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31 results on '"Krisnadhi, Adila"'

1. GeoLink Data Set: A Complex Alignment Benchmark from Real-world Ontology

Author

Zhou, Lu, Cheatham, Michelle, Krisnadhi, Adila, and Hitzler, Pascal

Subjects
Information technology, T58.5-58.64
Abstract

Ontology alignment has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships. In this paper, we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark. The data set consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions. The alignment includes 1:1, 1:n, and m:n equivalence and subsumption correspondences, and is available in both Expressive and Declarative Ontology Alignment Language (EDOAL) and rule syntax. The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies. This allows it to be used by extensional alignment systems or those that require training data. This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative (OAEI) complex track to help researchers test their automated alignment systems and algorithms. This paper also analyzes the challenges inherent in effectively generating, detecting, and evaluating complex ontology alignments and provides a road map for future work on this topic.

Published
2020
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2. Replicable Benchmarking of Neural Machine Translation (NMT) on Low-Resource Local Languages in Indonesia

Author

Susanto, Lucky, Diandaru, Ryandito, Krisnadhi, Adila, Purwarianti, Ayu, and Wijaya, Derry

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Neural machine translation (NMT) for low-resource local languages in Indonesia faces significant challenges, including the need for a representative benchmark and limited data availability. This work addresses these challenges by comprehensively analyzing training NMT systems for four low-resource local languages in Indonesia: Javanese, Sundanese, Minangkabau, and Balinese. Our study encompasses various training approaches, paradigms, data sizes, and a preliminary study into using large language models for synthetic low-resource languages parallel data generation. We reveal specific trends and insights into practical strategies for low-resource language translation. Our research demonstrates that despite limited computational resources and textual data, several of our NMT systems achieve competitive performances, rivaling the translation quality of zero-shot gpt-3.5-turbo. These findings significantly advance NMT for low-resource languages, offering valuable guidance for researchers in similar contexts., Comment: Accepted on SEALP 2023, Workshop in IJCNLP-AACL 2023

Published
2023

3. Rule-based OWL Modeling with ROWLTab Protege Plugin

Author

Sarker, Md. Kamruzzaman, Krisnadhi, Adila, Carral, David, and Hitzler, Pascal

Subjects
Computer Science - Artificial Intelligence, Computer Science - Databases, Computer Science - Logic in Computer Science
Abstract

It has been argued that it is much easier to convey logical statements using rules rather than OWL (or description logic (DL)) axioms. Based on recent theoretical developments on transformations between rules and DLs, we have developed ROWLTab, a Protege plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL 2 DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule. In this paper, we present ROWLTab, together with a user evaluation of its effectiveness compared to entering axioms using the standard Protege interface. Our evaluation shows that modeling with ROWLTab is much quicker than the standard interface, while at the same time, also less prone to errors for hard modeling tasks., Comment: Accepted at ESWC 2017

Published
2018

4. Modeling OWL with Rules: The ROWL Protege Plugin

Author

Sarker, Md. Kamruzzaman, Carral, David, Krisnadhi, Adila A., and Hitzler, Pascal

Subjects
Computer Science - Artificial Intelligence
Abstract

In our experience, some ontology users find it much easier to convey logical statements using rules rather than OWL (or description logic) axioms. Based on recent theoretical developments on transformations between rules and description logics, we develop ROWL, a Protege plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule., Comment: Accepted at ISWC 2016

Published
2018

5. OWLAx: A Protege Plugin to Support Ontology Axiomatization through Diagramming

Author

Sarker, Md. Kamruzzaman, Krisnadhi, Adila A., and Hitzler, Pascal

Subjects
Computer Science - Artificial Intelligence, Computer Science - Databases
Abstract

Once the conceptual overview, in terms of a somewhat informal class diagram, has been designed in the course of engineering an ontology, the process of adding many of the appropriate logical axioms is mostly a routine task. We provide a Protege plugin which supports this task, together with a visual user interface, based on established methods for ontology design pattern modeling., Comment: Poster in ISWC 2016

Published
2018

10. MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors.

Author

Pusparini, Rizki Triyani, Krisnadhi, Adila Alfa, and Firdayani

Subjects
*ESTROGEN receptors, *DEEP learning, *SUPERVISED learning, *DRUG discovery, *MOLECULAR graphs, *MATHEMATICS, *HORMONE therapy
Abstract

Breast cancer ranks as the second leading cause of death among women, but early screening and self-awareness can help prevent it. Hormone therapy drugs that target estrogen levels offer potential treatments. However, conventional drug discovery entails extensive, costly processes. This study presents a framework for analyzing the quantitative structure–activity relationship (QSAR) of estrogen receptor alpha inhibitors. Our approach utilizes supervised learning, integrating self-attention Transformer and molecular graph information, to predict estrogen receptor alpha inhibitors. We established five classification models for predicting these inhibitors in breast cancer. Among these models, our proposed MATH model achieved remarkable precision, recall, F1 score, and specificity, with values of 0.952, 0.972, 0.960, and 0.922, respectively, alongside an ROC AUC of 0.977. MATH exhibited robust performance, suggesting its potential to assist pharmaceutical and health researchers in identifying candidate compounds for estrogen alpha inhibitors and guiding drug discovery pathways. [ABSTRACT FROM AUTHOR]

Published
2023
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15. Capturing Cultural Heritage in East Asia and Oceania.

Author

CUNNINGHAM, SALLY JO, DARARI, FARIZ, KRISNADHI, ADILA, and HINZE, ANNIKA

Subjects
CULTURAL property, INDONESIAN cooking, DIGITAL media
Abstract

The article discusses various methods of capturing cultural objects in digital form, utilizing the Indonesian dish rendang as an example. It also discusses the development of the cultural heritage knowledge storage base BudayaKB.

Published
2020
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17. Preface

Author

Janowicz, Krzysztof, Krisnadhi, Adila Alfa, Poveda Villalón, María, Hammar, Karl, and Shimizu, Cogan

Subjects
Datavetenskap (datalogi), Computer Sciences
Published
2019

19. Ontologi dalam Wikidata

Author

Krisnadhi, Adila

Abstract

Ontology in Wikidata (Indonesian) by Adila Krisnadhi of Faculty of Computer Science, Universitas Indonesia, presented at the Wikidata for AI Workshop

Published
2018
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20. GeoLink Triple Store Data

Author

Arko, Robert A., Carbotte, Suzanne M., Chandler, Cynthia, Cheatham, Michelle, Fils, Douglas, Hitzler, Pascal, Hu, Yingjie, Janowicz, Kyzysztof, Ji, Peng, Jones, Matt, Krisnadhi, Adila, Lehnert, Kerstin A., Mecum, Bryce, Mickle, Audrey, Narock, Tom, Raymond, Lisa, Schildhauer, Mark, Shepherd, Adam, Wiebe, Peter H., Arko, Robert A., Carbotte, Suzanne M., Chandler, Cynthia, Cheatham, Michelle, Fils, Douglas, Hitzler, Pascal, Hu, Yingjie, Janowicz, Kyzysztof, Ji, Peng, Jones, Matt, Krisnadhi, Adila, Lehnert, Kerstin A., Mecum, Bryce, Mickle, Audrey, Narock, Tom, Raymond, Lisa, Schildhauer, Mark, Shepherd, Adam, and Wiebe, Peter H.

Abstract

A growing collection of standard protocols, formats, and vocabularies, often characterized as the Semantic Web, offers a powerful approach for publishing research data online. The GeoLink project brings together experts from the geosciences, computer science, and library science in an effort to develop Semantic Web components that support discovery and reuse of data and knowledge. GeoLink's participating repositories include content from field expeditions, laboratory analyses, journal publications, conference presentations, theses/reports, and funding awards that span scientific studies from marine geology to marine ecosystems and biogeochemistry to paleoclimatology. One of the outcomes of this project is a network of Linked Data published by participating repositories using those ODPs, and tools to facilitate discovery of related content in multiple repositories. This item will be versioned periodically as the data is re-harvested and processed. The live dataset is currently available for query at http://data.geolink.org/sparql. A demo data application is available at http://demo.geolink.org/., This work is sponsored by NSF-1440114 and the EarthCube program.

Published
2018

21. The GeoLink knowledge graph

Author

Cheatham, Michelle, primary, Krisnadhi, Adila, additional, Amini, Reihaneh, additional, Hitzler, Pascal, additional, Janowicz, Krzysztof, additional, Shepherd, Adam, additional, Narock, Tom, additional, Jones, Matt, additional, and Ji, Peng, additional

Published
2018
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22. Advances in Ontology Design and Patterns

Author

Hammar, Karl, Hitzler, Pascal, Krisnadhi, Adila, Ławrynowicz, Agnieszka, Nuzzolese, Andrea Giovanni, Solanki, Monika, Hammar, Karl, Hitzler, Pascal, Krisnadhi, Adila, Ławrynowicz, Agnieszka, Nuzzolese, Andrea Giovanni, and Solanki, Monika

Abstract

The study of patterns in the context of ontology engineering for the semantic web was pioneered more than a decade ago by Blomqvist, Sandkuhl and Gangemi. Since then, this line of research has flourished and led to the development of ontology design patterns, knowledge patterns, and linked data patterns: the patterns as they are known by ontology designers, knowledge engineers, and linked data publishers, respectively. A key characteristic of those patterns is that they are modular and reusable solutions to recurrent problems in ontology engineering and linked data publishing. This book contains recent contributions which advance the state of the art on theory and use of ontology design patterns. The papers collected in this book cover a range of topics, from a method to instantiate content patterns, a proposal on how to document a content pattern, to a number of patterns emerging in ontology modeling in various situations.

Published
2017

23. Preface

Author

Hammar, Karl, Hitzler, Pascal, Krisnadhi, Adila, Ławrynowicz, Agnieszka, Nuzzolese, Andrea Giovanni, Solanki, Monika, Hammar, Karl, Hitzler, Pascal, Krisnadhi, Adila, Ławrynowicz, Agnieszka, Nuzzolese, Andrea Giovanni, and Solanki, Monika

Published
2017

24. Collected Research Questions Concerning Ontology Design Patterns

Author

Hammar, Karl, Blomqvist, Eva, Carral, David, Van Erp, Marieke, Fokkens, Antske, Gangemi, Aldo, Van Hage, Willem Robert, Hitzler, Pascal, Janowicz, Krzysztof, Karima, Nazifa, Krisnadhi, Adila, Narock, Tom, Segers, Roxane, Solanki, Monika, Svatek, Vojtech, Pascal Hitzler , Aldo Gangemi , Krzysztof Janowicz , Adila Krisnadhi , Valentina Presutti, and Karl Hammar , Eva Blomqvist , David Carral , Marieke van Erp , Antske Fokkens , Aldo Gangemi , Willem Robert van Hage , Pascal Hitzler , Krzysztof Janowicz , Nazifa Karima , Adila Krisnadhi , Tom Narock , Roxane Segers , Monika Solanki , Vojtech Svatek

Subjects
Datavetenskap (datalogi), Computer Sciences, ontology design patterns, research question, semantic web
Abstract

This chapter lists and discusses open challenges for the ODP community in the coming years, both in terms of research questions that will need be answered, and in terms of tooling and infrastructure that will need be developed to increase adoption of ODPs in academia and industry. The chapter is organised into three sections: Section 1 focuses on issues pertaining to the patterns themselves, including understanding their features and qualities, and developing pattern languages and standards. Section 2 concerns the evaluation and development of methods for using, constructing, and extracting ODPs. Finally, Section 3 focuses on tooling and infrastructure development, including Ontology Engineering environments that support pattern use, pattern repository development, and sustainability and versioning issues.

Published
2016

28. Collaborative research : EarthCube building blocks, leveraging semantics and linked data for geoscience data sharing and discovery, OceanLink

Author

Wiebe, Peter H., Chandler, Cynthia L., Raymond, Lisa, Shepherd, Adam, Finin, Tim, Narock, Tom, Arko, Robert A., Carbotte, Suzanne M., Hitzler, Pascal, Cheatham, Michelle, Krisnadhi, Adila, Wiebe, Peter H., Chandler, Cynthia L., Raymond, Lisa, Shepherd, Adam, Finin, Tim, Narock, Tom, Arko, Robert A., Carbotte, Suzanne M., Hitzler, Pascal, Cheatham, Michelle, and Krisnadhi, Adila

Abstract

The OceanLink EarthCube project will apply state-of-the-art Semantic Web Technologies to support data representation, discovery, analysis, sharing, and integration of datasets from the global oceans, and related resources including meeting abstracts and library holdings. Ships are a principal platform from which a wide spectrum of oceanographic data are collected. At the University of Maryland, Baltimore County, semantic relationships will be extracted from text for use in developing methods that efficiently identify relationships across distributed oceanographic datasets. At Wright State University integration of disparate data will occur by refining and applying leading edge technology from the Semantic Web, ontologies, and linked data. From the MBLWHOI Library, DSpace content will be published as Linked Open Data, providing relationships between oceanographic datasets, publications, conference presentations, and funded National Science Foundation projects. Teams of researchers at the Lamont-Doherty Earth Observatory and the Woods Hole Oceanographic Institution will develop Use Cases that represent the needs of the oceanographic research community and will publish oceanographic dataset catalogs as Linked Open Data. A key contribution will be semantically-enabled cyberinfrastructure components capable of automated data integration across distributed repositories. These efforts will ultimately lead to generalized computational techniques applicable to all of EarthCube.

Published
2013

29. Considerations regarding Ontology Design Patterns.

Author

Blomqvist, Eva, Hitzler, Pascal, Krisnadhi, Adila, Janowicz, Krzysztof, Narock, Tom, and Solanki, Monika

Subjects
ONTOLOGY, SEMANTIC Web, CONFERENCES & conventions
Abstract

The author focuses on the advantages of ontology design patterns that was discussed at the Worskhop on Ontology and Semantic Web Patterns held on October 11, 2015 in Bethlehem, Pennsylvania.

Published
2016
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31. A minimal ontology pattern for life cycle assessment data

Author

Janowicz, K., Krisnadhi, A. A., Hu, Y., Suh, S., Bo Weidema, Rivela, B., Tivander, J., Meyer, D. E., Berg-Cross, G., Hitzler, P., Ingwersen, W., Kuczenski, B., Vardeman, C., Ju, Y., Cheatham, M., Blomqvist, Eva, Hitzler, Pascal, Krisnadhi, Adila, Narock, Tom, and Solanki, Monika

Abstract

Life Cycle Assessment (LCA) studies the environmental impact of products taking into account their entire life-span and production chain. This requires gathering data from a variety of heterogeneous sources into a Life Cycle Inventory (LCI). LCI preparation involves the integration of observations and engineering models with reference data and literature results from around the world, from different domains, and at varying levels of granularity. Existing LCA data formats only address syntactic interoperability, thereby ignoring semantics. This leads to a variety of challenges, e.g., difficulties in reproducing assessments published in the literature. In this work, we present an ontology pattern that specifies key aspects of LCA/LCI data models, namely the notions of ows, activities, agents, and products, as well as their properties.

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