Search

Your search keyword '"Tiddi, Ilaria"' showing total 277 results
Start Over Author "Tiddi, Ilaria"
277 results on '"Tiddi, Ilaria"'

1. Advancing Robotic Perception with Perceived-Entity Linking

Author

Adamik, Mark, Pernisch, Romana, Tiddi, Ilaria, Schlobach, Stefan, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Demartini, Gianluca, editor, Hose, Katja, editor, Acosta, Maribel, editor, Palmonari, Matteo, editor, Cheng, Gong, editor, Skaf-Molli, Hala, editor, Ferranti, Nicolas, editor, Hernández, Daniel, editor, and Hogan, Aidan, editor

Published
2025
Full Text
View/download PDF

2. Structured Representations for Narratives

Author

Blin, Inès, ten Teije, Annette, van Harmelen, Frank, Tiddi, Ilaria, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Alam, Mehwish, editor, Rospocher, Marco, editor, van Erp, Marieke, editor, Hollink, Laura, editor, and Gesese, Genet Asefa, editor

Published
2025
Full Text
View/download PDF

3. ORKA: An Ontology for Robotic Knowledge Acquisition

Author

Adamik, Mark, Pernisch, Romana, Tiddi, Ilaria, Schlobach, Stefan, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Alam, Mehwish, editor, Rospocher, Marco, editor, van Erp, Marieke, editor, Hollink, Laura, editor, and Gesese, Genet Asefa, editor

Published
2025
Full Text
View/download PDF

4. The Privacy-Value-App Relationship and the Value-Centered Privacy Assistant

Author

Carter, Sarah E., d'Aquin, Mathieu, Spagnuelo, Dayana, Tiddi, Ilaria, Cormican, Kathryn, and Felzmann, Heike

Subjects
Computer Science - Cryptography and Security, Computer Science - Computers and Society, Computer Science - Human-Computer Interaction
Abstract

Many of us make quick decisions that affect our data privacy on our smartphones without due consideration of our values. One such decision point is establishing whether to download a smartphone app or not. In this work, we aim to better understand the relationship between our values, our privacy preferences, and our app choices, as well as explore the effectiveness of a smartphone value-centered privacy assistant (VcPA) at promoting value-centered app selection. To do this, we conducted a mixed-methods study that involved two phases. The first was an online survey of 273 smartphone user's values and privacy preferences when considering whether to download one of two apps (Lose It! and OpenLitterMap). Our results suggest that values and privacy preferences are related in an app or context-dependent manner. The second phase was testing the VcPA with 77 users in a synthetic Mock App Store setting. We established usability of a VcPA, with the VcPA helping some users more than others with selecting apps consistent with their selected value profile. Future qualitative and context-specific explorations of user perspectives could contribute to adequately capturing the specific role of values for privacy decision-making and improving the VcPA., Comment: 14 pages, Preprint; under review. Supplementary available at: https://zenodo.org/record/8234190

Published
2023

5. Establishing Shared Query Understanding in an Open Multi-Agent System

Author

Kondylidis, Nikolaos, Tiddi, Ilaria, and Teije, Annette ten

Subjects
Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science - Multiagent Systems
Abstract

We propose a method that allows to develop shared understanding between two agents for the purpose of performing a task that requires cooperation. Our method focuses on efficiently establishing successful task-oriented communication in an open multi-agent system, where the agents do not know anything about each other and can only communicate via grounded interaction. The method aims to assist researchers that work on human-machine interaction or scenarios that require a human-in-the-loop, by defining interaction restrictions and efficiency metrics. To that end, we point out the challenges and limitations of such a (diverse) setup, while also restrictions and requirements which aim to ensure that high task performance truthfully reflects the extent to which the agents correctly understand each other. Furthermore, we demonstrate a use-case where our method can be applied for the task of cooperative query answering. We design the experiments by modifying an established ontology alignment benchmark. In this example, the agents want to query each other, while representing different databases, defined in their own ontologies that contain different and incomplete knowledge. Grounded interaction here has the form of examples that consists of common instances, for which the agents are expected to have similar knowledge. Our experiments demonstrate successful communication establishment under the required restrictions, and compare different agent policies that aim to solve the task in an efficient manner., Comment: 9 pages. International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), London, United Kingdom

Published
2023

7. Enabling Social Demography Research Using Semantic Technologies

Author

Stork, Lise, Zijdeman, Richard L., Tiddi, Ilaria, ten Teije, Annette, Hartmanis, Juris, Founding Editor, Goos, Gerhard, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Meroño Peñuela, Albert, editor, Dimou, Anastasia, editor, Troncy, Raphaël, editor, Hartig, Olaf, editor, Acosta, Maribel, editor, Alam, Mehwish, editor, Paulheim, Heiko, editor, and Lisena, Pasquale, editor

Published
2024
Full Text
View/download PDF

8. Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods

Author

Höpner, Niklas, Tiddi, Ilaria, and van Hoof, Herke

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Enabling reinforcement learning (RL) agents to leverage a knowledge base while learning from experience promises to advance RL in knowledge intensive domains. However, it has proven difficult to leverage knowledge that is not manually tailored to the environment. We propose to use the subclass relationships present in open-source knowledge graphs to abstract away from specific objects. We develop a residual policy gradient method that is able to integrate knowledge across different abstraction levels in the class hierarchy. Our method results in improved sample efficiency and generalisation to unseen objects in commonsense games, but we also investigate failure modes, such as excessive noise in the extracted class knowledge or environments with little class structure.

Published
2022

9. Local Explanations for Clinical Search Engine results

Author

Contempré, Edeline, Szlávik, Zoltán, Mohammadi, Majid, Velazquez, Erick, Teije, Annette ten, and Tiddi, Ilaria

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

Health care professionals rely on treatment search engines to efficiently find adequate clinical trials and early access programs for their patients. However, doctors lose trust in the system if its underlying processes are unclear and unexplained. In this paper, a model-agnostic explainable method is developed to provide users with further information regarding the reasons why a clinical trial is retrieved in response to a query. To accomplish this, the engine generates features from clinical trials using by using a knowledge graph, clinical trial data and additional medical resources. and a crowd-sourcing methodology is used to determine their importance. Grounded on the proposed methodology, the rationale behind retrieving the clinical trials is explained in layman's terms so that healthcare processionals can effortlessly perceive them. In addition, we compute an explainability score for each of the retrieved items, according to which the items can be ranked. The experiments validated by medical professionals suggest that the proposed methodology induces trust in targeted as well as in non-targeted users, and provide them with reliable explanations and ranking of retrieved items.

Published
2021

10. Explainable Drug Repurposing in Context via Deep Reinforcement Learning

Author

Stork, Lise, Tiddi, Ilaria, Spijker, René, ten Teije, Annette, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pesquita, Catia, editor, Jimenez-Ruiz, Ernesto, editor, McCusker, Jamie, editor, Faria, Daniel, editor, Dragoni, Mauro, editor, Dimou, Anastasia, editor, Troncy, Raphael, editor, and Hertling, Sven, editor

Published
2023
Full Text
View/download PDF

15. A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling

Author

Mensio, Martino, Bastianelli, Emanuele, Tiddi, Ilaria, and Rizzo, Giuseppe

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

As the first robotic platforms slowly approach our everyday life, we can imagine a near future where service robots will be easily accessible by non-expert users through vocal interfaces. The capability of managing natural language would indeed speed up the process of integrating such platform in the ordinary life. Semantic parsing is a fundamental task of the Natural Language Understanding process, as it allows extracting the meaning of a user utterance to be used by a machine. In this paper, we present a preliminary study to semantically parse user vocal commands for a House Service robot, using a multi-layer Long-Short Term Memory neural network with attention mechanism. The system is trained on the Human Robot Interaction Corpus, and it is preliminarily compared with previous approaches., Comment: Workshop on Language and Robotics, IROS 2018

Published
2018

17. Discovering Research Hypotheses in Social Science Using Knowledge Graph Embeddings

Author

de Haan, Rosaline, Tiddi, Ilaria, Beek, Wouter, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Verborgh, Ruben, editor, Hose, Katja, editor, Paulheim, Heiko, editor, Champin, Pierre-Antoine, editor, Maleshkova, Maria, editor, Corcho, Oscar, editor, Ristoski, Petar, editor, and Alam, Mehwish, editor

Published
2021
Full Text
View/download PDF

19. Fostering Scientific Meta-analyses with Knowledge Graphs: A Case-Study

Author

Tiddi, Ilaria, Balliet, Daniel, ten Teije, Annette, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Harth, Andreas, editor, Kirrane, Sabrina, editor, Ngonga Ngomo, Axel-Cyrille, editor, Paulheim, Heiko, editor, Rula, Anisa, editor, Gentile, Anna Lisa, editor, Haase, Peter, editor, and Cochez, Michael, editor

Published
2020
Full Text
View/download PDF

21. Explaining data patterns using knowledge from the Web of Data

Author

Tiddi, Ilaria

Subjects
006.3
Abstract

Knowledge Discovery (KD) is a long-tradition field aiming at developing methodologies to detect hidden patterns and regularities in large datasets, using techniques from a wide range of domains, such as statistics, machine learning, pattern recognition or data visualisation. In most real world contexts, the interpretation and explanation of the discovered patterns is left to human experts, whose work is to use their background knowledge to analyse, refine and make the patterns understandable for the intended purpose. Explaining patterns is therefore an intensive and time-consuming process, where parts of the knowledge can remain unrevealed, especially when the experts lack some of the required background knowledge. In this thesis, we investigate the hypothesis that such interpretation process can be facilitated by introducing background knowledge from the Web of (Linked) Data. In the last decade, many areas started publishing and sharing their domain-specific knowledge in the form of structured data, with the objective of encouraging information sharing, reuse and discovery. With a constantly increasing amount of shared and connected knowledge, we thus assume that the process of explaining patterns can become easier, faster, and more automated. To demonstrate this, we developed Dedalo, a framework that automatically provides explanations to patterns of data using the background knowledge extracted from the Web of Data. We studied the elements required for a piece of information to be considered an explanation, identified the best strategies to automatically find the right piece of information in the Web of Data, and designed a process able to produce explanations to a given pattern using the background knowledge autonomously collected from the Web of Data. The final evaluation of Dedalo involved users within an empirical study based on a real-world scenario. We demonstrated that the explanation process is complex when not being familiar with the domain of usage, but also that this can be considerably simplified when using the Web of Data as a source of background knowledge.

Published
2016
Full Text
View/download PDF

24. Correction to: The Semantic Web: ESWC 2021 Satellite Events

Author

Verborgh, Ruben, primary, Dimou, Anastasia, additional, Hogan, Aidan, additional, d’Amato, Claudia, additional, Tiddi, Ilaria, additional, Bröring, Arne, additional, Mayer, Simon, additional, Ongenae, Femke, additional, Tommasini, Riccardo, additional, and Alam, Mehwish, additional

Published
2021
Full Text
View/download PDF

25. Correction to: The Semantic Web: ESWC 2021 Satellite Events

Author

Verborgh, Ruben, Dimou, Anastasia, Hogan, Aidan, d’Amato, Claudia, Tiddi, Ilaria, Bröring, Arne, Mayer, Simon, Ongenae, Femke, Tommasini, Riccardo, Alam, Mehwish, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Verborgh, Ruben, editor, Dimou, Anastasia, editor, Hogan, Aidan, editor, d'Amato, Claudia, editor, Tiddi, Ilaria, editor, Bröring, Arne, editor, Mayer, Simon, editor, Ongenae, Femke, editor, Tommasini, Riccardo, editor, and Alam, Mehwish, editor

Published
2021
Full Text
View/download PDF

28. Towards the Temporal Streaming of Graph Data on Distributed Ledgers

Author

Third, Allan, Tiddi, Ilaria, Bastianelli, Emanuele, Valentine, Chris, Domingue, John, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Blomqvist, Eva, editor, Hose, Katja, editor, Paulheim, Heiko, editor, Ławrynowicz, Agnieszka, editor, Ciravegna, Fabio, editor, and Hartig, Olaf, editor

Published
2017
Full Text
View/download PDF

29. DKA-robo: Dynamically Updating Time-Invalid Knowledge Bases Using Robots

Author

Tiddi, Ilaria, Bastianelli, Emanuele, Daga, Enrico, d’Aquin, Mathieu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ciancarini, Paolo, editor, Poggi, Francesco, editor, Horridge, Matthew, editor, Zhao, Jun, editor, Groza, Tudor, editor, Suarez-Figueroa, Mari Carmen, editor, d'Aquin, Mathieu, editor, and Presutti, Valentina, editor

Published
2017
Full Text
View/download PDF

30. Learning to Assess Linked Data Relationships Using Genetic Programming

Author

Tiddi, Ilaria, d’Aquin, Mathieu, Motta, Enrico, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Groth, Paul, editor, Simperl, Elena, editor, Gray, Alasdair, editor, Sabou, Marta, editor, Krötzsch, Markus, editor, Lecue, Freddy, editor, Flöck, Fabian, editor, and Gil, Yolanda, editor

Published
2016
Full Text
View/download PDF

31. Commonsense knowledge in cognitive robotics: a systematic literature review.

Author

Töberg, Jan-Philipp, Ngomo, Axel-Cyrille Ngonga, Beetz, Michael, Cimiano, Philipp, Wang, Lichun, Tiddi, Ilaria, and Bardaro, Gianluca

Subjects
COGNITIVE robotics, KNOWLEDGE representation (Information theory), SEARCH engines, KEYWORD searching, MENTAL representation
Abstract

One of the big challenges in robotics is the generalization necessary for performing unknown tasks in unknown environments on unknown objects. For us humans, this challenge is simplified by the commonsense knowledge we can access. For cognitive robotics, representing and acquiring commonsense knowledge is a relevant problem, so we perform a systematic literature review to investigate the current state of commonsense knowledge exploitation in cognitive robotics. For this review, we combine a keyword search on six search engines with a snowballing search on six related reviews, resulting in 2,048 distinct publications. After applying pre-defined inclusion and exclusion criteria, we analyse the remaining 52 publications. Our focus lies on the use cases and domains for which commonsense knowledge is employed, the commonsense aspects that are considered, the datasets/resources used as sources for commonsense knowledge and the methods for evaluating these approaches. Additionally, we discovered a divide in terminology between research from the knowledge representation and reasoning and the cognitive robotics community. This divide is investigated by looking at the extensive review performed by Zech et al. (The International Journal of Robotics Research, 2019, 38, 518-562), with whom we have no overlapping publications despite the similar goals. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

32. Data Patterns Explained with Linked Data

Author

Tiddi, Ilaria, d’Aquin, Mathieu, Motta, Enrico, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Bifet, Albert, editor, May, Michael, editor, Zadrozny, Bianca, editor, Gavalda, Ricard, editor, Pedreschi, Dino, editor, Bonchi, Francesco, editor, Cardoso, Jaime, editor, and Spiliopoulou, Myra, editor

Published
2015
Full Text
View/download PDF

33. Joint Proceedings of the ESWC 2023 Workshops and Tutorials co-located with 20th European Semantic Web Conference (ESWC)

Author

Alam, Mehwish, Trojahn, Cássia, Hertling, Sven, Pesquita, Catia, Aebeloe, Christian, Aras, Hidir, Azzam, Amr, Cano, Juan, Domingue, John, Gottschalk, Simon, Hartig, Olaf, Hose, Katja, Kirrane, Sabrina, Lisena, Pasquale, Osborne, Francesco, Rohde, Philipp D., Steels, Luc, Taelman, Ruben, Third, Aisling, Tiddi, Ilaria, Türker, Rima, Alam, Mehwish, Trojahn, Cássia, Hertling, Sven, Pesquita, Catia, Aebeloe, Christian, Aras, Hidir, Azzam, Amr, Cano, Juan, Domingue, John, Gottschalk, Simon, Hartig, Olaf, Hose, Katja, Kirrane, Sabrina, Lisena, Pasquale, Osborne, Francesco, Rohde, Philipp D., Steels, Luc, Taelman, Ruben, Third, Aisling, Tiddi, Ilaria, and Türker, Rima

Published
2023

34. Using Neural Networks to Aggregate Linked Data Rules

Author

Tiddi, Ilaria, d’Aquin, Mathieu, Motta, Enrico, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Janowicz, Krzysztof, editor, Schlobach, Stefan, editor, Lambrix, Patrick, editor, and Hyvönen, Eero, editor

Published
2014
Full Text
View/download PDF

35. Quantifying the Bias in Data Links

Author

Tiddi, Ilaria, d’Aquin, Mathieu, Motta, Enrico, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Janowicz, Krzysztof, editor, Schlobach, Stefan, editor, Lambrix, Patrick, editor, and Hyvönen, Eero, editor

Published
2014
Full Text
View/download PDF

36. Dedalo: Looking for Clusters Explanations in a Labyrinth of Linked Data

Author

Tiddi, Ilaria, d’Aquin, Mathieu, Motta, Enrico, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Presutti, Valentina, editor, d’Amato, Claudia, editor, Gandon, Fabien, editor, d’Aquin, Mathieu, editor, Staab, Steffen, editor, and Tordai, Anna, editor

Published
2014
Full Text
View/download PDF

37. Testing prompt engineering methods for knowledge extraction from text

Author

Polat, Fina, Tiddi, Ilaria, and Groth, Paul

Abstract

The capabilities of Large Language Models (LLMs,) such as Mistral 7B, Llama 3, GPT-4, present a significant opportunity for knowledge extraction (KE) from text. However, LLMs’ context-sensitivity can hinder obtaining precise and task-aligned outcomes, thereby requiring prompt engineering. This study explores the efficacy of five prompt methods with different task demonstration strategies across 17 different prompt templates, utilizing a relation extraction dataset (RED-FM) with the aforementioned LLMs. To facilitate evaluation, we introduce a novel framework grounded in Wikidata’s ontology. The findings demonstrate that LLMs are capable of extracting a diverse array of facts from text. Notably, incorporating a simple instruction accompanied by a task demonstration – comprising three examples selected via a retrieval mechanism – significantly enhances performance across Mistral 7B, Llama 3, and GPT-4. The effectiveness of reasoning-oriented prompting methods such as Chain-of-Thought, Reasoning and Acting, while improved with task demonstrations, does not surpass alternative methods. This suggests that framing extraction as a reasoning task may not be necessary for KE. Notably, task demonstrations leveraging examples selected via retrieval mechanisms facilitate effective knowledge extraction across all tested prompting strategies and LLMs.

Published
2024
Full Text
View/download PDF

38. Towards Robots That Meet Users’ Need for Explanation

Author

Stange, Sonja, Kopp, Stefan, Lukowicz, Paul, Mayer, Sven, Koch, Janin, Shawe-Taylor, John, and Tiddi, Ilaria

Abstract

While humans are used to reason about other humans’ behavior, they are not readily able to understand the decision processes of artificial agents. This can be harmful in human-robot interaction (HRI) settings where a user may suspect erroneous or, even worse, intentionally non-cooperative behavior, resulting in reduced acceptance of the robot. In order to mitigate such negative effects, autonomous robots may be equipped with the ability to adequately explain their behavior. To that end, a robot is required to have the ability to (1) robustly detect a user’s need for explanation and (2) identify the situation-specific nature of the explanation need. Further it needs to be endowed with (3) communicative capabilities in order to deliver suitable explanations and ensure sufficient understanding. This extended abstract presents recent work towards endowing a social robot with such qualities and discusses how robots can meet users’ explanation needs more adequately.

Published
2023
Full Text
View/download PDF

39. Ontology Learning from Open Linked Data and Web Snippets

Author

Tiddi, Ilaria, Mustapha, Nesrine Ben, Vanrompay, Yves, Aufaure, Marie-Aude, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Herrero, Pilar, editor, Panetto, Hervé, editor, Meersman, Robert, editor, and Dillon, Tharam, editor

Published
2012
Full Text
View/download PDF

40. Building Narrative Structures from Knowledge Graphs

Author

Blin, Inès, Groth, Paul, Rula, Anisa, Schneider, Jodi, Tiddi, Ilaria, Simperl, Elena, Alexopoulos, Panos, Hoekstra, Rinke, Alam, Mehwish, Dimou, Anastasia, Tamper, Minna, Groth, Paul, Rula, Anisa, Schneider, Jodi, Tiddi, Ilaria, Simperl, Elena, Alexopoulos, Panos, Hoekstra, Rinke, Alam, Mehwish, Dimou, Anastasia, Tamper, Minna, and Artificial intelligence

Subjects
SDG 16 - Peace, SDG 16 - Peace, Justice and Strong Institutions, Ontologies, Narratives, Reasoning, Justice and Strong Institutions, Semantic web
Abstract

Humans constantly create narratives to provide explanations for how and why something happens. Designing systems able to build such narratives would therefore contribute to building more human-centric systems, and to support uses like decision-making processes. Here, a narrative is seen as a sequence of events. My thesis investigates how a narrative can be built computationally. Four research questions are identified: representation, construction, link prediction and evaluation. A case study on the French Revolution, based upon Wikidata and Wikipedia is presented. This prototype helps identifying the first challenges such as dynamic representation and evaluation of a narrative.

Published
2022
Full Text
View/download PDF

41. Using Referential Language Games for Task-oriented Ontology Alignment

Author

Kondylidis, Nikolaos, Groth, Paul, Rula, Anisa, Schneider, Jodi, Tiddi, Ilaria, Simperl, Elena, Alexopoulos, Panos, Hoekstra, Rinke, Alam, Mehwish, Dimou, Anastasia, Tamper, Minna, Artificial Intelligence (section level), Network Institute, Artificial intelligence, Groth, Paul, Rula, Anisa, Schneider, Jodi, Tiddi, Ilaria, Simperl, Elena, Alexopoulos, Panos, Hoekstra, Rinke, Alam, Mehwish, Dimou, Anastasia, and Tamper, Minna

Subjects
SDG 16 - Peace, SDG 16 - Peace, Justice and Strong Institutions, Instance-based Ontology Matching, Task-oriented Ontology Alignment, Multi-agent communication, Justice and Strong Institutions
Abstract

Ontology Alignment (OA) is generally performed by requesting two parties to provide their complete knowledge to a third party that suggests potential schema alignments. This might however not always be possible or helpful, as for example, when two organisations want to query each other’s knowledge, and none of them is willing to share their schema due to information privacy considerations. This Ph.D. explores how to allow multi-agent communication in cases where agents operate using different ontologies that cannot be fully exposed or shared. Our preliminary experiments focus on the case where agents’ knowledge is describing a common set of entities and has the form of Knowledge Graphs (KGs). The suggested methodology is based on the grounded naming game, where agents are forced to develop their own language in order to refer to corresponding schema concepts of different ontologies. This way, agents that use different ontologies can still communicate successfully for a task at hand, without revealing any private information. We have performed some proof of concept experiments applying our suggested method on artificial cases and we are on the process of extending our methodology so that it can be applied in real-world KGs.

Published
2022
Full Text
View/download PDF

43. A knowledge graph approach for interpretable prediction of pathogenic genetic interactions

Author

Renaux, Alexandre, Terwagne, Chloé CT, Cochez, Michael, Tiddi, Ilaria, Nowé, Ann, Lenaerts, Tom, Renaux, Alexandre, Terwagne, Chloé CT, Cochez, Michael, Tiddi, Ilaria, Nowé, Ann, and Lenaerts, Tom

Abstract

An increasing number of clinical studies are reporting patterns of oligogenic inheritance in genetic diseases. Despite the advent of methods able to predict the pathogenicity of variant combinations, the underlying biological mechanisms remain unknown since these models offer limited interpretability. To advance towards a better understanding of oligogenic disease aetiology, we developed a new interpretable predictive method based on a knowledge graph. This heterogenous network integrates curated oligogenic combinations together with multiple biological networks and biomedical ontologies. Our approach successfully captures association rules solely based on multi-hop relationships between genes. It combines them as a decision set model which can predict the pathogenicity of new gene pairs. These predictions come with explanations, obtained by querying the knowledge graph, which highlight relevant paths. The benchmarking of this model in a cross-validation setting achieves a ROC AUC of 0.81 and could consistently recall 13/22 independant gene pairs from recently published digenic combinations. The analysis of the rule-based paths highlights relevant contributors to the disease and demonstrates the ability of this approach to generate knowledge-based hypotheses to investigate new disease mechanisms., info:eu-repo/semantics/nonPublished

Published
2022

44. A knowledge graph approach for interpretable prediction of pathogenic genetic interactions

Author

European Conference on Computational Biology (ECCB) 2022 (2022-07: Sitges, Spain), Renaux, Alexandre, Terwagne, Chloé CT, Cochez, Michael, Tiddi, Ilaria, Nowé, Ann, Lenaerts, Tom, European Conference on Computational Biology (ECCB) 2022 (2022-07: Sitges, Spain), Renaux, Alexandre, Terwagne, Chloé CT, Cochez, Michael, Tiddi, Ilaria, Nowé, Ann, and Lenaerts, Tom

Abstract

An increasing number of clinical studies are reporting patterns of oligogenic inheritance in genetic diseases. Despite the advent of methods able to predict the pathogenicity of variant combinations, the underlying biological mechanisms remain unknown, since these models offer limited interpretability. To advance towards a better understanding of oligogenic disease aetiology, we developed a new interpretable predictive method based on a knowledge graph. This heterogenous network integrates curated oligogenic combinations together with multiple biological networks and biomedical ontologies. Our approach successfully captures association rules solely based on multi-hop relationships between genes. It combines them as a decision set model which can predict the pathogenicity of new gene pairs. These predictions come with explanations, obtained by querying the knowledge graph, which highlight relevant paths. The benchmarking of this model in a cross-validation setting achieves high accuracy and recalls independent gene pairs from recently published digenic combinations. The analysis of the rule-based paths highlights relevant contributors to the disease and shows the ability of this approach to generate knowledge-based hypotheses to investigate new disease mechanisms., info:eu-repo/semantics/published

Published
2022

45. Local Explanations for Clinical Search Engine Results

Author

Contempr��, Edeline, Szl��vik, Zolt��n, Mohammadi, Majid, Velazquez, Erick, Teije, Annette ten, and Tiddi, Ilaria

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Information Retrieval (cs.IR), Computer Science - Information Retrieval
Abstract

Health care professionals rely on treatment search engines to efficiently find adequate clinical trials and early access programs for their patients. However, doctors lose trust in the system if its underlying processes are unclear and unexplained. In this paper, a model-agnostic explainable method is developed to provide users with further information regarding the reasons why a clinical trial is retrieved in response to a query. To accomplish this, the engine generates features from clinical trials using by using a knowledge graph, clinical trial data and additional medical resources. and a crowd-sourcing methodology is used to determine their importance. Grounded on the proposed methodology, the rationale behind retrieving the clinical trials is explained in layman's terms so that healthcare processionals can effortlessly perceive them. In addition, we compute an explainability score for each of the retrieved items, according to which the items can be ranked. The experiments validated by medical professionals suggest that the proposed methodology induces trust in targeted as well as in non-targeted users, and provide them with reliable explanations and ranking of retrieved items.

Published
2022
Full Text
View/download PDF

46. A Mock App Store Interface for Virtual Privacy Assistants

Author

Carter, Sarah E., Tiddi, Ilaria, Spagnuelo, Dayana, Schlobach, Stefan, Perez-Ortiz, Maria, Tielman, Myrthe, Schlobach, Stefan, Perez-Ortiz, Maria, Tielman, Myrthe, Artificial intelligence, Network Institute, Artificial Intelligence (section level), and Business Web and Media

Subjects
Privacy assistant, user studies, mobile applications
Abstract

Privacy assistants aim to provide meaningful privacy recommendations to users. Here, we describe a web-based testing environment for smartphone privacy assistants called the “Mock App Store” (MAS). The MAS was developed to test a particular privacy assistant – the value-centered privacy assistant (VcPA) – which assists users in selecting applications based on their value profile. While the MAS was designed with the VcPA in mind, it could also be utilized to test other state-of-the-art privacy assistant technology.

Published
2022
Full Text
View/download PDF

47. Preface

Author

Groth, Paul, Schneider, Jodi, Rula, Anisa, Tiddi, Ilaria, Simperl, Elena, Hoekstra, Rinke, Alexopoulos, Panos, Alam, Mehwish, Dimou, Anastasia, Tamper, Minna, Artificial intelligence, Network Institute, and Artificial Intelligence (section level)

Published
2022

48. sj-pdf-1-pps-10.1177_17456916211053319 – Supplemental material for The Cooperation Databank: Machine-Readable Science Accelerates Research Synthesis

Author

Spadaro, Giuliana, Tiddi, Ilaria, Columbus, Simon, Jin, Shuxian, ten Teije, Annette, and Balliet, Daniel

Subjects
FOS: Psychology, FOS: Clinical medicine, 170199 Psychology not elsewhere classified, 110319 Psychiatry (incl. Psychotherapy), 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, sj-pdf-1-pps-10.1177_17456916211053319 for The Cooperation Databank: Machine-Readable Science Accelerates Research Synthesis by Giuliana Spadaro, Ilaria Tiddi, Simon Columbus, Shuxian Jin, Annette ten Teije, CoDa Team and Daniel Balliet in Perspectives on Psychological Science

Published
2022
Full Text
View/download PDF

49. Towards Easy Vocabulary Drafts with Neologism 2.0

Author

Lipp, Johannes, Gleim, Lars, Cochez, Michael, Dimitriadis, Iraklis, Ali, Hussain, Alvarez, Daniel Hoppe, Lange, Christoph, Decker, Stefan, Verborgh, Ruben, Dimou, Anastasia, Hogan, Aidan, d’Amato, Claudia, Tiddi, Ilaria, Bröring, Arne, Maier, Simon, Ongenae, Femke, Tommasini, Riccardo, Alam, Mehwish, Artificial intelligence, Network Institute, Artificial Intelligence (section level), Verborgh, Ruben, Dimou, Anastasia, Hogan, Aidan, d’Amato, Claudia, Tiddi, Ilaria, Bröring, Arne, Maier, Simon, Ongenae, Femke, Tommasini, Riccardo, and Alam, Mehwish

Subjects
Knowledge graph, Vocabulary, Computer science, media_common.quotation_subject, Bootstrapping (linguistics), computer.file_format, computer.software_genre, Ontology engineering, Sketch, Domain (software engineering), World Wide Web, Vocabulary drafts, Ontology creation, SDG 17 - Partnerships for the Goals, Vocabulary creation, RDF, Semantic Web, computer, Neologism, media_common
Abstract

Shared vocabularies and ontologies are essential for many applications. Although standards and recommendations already cover many areas, adaptations are usually necessary to represent concrete use-cases properly. Domain experts are unfamiliar with ontology engineering, which creates special requirements for needed tool support. Simple sketch applications are usually too imprecise, while comprehensive ontology editors are often too complicated for non-experts. We present Neologism 2.0 – an open-source tool for quick vocabulary creation through domain experts. Its guided vocabulary creation and its collaborative graph editor enable the quick creation of proper vocabularies, even for non-experts, and dramatically reduces the time and effort to draft vocabularies collaboratively. An RDF export allows quick bootstrapping of any other Semantic Web tool.

Published
2021
Full Text
View/download PDF

50. Using Knowledge Graphs for Machine Learning in Smart Home Forecasters

Author

van der Weerdt, Roderick, Verborgh, Ruben, Dimou, Anastasia, Hogan, Aidan, d’Amato, Claudia, Tiddi, Ilaria, Bröring, Arne, Maier, Simon, Ongenae, Femke, Tommasini, Riccardo, Alam, Mehwish, Verborgh, Ruben, Dimou, Anastasia, Hogan, Aidan, d’Amato, Claudia, Tiddi, Ilaria, Bröring, Arne, Maier, Simon, Ongenae, Femke, Tommasini, Riccardo, Alam, Mehwish, Business Web and Media, and Network Institute

Subjects
Focus (computing), Computer science, business.industry, Ontology, Internet of Things, Federated learning, Ontology (information science), Data mapping, Transformation (function), Knowledge graph, Smart home, Human–computer interaction, Home automation, Machine learning, SDG 7 - Affordable and Clean Energy, business, Forecasting
Abstract

Internet of Things (IoT) brings together heterogeneous data from smart devices in smart homes. Smart devices operate within different platforms, but ontologies can be used to create a common middle ground that allows communications between these smart devices outside of those platforms. The data communicated by the smart devices can be used to train the prediction algorithms used in forecasters. This research will first focus on the creation of a mapping to transform IoT data into a knowledge graph than can be used in the common middle ground and investigate the effect of using that IoT knowledge graph data as input for prediction algorithms. Experiments to determine the impact of incorporating other related information in the training of the prediction algorithms will be performed by using external datasources that can be linked to the knowledge graph and by using federated learning over IoT data from other smart homes. Initial results on the transformation mapping of IoT data to an ontology is presented.

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results