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Found 64 papers, 30 papers with code
Peeling Back the Layers: An In-Depth Evaluation of Encoder Architectures in Neural News Recommenders
In this work, we perform a comprehensive analysis of encoder architectures in neural news recommender systems.
SnapE -- Training Snapshot Ensembles of Link Prediction Models
In this paper, we introduce an approach to transfer the idea of snapshot ensembles to link prediction models in knowledge graphs.
Do LLMs Really Adapt to Domains? An Ontology Learning Perspective
Empirical results show that, while adapting to the gibberish corpora, off-the-shelf LLMs do not consistently reason over semantic relationships between concepts, and instead leverage senses and their frame.
News Without Borders: Domain Adaptation of Multilingual Sentence Embeddings for Cross-lingual News Recommendation
Rapidly growing numbers of multilingual news consumers pose an increasing challenge to news recommender systems in terms of providing customized recommendations.
By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning
In the rapidly evolving landscape of eCommerce, Artificial Intelligence (AI) based pricing algorithms, particularly those utilizing Reinforcement Learning (RL), are becoming increasingly prevalent.
A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid
Geospatial data plays a central role in modeling our world, for which OpenStreetMap (OSM) provides a rich source of such data.
Integrating Heterogeneous Gene Expression Data through Knowledge Graphs for Improving Diabetes Prediction
Diabetes is a worldwide health issue affecting millions of people.
MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation
Our findings reveal that (i) current NNRs, even when based on a multilingual language model, suffer from substantial performance losses under ZS-XLT and that (ii) inclusion of target-language data in FS-XLT training has limited benefits, particularly when combined with a bilingual news consumption.
Do Similar Entities have Similar Embeddings?
A common tacit assumption is the KGE entity similarity assumption, which states that these KGEMs retain the graph's structure within their embedding space, \textit{i. e.}, position similar entities within the graph close to one another.
Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs
Consequently, a larger body of works focuses on the completion of missing information in KGs, which is commonly referred to as link prediction (LP).
OLaLa: Ontology Matching with Large Language Models
Ontology (and more generally: Knowledge Graph) Matching is a challenging task where information in natural language is one of the most important signals to process.
NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation
NewsRecLib is an open-source library based on Pytorch-Lightning and Hydra developed for training and evaluating neural news recommendation models.
The Time Traveler's Guide to Semantic Web Research: Analyzing Fictitious Research Themes in the ESWC "Next 20 Years" Track
What will Semantic Web research focus on in 20 years from now?
Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals
Knowledge graph embeddings are dense numerical representations of entities in a knowledge graph (KG).
NeMig -- A Bilingual News Collection and Knowledge Graph about Migration
News recommendation plays a critical role in shaping the public's worldviews through the way in which it filters and disseminates information about different topics.
KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks
In recent years, countless research papers have addressed the topics of knowledge graph creation, extension, or completion in order to create knowledge graphs that are larger, more correct, or more diverse.
Biomedical Knowledge Graph Embeddings with Negative Statements
Explicitly considering negative statements has been shown to improve performance on tasks such as entity summarization and question answering or domain-specific tasks such as protein function prediction.
Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation
Recent neural news recommenders (NNRs) extend content-based recommendation (1) by aligning additional aspects (e. g., topic, sentiment) between candidate news and user history or (2) by diversifying recommendations w. r. t.
Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
These models learn a vector representation of knowledge graph entities and relations, a. k. a.
ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics
Many machine learning (ML) libraries are accessible online for ML practitioners.
Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives
Most neural news recommenders rely on user click behavior and typically introduce dedicated user encoders that aggregate the content of clicked news into user embeddings (early fusion).
Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG
In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community - Semantic Web Machine Learning (SWeML for short).
NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker
With NASTyLinker, we introduce an EL approach that is aware of NIL entities and produces corresponding mention clusters while maintaining high linking performance for known entities.
DBkWik++ -- Multi Source Matching of Knowledge Graphs
In this paper, we present the approach and analysis of DBkWik++, a fused Knowledge Graph from thousands of wikis.
Transformer-based Subject Entity Detection in Wikipedia Listings
In tasks like question answering or text summarisation, it is essential to have background knowledge about the relevant entities.
Gollum: A Gold Standard for Large Scale Multi Source Knowledge Graph Matching
The number of Knowledge Graphs (KGs) generated with automatic and manual approaches is constantly growing.
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions
Entity typing is the task of assigning or inferring the semantic type of an entity in a KG.
On a Generalized Framework for Time-Aware Knowledge Graphs
Knowledge graphs have emerged as an effective tool for managing and standardizing semistructured domain knowledge in a human- and machine-interpretable way.
The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings
To demonstrate the use of DLCC, we compare multiple embedding approaches using the gold standards.
KERMIT - A Transformer-Based Approach for Knowledge Graph Matching
One of the strongest signals for automated matching of knowledge graphs and ontologies are textual concept descriptions.
Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph
In this paper, we present an approach using diagnosis paths in a medical knowledge graph.
Ontology Matching Through Absolute Orientation of Embedding Spaces
Ontology matching is a core task when creating interoperable and linked open datasets.
Walk this Way! Entity Walks and Property Walks for RDF2vec
RDF2vec is a knowledge graph embedding mechanism which first extracts sequences from knowledge graphs by performing random walks, then feeds those into the word embedding algorithm word2vec for computing vector representations for entities.
Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection
News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users.
Order Matters: Matching Multiple Knowledge Graphs
Knowledge graphs (KGs) provide information in machine interpretable form.
The CaLiGraph Ontology as a Challenge for OWL Reasoners
CaLiGraph is a large-scale cross-domain knowledge graph generated from Wikipedia by exploiting the category system, list pages, and other list structures in Wikipedia, containing more than 15 million typed entities and around 10 million relation assertions.
Matching with Transformers in MELT
One of the strongest signals for automated matching of ontologies and knowledge graphs are the textual descriptions of the concepts.
Putting RDF2vec in Order
The RDF2vec method for creating node embeddings on knowledge graphs is based on word2vec, which, in turn, is agnostic towards the position of context words.
Winning at Any Cost -- Infringing the Cartel Prohibition With Reinforcement Learning
Thanks to their ability to train with live market data while making decisions on the fly, deep reinforcement learning algorithms are especially effective in taking such pricing decisions.
On-Demand and Lightweight Knowledge Graph Generation -- a Demonstration with DBpedia
Modern large-scale knowledge graphs, such as DBpedia, are datasets which require large computational resources to serve and process.
Background Knowledge in Schema Matching: Strategy vs. Data
The use of external background knowledge can be beneficial for the task of matching schemas or ontologies automatically.
GraphConfRec: A Graph Neural Network-Based Conference Recommender System
In today's academic publishing model, especially in Computer Science, conferences commonly constitute the main platforms for releasing the latest peer-reviewed advancements in their respective fields.
Large-scale Taxonomy Induction Using Entity and Word Embeddings
Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies.
Bias in Knowledge Graphs -- an Empirical Study with Movie Recommendation and Different Language Editions of DBpedia
Public knowledge graphs such as DBpedia and Wikidata have been recognized as interesting sources of background knowledge to build content-based recommender systems.
FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs
This paper presents the FinMatcher system and its results for the FinSim 2021 shared task which is co-located with the Workshop on Financial Technology on the Web (FinWeb) in conjunction with The Web Conference.
Web Table Classification based on Visual Features
Tables on the web constitute a valuable data source for many applications, like factual search and knowledge base augmentation.
Information Extraction From Co-Occurring Similar Entities
In this paper, we explore how information extracted from similar entities that co-occur in structures like tables or lists can help to increase the coverage of such knowledge graphs.
Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019
Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything.
Supervised Ontology and Instance Matching with MELT
In this paper, we present MELT-ML, a machine learning extension to the Matching and EvaLuation Toolkit (MELT) which facilitates the application of supervised learning for ontology and instance matching.
RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings
Knowledge graph embedding approaches represent nodes and edges of graphs as mathematical vectors.
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs
As KGs are symbolic constructs, specialized techniques have to be applied in order to make them compatible with data mining techniques.
More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings
RDF2vec is an embedding technique for representing knowledge graph entities in a continuous vector space.
A Knowledge Graph for Assessing Aggressive Tax Planning Strategies
The taxation of multi-national companies is a complex field, since it is influenced by the legislation of several states.
Visual Analysis of Ontology Matching Results with the MELT Dashboard
In this demo, we introduce MELT Dashboard, an interactive Web user interface for ontology alignment evaluation which is created with the existing Matching EvaLuation Toolkit (MELT).
Towards Exploiting Implicit Human Feedback for Improving RDF2vec Embeddings
In a second step, those sequences are processed by the word2vec algorithm for creating the actual embeddings.
Entity Extraction from Wikipedia List Pages
In this paper, we present a two-phased approach for the extraction of entities from Wikipedia's list pages, which have proven to serve as a valuable source of information.
KGvec2go -- Knowledge Graph Embeddings as a Service
In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications.
Knowledge Graphs on the Web -- an Overview
Knowledge Graphs are an emerging form of knowledge representation.
The Knowledge Graph Track at OAEI -- Gold Standards, Baselines, and the Golden Hammer Bias
The Ontology Alignment Evaluation Initiative (OAEI) is an annual evaluation of ontology matching tools.
Uncovering the Semantics of Wikipedia Categories
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like YAGO or Probase, and has been used extensively for tasks like entity disambiguation or semantic similarity estimation.
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones.
RDF2Vec: RDF Graph Embeddings and Their Applications
Linked Open Data has been recognized as a valuable source for background information in many data mining and information retrieval tasks.
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Evaluating Entity Linking: An Analysis of Current Benchmark Datasets and a Roadmap for Doing a Better Job
Entity linking has become a popular task in both natural language processing and semantic web communities.
A Large DataBase of Hypernymy Relations Extracted from the Web.
Hypernymy relations (those where an hyponym term shares a {``}isa{''} relationship with his hypernym) play a key role for many Natural Language Processing (NLP) tasks, e. g. ontology learning, automatically building or extending knowledge bases, or word sense disambiguation and induction.
