Collaborating Authors

Semantic Web

Web 3.0: The Future Of The Internet - DPN


While blockchain can ensure decentralization as one of the top web 3.0 features, you must also identify how the semantic web defines web3 functionalities. The semantic web basically ensures that machines in the web3 ecosystem cannot only understand data but also interpret the underlying context. Decentralisation remained one of the elusive concepts in the domain of technology for many years. The use of decentralization could put a system at risk of new vulnerabilities alongside increasing the need for resources. However, blockchain and the notion of web 3.0 have proved the possibility of introducing decentralization in an efficient, secure, and resourceful manner.

Web 3.0 in 2023: A Look Ahead. The Future of the Internet: How Web 3.0…


As we move closer to the year 2023, the concept of Web 3.0 is gaining more and more attention. Also known as the "Semantic Web," Web 3.0 represents the next generation of the Internet, and has the potential to revolutionize how we interact with and use the web. At its core, the Semantic Web is about adding meaning and context to the vast amount of data that is available online. It does this through the use of semantic technologies, which enable computers to understand the meaning and context of data, rather than just its raw form. One way that the Semantic Web achieves this is through the use of semantic markup languages, such as RDF (Resource Description Framework) and OWL (Web Ontology Language).

19th Extended Semantic Web Conference (ESWC), Heraklion 2022


The ESWC is a major venue for discussing the latest scientific results and technology innovations around semantic technologies including in knowledge graphs, web data, linked data and the semantic web. The goal of the Semantic Web is to create a Web of knowledge and services in which the semantics of content is made explicit and content is linked to both other content and services allowing novel applications to combine content from heterogeneous sites in unforeseen ways and support enhanced matching between users needs and content. This network of knowledge-based functionality weaves together a large network of human knowledge, and make this knowledge machine-processable to support intelligent behaviour by machines. Creating such an interlinked Web of knowledge which spans unstructured text, structured data as well as multimedia content and services requires the collaboration of many disciplines, including but not limited to: Artificial Intelligence, Natural Language Processing, Databases and Information Systems, Information Retrieval, Machine Learning, Multimedia, Distributed Systems, Social Networks, Web Engineering, and Web Science. For more information about the event please visit the ESWC 2022 website.

Why JSON Users Should Learn Turtle -


The Semantic Web has garnered a reputation for complexity among both Javascript and Python developers, primarily because, well, it's not JSON, and JSON has become the data language of the web. Why learn some obscure language when JSON is perfectly capable of describing everything, right? The problem that JSON faces, is actually a pretty subtle one, and has to do with the distinction between something occurring by value rather than by reference. Let's say that you have an education setting involving three courses and two teachers, where the two teachers co-teach one of the classes. The description is straightforward until you get to the very last teacher entry.


AAAI Conferences

SPARQL, a query language for RDF graphs, is one of the key technologies for the Semantic Web. The expressivity and complexity of various fragments of SPARQL have been studied extensively. It is usually assumed that the optional matching operator OPTIONAL has only two graph patterns as arguments. The specification of SPARQL, however, defines it as a ternary operator, with an additional filter condition. We address the problem of expressibility of the full ternary OPTIONAL via the simplified binary version and show that it is possible, but only with an exponential blowup in the size of the query (under common complexity-theoretic assumptions). We also study expressibility of other non-monotone SPARQL operators via optional matching and each other.


AAAI Conferences

Static analysis is a core task in query optimization and knowledge base verification. We study static analysis techniques for SPARQL, the standard language for querying Semantic Web data. Specifically, we investigate the query containment problem and query-update independence analysis. We are interested in developing techniques through reductions to the validity problem in logic.


AAAI Conferences

MetaShare is a knowledge-based system that supports the creation of data management plans and provides the functionality to support researchers as they implement those plans. MetaShare is a community-based, user-driven system that is being designed around the parallels of the scientific data life cycle and the development cycle of knowledge-based systems. MetaShare will provide recommendations and guidance to researchers based on the practices and decisions of similar projects. Using formal knowledge representation in the form of ontologies and rules, the system will be able to generate data collection, dissemination, and management tools to facilitate tasks with respect to using and sharing scientific data. MetaShare, which is initially targeting the research community at the University of Texas at El Paso, is being developed on a Web platform, using Semantic Web technologies. This paper presents a roadmap for the development of MetaShare, justifying the functionality and implementation decisions. In addition, the paper presents an argument concerning the return on investment for researchers and the planned evaluation for the system.

Del Rio

AAAI Conferences

In this paper, we describe the approach of the Earth, Life and Semantic Web (ELSEWeb) project that facilitates the discovery and transformation of Earth observation data sources for the creation of species distribution models (data-to-model) transformations. ELSEWeb automates the discovery and processing of voluminous, heterogeneous satellite imagery and other geospatial data available at the Earth Data Analysis Center to be included in Lifemapper Species Distribution models by using AI knowledge representation and reasoning techniques developed by the Semantic Web community. The realization of the ELSEWeb semantic infrastructure provides the possibility of combinatoric explosions of scientific results, automatically generated by orchestrations of data mash-ups and service composition. We report on the key elements that contributed to the ELSEWeb project and the role of automated reasoning in streamlining the Species Distribution Model generation and execution.


AAAI Conferences

Abstracts of the invited talks presented at the AAAI Fall Symposium on Discovery Informatics: AI Takes a Science-Centered View on Big Data. Talks include A Data Lifecycle Approach to Discovery Informatics, Generating Biomedical Hypotheses Using Semantic Web Technologies, Socially Intelligent Science, Representing and Reasoning with Experimental and Quasi-Experimental Designs, Bioinformatics Computation of Metabolic Models from Sequenced Genomes, Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science, Predictive Modeling of Patient State and Therapy Optimization, Case Studies in Data-Driven Systems: Building Carbon Maps to Finding Neutrinos, Computational Analysis of Complex Human Disorders, and Look at This Gem: Automated Data Prioritization for Scientific Discovery of Exoplanets, Mineral Deposits, and More.

Semantic Answer Type and Relation Prediction Task (SMART 2021) Artificial Intelligence

Each year the International Semantic Web Conference organizes a set of Semantic Web Challenges to establish competitions that will advance state-of-the-art solutions in some problem domains. The Semantic Answer Type and Relation Prediction Task (SMART) task is one of the ISWC 2021 Semantic Web challenges. This is the second year of the challenge after a successful SMART 2020 at ISWC 2020. This year's version focuses on two sub-tasks that are very important to Knowledge Base Question Answering (KBQA): Answer Type Prediction and Relation Prediction. Question type and answer type prediction can play a key role in knowledge base question answering systems providing insights about the expected answer that are helpful to generate correct queries or rank the answer candidates. More concretely, given a question in natural language, the first task is, to predict the answer type using a target ontology (e.g., DBpedia or Wikidata. Similarly, the second task is to identify relations in the natural language query and link them to the relations in a target ontology. This paper discusses the task descriptions, benchmark datasets, and evaluation metrics. For more information, please visit