Learning Distributional Programs for Relational Autocompletion
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00356029" target="_blank" >RIV/68407700:21230/22:00356029 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1017/S1471068421000144" target="_blank" >https://doi.org/10.1017/S1471068421000144</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1017/S1471068421000144" target="_blank" >10.1017/S1471068421000144</a>
Alternative languages
Result language
angličtina
Original language name
Learning Distributional Programs for Relational Autocompletion
Original language description
Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses (DCs), which supports both discrete and continuous probability distributions. Within this framework, we introduce DiceML - an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data). To realize this, DiceML integrates statistical modeling and DCs with rule learning. The distinguishing features of DiceML are that it (1) tackles autocompletion in relational data, (2) learns DCs extended with statistical models, (3) deals with both discrete and continuous distributions, (4) can exploit background knowledge, and (5) uses an expectation-maximization-based (EM) algorithm to cope with missing data. The empirical results show the promise of the approach, even when there is missing data.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Theory and Practice of Logic Programming
ISSN
1471-0684
e-ISSN
1475-3081
Volume of the periodical
22
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
34
Pages from-to
81-114
UT code for WoS article
000889151500004
EID of the result in the Scopus database
2-s2.0-85114353876