activity
20222026
most citedA Decade of Knowledge Graphs in Natural Language Processing: A Survey

20 citations · 27 across the 16 of their papers we have counts for

collaborators

16 papers

cs.CL2026

Not All Fallbacks Are Failures: Understanding and Recovering from Fallbacks in Mobile Voice Assistants

Phillip Schneider, Alexandre Mercier, Joshua Oehms +2

Robust understanding of user input is a core requirement for voice assistants deployed in real-world environments. In practice, these systems encounter heterogeneous fallback situa…

cs.AI2025

CarMem: Enhancing Long-Term Memory in LLM Voice Assistants through Category-Bounding

Johannes Kirmayr, Lukas Stappen, Phillip Schneider +2

In today's assistant landscape, personalisation enhances interactions, fosters long-term relationships, and deepens engagement. However, many systems struggle with retaining user p…

cs.CL2024

Conversational Exploratory Search of Scholarly Publications Using Knowledge Graphs

Phillip Schneider, Florian Matthes

Traditional search methods primarily depend on string matches, while semantic search targets concept-based matches by recognizing underlying intents and contextual meanings of sear…

cs.CL2024

Bridging Information Gaps in Dialogues With Grounded Exchanges Using Knowledge Graphs

Phillip Schneider, Nektarios Machner, Kristiina Jokinen +1

Knowledge models are fundamental to dialogue systems for enabling conversational interactions, which require handling domain-specific knowledge. Ensuring effective communication in…

cs.CL2024★ 1 cited

Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components

Phillip Schneider, Wessel Poelman, Michael Rovatsos +1

Conversational search systems enable information retrieval via natural language interactions, with the goal of maximizing users' information gain over multiple dialogue turns. The…

cs.CL2024★ 1 cited

MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering

Juraj Vladika, Phillip Schneider, Florian Matthes

In recent years, Large Language Models (LLMs) have demonstrated an impressive ability to encode knowledge during pre-training on large text corpora. They can leverage this knowledg…

cs.CL2024

Towards Harnessing Large Language Models for Comprehension of Conversational Grounding

Kristiina Jokinen, Phillip Schneider, Taiga Mori

Conversational grounding is a collaborative mechanism for establishing mutual knowledge among participants engaged in a dialogue. This experimental study analyzes information-seeki…

cs.CL2024

Enterprise Use Cases Combining Knowledge Graphs and Natural Language Processing

Phillip Schneider, Tim Schopf, Juraj Vladika +1

Knowledge management is a critical challenge for enterprises in today's digital world, as the volume and complexity of data being generated and collected continue to grow incessant…

cs.CL2024★ 1 cited

A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation

Phillip Schneider, Manuel Klettner, Elena Simperl +1

Generating natural language text from graph-structured data is essential for conversational information seeking. Semantic triples derived from knowledge graphs can serve as a valua…

cs.CL2024★ 1 cited

Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs

Phillip Schneider, Manuel Klettner, Kristiina Jokinen +2

Conversational question answering systems often rely on semantic parsing to enable interactive information retrieval, which involves the generation of structured database queries f…

cs.CL2023★ 1 cited

From Data to Dialogue: Leveraging the Structure of Knowledge Graphs for Conversational Exploratory Search

Phillip Schneider, Nils Rehtanz, Kristiina Jokinen +1

Exploratory search is an open-ended information retrieval process that aims at discovering knowledge about a topic or domain rather than searching for a specific answer or piece of…

cs.CL2023

HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking

Juraj Vladika, Phillip Schneider, Florian Matthes

In the digital age, seeking health advice on the Internet has become a common practice. At the same time, determining the trustworthiness of online medical content is increasingly…

cs.CL2023

Voice-Based Conversational Agents and Knowledge Graphs for Improving News Search in Assisted Living

Phillip Schneider, Nils Rehtanz, Kristiina Jokinen +1

As the healthcare sector is facing major challenges, such as aging populations, staff shortages, and common chronic diseases, delivering high-quality care to individuals has become…

cs.CL2023★ 1 cited

Investigating Conversational Search Behavior For Domain Exploration

Phillip Schneider, Anum Afzal, Juraj Vladika +2

Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search age…

cs.CL2022★ 1 cited

Semantic Similarity-Based Clustering of Findings From Security Testing Tools

Phillip Schneider, Markus Voggenreiter, Abdullah Gulraiz +1

Over the last years, software development in domains with high security demands transitioned from traditional methodologies to uniting modern approaches from software development a…

cs.CL2022★ 20 cited

A Decade of Knowledge Graphs in Natural Language Processing: A Survey

Phillip Schneider, Tim Schopf, Juraj Vladika +3

In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a represen…