AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Dong, Y.
Enhancing Painting Exhibition Experiences with the Application of Augmented Reality-Based AI Video Generation Technology Proceedings Article
In: P., Zaphiris; A., Ioannou; A., Ioannou; R.A., Sottilare; J., Schwarz; M., Rauterberg (Ed.): Lect. Notes Comput. Sci., pp. 256–262, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303176814-9 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, AI-generated art, Art and Technology, Arts computing, Augmented Reality, Augmented reality technology, Digital Exhibition Design, Dynamic content, E-Learning, Education computing, Generation technologies, Interactive computer graphics, Knowledge Management, Multi dimensional, Planning designs, Three dimensional computer graphics, Video contents, Video generation
@inproceedings{dong_enhancing_2025,
title = {Enhancing Painting Exhibition Experiences with the Application of Augmented Reality-Based AI Video Generation Technology},
author = {Y. Dong},
editor = {Zaphiris P. and Ioannou A. and Ioannou A. and Sottilare R.A. and Schwarz J. and Rauterberg M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213302959&doi=10.1007%2f978-3-031-76815-6_18&partnerID=40&md5=35484f5ed199a831f1a30f265a0d32d5},
doi = {10.1007/978-3-031-76815-6_18},
isbn = {03029743 (ISSN); 978-303176814-9 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15378 LNCS},
pages = {256–262},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Traditional painting exhibitions often rely on flat presentation methods, such as walls and stands, limiting their impact. Augmented Reality (AR) technology presents an opportunity to transform these experiences by turning static, flat artwork into dynamic, multi-dimensional presentations. However, creating and integrating video or dynamic content can be time-consuming and challenging, requiring meticulous planning, design, and production. In the context of urban renewal and community revitalization, particularly in China’s first-tier cities where real estate development has saturated the market, there is a growing trend to repurpose traditional commercial and office spaces with cultural and artistic exhibitions. These exhibitions not only enhance the spatial quality but also elevate the user experience, making the spaces more competitive. However, these non-traditional exhibition venues often lack the amenities of professional galleries, relying on walls, windows, and corners for displays, and requiring quick setup times. For visitors, who are often office workers or shoppers with limited time, the use of personal mobile devices for interaction is common. WeChat, China’s most widely used mobile application, provides a platform for convenient digital interactive experiences through mini-programs, which can support lightweight AR applications. AI video generation technologies, such as Conditional Generative Adversarial Networks (ControlNet) and Latent Consistency Models (LCM), have seen significant advancements. These technologies now allow for the creation of 3D models and video content from text and images. Tools like Meshy and Pika provide the ability to generate various video styles and offer precise control over video content. New AI video applications like Stable Video further expand the possibilities by rapidly converting static images into dynamic videos, facilitating easy adjustments and edits. This paper explores the application of AR-based AI video generation technology in enhancing the experience of painting exhibitions. By integrating these technologies, traditional paintings can be transformed into interactive, engaging displays that enrich the viewer’s experience. The study demonstrates the potential of these innovations to make art exhibitions more appealing and competitive in various public spaces, thereby improving both artistic expression and audience engagement. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {3D modeling, AI-generated art, Art and Technology, Arts computing, Augmented Reality, Augmented reality technology, Digital Exhibition Design, Dynamic content, E-Learning, Education computing, Generation technologies, Interactive computer graphics, Knowledge Management, Multi dimensional, Planning designs, Three dimensional computer graphics, Video contents, Video generation},
pubstate = {published},
tppubtype = {inproceedings}
}
Traditional painting exhibitions often rely on flat presentation methods, such as walls and stands, limiting their impact. Augmented Reality (AR) technology presents an opportunity to transform these experiences by turning static, flat artwork into dynamic, multi-dimensional presentations. However, creating and integrating video or dynamic content can be time-consuming and challenging, requiring meticulous planning, design, and production. In the context of urban renewal and community revitalization, particularly in China’s first-tier cities where real estate development has saturated the market, there is a growing trend to repurpose traditional commercial and office spaces with cultural and artistic exhibitions. These exhibitions not only enhance the spatial quality but also elevate the user experience, making the spaces more competitive. However, these non-traditional exhibition venues often lack the amenities of professional galleries, relying on walls, windows, and corners for displays, and requiring quick setup times. For visitors, who are often office workers or shoppers with limited time, the use of personal mobile devices for interaction is common. WeChat, China’s most widely used mobile application, provides a platform for convenient digital interactive experiences through mini-programs, which can support lightweight AR applications. AI video generation technologies, such as Conditional Generative Adversarial Networks (ControlNet) and Latent Consistency Models (LCM), have seen significant advancements. These technologies now allow for the creation of 3D models and video content from text and images. Tools like Meshy and Pika provide the ability to generate various video styles and offer precise control over video content. New AI video applications like Stable Video further expand the possibilities by rapidly converting static images into dynamic videos, facilitating easy adjustments and edits. This paper explores the application of AR-based AI video generation technology in enhancing the experience of painting exhibitions. By integrating these technologies, traditional paintings can be transformed into interactive, engaging displays that enrich the viewer’s experience. The study demonstrates the potential of these innovations to make art exhibitions more appealing and competitive in various public spaces, thereby improving both artistic expression and audience engagement. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
2024
Chheang, V.; Sharmin, S.; Marquez-Hernandez, R.; Patel, M.; Rajasekaran, D.; Caulfield, G.; Kiafar, B.; Li, J.; Kullu, P.; Barmaki, R. L.
Towards Anatomy Education with Generative AI-based Virtual Assistants in Immersive Virtual Reality Environments Proceedings Article
In: Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR, pp. 21–30, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835037202-1 (ISBN).
Abstract | Links | BibTeX | Tags: 3-D visualization systems, Anatomy education, Anatomy educations, Cognitive complexity, E-Learning, Embodied virtual assistant, Embodied virtual assistants, Generative AI, generative artificial intelligence, Human computer interaction, human-computer interaction, Immersive virtual reality, Interactive 3d visualizations, Knowledge Management, Medical education, Three dimensional computer graphics, Verbal communications, Virtual assistants, Virtual Reality, Virtual-reality environment
@inproceedings{chheang_towards_2024,
title = {Towards Anatomy Education with Generative AI-based Virtual Assistants in Immersive Virtual Reality Environments},
author = {V. Chheang and S. Sharmin and R. Marquez-Hernandez and M. Patel and D. Rajasekaran and G. Caulfield and B. Kiafar and J. Li and P. Kullu and R. L. Barmaki},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187216893&doi=10.1109%2fAIxVR59861.2024.00011&partnerID=40&md5=33e8744309add5fe400f4f341326505f},
doi = {10.1109/AIxVR59861.2024.00011},
isbn = {979-835037202-1 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},
pages = {21–30},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Virtual reality (VR) and interactive 3D visualization systems have enhanced educational experiences and environments, particularly in complicated subjects such as anatomy education. VR-based systems surpass the potential limitations of traditional training approaches in facilitating interactive engagement among students. However, research on embodied virtual assistants that leverage generative artificial intelligence (AI) and verbal communication in the anatomy education context is underrepresented. In this work, we introduce a VR environment with a generative AI-embodied virtual assistant to support participants in responding to varying cognitive complexity anatomy questions and enable verbal communication. We assessed the technical efficacy and usability of the proposed environment in a pilot user study with 16 participants. We conducted a within-subject design for virtual assistant configuration (avatar- and screen-based), with two levels of cognitive complexity (knowledge- and analysis-based). The results reveal a significant difference in the scores obtained from knowledge- and analysis-based questions in relation to avatar configuration. Moreover, results provide insights into usability, cognitive task load, and the sense of presence in the proposed virtual assistant configurations. Our environment and results of the pilot study offer potential benefits and future research directions beyond medical education, using generative AI and embodied virtual agents as customized virtual conversational assistants. © 2024 IEEE.},
keywords = {3-D visualization systems, Anatomy education, Anatomy educations, Cognitive complexity, E-Learning, Embodied virtual assistant, Embodied virtual assistants, Generative AI, generative artificial intelligence, Human computer interaction, human-computer interaction, Immersive virtual reality, Interactive 3d visualizations, Knowledge Management, Medical education, Three dimensional computer graphics, Verbal communications, Virtual assistants, Virtual Reality, Virtual-reality environment},
pubstate = {published},
tppubtype = {inproceedings}
}
Virtual reality (VR) and interactive 3D visualization systems have enhanced educational experiences and environments, particularly in complicated subjects such as anatomy education. VR-based systems surpass the potential limitations of traditional training approaches in facilitating interactive engagement among students. However, research on embodied virtual assistants that leverage generative artificial intelligence (AI) and verbal communication in the anatomy education context is underrepresented. In this work, we introduce a VR environment with a generative AI-embodied virtual assistant to support participants in responding to varying cognitive complexity anatomy questions and enable verbal communication. We assessed the technical efficacy and usability of the proposed environment in a pilot user study with 16 participants. We conducted a within-subject design for virtual assistant configuration (avatar- and screen-based), with two levels of cognitive complexity (knowledge- and analysis-based). The results reveal a significant difference in the scores obtained from knowledge- and analysis-based questions in relation to avatar configuration. Moreover, results provide insights into usability, cognitive task load, and the sense of presence in the proposed virtual assistant configurations. Our environment and results of the pilot study offer potential benefits and future research directions beyond medical education, using generative AI and embodied virtual agents as customized virtual conversational assistants. © 2024 IEEE.
2016
Spiccia, Carmelo; Augello, Agnese; Pilato, Giovanni
A Word Prediction Methodology Based on Posgrams Journal Article
In: Communications in Computer and Information Science, vol. 631, pp. 139–154, 2016, ISSN: 18650929.
Abstract | Links | BibTeX | Tags: Knowledge Management, Semantic Computing
@article{spicciaWordPredictionMethodology2016,
title = {A Word Prediction Methodology Based on Posgrams},
author = { Carmelo Spiccia and Agnese Augello and Giovanni Pilato},
editor = { Fred A. Dietz J.L.G. Liu K. Aveiro D. Filipe J.},
doi = {10.1007/978-3-319-52758-1_9},
issn = {18650929},
year = {2016},
date = {2016-01-01},
journal = {Communications in Computer and Information Science},
volume = {631},
pages = {139--154},
abstract = {This work introduces a two steps methodology for the prediction of missing words in incomplete sentences. In a first step the number of candidate words is restricted to the ones fulfilling the predicted part of speech; to this aim a novel algorithm based on ``posgrams'' analysis is also proposed. Then, in a second step, a word prediction algorithm is applied on the reduced words set. The work quantifies the advantages in predicting a word part of speech before predicting the word itself, in terms of accuracy and execution time. The methodology can be applied in several tasks, such as Text Autocompletion, Speech Recognition and Optical Text Recognition. textcopyright Springer International Publishing AG 2016.},
keywords = {Knowledge Management, Semantic Computing},
pubstate = {published},
tppubtype = {article}
}
This work introduces a two steps methodology for the prediction of missing words in incomplete sentences. In a first step the number of candidate words is restricted to the ones fulfilling the predicted part of speech; to this aim a novel algorithm based on ``posgrams'' analysis is also proposed. Then, in a second step, a word prediction algorithm is applied on the reduced words set. The work quantifies the advantages in predicting a word part of speech before predicting the word itself, in terms of accuracy and execution time. The methodology can be applied in several tasks, such as Text Autocompletion, Speech Recognition and Optical Text Recognition. textcopyright Springer International Publishing AG 2016.
Spiccia, Carmelo; Augello, Agnese; Pilato, Giovanni
A word prediction methodology based on posgrams Journal Article
In: Communications in Computer and Information Science, vol. 631, pp. 139–154, 2016, ISSN: 18650929.
Abstract | Links | BibTeX | Tags: Knowledge Management, Semantic Computing
@article{spiccia_word_2016,
title = {A word prediction methodology based on posgrams},
author = {Carmelo Spiccia and Agnese Augello and Giovanni Pilato},
editor = {Dietz J. L. G. Fred A. Filipe J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011357024&doi=10.1007%2f978-3-319-52758-1_9&partnerID=40&md5=ab1115d3da5af0239340ab31566e7401},
doi = {10.1007/978-3-319-52758-1_9},
issn = {18650929},
year = {2016},
date = {2016-01-01},
journal = {Communications in Computer and Information Science},
volume = {631},
pages = {139–154},
abstract = {This work introduces a two steps methodology for the prediction of missing words in incomplete sentences. In a first step the number of candidate words is restricted to the ones fulfilling the predicted part of speech; to this aim a novel algorithm based on “posgrams” analysis is also proposed. Then, in a second step, a word prediction algorithm is applied on the reduced words set. The work quantifies the advantages in predicting a word part of speech before predicting the word itself, in terms of accuracy and execution time. The methodology can be applied in several tasks, such as Text Autocompletion, Speech Recognition and Optical Text Recognition. © Springer International Publishing AG 2016.},
keywords = {Knowledge Management, Semantic Computing},
pubstate = {published},
tppubtype = {article}
}
This work introduces a two steps methodology for the prediction of missing words in incomplete sentences. In a first step the number of candidate words is restricted to the ones fulfilling the predicted part of speech; to this aim a novel algorithm based on “posgrams” analysis is also proposed. Then, in a second step, a word prediction algorithm is applied on the reduced words set. The work quantifies the advantages in predicting a word part of speech before predicting the word itself, in terms of accuracy and execution time. The methodology can be applied in several tasks, such as Text Autocompletion, Speech Recognition and Optical Text Recognition. © Springer International Publishing AG 2016.
2015
Ditta, Marilena; Milazzo, Fabrizio; Raví, Valentina; Pilato, Giovanni; Augello, Agnese
Data-Driven Relation Discovery from Unstructured Texts Proceedings Article
In: A., Filipe J. Liu K. Aveiro D. Dietz J. Filipe J. Fred (Ed.): IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 597–602, SciTePress, 2015, ISBN: 978-989-758-158-8.
Abstract | BibTeX | Tags: Knowledge Management, Knowledge Representation, Latent Semantic Analysis, Semantic Computing
@inproceedings{dittaDatadrivenRelationDiscovery2015,
title = {Data-Driven Relation Discovery from Unstructured Texts},
author = { Marilena Ditta and Fabrizio Milazzo and Valentina Raví and Giovanni Pilato and Agnese Augello},
editor = { Filipe J. Liu K. Aveiro D. Dietz J. Filipe J. Fred A.},
isbn = {978-989-758-158-8},
year = {2015},
date = {2015-01-01},
booktitle = {IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management},
volume = {1},
pages = {597--602},
publisher = {SciTePress},
abstract = {This work proposes a data driven methodology for the extraction of subject-verb-object triplets from a text corpus. Previous works on the field solved the problem by means of complex learning algorithms requiring hand-crafted examples; our proposal completely avoids learning triplets from a dataset and is built on top of a well-known baseline algorithm designed by Delia Rusu et al.. The baseline algorithm uses only syntactic information for generating triplets and is characterized by a very low precision i.e., very few triplets are meaningful. Our idea is to integrate the semantics of the words with the aim of filtering out the wrong triplets, thus increasing the overall precision of the system. The algorithm has been tested over the Reuters Corpus and has it as shown good performance with respect to the baseline algorithm for triplet extraction. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.},
keywords = {Knowledge Management, Knowledge Representation, Latent Semantic Analysis, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
This work proposes a data driven methodology for the extraction of subject-verb-object triplets from a text corpus. Previous works on the field solved the problem by means of complex learning algorithms requiring hand-crafted examples; our proposal completely avoids learning triplets from a dataset and is built on top of a well-known baseline algorithm designed by Delia Rusu et al.. The baseline algorithm uses only syntactic information for generating triplets and is characterized by a very low precision i.e., very few triplets are meaningful. Our idea is to integrate the semantics of the words with the aim of filtering out the wrong triplets, thus increasing the overall precision of the system. The algorithm has been tested over the Reuters Corpus and has it as shown good performance with respect to the baseline algorithm for triplet extraction. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.
Spiccia, Carmelo; Augello, Agnese; Pilato, Giovanni
Posgram Driven Word Prediction Proceedings Article
In: A., Dietz J. Aveiro D. Liu K. Filipe J. Filipe J. Fred (Ed.): IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 589–596, SciTePress, 2015, ISBN: 978-989-758-158-8.
Abstract | Links | BibTeX | Tags: Knowledge Management, Knowledge Representation, Semantic Computing
@inproceedings{spicciaPosgramDrivenWord2015,
title = {Posgram Driven Word Prediction},
author = { Carmelo Spiccia and Agnese Augello and Giovanni Pilato},
editor = { Dietz J. Aveiro D. Liu K. Filipe J. Filipe J. Fred A.},
doi = {10.5220/0005613305890596},
isbn = {978-989-758-158-8},
year = {2015},
date = {2015-01-01},
booktitle = {IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management},
volume = {1},
pages = {589--596},
publisher = {SciTePress},
abstract = {Several word prediction algorithms have been described in literature for automatic sentence completion from a finite candidate words set. However, at the best of our knowledge, very little or no work has been done on reducing the cardinality of this set. To address this issue, we use posgrams to predict the part of speech of the missing word first. Candidate words are then restricted to the ones fulfilling the predicted part of speech. We show how this additional step can improve the processing speed and the accuracy of word predictors. Experimental results are provided for the Italian language. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.},
keywords = {Knowledge Management, Knowledge Representation, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Several word prediction algorithms have been described in literature for automatic sentence completion from a finite candidate words set. However, at the best of our knowledge, very little or no work has been done on reducing the cardinality of this set. To address this issue, we use posgrams to predict the part of speech of the missing word first. Candidate words are then restricted to the ones fulfilling the predicted part of speech. We show how this additional step can improve the processing speed and the accuracy of word predictors. Experimental results are provided for the Italian language. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.
Spiccia, Carmelo; Augello, Agnese; Pilato, Giovanni
Posgram driven word prediction Proceedings Article
In: A., Aveiro D. Dietz J. Fred (Ed.): IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 589–596, SciTePress, 2015, ISBN: 978-989-758-158-8.
Abstract | Links | BibTeX | Tags: Knowledge Management, Knowledge Representation, Semantic Computing
@inproceedings{spiccia_posgram_2015,
title = {Posgram driven word prediction},
author = {Carmelo Spiccia and Agnese Augello and Giovanni Pilato},
editor = {Aveiro D. Dietz J. Fred A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960852021&doi=10.5220%2f0005613305890596&partnerID=40&md5=8ac11d2bbf2bb8953abb4c966c37eea1},
doi = {10.5220/0005613305890596},
isbn = {978-989-758-158-8},
year = {2015},
date = {2015-01-01},
booktitle = {IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management},
volume = {1},
pages = {589–596},
publisher = {SciTePress},
abstract = {Several word prediction algorithms have been described in literature for automatic sentence completion from a finite candidate words set. However, at the best of our knowledge, very little or no work has been done on reducing the cardinality of this set. To address this issue, we use posgrams to predict the part of speech of the missing word first. Candidate words are then restricted to the ones fulfilling the predicted part of speech. We show how this additional step can improve the processing speed and the accuracy of word predictors. Experimental results are provided for the Italian language. © 2015 by SCITEPRESS - Science and Technology Publications, Lda.},
keywords = {Knowledge Management, Knowledge Representation, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Several word prediction algorithms have been described in literature for automatic sentence completion from a finite candidate words set. However, at the best of our knowledge, very little or no work has been done on reducing the cardinality of this set. To address this issue, we use posgrams to predict the part of speech of the missing word first. Candidate words are then restricted to the ones fulfilling the predicted part of speech. We show how this additional step can improve the processing speed and the accuracy of word predictors. Experimental results are provided for the Italian language. © 2015 by SCITEPRESS - Science and Technology Publications, Lda.
Ditta, Marilena; Milazzo, Fabrizio; Raví, Valentina; Pilato, Giovanni; Augello, Agnese
Data-driven relation discovery from unstructured texts Proceedings Article
In: A., Liu K. Filipe J. Fred (Ed.): IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 597–602, SciTePress, 2015, ISBN: 978-989-758-158-8.
Abstract | Links | BibTeX | Tags: Knowledge Management, Knowledge Representation, Latent Semantic Analysis, Semantic Computing
@inproceedings{ditta_data-driven_2015,
title = {Data-driven relation discovery from unstructured texts},
author = {Marilena Ditta and Fabrizio Milazzo and Valentina Raví and Giovanni Pilato and Agnese Augello},
editor = {Liu K. Filipe J. Fred A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960877482&partnerID=40&md5=3e9c3192a44eab571fd16c461fc4008d},
isbn = {978-989-758-158-8},
year = {2015},
date = {2015-01-01},
booktitle = {IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management},
volume = {1},
pages = {597–602},
publisher = {SciTePress},
abstract = {This work proposes a data driven methodology for the extraction of subject-verb-object triplets from a text corpus. Previous works on the field solved the problem by means of complex learning algorithms requiring hand-crafted examples; our proposal completely avoids learning triplets from a dataset and is built on top of a well-known baseline algorithm designed by Delia Rusu et al.. The baseline algorithm uses only syntactic information for generating triplets and is characterized by a very low precision i.e., very few triplets are meaningful. Our idea is to integrate the semantics of the words with the aim of filtering out the wrong triplets, thus increasing the overall precision of the system. The algorithm has been tested over the Reuters Corpus and has it as shown good performance with respect to the baseline algorithm for triplet extraction. © 2015 by SCITEPRESS - Science and Technology Publications, Lda.},
keywords = {Knowledge Management, Knowledge Representation, Latent Semantic Analysis, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
This work proposes a data driven methodology for the extraction of subject-verb-object triplets from a text corpus. Previous works on the field solved the problem by means of complex learning algorithms requiring hand-crafted examples; our proposal completely avoids learning triplets from a dataset and is built on top of a well-known baseline algorithm designed by Delia Rusu et al.. The baseline algorithm uses only syntactic information for generating triplets and is characterized by a very low precision i.e., very few triplets are meaningful. Our idea is to integrate the semantics of the words with the aim of filtering out the wrong triplets, thus increasing the overall precision of the system. The algorithm has been tested over the Reuters Corpus and has it as shown good performance with respect to the baseline algorithm for triplet extraction. © 2015 by SCITEPRESS - Science and Technology Publications, Lda.