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Found 28 papers, 12 papers with code
SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning
We present SlotGAN, a framework for training a mention detection model that only requires unlabeled text and a gazetteer.
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"
Data scientists develop ML pipelines in an iterative manner: they repeatedly screen a pipeline for potential issues, debug it, and then revise and improve its code according to their findings.
SHROOM-INDElab at SemEval-2024 Task 6: Zero- and Few-Shot LLM-Based Classification for Hallucination Detection
We describe the University of Amsterdam Intelligent Data Engineering Lab team's entry for the SemEval-2024 Task 6 competition.
AE SemRL: Learning Semantic Association Rules with Autoencoders
In this study, we propose an Autoencoder-based approach to learn and extract association rules from time series data (AE SemRL).
AdaTyper: Adaptive Semantic Column Type Detection
A key source for understanding a table is the semantics of its columns.
Semantic Association Rule Learning from Time Series Data and Knowledge Graphs
Building on this move, this paper proposes a pipeline for semantic association rule learning in DTs using KGs and time series data.
Observatory: Characterizing Embeddings of Relational Tables
Based on these properties, we define an extensible framework to evaluate language and table embedding models.
Knowledge Engineering using Large Language Models
Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge.
Ontologies in Digital Twins: A Systematic Literature Review
Digital Twins (DT) facilitate monitoring and reasoning processes in cyber-physical systems.
Harnessing the Web and Knowledge Graphs for Automated Impact Investing Scoring
The Sustainable Development Goals (SDGs) were introduced by the United Nations in order to encourage policies and activities that help guarantee human prosperity and sustainability.
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation
However, Knowledge Graphs are not just sets of links but also have semantics underlying their structure.
BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs
We train models using a biomedical KG containing approximately 2 million triples, and evaluate the performance of the resulting entity embeddings on the tasks of link prediction, and drug-protein interaction prediction, comparing against methods that do not take attribute data into account.
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression
Motivated by the efficiency of $L_2$-based learning, we propose to jointly learn expectiles and quantiles of the return distribution in a way that allows efficient learning while keeping an estimate of the full distribution of returns.
Self-Contained Entity Discovery from Captioned Videos
This paper introduces the task of visual named entity discovery in videos without the need for task-specific supervision or task-specific external knowledge sources.
E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes
Node classification utilizing text-based node attributes has many real-world applications, ranging from prediction of paper topics in academic citation graphs to classification of user characteristics in social media networks.
Ranked #38 on
Node Property Prediction
on ogbn-products
A Simulation Environment and Reinforcement Learning Method for Waste Reduction
We frame inventory restocking as a new reinforcement learning task that exhibits stochastic behavior conditioned on the agent's actions, making the environment partially observable.
Distributional Reinforcement Learning
reinforcement-learning
+1
Making Table Understanding Work in Practice
However, we observe that there exists a gap between the performance of these models on these benchmarks and their applicability in practice.
SemEval-2021 Task 8: MeasEval -- Extracting Counts and Measurements and their Related Contexts
We describe MeasEval, a SemEval task of extracting counts, measurements, and related context from scientific documents, which is of significant importance to the creation of Knowledge Graphs that distill information from the scientific literature.
Relational Graph Convolutional Networks: A Closer Look
In this paper, we describe a reproduction of the Relational Graph Convolutional Network (RGCN).
GitTables: A Large-Scale Corpus of Relational Tables
Existing table corpora primarily contain tables extracted from HTML pages, limiting the capability to represent offline database tables.
Reinforcement Learning based Collective Entity Alignment with Adaptive Features
Entity alignment (EA) is the task of identifying the entities that refer to the same real-world object but are located in different knowledge graphs (KGs).
Towards Olfactory Information Extraction from Text: A Case Study on Detecting Smell Experiences in Novels
Environmental factors determine the smells we perceive, but societal factors factors shape the importance, sentiment and biases we give to them.
Inductive Entity Representations from Text via Link Prediction
However, the extent to which these representations learned for link prediction generalize to other tasks is unclear.
Ranked #1 on
Inductive knowledge graph completion
on WN18RR-ind
Inductive knowledge graph completion
Inductive Link Prediction
+8
Towards Entity Spaces
Entities are a central element of knowledge bases and are important input to many knowledge-centric tasks including text analysis.
End-to-End Learning for Answering Structured Queries Directly over Text
Structured queries expressed in languages (such as SQL, SPARQL, or XQuery) offer a convenient and explicit way for users to express their information needs for a number of tasks.
Open Information Extraction on Scientific Text: An Evaluation
Open Information Extraction (OIE) is the task of the unsupervised creation of structured information from text.
