Search Results for author: Ibrahim Abdelaziz

Found 30 papers, 10 papers with code

Formally Specifying the High-Level Behavior of LLM-Based Agents

no code implementations12 Oct 2023 Maxwell Crouse, Ibrahim Abdelaziz, Ramon Astudillo, Kinjal Basu, Soham Dan, Sadhana Kumaravel, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Luis Lastras

We demonstrate how the proposed framework can be used to implement recent LLM-based agents (e. g., ReACT), and show how the flexibility of our approach can be leveraged to define a new agent with more complex behavior, the Plan-Act-Summarize-Solve (PASS) agent.

Question Answering

An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations

1 code implementation15 May 2023 Achille Fokoue, Ibrahim Abdelaziz, Maxwell Crouse, Shajith Ikbal, Akihiro Kishimoto, Guilherme Lima, Ndivhuwo Makondo, Radu Marinescu

NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving.

Automated Theorem Proving Transfer Learning

Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning

no code implementations5 Jan 2023 Wenting Zhao, Ibrahim Abdelaziz, Julian Dolby, Kavitha Srinivas, Mossad Helali, Essam Mansour

We demonstrate the efficiency and usefulness of Serenity's analysis in two applications: code completion and automated machine learning.

Code Completion

Expressive Reasoning Graph Store: A Unified Framework for Managing RDF and Property Graph Databases

1 code implementation13 Sep 2022 Sumit Neelam, Udit Sharma, Sumit Bhatia, Hima Karanam, Ankita Likhyani, Ibrahim Abdelaziz, Achille Fokoue, L. V. Subramaniam

Resource Description Framework (RDF) and Property Graph (PG) are the two most commonly used data models for representing, storing, and querying graph data.


A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases

no code implementations15 Jan 2022 Sumit Neelam, Udit Sharma, Hima Karanam, Shajith Ikbal, Pavan Kapanipathi, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Young-suk Lee, Santosh Srivastava, Cezar Pendus, Saswati Dana, Dinesh Garg, Achille Fokoue, G P Shrivatsa Bhargav, Dinesh Khandelwal, Srinivas Ravishankar, Sairam Gurajada, Maria Chang, Rosario Uceda-Sosa, Salim Roukos, Alexander Gray, Guilherme Lima, Ryan Riegel, Francois Luus, L Venkata Subramaniam

Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata.

Knowledge Base Question Answering Semantic Parsing

Learning to Transpile AMR into SPARQL

no code implementations15 Dec 2021 Mihaela Bornea, Ramon Fernandez Astudillo, Tahira Naseem, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Pavan Kapanipathi, Radu Florian, Salim Roukos

We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA).

Knowledge Base Question Answering Semantic Parsing

A Two-Stage Approach towards Generalization in Knowledge Base Question Answering

no code implementations10 Nov 2021 Srinivas Ravishankar, June Thai, Ibrahim Abdelaziz, Nandana Mihidukulasooriya, Tahira Naseem, Pavan Kapanipathi, Gaetano Rossiello, Achille Fokoue

Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes.

Knowledge Base Question Answering Knowledge Graphs +3

A Scalable AutoML Approach Based on Graph Neural Networks

1 code implementation29 Oct 2021 Mossad Helali, Essam Mansour, Ibrahim Abdelaziz, Julian Dolby, Kavitha Srinivas

AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner.

AutoML Graph Generation +2

Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion

no code implementations16 Sep 2021 Prithviraj Sen, Breno W. S. R. Carvalho, Ibrahim Abdelaziz, Pavan Kapanipathi, Francois Luus, Salim Roukos, Alexander Gray

Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings.

Inductive logic programming Knowledge Base Completion +1

Learning to Guide a Saturation-Based Theorem Prover

no code implementations7 Jun 2021 Ibrahim Abdelaziz, Maxwell Crouse, Bassem Makni, Vernon Austil, Cristina Cornelio, Shajith Ikbal, Pavan Kapanipathi, Ndivhuwo Makondo, Kavitha Srinivas, Michael Witbrock, Achille Fokoue

In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more problems).

Automated Theorem Proving reinforcement-learning +1

A Toolkit for Generating Code Knowledge Graphs

1 code implementation21 Feb 2020 Ibrahim Abdelaziz, Julian Dolby, Jamie McCusker, Kavitha Srinivas

We make the toolkit to build such graphs as well as the sample extraction of the 2 billion triples graph publicly available to the community for use.

Code Search Image Classification +2

Explainable Deep RDFS Reasoner

no code implementations10 Feb 2020 Bassem Makni, Ibrahim Abdelaziz, James Hendler

Recent research efforts aiming to bridge the Neural-Symbolic gap for RDFS reasoning proved empirically that deep learning techniques can be used to learn RDFS inference rules.

Machine Translation Translation

An Experimental Study of Formula Embeddings for Automated Theorem Proving in First-Order Logic

no code implementations2 Feb 2020 Ibrahim Abdelaziz, Veronika Thost, Maxwell Crouse, Achille Fokoue

Automated theorem proving in first-order logic is an active research area which is successfully supported by machine learning.

Automated Theorem Proving

Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling

1 code implementation arXiv 2020 Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth Forbus, Achille Fokoue

Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems.

Automated Theorem Proving

Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks

no code implementations5 Nov 2019 Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij Fadnis, Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, Achille Fokoue

A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.

Knowledge Graphs Natural Language Inference

AltecOnDB: A Large-Vocabulary Arabic Online Handwriting Recognition Database

no code implementations24 Dec 2014 Ibrahim Abdelaziz, Sherif Abdou

Currently, there is no online Arabic handwriting database with large lexicon, high coverage, large number of writers and training/testing data.

Handwriting Recognition Sentence +1

Large Vocabulary Arabic Online Handwriting Recognition System

no code implementations17 Oct 2014 Ibrahim Abdelaziz, Sherif Abdou, Hassanin Al-Barhamtoshy

In this paper, we introduce a fully-fledged Hidden Markov Model (HMM) based system for Arabic online handwriting recognition that provides solutions for most of the difficulties inherent in recognizing the Arabic script.

Handwriting Recognition Language Modelling

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