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Jeff Heflin

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5 papers

2 author rows

AAAI Conference 2025 Short Paper

An Evaluation of Approaches to Train Embeddings for Logical Inference (Student Abstract)

Yasir White
Jevon Lipsey
Jeff Heflin

Knowledge bases traditionally require manual optimization to ensure reasonable performance when answering queries. We build on previous neurosymbolic approaches by improving the training of an embedding model for logical statements that maximizes similarity between unifying atoms and minimizes similarity of non-unifying atoms. In particular, we evaluate different approaches to training this model.

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AAAI Conference 2025 Short Paper

Efficient Image Similarity Search with Quadtrees (Student Abstract)

Yifan Zhang
Jeff Heflin

In this paper, we present a new image similarity search algorithm designed to enhance traditional information retrieval(IR) by adding an image search capability. Our approach uses a quadtree data structure to organize image data, significantly reducing search space and improving retrieval efficiency. We describe an indexing strategy and two query algorithms that can be implemented in any IR system. We tested our method on a 70K material microscopy image dataset, achieving a 25 times improvement in retrieval speed with only a 20% reduction in ranking accuracy.

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NeSy Conference 2025 Conference Paper

High Quality Embeddings for Horn Logic Reasoning

Yifan Zhang
Yasir White
Dean Clark
Joseph Sanchez
Jevon Lipsey
Ashely Hirst
Jeff Heflin

Neural networks can be trained to rank the choices made by logical reasoners, resulting in more efficient searches for answers. A key step in this process is creating useful embeddings, i. e. , numeric representations of logical statements. This paper introduces and evaluates several approaches to creating embeddings that result in better downstream results. We train embeddings using triplet loss, which requires examples consisting of an anchor, a positive example, and a negative example. We introduce three ideas: generating anchors that are more likely to have repeated terms, generating positive and negative examples in a way that ensures a good balance between easy, medium, and hard examples, and periodically emphasizing the hardest examples during training. We conduct several experiments to evaluate this approach, including a comparison of different embeddings across different knowledge bases, in an attempt to identify what characteristics make an embedding well-suited to a particular reasoning task.

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AAAI Conference 2016 Conference Paper

Ontology Instance Linking: Towards Interlinked Knowledge Graphs

Jeff Heflin
Dezhao Song

Due to the decentralized nature of the Semantic Web, the same real-world entity may be described in various data sources with different ontologies and assigned syntactically distinct identiﬁers. In order to facilitate data utilization and consumption in the Semantic Web, without compromising the freedom of people to publish their data, one critical problem is to appropriately interlink such heterogeneous data. This interlinking process is sometimes referred to as Entity Coreference, i. e. , ﬁnding which identiﬁers refer to the same realworld entity. In this paper, we ﬁrst summarize state-of-theart algorithms in detecting such coreference relationships between ontology instances. We then discuss various techniques in scaling entity coreference to large-scale datasets. Finally, we present well-adopted evaluation datasets and metrics, and compare the performance of the state-of-the-art algorithms on such datasets.

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AAAI Conference 2000 Short Paper

Knowledge Representation on the Internet: Achieving Interoperability in a Dynamic, Distributed Environment

Jeff Heflin

The Internet’s explosive growth is making it harder and harder to harness its potential. However, the field of knowledge representation, particularly the subfield of ontologies, can provide techniques for improving the ability of agents to work with Internet information. SHOE (Simple HTML Ontology Extensions) is a semantic markup language designed specifically for the Internet. It includes features that allow knowledge representation in distributed enviroments, and since the Internet is dynamic, allows ontologies to evolve in a controlled way.

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