#knowledgegraphs

How much critical life sciences insight is still buried in unstructured text?

Discover the innovative framework behind the Connected Multi-Hierarchies Lightweight Global Hierarchical Model in Hyper-Relational Knowledge Graphs. This video breaks down how multi-level hierarchical structures can be effectively connected and embedded in hyper-relational knowledge graphs (HKGs), enabling richer semantic modeling and improved reasoning performance.

Learn how the model captures complex n-ary relations, preserves hierarchical depth, and avoids information loss-while remaining computationally lightweight and globally consistent. Ideal for researchers, AI practitioners, and data scientists working on advanced knowledge representation, semantic reasoning, and graph-based machine learning.

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🏆 Award Nomination: statisticsaward.com/award-nomination/?ecategory=Awards&rcategory=Awardee

In September, we submitted our manuscript "SEOntology: Writing the Future of SEO" to the Semantic Web Journal, for peer-review.

Given the nature of open and transparent review process of the journal, the paper is publicly available as a preprint on the journal website, at https://www.semantic-web-journal.net/content/seontology-writing-future-seo.

SEOntology, our proposal for an overarching ontology for the SEO domain, was developed in a fruitful collaboration between my master student Emilija Gjorgjevska and the amazing people at WordLift, especially David Riccitelli and Andrea Volpini.

If you are interested in the topic of ontology engineering, or SEO in general, you can give it a look and potentially provide a public (or anonymous) review.

What's the manuscript about?

In the paper, we introduce SEOntology as a pioneering ontology designed to formalize and systematize SEO-related knowledge, effectively bridging the gap between human and machine understanding. SEOntology seeks to standardize the complex vocabulary and interrelationships of SEO concepts and enhance the transparency, consistency, and efficiency of SEO practices. This paper details the design and implementation of SEOntology and examines its potential to future-proof the SEO industry by enabling more dynamic content interactions, improving data quality, and supporting the responsible integration of AI technologies.

The SEOntology is an open-source endeavour and is publicly available on GitHub at https://github.com/seontology/seontology.

Our previously published paper "Review of Natural Language Processing in Pharmacology" is now available as open access (OA), after the 12 months of embargo are over. You can now access the paper directly on the Pharmacological Reviews journal website, for free.

In the past year or so, our research team designed, developed and published RDFGraphGen, a general-purpose, domain-independent generator of synthetic RDF knowledge graphs, based on SHACL constraints. Today, we published a preprint detailing its design and implementation: "RDFGraphGen: A Synthetic RDF Graph Generator based on SHACL Constraints".

So, how does RDFGraphGen work, and why was it needed?

The Shapes Constraint Language (SHACL) is a W3C standard which specifies ways to validate data in RDF graphs, by defining constraining shapes. However, even though the main purpose of SHACL is validation of existing RDF data, in order to solve the problem with the lack of available RDF datasets in multiple RDF-based application development processes, we envisioned and implemented a reverse role for SHACL: we use SHACL shape definitions as a starting point to generate synthetic data for an RDF graph. The generation process involves extracting the constraints from the SHACL shapes, converting the specified constraints into rules, and then generating artificial data for a predefined number of RDF entities, based on these rules. The purpose of RDFGraphGen is the generation of small, medium or large RDF knowledge graphs for the purpose of benchmarking, testing, quality control, training and other similar purposes for applications from the RDF, Linked Data and Semantic Web domain.

RDFGraphGen is open-source and is available as a ready-to-use Python package.

Preprint: https://arxiv.org/abs/2407.17941 Authors: Marija Vecovska and Milos Jovanovik RDFGraphGen on GitHub: https://github.com/mveco/RDFGraphGen RDFGraphGen on PyPi: https://pypi.org/project/rdf-graph-gen/

The new year is off to a great start! Our paper "PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts Using Transfer Learning" has just been published in the MDPI Computers journal. It highlights the work by our team at the Faculty of Computer Science and Engineering - Skopje in the field of transfer learning in pharmacology.

More specifically, in it we introduce PharmKE, a text analysis platform tailored to the pharmaceutical industry that uses deep learning at several stages to perform an in-depth semantic analysis of relevant publications. With the platform, pharmaceutical domain specialists can easily identify and visualize the knowledge extracted from the input texts.

Shout out to our team: Nasi Jofche, Kostadin Mishev, Riste Stojanov, Milos Jovanovik, Eftim Zdravevski and Dimitar Trajanov.

Paper: https://www.mdpi.com/2073-431X/12/1/17

Our paper from the IEEE Conference on Big Data 2022 (Osaka, Japan) has now been published online. "Learning Robust Food Ontology Alignment" is a work by our team at the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Macedonia.

In the paper, we show how ontology alignment can be performed using neural networks. Each semantic resource is represented as a combination of graph based representations (#RDF2vec) and text representations (#BERT), in order to capture its semantic and structural features.

With this, we get a methodology for ontology alignment that is both robust and ontology-agnostic. It can be applied to any ontology, regardless of the domain.

Congrats to the team: Viktorija Mijalcheva, Ana Davcheva, Sasho Gramatikov, Milos Jovanovik, Dimitar Trajanov and Riste Stojanov.

Paper: https://ieeexplore.ieee.org/document/10020417

Our latest research has just been published in the MDPI Information Journal.

The paper, titled "Knowledge Graph Based Recommender for Automatic Playlist Continuation" explores and demonstrates the potential of using Knowledge Graphs in the domain of music recommendation and playlist continuation. More specifically, by integrating representational learning with Graph Neural Networks (GNNs) and fusing multiple data streams, our approach effectively models user behavior, leading to accurate and personalized music recommendations.

✍️ Authors: Aleksandar Ivanovski, Milos Jovanovik, Riste Stojanov and Dimitar Trajanov.

📄 Paper (HTML): https://www.mdpi.com/2078-2489/14/9/510 📄 Paper (PDF): https://www.mdpi.com/2078-2489/14/9/510/pdf