In this paper, we present a novel method named RECON, that automatically identifies relations in sentence (sentential relation extraction) and aligns to knowledge graph (KG). RECON uses neural network learn representations of both the as well facts stored KG, improving overall extraction quality. These facts, including entity attributes (label, alias, description, instance-of) factual triples, have not been collectively used state art methods. We evaluate effect various forms representing KG...

Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang', Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021.

Recently, several Knowledge Graph Embedding (KGE) approaches have been devised to represent entities and relations in a dense vector space employed downstream tasks such as link prediction. A few KGE techniques address interpretability, i.e., mapping the connectivity patterns of (symmetric/asymmetric, inverse, composition) geometric interpretation rotation. Other model representations higher dimensional four-dimensional (4D) enhance ability infer (i.e., expressiveness). However, modeling...

Extracting knowledge from unstructured data silos, a legacy of old applications, is mandatory for improving the governance today's cities and fostering creation smart cities. Texts in natural language often compose such data. Nevertheless, inference useful information linguistic-computational analysis an open challenge. In this paper, we propose clustering method to analyze textual employing unsupervised machine learning algorithms k-means hierarchical clustering. We assess different vector...

Learning on evolving(dynamic) graphs has caught the attention of researchers as static methods exhibit limited performance in this setting. The existing for dynamic learn spatial features by local neighborhood aggregation, which essentially only captures low pass signals and interactions. In work, we go beyond current approaches to incorporate global effectively learning representations a dynamically evolving graph. We propose do so capturing spectrum Since graph would not consider history...

In this paper we present a novel method, Knowledge Persistence (), for faster evaluation of Graph (KG) completion approaches. Current ranking-based is quadratic in the size KG, leading to long times and consequently high carbon footprint. addresses by representing topology KG methods through lens topological data analysis, concretely using persistent homology. The characteristics homology allow evaluate quality looking only at fraction data. Experimental results on standard datasets show...

The recent works proposing transformer-based models for graphs have proven the inadequacy of Vanilla Transformer graph representation learning. To understand this inadequacy, there is a need to investigate if spectral analysis transformer will reveal insights into its expressive power. Similar studies already established that Graph neural networks (GNNs) provides extra perspectives on their expressiveness. In work, we systematically study and establish link between spatial domain in realm...

We present the Evolving Graph Fourier Transform (EFT), first invertible spectral transform that captures evolving representations on temporal graphs. motivate our work by inadequacy of existing methods for capturing graph spectra, which are also computationally expensive due to aspect along with vertex domain. view problem as an optimization over Laplacian continuous time dynamic graph. Additionally, we propose pseudo-spectrum relaxations decompose transformation process, making it highly...

Learning sentence vectors that generalise well is a challenging task. In this paper we compare three methods of learning phrase embeddings: 1) Using LSTMs, 2) using recursive nets, 3) A variant the method 2 POS information phrase. We train our models on dictionary definitions words to obtain reverse application similar Felix et al. [1]. To see if embeddings can be transferred new task also and test rotten tomatoes dataset [2]. keeping fixed as with fine tuning.

Recently, several Knowledge Graph Embedding (KGE) approaches have been devised to represent entities and relations in dense vector space employed downstream tasks such as link prediction. A few KGE techniques address interpretability, i.e., mapping the connectivity patterns of (i.e., symmetric/asymmetric, inverse, composition) a geometric interpretation rotations. Other model representations higher dimensional four-dimensional (4D) enhance ability infer expressiveness). However, modeling...

Active learning has emerged as a standard paradigm in areas with scarcity of labeled training data, such the medical domain. Language models have prevalent choice several natural language tasks due to performance boost offered by these models. However, domains, medicine, data is common issue. Also, may not work well cases where class imbalance prevalent. prove helpful limited label budget. To this end, we propose novel method using sampling techniques based on submodular optimization and...

We present a novel method for relation extraction (RE) from single sentence, mapping the sentence and two given entities to canonical fact in knowledge graph (KG). Especially this presumed sentential RE setting, context of is often sparse. This paper introduces KGPool address sparsity, dynamically expanding with additional facts KG. It learns representation these (entity alias, entity descriptions, etc.) using neural methods, supplementing context. Unlike existing methods that statically use...

In this paper we present a novel method, $\textit{Knowledge Persistence}$ ($\mathcal{KP}$), for faster evaluation of Knowledge Graph (KG) completion approaches. Current ranking-based is quadratic in the size KG, leading to long times and consequently high carbon footprint. $\mathcal{KP}$ addresses by representing topology KG methods through lens topological data analysis, concretely using persistent homology. The characteristics homology allow evaluate quality looking only at fraction data....

In this paper, we present a novel method named RECON, that automatically identifies relations in sentence (sentential relation extraction) and aligns to knowledge graph (KG). RECON uses neural network learn representations of both the as well facts stored KG, improving overall extraction quality. These facts, including entity attributes (label, alias, description, instance-of) factual triples, have not been collectively used state art methods. We evaluate effect various forms representing KG...

Learning on evolving(dynamic) graphs has caught the attention of researchers as static methods exhibit limited performance in this setting. The existing for dynamic learn spatial features by local neighborhood aggregation, which essentially only captures low pass signals and interactions. In work, we go beyond current approaches to incorporate global effectively learning representations a dynamically evolving graph. We propose do so capturing spectrum Since graph would not consider history...