Learning Distributional Programs for Relational Autocompletion

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning Distributional Programs for Relational Autocompletion

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Learning Distributional Programs for Relational Autocompletion

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Theory and Practice of Logic Programming

ISSN

1471-0684

e-ISSN

1475-3081

Svazek periodika

22

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

34

Strana od-do

81-114

Kód UT WoS článku

000889151500004

EID výsledku v databázi Scopus

2-s2.0-85114353876