Now showing items 1-11 of 11

    • Cleaning Noisy Knowledge Graphs 

      Padia, Ankur (CEUR Workshop Proceedings, 2017-10-22)
      My dissertation research is developing an approach to identify and explain errors in a knowledge graph constructed by extracting entities and relations from text. Information extraction systems can automatically construct ...
    • Inferring Relations in Knowledge Graphs with Tensor Decompositions 

      Padia, Ankur; Kalpakis, Kostantinos; Finin, Tim (IEEE, 2017-02-06)
      Multi-relational data, like knowledge graphs, are generated from multiple data sources by extracting entities and their relationships. We often want to include inferred, implicit or likely relationships that are not ...
    • KGCleaner : Identifying and Correcting Errors Produced by Information Extraction Systems 

      Padia, Ankur; Ferraro, Frank; Finin, Tim
      KG Cleaner is a framework to identify and correct errors in data produced and delivered by an information extraction system. These tasks have been understudied and KG Cleaner is the first to address both. We introduce a ...
    • Knowledge graph fact prediction via knowledge-enriched tensor factorization 

      Padia, Ankur; Kalpakis, Konstantinos; Ferraro, Francis; Finin, Tim (Elsevier, 2019-02-15)
      We present a family of novel methods for embedding knowledge graphs into real-valued tensors. These tensor-based embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web ...
    • Named Entity Recognition for Nepali Language 

      Singh, Oyesh Mann; Padia, Ankur; Joshi, Anupam (2019-08-16)
      Named Entity Recognition have been studied for different languages like English, German, Spanish and many others but no study have focused on Nepali language. In this paper we propose a neural based Nepali NER using latest ...
    • Reflections on: Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization 

      Padia, Ankur; Kalpakis, Konstantinos; Ferraro, Francis; Finin, Tim
      We present a family of four novel methods for embedding knowledge graphs into real-valued tensors that capture the ordered relations found in RDF. Unlike many previous models, these can easily use prior background knowledge ...
    • Supporting Situationally Aware Cybersecurity Systems 

      Syed, Zareen; Finin, Tim; Padia, Ankur; Mathews, M. Lisa (University of Maryland, Baltimore County, 2015-09-30)
      In this report, we describe the Unified Cyber Security ontology (UCO) to support situational awareness in cyber security systems. The ontology is an effort to incorporate and integrate heterogeneous information available ...
    • SURFACE: Semantically Rich Fact Validation with Explanations 

      Padia, Ankur; Ferraro, Francis; Finin, Tim (2018-10-31)
      Judging the veracity of a sentence making one or more claims is an important and challenging problem with many dimensions. The recent FEVER task asked participants to classify input sentences as either SUPPORTED, REFUTED ...
    • Team UMBC-FEVER: Claim verification using Semantic Lexical Resources 

      Padia, Ankur; Ferraro, Francis; Finin, Tim (2018-11-01)
      We describe our system used in the 2018 FEVER shared task. The system employed a frame-based information retrieval approach to select Wikipedia sentences providing evidence and used a two-layer multilayer perceptron to ...
    • UCO: A Unified Cybersecurity Ontology 

      Syed, Zareen; Padia, Ankur; Finin, Tim; Mathews, Lisa; Joshi, Anupam (AAAI Press, 2016-02-12)
      In this paper we describe the Unified Cybersecurity Ontology (UCO) that is intended to support information integration and cyber situational awareness in cybersecurity systems. The ontology incorporates and integrates ...
    • UMBC at SemEval-2018 Task 8: Understanding Text about Malware 

      Padia, Ankur; Roy, Arpita; Satyapanich, Taneeya W.; Ferraro, Francis; Pan, Shimei; Park, Youngja; Joshi, Anupam; Finin, Tim (2018-06-05)
      We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing). We participated in three of the sub-tasks: (1) ...