Search Results for author:
Found 13 papers, 4 papers with code
FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge
Detecting factual errors in textual information, whether generated by large language models (LLM) or curated by humans, is crucial for making informed decisions.
Growing and Serving Large Open-domain Knowledge Graphs
We then describe how our platform, including graph embeddings, can be leveraged to create a Semantic Annotation service that links unstructured Web documents to entities in our KG.
High-Throughput Vector Similarity Search in Knowledge Graphs
Motivated by the tasks of finding related KG queries and entities for past KG query workloads, we focus on hybrid vector similarity search (hybrid queries for short) where part of the query corresponds to vector similarity search and part of the query corresponds to predicates over relational attributes associated with the underlying data vectors.
Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale
We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale.
Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins
Context enrichment, or rebuilding fragmented context, using keyless joins is an implicit or explicit step in machine learning (ML) pipelines over structured data sources.
Kamino: Constraint-Aware Differentially Private Data Synthesis
Existing differentially private data synthesis methods aim to generate useful data based on applications, but they fail in keeping one of the most fundamental data properties of the structured data -- the underlying correlations and dependencies among tuples and attributes (i. e., the structure of the data).
Databases Cryptography and Security
Batchwise Probabilistic Incremental Data Cleaning
Lack of data and data quality issues are among the main bottlenecks that prevent further artificial intelligence adoption within many organizations, pushing data scientists to spend most of their time cleaning data before being able to answer analytical questions.
On sampling from data with duplicate records
Our goal is to develop a procedure that samples uniformly from the set of entities present in the database in the presence of duplicates.
Record fusion: A learning approach
We use this feature vector alongwith the ground-truth information to learn a classifier for each of the attributes of the database.
Scalable Knowledge Graph Construction from Text Collections
We present a scalable, open-source platform that {``}distills{''} a potentially large text collection into a knowledge graph.
Approximate Inference in Structured Instances with Noisy Categorical Observations
We study the problem of recovering the latent ground truth labeling of a structured instance with categorical random variables in the presence of noisy observations.
Matching Entities Across Different Knowledge Graphs with Graph Embeddings
This paper explores the problem of matching entities across different knowledge graphs.
APEx: Accuracy-Aware Differentially Private Data Exploration
Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets.
Databases
