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Geometric Representation Learning
Vector embedding models are a cornerstone of modern machine learning methods for knowledge representation and reasoning. These methods aim to turn semantic questions into geometric questions by learning representations of concepts and other domain objects in a lower-dimensional vector space. In that spirit, this work advocates for density- and region-based representation learning. Embedding domain elements as geometric objects beyond a single point enables us to naturally represent breadth and polysemy, make asymmetric comparisons, answer complex queries, and provides a strong inductive bias when labeled data is scarce. We present a model for word representation using Gaussian densities, enabling asymmetric entailment judgments between concepts, and a probabilistic model for weighted transitive relations and multivariate discrete data based on a lattice of axis-aligned hyperrectangle representations (boxes). We explore the suitability of these embedding methods in different regimes of sparsity, edge weight, correlation, and independence structure, as well as extensions of the representation and different optimization strategies. We make a theoretical investigation of the representational power of the box lattice, and propose extensions to address shortcomings in modeling difficult distributions and graphs
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Probabilistic Commonsense Knowledge
Commonsense knowledge is critical to achieving artificial general intelligence. This shared common background knowledge is implicit in all human communication, facilitating efficient information exchange and understanding. But commonsense research is hampered by its immense quantity of knowledge because an explicit categorization is impossible. Furthermore, a plumber could repair a sink in a kitchen or a bathroom, indicating that common sense reveals a probable assumption rather than a definitive answer. To align with these properties of commonsense fundamentally, we want to not only model but also evaluate such knowledge human-like using abstractions and probabilistic principles. Traditional combinatorial probabilistic models, e.g., probabilistic graphical model approaches, have limitations to modeling large-scale probability distributions containing thousands or even millions of commonsensical events. On the other hand, although embedding-based representation learning has the advantage of generalizing to large combinations of events, they suffer from producing consistent probabilities under different styles of queries. Combining benefits from both sides, we introduce probabilistic box embeddings, which represent joint probability distributions on a learned latent space of geometric embeddings. By using box embeddings, it is now possible to handle queries with intersections, unions, and negations in a way similar to Venn diagram reasoning, which has faced difficulty even when using large language models. Meanwhile, existing evaluations do not reflect the probabilistic nature of commonsense knowledge. The popular multiple-choice evaluation style often misleads us into the paradigm that commonsense solved. To fill in the gap, we propose a method of retrieving commonsense related question answer distributions from human annotators as well as a novel method of generative evaluation. We utilize these approaches in two new commonsense datasets. Finally, we draw a connection between the-state-of-art NLP models --- large language models and their ability to perform commonsense reasoning tasks. According to the previous study, large language models would make inconsistent predictions while given different input texts for plausible commonsense situations. We intend to evaluate their performance using more rigorous probabilistic measurements
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
We consider the task of inferring is-a relationships from large text corpora.
For this purpose, we propose a new method combining hyperbolic embeddings and
Hearst patterns. This approach allows us to set appropriate constraints for
inferring concept hierarchies from distributional contexts while also being
able to predict missing is-a relationships and to correct wrong extractions.
Moreover -- and in contrast with other methods -- the hierarchical nature of
hyperbolic space allows us to learn highly efficient representations and to
improve the taxonomic consistency of the inferred hierarchies. Experimentally,
we show that our approach achieves state-of-the-art performance on several
commonly-used benchmarks
Bond percolation on isoradial graphs: criticality and universality
In an investigation of percolation on isoradial graphs, we prove the
criticality of canonical bond percolation on isoradial embeddings of planar
graphs, thus extending celebrated earlier results for homogeneous and
inhomogeneous square, triangular, and other lattices. This is achieved via the
star-triangle transformation, by transporting the box-crossing property across
the family of isoradial graphs. As a consequence, we obtain the universality of
these models at the critical point, in the sense that the one-arm and
2j-alternating-arm critical exponents (and therefore also the connectivity and
volume exponents) are constant across the family of such percolation processes.
The isoradial graphs in question are those that satisfy certain weak conditions
on their embedding and on their track system. This class of graphs includes,
for example, isoradial embeddings of periodic graphs, and graphs derived from
rhombic Penrose tilings.Comment: In v2: extended title, and small changes in the tex
GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs
Embedding knowledge graphs (KGs) for multi-hop logical reasoning is a
challenging problem due to massive and complicated structures in many KGs.
Recently, many promising works projected entities and queries into a geometric
space to efficiently find answers. However, it remains challenging to model the
negation and union operator. The negation operator has no strict boundaries,
which generates overlapped embeddings and leads to obtaining ambiguous answers.
An additional limitation is that the union operator is non-closure, which
undermines the model to handle a series of union operators. To address these
problems, we propose a novel probabilistic embedding model, namely Gamma
Embeddings (GammaE), for encoding entities and queries to answer different
types of FOL queries on KGs. We utilize the linear property and strong boundary
support of the Gamma distribution to capture more features of entities and
queries, which dramatically reduces model uncertainty. Furthermore, GammaE
implements the Gamma mixture method to design the closed union operator. The
performance of GammaE is validated on three large logical query datasets.
Experimental results show that GammaE significantly outperforms
state-of-the-art models on public benchmarks
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