no code implementations • 1 Jul 2022 • Bohui Zhang, Filip Ilievski, Pedro Szekely
We present a novel workflow that includes gap detection, source selection, schema alignment, and semantic validation.
1 code implementation • NAACL 2022 • Fei Wang, Zhewei Xu, Pedro Szekely, Muhao Chen
This prunes the full self-attention structure into an order-invariant graph attention that captures the connected graph structure of cells belonging to the same row or column, and it differentiates between relevant cells and irrelevant cells from the structural perspective.
Ranked #2 on Data-to-Text Generation on ToTTo
1 code implementation • 26 Mar 2022 • Jiang Wang, Filip Ilievski, Pedro Szekely, Ke-Thia Yao
Experiments on legacy benchmarks and a new large benchmark, DWD, show that augmenting the knowledge graph with quantities and years is beneficial for predicting both entities and numbers, as KGA outperforms the vanilla models and other relevant baselines.
no code implementations • 19 Jan 2022 • Ehsan Qasemi, Lee Kezar, Jay Pujara, Pedro Szekely
Language models (LMs) show state of the art performance for common sense (CS) question answering, but whether this ability implies a human-level mastery of CS remains an open question.
1 code implementation • Findings (EMNLP) 2021 • Fei Wang, Kexuan Sun, Jay Pujara, Pedro Szekely, Muhao Chen
From one perspective, our system conducts masked salient token prediction to enhance the model for alignment and reasoning between the table and the statement.
Ranked #8 on Table-based Fact Verification on TabFact
1 code implementation • 11 Aug 2021 • Filip Ilievski, Pedro Szekely, Gleb Satyukov, Amandeep Singh
While the similarity between two concept words has been evaluated and studied for decades, much less attention has been devoted to algorithms that can compute the similarity of nodes in very large knowledge graphs, like Wikidata.
no code implementations • 6 Aug 2021 • Hans Chalupsky, Pedro Szekely, Filip Ilievski, Daniel Garijo, Kartik Shenoy
Application developers today have three choices for exploiting the knowledge present in Wikidata: they can download the Wikidata dumps in JSON or RDF format, they can use the Wikidata API to get data about individual entities, or they can use the Wikidata SPARQL endpoint.
1 code implementation • 1 Jul 2021 • Kartik Shenoy, Filip Ilievski, Daniel Garijo, Daniel Schwabe, Pedro Szekely
Wikidata has been increasingly adopted by many communities for a wide variety of applications, which demand high-quality knowledge to deliver successful results.
1 code implementation • 4 May 2021 • Fei Wang, Kexuan Sun, Muhao Chen, Jay Pujara, Pedro Szekely
The task of natural language table retrieval (NLTR) seeks to retrieve semantically relevant tables based on natural language queries.
1 code implementation • 18 Apr 2021 • Ehsan Qasemi, Filip Ilievski, Muhao Chen, Pedro Szekely
To address this gap, we propose a novel challenge of reasoning with circumstantial preconditions.
no code implementations • 12 Jan 2021 • Filip Ilievski, Alessandro Oltramari, Kaixin Ma, Bin Zhang, Deborah L. McGuinness, Pedro Szekely
Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application to textual tasks, typically at the expense of the semantics of the sources and their harmonization.
1 code implementation • 21 Dec 2020 • Filip Ilievski, Pedro Szekely, Bin Zhang
Sources of commonsense knowledge support applications in natural language understanding, computer vision, and knowledge graphs.
no code implementations • 18 Aug 2020 • Filip Ilievski, Pedro Szekely, Daniel Schwabe
Our experiments reveal that: 1) albeit Wikidata-CS represents a small portion of Wikidata, it is an indicator that Wikidata contains relevant commonsense knowledge, which can be mapped to 15 ConceptNet relations; 2) the overlap between Wikidata-CS and other commonsense sources is low, motivating the value of knowledge integration; 3) Wikidata-CS has been evolving over time at a slightly slower rate compared to the overall Wikidata, indicating a possible lack of focus on commonsense knowledge.
no code implementations • 10 Jun 2020 • Filip Ilievski, Pedro Szekely, Jingwei Cheng, Fu Zhang, Ehsan Qasemi
Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities.
1 code implementation • 29 May 2020 • Filip Ilievski, Daniel Garijo, Hans Chalupsky, Naren Teja Divvala, Yixiang Yao, Craig Rogers, Rongpeng Li, Jun Liu, Amandeep Singh, Daniel Schwabe, Pedro Szekely
Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Peifeng Wang, Nanyun Peng, Filip Ilievski, Pedro Szekely, Xiang Ren
In this paper, we augment a general commonsense QA framework with a knowledgeable path generator.
no code implementations • 5 Dec 2017 • Mayank Kejriwal, Jiayuan Ding, Runqi Shao, Anoop Kumar, Pedro Szekely
In this paper, we describe and study the indicator mining problem in the online sex advertising domain.
no code implementations • 19 Apr 2017 • Rahul Kapoor, Mayank Kejriwal, Pedro Szekely
Extracting geographical tags from webpages is a well-motivated application in many domains.
no code implementations • 22 Mar 2017 • Mayank Kejriwal, Pedro Szekely
We propose a supervised algorithm for generating type embeddings in the same semantic vector space as a given set of entity embeddings.
no code implementations • 9 Mar 2017 • Mayank Kejriwal, Pedro Szekely
Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact.
no code implementations • 16 Jan 2016 • Mohsen Taheriyan, Craig A. Knoblock, Pedro Szekely, Jose Luis Ambite
This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology.
no code implementations • 1 Jan 2014 • Reza Nourjou, Michinori Hatayama, Stephen F. Smith, Atabak Sadeghi, Pedro Szekely
Problem: This paper addresses the design of an intelligent software system for the IC (incident commander) of a team in order to coordinate actions of agents (field units or robots) in the domain of emergency/crisis response operations.