RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models

Noessner, Jan; Niepert, Mathias; Stuckenschmidt, Heiner

doi:10.1609/aaai.v27i1.8579

Cited by 27 publications

(10 citation statements)

References 17 publications

(16 reference statements)

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“…We evaluate our algorithm on three benchmarks with three different input datasets generated from real-world information retrieval and program analysis applications. Our empirical evaluation shows that our approach achieves significant improvement over three state-of-art approaches, CPI (Riedel 2008;2009), RockIt (Noessner, Niepert, and Stuckenschmidt 2013), and Tuffy (Niu et al 2011), in running time as well as the quality of the solution.…”

Section: ∀N1mentioning

confidence: 87%

“…Several techniques have been proposed to lazily ground constraints (Niu et al 2011;Kok et al 2007;Chaganty et al 2013;Noessner, Niepert, and Stuckenschmidt 2013;Riedel 2008;2009). For the scale of problems we consider, however, these techniques are either too lazy and converge very slowly, or too eager and produce instances that are beyond the reach of sound WPMS solvers.…”

Section: ∀N1mentioning

confidence: 99%

“…Note that reducing relational constraints to WPMS via full grounding (the eager approach) is not tractable as the size of such generated WPMS instances is beyond the scope of the state-of-the-art solvers. Existing techniques (Kok et al 2007;Niu et al 2011;Chaganty et al 2013;Riedel 2008;2009;Noessner, Niepert, and Stuckenschmidt 2013) reduce the burden on the WPMS solver by considering subsets of the full grounding (the lazy approach) in an iterative manner. These approaches, however, are either overly conservative in growing the considered subsets resulting in too many iterations, or are excessively aggressive leading to intractable instances.…”

Section: Preliminariesmentioning

confidence: 99%

“…In this section, we evaluate IPR and compare it with three state-of-the-art approaches, CPI (Riedel 2008;2009), ROCKIT (Noessner, Niepert, and Stuckenschmidt 2013), and TUFFY (Niu et al 2011), on three different benchmarks with three different-sized inputs per benchmark.…”

Section: Empirical Evaluationmentioning

confidence: 99%

“…Such techniques apply a loose termination check, and therefore do not guarantee soundness nor optimality of the final solution. Iterative ground-and-solve approaches such as CPI (Riedel 2008) and RockIt (Noessner, Niepert, and Stuckenschmidt 2013) solve constraints lazily by iteratively grounding only those constraints that are violated by the current solution. Compared to lazy inference techniques, they apply a conservative termination check which guarantees the soundness and optimality of the solution.…”

Section: Related Workmentioning

confidence: 99%

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Scaling Relational Inference Using Proofs and Refutations

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Many inference problems are naturally formulated using hard and soft constraints over relational domains: the desired solution must satisfy the hard constraints, while optimizing the objectives expressed by the soft constraints. Existing techniques for solving such constraints rely on efficiently grounding a sufficient subset of constraints that is tractable to solve. We present an eager-lazy grounding algorithm that eagerly exploits proofs and lazily refutes counterexamples. We show that our algorithm achieves significant speedup over existing approaches without sacrificing soundness for real-world applications from information retrieval and program analysis.

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Section: ∀N1mentioning

confidence: 87%

Section: ∀N1mentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: Empirical Evaluationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Supervised Knowledge Aggregation for Knowledge Graph Completion

Betz

Meilicke

Stuckenschmidt

2022

Lecture Notes in Computer Science

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Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding one plausibility score for a candidate fact which was simultaneously predicted by multiple rules. Although the problem is ubiquitous, as data-driven rule learning can result in noisy and large rule sets, it is underrepresented in the literature and its theoretical foundations have not been studied before in this context. In this work, we demonstrate that existing aggregation approaches can be expressed as marginal inference operations over the predicting rules. In particular, we show that the common Max-aggregation strategy, which scores candidates based on the rule with the highest confidence, has a probabilistic interpretation. Finally, we propose an efficient and overlooked baseline which combines the previous strategies and is competitive to computationally more expensive approaches.

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A Probabilistic Approach for Integrating Heterogeneous Knowledge Sources

Dutta

Meilicke

Ponzetto

2014

Lecture Notes in Computer Science

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Abstract. Open Information Extraction (OIE) systems like Nell andReVerb have achieved impressive results by harvesting massive amounts of machine-readable knowledge with minimal supervision. However, the knowledge bases they produce still lack a clean, explicit semantic data model. This, on the other hand, could be provided by full-fledged semantic networks like DBpedia or Yago, which, in turn, could benefit from the additional coverage provided by Web-scale IE. In this paper, we bring these two strains of research together, and present a method to align terms from Nell with instances in DBpedia. Our approach is unsupervised in nature and relies on two key components. First, we automatically acquire probabilistic type information for Nell terms given a set of matching hypotheses. Second, we view the mapping task as the statistical inference problem of finding the most likely coherent mapping -i.e., the maximum a posteriori (MAP) mapping -based on the outcome of the first component used as soft constraint. These two steps are highly intertwined: accordingly, we propose an approach that iteratively refines type acquisition based on the output of the mapping generator, and vice versa. Experimental results on gold-standard data indicate that our approach outperforms a strong baseline, and is able to produce everimproving mappings consistently across iterations.

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RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models

Cited by 27 publications

References 17 publications

Scaling Relational Inference Using Proofs and Refutations

Scaling Relational Inference Using Proofs and Refutations

Supervised Knowledge Aggregation for Knowledge Graph Completion

A Probabilistic Approach for Integrating Heterogeneous Knowledge Sources

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