Papers for

data integration teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Snap KG’s success depends on graph link similarity

When Connected Does Not Mean Similar: Charting the Homophily Boundary of SNAP-KG for Streaming Entity Integration

Abstract: SNAP-KG is a framework for assigning newly arriving entities to semantic communities in a growing knowledge graph (KG) using only their raw features, with no graph access and no retraining at inference time. It was evaluated on five multi-view benchmarks and a 2.4M-node OGB-WikiKG2 KG. In each of these datasets, at least one graph view is homophilous, meaning that connected nodes usually belong to the same class, and SNAP-KG performs well on all of them. This paper asks what happens outside that setting. We extend the evaluation to three heterophilous graphs (Texas, Wisconsin, Chameleon) and measure the edge homophily of every view. When no homophilous view is available, clustering quality drops sharply for both SNAP-KG and the transductive baselines used in its original evaluation. What decides this is the homophily of the relation, not the number of relations. Multi-view fusion still helps, but only when at least one homophilous relation provides a reliable foundation. The homophily assumption is therefore shared by the whole method family, not specific to SNAP-KG. We argue that heterophilous multi-view clustering is a separate research problem, outside the scope of this work. As future work, we outline how a heterophily-aware teacher could be distilled into SNAP-KG's projector to serve both homophilous and heterophilous KGs.

Fri 11 SeptMachine Learning
The gist
This paper looks at how a tool called SNAP-KG groups new pieces of information in growing knowledge maps based only on their own details, not their connections. The researchers found that SNAP-KG works well only when the linked parts of the map are similar, a quality called homophily. When the connected nodes are different (heterophily), SNAP-KG and similar tools struggle to group information correctly. The paper also suggests this problem is common for many similar methods and points to future directions for handling dissimilar connections.
Open → 2609.12356v1

Large language models struggle to normalize databases accurately

Can LLMs Normalize Databases? A Benchmark and Multi-Agent Framework for Schema Normalization

Abstract: Large Language Models (LLMs) are increasingly used to generate structured outputs, but their reliability remains unclear when those outputs must satisfy database-level constraints. We study this issue through database normalization, involving reasoning about functional dependencies, lossless join decompositions, and inter-table constraints. We introduce a Database Normalization Benchmark (DNBENCH), comprising 3,275 samples for evaluating LLM-driven database normalization from 1NF to BCNF. DNBENCH uses a three-axis protocol to measure semantic equivalence, structural accuracy, and logical validity. Across Single, Complex, and Real World levels, DNBENCH uncovers recurring failures in dependency inference, schema decomposition, and inter-table constraint reconstruction. We further propose Multi-Agent Reasoning for Schemas (MARS), which separates evidence extraction, violation diagnosis, and decomposition planning from schema generation and verification. MARS improves the DNB-SCORE by 82.0% over the single-prompt baseline. All artifacts will be released upon acceptance.

Thu 10 SeptComputation and Language
The gist
Making sure databases are organized properly is hard because it needs checking complex rules and relationships between data. The authors created a big test set to see how well large language models do this kind of organization, called normalization. They found these models often make mistakes understanding the necessary rules and breaking the databases into correct parts. To fix this, the authors designed a new system where multiple special agents work together to better understand and organize the database schemas, improving accuracy a lot.
Open → 2609.11141v1

Genetic algorithm finds simple consensus Bayesian networks with limited complexity

Structural Fusion of Bayesian Networks with Limited Treewidth Using Genetic Algorithms

Abstract: This paper introduces an evolutionary computation approach for consensus in structural Bayesian Network (BN) fusion under the constraint of limited treewidth. The consensus BN aims to reconcile multiple input BNs into a single one that retains key structural features present in the original networks. Treewidth, a graph-based parameter associated with computationally tractable inference, is utilized to restrict the complexity of the resulting network. A genetic algorithm is proposed to look for a BN that codifies as much information about the unrestricted fusion as possible while ensuring the treewidth restriction. Experimental evaluation demonstrates the genetic algorithm's ability to obtain consensus BNs with limited treewidth, providing a valuable tool for aggregating information from diverse sources while returning a computationally actionable model.

Wed 9 SeptNeural and Evolutionary ComputingMachine Learning
The gist
Combining many Bayesian networks into one can be hard because the combined network might become too complex to use for quick decisions. The authors introduce a genetic algorithm, which is a way of trying many solutions and keeping the best ones, to create a simpler combined network that still captures important information from the originals. They keep the network complexity low by limiting a property called treewidth, which helps make sure computations stay manageable. Their experiments show this method can effectively merge networks while keeping them practical.
Open → 2609.10276v1