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Found 13 papers, 4 papers with code
SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models
Causal information extraction is an important task in natural language processing, particularly in finance domain.
Distilling Event Sequence Knowledge From Large Language Models
In this work, we explore the use of Large Language Models (LLMs) to generate event sequences that can effectively be used for probabilistic event model construction.
An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph
In this paper, we present a methodology for creating a benchmark dataset of news headlines mapped to event classes in Wikidata, and resources for the evaluation of methods that perform the mapping.
Event Prediction using Case-Based Reasoning over Knowledge Graphs
To generalize our methods beyond the domain of event prediction, we frame our task as a 2-hop LP task, where the first hop is a causal relation connecting a cause event to a new effect event and the second hop is a property about the new event which we wish to predict.
Matching Table Metadata with Business Glossaries Using Large Language Models
The resulting matching enables the use of an available or curated business glossary for retrieval and analysis without or before requesting access to the data contents.
Improving Neural Ranking Models with Traditional IR Methods
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions.
A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation.
LakeBench: Benchmarks for Data Discovery over Data Lakes
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery.
Results of SemTab 2022
SemTab 2022 was the fourth edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 21st International Semantic Web Conference (ISWC) and the 17th Ontology Matching (OM) Workshop.
Ranked #2 on
Cell Entity Annotation
on ToughTables-WD
Summary Markov Models for Event Sequences
Datasets involving sequences of different types of events without meaningful time stamps are prevalent in many applications, for instance when extracted from textual corpora.
Results of SemTab 2021
SemTab 2021 was the third edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 20th International Semantic Web Conference (ISWC) and the 16th Ontology Matching (OM) Workshop.
Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings
Our results show that: (1) our novel lookup-based method outperforms state-of-the-art lookup-based methods, (2) the semantic embeddings method outperforms lookup-based methods in one benchmark data set, and (3) the lack of a rich schema in Web tables can limit the ability of ontology matching tools in performing high-quality table annotation.
Joint Learning of Local and Global Features for Entity Linking via Neural Networks
Previous studies have highlighted the necessity for entity linking systems to capture the local entity-mention similarities and the global topical coherence.
