Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Multivalued dependencies and a new normal form for relational databases
ACM Transactions on Database Systems (TODS)
Inferences involving embedded multivalued dependencies and transitive dependencies
SIGMOD '80 Proceedings of the 1980 ACM SIGMOD international conference on Management of data
Subjective bayesian methods for rule-based inference systems
AFIPS '76 Proceedings of the June 7-10, 1976, national computer conference and exposition
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Data-dependencies of the type "x can tell us more about y given that we already know z" can be represented in various formalisms: Probabilistic Dependencies, Embedded-Multi-Valued Dependencies, Undirected Graphs and Directed-Acyclic Graphs (DAGs). This paper provides an axiomatic basis, called a semigraphoid, which captures the structure common to all four types of dependencies and explores the expressive power of DAGs in representing various types of data dependencies. It is shown that DAGs can represent a richer set of dependencies than undirected graphs, that DAGs completely represent the closure of their specification bases, and that they offer an effective computational device for testing membership in that closure as well as inferring new dependencies from given inputs. These properties might explain the prevailing use of DAGs in causal reasoning and semantic nets.