4 research outputs found
Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning
Knowledge Graph Construction (KGC) can be seen as an iterative process
starting from a high quality nucleus that is refined by knowledge extraction
approaches in a virtuous loop. Such a nucleus can be obtained from knowledge
existing in an open KG like Wikidata. However, due to the size of such generic
KGs, integrating them as a whole may entail irrelevant content and scalability
issues. We propose an analogy-based approach that starts from seed entities of
interest in a generic KG, and keeps or prunes their neighboring entities. We
evaluate our approach on Wikidata through two manually labeled datasets that
contain either domain-homogeneous or -heterogeneous seed entities. We
empirically show that our analogy-based approach outperforms LSTM, Random
Forest, SVM, and MLP, with a drastically lower number of parameters. We also
evaluate its generalization potential in a transfer learning setting. These
results advocate for the further integration of analogy-based inference in
tasks related to the KG lifecycle
Probability Semantics for Aristotelian Syllogisms
We present a coherence-based probability semantics for (categorical)
Aristotelian syllogisms. For framing the Aristotelian syllogisms as
probabilistic inferences, we interpret basic syllogistic sentence types A, E,
I, O by suitable precise and imprecise conditional probability assessments.
Then, we define validity of probabilistic inferences and probabilistic notions
of the existential import which is required, for the validity of the
syllogisms. Based on a generalization of de Finetti's fundamental theorem to
conditional probability, we investigate the coherent probability propagation
rules of argument forms of the syllogistic Figures I, II, and III,
respectively. These results allow to show, for all three Figures, that each
traditionally valid syllogism is also valid in our coherence-based probability
semantics. Moreover, we interpret the basic syllogistic sentence types by
suitable defaults and negated defaults. Thereby, we build a knowledge bridge
from our probability semantics of Aristotelian syllogisms to nonmonotonic
reasoning. Finally, we show how the proposed semantics can be used to analyze
syllogisms involving generalized quantifiers
The Schulze Method of Voting
We propose a new single-winner election method ("Schulze method") and prove
that it satisfies many academic criteria (e.g. monotonicity, reversal symmetry,
resolvability, independence of clones, Condorcet criterion, k-consistency,
polynomial runtime). We then generalize this method to proportional
representation by the single transferable vote ("Schulze STV") and to methods
to calculate a proportional ranking ("Schulze proportional ranking").
Furthermore, we propose a generalization of the Condorcet criterion to
multi-winner elections. This paper contains a large number of examples to
illustrate the proposed methods