Search Results for author: Umar Farooq Minhas

Found 10 papers, 1 papers with code

KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs

no code implementations7 Jan 2025 Zelin Zhou, Simone Conia, Daniel Lee, Min Li, Shenglei Huang, Umar Farooq Minhas, Saloni Potdar, Henry Xiao, Yunyao Li

Multilingual knowledge graphs (KGs) provide high-quality relational and textual information for various NLP applications, but they are often incomplete, especially in non-English languages.

Incremental IVF Index Maintenance for Streaming Vector Search

no code implementations1 Nov 2024 Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Umar Farooq Minhas, Jeffery Pound, Cedric Renggli, Nima Reyhani, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman

The prevalence of vector similarity search in modern machine learning applications and the continuously changing nature of data processed by these applications necessitate efficient and effective index maintenance techniques for vector search indexes.

Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs

no code implementations17 Oct 2024 Simone Conia, Daniel Lee, Min Li, Umar Farooq Minhas, Saloni Potdar, Yunyao Li

Translating text that contains entity names is a challenging task, as cultural-related references can vary significantly across languages.

Knowledge Graphs Retrieval +3

Entity Disambiguation via Fusion Entity Decoding

no code implementations2 Apr 2024 Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander M. Rush, Umar Farooq Minhas, Yunyao Li

Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark.

 Ranked #1 on Entity Linking on KORE50 (Micro-F1 strong metric)

Decoder Entity Disambiguation +2

Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs

1 code implementation27 Nov 2023 Simone Conia, Min Li, Daniel Lee, Umar Farooq Minhas, Ihab Ilyas, Yunyao Li

Recent work in Natural Language Processing and Computer Vision has been using textual information -- e. g., entity names and descriptions -- available in knowledge graphs to ground neural models to high-quality structured data.

Entity Linking Machine Translation +1

Growing and Serving Large Open-domain Knowledge Graphs

no code implementations16 May 2023 Ihab F. Ilyas, JP Lacerda, Yunyao Li, Umar Farooq Minhas, Ali Mousavi, Jeffrey Pound, Theodoros Rekatsinas, Chiraag Sumanth

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.

Entity Linking Fact Verification +2

High-Throughput Vector Similarity Search in Knowledge Graphs

no code implementations4 Apr 2023 Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Ali Mousavi, Ihab F. Ilyas, Umar Farooq Minhas, Jeffrey Pound, Theodoros Rekatsinas

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.

Knowledge Graphs Vocal Bursts Intensity Prediction

Bounding the Last Mile: Efficient Learned String Indexing

no code implementations29 Nov 2021 Benjamin Spector, Andreas Kipf, Kapil Vaidya, Chi Wang, Umar Farooq Minhas, Tim Kraska

RSS achieves this by using the minimal string prefix to sufficiently distinguish the data unlike most learned approaches which index the entire string.

Qd-tree: Learning Data Layouts for Big Data Analytics

no code implementations22 Apr 2020 Zongheng Yang, Badrish Chandramouli, Chi Wang, Johannes Gehrke, Yi-Nan Li, Umar Farooq Minhas, Per-Åke Larson, Donald Kossmann, Rajeev Acharya

For a given workload, however, such techniques are unable to optimize for the important metric of the number of blocks accessed by a query.

Blocking Deep Reinforcement Learning

ALEX: An Updatable Adaptive Learned Index

no code implementations21 May 2019 Jialin Ding, Umar Farooq Minhas, JIA YU, Chi Wang, Jaeyoung Do, Yi-Nan Li, Hantian Zhang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David Lomet, Tim Kraska

The original work by Kraska et al. shows that a learned index beats a B+Tree by a factor of up to three in search time and by an order of magnitude in memory footprint.

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