
Preface [to Inductive Logic Programming], file e309ade1-f447-3969-e053-3a05fe0a2c94
|
12
|
|
Probabilistic Description Logics under the distribution semantics, file e309ade0-fdd2-3969-e053-3a05fe0a2c94
|
11
|
|
Improving the efficiency of euclidean TSP solving in constraint programming by predicting effective nocrossing constraints, file e309ade3-7af0-3969-e053-3a05fe0a2c94
|
11
|
|
cplint on SWISH: Probabilistic Logical Inference with a Web Browser, file e309ade1-b568-3969-e053-3a05fe0a2c94
|
10
|
|
Preface [Up-and-Coming and Short Papers of ILP 2018], file e309ade1-e94b-3969-e053-3a05fe0a2c94
|
10
|
|
Structure learning of Probabilistic Logic Programs by searching the clause space, file e309ade2-e4ac-3969-e053-3a05fe0a2c94
|
10
|
|
Special Issue on Probabilistic Logic Programming (PLP 2018), file e309ade2-f708-3969-e053-3a05fe0a2c94
|
10
|
|
Probabilistic inductive constraint logic, file e309ade4-722b-3969-e053-3a05fe0a2c94
|
10
|
|
A probabilistic logic model of Lightning Network, file e309ade5-12c0-3969-e053-3a05fe0a2c94
|
10
|
|
Causal inference in cplint, file e309ade1-b92e-3969-e053-3a05fe0a2c94
|
9
|
|
Modeling Bitcoin protocols with probabilistic logic programming, file e309ade2-0e29-3969-e053-3a05fe0a2c94
|
9
|
|
Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach, file e309ade2-3e9a-3969-e053-3a05fe0a2c94
|
9
|
|
Preface to special issue on Inductive Logic Programming, ILP 2017 and 2018, file e309ade2-f86a-3969-e053-3a05fe0a2c94
|
9
|
|
null, file e309ade0-b0d9-3969-e053-3a05fe0a2c94
|
8
|
|
Learning the structure of probabilistic logic programs, file e309ade1-b3fe-3969-e053-3a05fe0a2c94
|
8
|
|
Modeling Bitcoin Lightning Network by Logic Programming, file e309ade5-9391-3969-e053-3a05fe0a2c94
|
8
|
|
null, file e309ade0-3fc3-3969-e053-3a05fe0a2c94
|
7
|
|
Distributed Parameter Learning for Probabilistic Ontologies, file e309ade1-b971-3969-e053-3a05fe0a2c94
|
7
|
|
A Modular Inference System for Probabilistic Description Logics, file e309ade1-ef88-3969-e053-3a05fe0a2c94
|
7
|
|
Expectation Maximization over Binary Decision Diagrams for Probabilistic Logic Programs, file e309ade1-4136-3969-e053-3a05fe0a2c94
|
6
|
|
MAP Inference for Probabilistic Logic Programming, file e309ade2-b549-3969-e053-3a05fe0a2c94
|
6
|
|
Neural Network Techniques for Detecting Intra-Domestic Water Leaks of Different Magnitude, file e309ade4-ee15-3969-e053-3a05fe0a2c94
|
6
|
|
Experimentation of an expectation maximization algorithm for probabilistic logic programs, file e309ade0-3911-3969-e053-3a05fe0a2c94
|
5
|
|
null, file e309ade0-3b52-3969-e053-3a05fe0a2c94
|
5
|
|
Preface [PLP 2018. Probabilistic Logic Programming], file e309ade2-264d-3969-e053-3a05fe0a2c94
|
5
|
|
Improving the Efficiency of Euclidean TSP Solving in Constraint Programming by Predicting Effective Nocrossing Constraints, file e309ade4-75b0-3969-e053-3a05fe0a2c94
|
5
|
|
MAP Inference in Probabilistic Answer Set Programs, file d822492c-01bf-4274-a864-0a78917285a3
|
4
|
|
null, file e309ade0-20a8-3969-e053-3a05fe0a2c94
|
4
|
|
null, file e309ade0-4495-3969-e053-3a05fe0a2c94
|
4
|
|
Structure learning of Probabilistic Logic Programs by searching the clause space, file e309ade0-9209-3969-e053-3a05fe0a2c94
|
4
|
|
Statistical relational learning for workflow mining, file e309ade0-d632-3969-e053-3a05fe0a2c94
|
4
|
|
null, file e309ade1-40ff-3969-e053-3a05fe0a2c94
|
4
|
|
A History of Probabilistic Inductive Logic Programming, file e309ade1-bb51-3969-e053-3a05fe0a2c94
|
4
|
|
A Framework for Reasoning on Probabilistic Description Logics, file e309ade2-fcf8-3969-e053-3a05fe0a2c94
|
4
|
|
Special Issue on Probabilistic Logic Programming (PLP 2018), file e309ade4-c295-3969-e053-3a05fe0a2c94
|
4
|
|
Statistical Statements in Probabilistic Logic Programming, file fe165fc8-2754-4c92-9b14-1d00f4349b2d
|
4
|
|
null, file e309ade0-3347-3969-e053-3a05fe0a2c94
|
3
|
|
null, file e309ade0-4144-3969-e053-3a05fe0a2c94
|
3
|
|
Lifted Variable Elimination for Probabilistic Logic Programming, file e309ade0-f941-3969-e053-3a05fe0a2c94
|
3
|
|
Bandit-based Monte-Carlo structure learning of probabilistic logic programs, file e309ade0-fdd4-3969-e053-3a05fe0a2c94
|
3
|
|
Reducing Laboratory Examinations by a Computer-aided Clinical Decision Support System, file e309ade1-0e9a-3969-e053-3a05fe0a2c94
|
3
|
|
null, file e309ade1-b3bd-3969-e053-3a05fe0a2c94
|
3
|
|
null, file e309ade1-bb43-3969-e053-3a05fe0a2c94
|
3
|
|
Preface [to Inductive Logic Programming], file e309ade1-efa8-3969-e053-3a05fe0a2c94
|
3
|
|
Summarizing significant subgraphs by probabilistic logic programming, file e309ade2-7e3a-3969-e053-3a05fe0a2c94
|
3
|
|
Abduction in (Probabilistic) Answer Set Programming, file f2cc3fb1-b8cf-493b-a8f2-dcfa550d0073
|
3
|
|
Approximate Inference in Probabilistic Answer Set Programming for Statistical Probabilities, file fea33135-b84e-4c23-9a96-9e618b447657
|
3
|
|
Nonground Abductive Logic Programming with Probabilistic Integrity Constraints, file 465ca7e7-1d81-4696-bef9-31f62689790e
|
2
|
|
Nonground Abductive Logic Programming with Probabilistic Integrity Constraints, file 61e0a1ce-44ec-4ed5-97a1-a128c510011e
|
2
|
|
Comparing Emotion and Sentiment Analysis Tools on Italian anti-vaccination for COVID-19 posts, file 6d35e358-d6de-4e81-b837-3d57582041fe
|
2
|
|
Modeling opinion polarization on social media: Application to Covid-19 vaccination hesitancy in Italy, file 7295f105-bced-4fb8-9f16-4cd4987970a3
|
2
|
|
Abduction in Probabilistic Logic Programs, file c9866771-3ed8-4fdd-9edd-36d91eec1fcf
|
2
|
|
null, file e309ade0-3398-3969-e053-3a05fe0a2c94
|
2
|
|
null, file e309ade0-3b51-3969-e053-3a05fe0a2c94
|
2
|
|
null, file e309ade0-4a95-3969-e053-3a05fe0a2c94
|
2
|
|
Parameter and Structure Learning Algorithms for Statistical Relational Learning, file e309ade0-e79c-3969-e053-3a05fe0a2c94
|
2
|
|
null, file e309ade1-b3be-3969-e053-3a05fe0a2c94
|
2
|
| Totale |
2.656 |