ULYSSES: Automated FreqUentLY ASked QueStions for KnowlEdge GraphS
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Published:2024-08-29
Issue:17
Volume:14
Page:7640
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Vassiliou Giannis1, Trouli Georgia Eirini12ORCID, Troullinou Georgia2, Spyridakis Nikolaos1ORCID, Bitzarakis George1, Droumalia Fotini1, Karagiannakis Antonis1, Skouteli Georgia1, Oikonomou Nikolaos1, Deka Dimitra1, Makaronas Emmanouil1, Pronoitis Georgios1, Alexandris Konstantinos1, Kostopoulos Stamatios1ORCID, Kazantzakis Yiannis1, Vlassis Nikolaos1, Sfinarolaki Eleftheria1, Daskalakis Vardis1, Giannakos Iakovos1, Stamatoukou Argyro1, Papadakis Nikolaos1, Kondylakis Haridimos23ORCID
Affiliation:
1. Department of Electrical and Computer Engineering, Hellenic Mediterranean University (HMU), 71309 Heraklion, Greece 2. Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Greece 3. Computer Science Department, University of Crete, 70013 Heraklion, Greece
Abstract
The exponential growth of Knowledge Graphs necessitates effective and efficient methods for their exploration and understanding. Frequently Asked Questions (FAQ) is a service that typically presents a list of questions and answers related to a specific topic, and which is intended to help people understand that topic. Although FAQ has already shown its value on large websites and is widely used, to the best of our knowledge it has not yet been exploited for Knowledge Graphs. In this paper, we present ULYSSES, the first system for automatically constructing FAQ lists for large Knowledge Graphs. Our method consists of three key steps. First, we select the most frequent queries by exploiting the available query logs. Next, we answer the selected queries, using the original graph. Finally, we construct textual descriptions of both the queries and the corresponding answers, exploring state-of-the-art transformer models, i.e., ChatGPT 3.5 and Gemini 1.5 Pro. We evaluate the results of each model, using a human-constructed FAQ list, contributing a unique dataset to the domain and showing the benefits of our approach.
Reference34 articles.
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