Probabilistic Databases

Suciu, Dan

doi:10.1007/978-1-4899-7993-3_275-2

Cited by 4 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While our possible worlds semantic is in line with standard approaches to probabilistic databases (Suciu, Olteanu, Ré, & Koch, 2011;Dalvi & Suciu, 2012;Fuhr & Rölleke, 1997), in such a setup one typically wants to make strong independence assumptions on the data (that is, between ABox assertions) which we have not considered in this paper. First steps into this direction have been made (Jung & Lutz, 2012;Ceylan & Peñaloza, 2015;Gottlob et al, 2013).…”

Section: Resultsmentioning

confidence: 97%

Probabilistic Description Logics for Subjective Uncertainty

Lutz¹,

Schröder

2017

jair

View full text Add to dashboard Cite

We propose a family of probabilistic description logics (DLs) that are derived in a principled way from Halpern's probabilistic first-order logic. The resulting probabilistic DLs have a two-dimensional semantics similar to temporal DLs and are well-suited for representing subjective probabilities. We carry out a detailed study of reasoning in the new family of logics, concentrating on probabilistic extensions of the DLs ALC and EL, and showing that the complexity ranges from PTime via ExpTime and 2ExpTime to undecidable.

show abstract

Section: Resultsmentioning

confidence: 97%

Probabilistic Description Logics for Subjective Uncertainty

Lutz¹,

Schröder

2017

jair

View full text Add to dashboard Cite

show abstract

“…Let φ j denote the user effort required for deciding about the relevance of entity y j . 1 Now consider two entities y 1 and y 2 with probabilities 0.51 and 0.49 and effort φ 1 = 10 and φ 2 = 1, respectively. If we rank y 1 before y 2 , the user would have to invest an expected effort of 0.51 · 10 + 0.49 · (10 + 1) = 10.49 when examining the ranked list top-down until the correct answer is found (assuming either y 1 or y 2 is correct, but not both).…”

Section: Results Rankingmentioning

confidence: 99%

“…Querying of uncertain data has been studied for probabilistic databases [1], but the query is given and specified precisely. Our approach can also be applied to probabilistic data.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

User-driven refinement of imprecise queries

Qarabaqi

Riedewald

2014

2014 IEEE 30th International Conference on Data Engineering Workshops

View full text Add to dashboard Cite

Abstract-We propose techniques for exploratory search in large databases. The goal is to provide new functionality that aids users in homing in on the right query conditions to find what they are looking for. Query refinement proceeds interactively by repeatedly consulting the user to manage query conditions. This process is characterized by three key challenges: (1) dealing with incomplete and imprecise user input, (2) keeping user effort low, and (3) guaranteeing interactive system response time. We address the first two challenges with a probability-based framework that guides the user to the most important query conditions. To recover from input errors, we introduce the notion of sensitivity and propose efficient algorithms for identifying the most sensitive user input, i.e., those inputs that had the greatest influence on the query results. For the third challenge, we develop techniques that can deliver estimates of the required probabilities within a given hard realtime limit and are able to adapt automatically as the interactive query refinement proceeds.

show abstract

“…Thus, they cannot be used in a spontaneous "on-the-fly" manner. Query-time IE, as pursued in our work, resembles the notion of querytime inference over probabilistic databases [18,53]. These methods operate on uncertain relational data as well as uncertain rules, and support flexible forms of top-k queries [17] and general inference [23,24,31].…”

Section: Introductionmentioning

confidence: 99%

Query-driven on-the-fly knowledge base construction

Nguyen¹,

Abujabal²,

Tran

et al. 2017

Proc. VLDB Endow.

View full text Add to dashboard Cite

Today's openly available knowledge bases, such as DBpedia, Yago, Wikidata or Freebase, capture billions of facts about the world's entities. However, even the largest among these (i) are still limited in up-to-date coverage of what happens in the real world, and (ii) miss out on many relevant predicates that precisely capture the wide variety of relationships among entities. To overcome both of these limitations, we propose a novel approach to build on-the-fly knowledge bases in a query-driven manner. Our system, called QKBfly, supports analysts and journalists as well as question answering on emerging topics, by dynamically acquiring relevant facts as timely and comprehensively as possible. QKBfly is based on a semantic-graph representation of sentences, by which we perform three key IE tasks, namely named-entity disambiguation, co-reference resolution and relation extraction, in a light-weight and integrated manner. In contrast to Open IE, our output is canonicalized. In contrast to traditional IE, we capture more predicates, including ternary and higher-arity ones. Our experiments demonstrate that QKBfly can build high-quality, on-the-fly knowledge bases that can readily be deployed, e.g., for the task of ad-hoc question answering. PVLDB Reference Format:

show abstract

Probabilistic Databases

Cited by 4 publications

References 11 publications

Probabilistic Description Logics for Subjective Uncertainty

Probabilistic Description Logics for Subjective Uncertainty

User-driven refinement of imprecise queries

Query-driven on-the-fly knowledge base construction

Contact Info

Product

Resources

About