Data, Interconnected.

I have joined TU Wien in Vienna, Austria. I'm a Knowledge Graphs Researcher (Postdoctoral Researcher) at the Databases and Artificial Intelligence (DBAI) group, Institute of Logic and Computation, Faculty of Informatics, at the Vienna University of Technology (TU Wien).

I'm part of the research lab of Prof. Katja Hose, and I will work as a Knowledge Graphs expert on the TARGET Project. The research lab also features Dr. Dominik Tomaszuk and Dr. Maxime Jakubowski.

Looking forward to this new academic journey!

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/

I'm happy to share that our paper "Sentiment Analysis in Finance: From Transformers Back to eXplainable Lexicons (XLex)" has just been published in the IEEE Access journal.

It showcases an amazing work done by Maryan Rizinski (Boston University, USA) and Hristijan Peshov (Faculty of Computer Science and Engineering - Skopje), under the guidance of prof. Dimitar Trajanov, with the help of the rest of our team: prof. Kostadin Mishev and myself (prof. Milos Jovanovik).

In this paper, we present a novel XLex methodology that leverages NLP transformer models and SHAP explainability to automatically enhance the vocabulary coverage of the Loughran-McDonald (LM) lexicon in sentiment analysis scenarios for financial applications. Our results demonstrate that standard domain-specific lexicons, such as the LM lexicon, can be expanded in an explainable way with new words without the need for laborious annotation involvement of human experts, a process that is both expensive and time-consuming. We have conducted 22 separate experiments, and in all of them, the proposed XLex methodology leads to increased performance compared to LM. Additionally, the use of generated (XLex) or combined lexicons (XLex+LM) leads to significant improvements in sentiment analysis results compared to using the manually annotated lexicon alone.

Overall, the proposed XLex methodology holds great promise in advancing the field of sentiment analysis, particularly in applications where interpretability is of utmost importance.

The paper is published as #OpenAccess and is freely available at the link below.

Paper: https://ieeexplore.ieee.org/document/10380556

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

I'm happy to share that after an extensive review, our paper "Review of Natural Language Processing in Pharmacology" has been published in the Pharmacological Reviews journal.

The paper is a result of the fruitful collaboration of our research team at the Faculty of Computer Science and Engineering - Skopje (Ss. Cyril and Methodius University in Skopje) with the University of Ljubljana, Faculty of Computer and Information Science, lead by prof. Dimitar Trajanov and prof. Marko Robnik-Sikonja.

The main objective of this paper is to survey the recent use of NLP in the field of pharmacology, in order to provide a comprehensive overview of the current state in the area after the rapid developments which occurred in the last few years. We believe the resulting survey to be useful to practitioners and interested observers in the domain.

The team: Dimitar Trajanov, Vangel Trajkovski, Makedonka Dimitrieva, Jovana Dobreva, Milos Jovanovik, Matej Klemen, Aleš Žagar, Marko Robnik-Sikonja.

The paper is available on the Pharmacological Reviews website: https://pharmrev.aspetjournals.org/content/75/4/714

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

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

The main contribution from my PhD Thesis has just been published in the Journal of Biomedical Semantics: "Consolidating Drug Data on a Global Scale Using Linked Data".

Results: We developed a methodology and a set of tools which support the process of generating Linked Data in the drug domain. Using them, we generated the LinkedDrugs dataset by seamlessly transforming, consolidating and publishing high-quality, 5-star Linked Drug Data from twenty-three countries, containing over 248,000 drug products, over 99,000,000 RDF triples and over 278,000 links to generic drugs from the LOD Cloud. Using the linked nature of the dataset, we demonstrate its ability to support advanced usage scenarios in the drug domain.

Conclusions: The process of generating the LinkedDrugs dataset demonstrates the applicability of the methodological guidelines and the supporting tools in transforming drug product data from various, independent and distributed sources, into a comprehensive Linked Drug Data dataset. The presented user-centric and analytical usage scenarios over the dataset show the advantages of having a de-siloed, consolidated and comprehensive dataspace of drug data available via the existing infrastructure of the Web.

Paper: https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0111-z

I'm happy to share that I've successfully defended my Doctorate (PhD), at the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje.

With this, I'm now a Doctor of Technical Sciences, specialty: Computer Science and Engineering.

You can access and download the full thesis text in Macedonian, or the shortened executive summary in English, at the links below.

PhD Thesis (ENG): https://repository.ukim.mk/handle/20.500.12188/253

PhD Thesis (MKD): https://repository.ukim.mk/handle/20.500.12188/17581

This morning I came across a Google+ post from Pierre-Yves Vandenbussche, one of the people behind the Linked Open Vocabularies (LOV) project. In it, Pierre-Yves details the newly added ontology tools on the LOV vocabulary pages: WebVOWL, Oops (Ontology Pitfall Scanner) and Parrot.

WebVOWL

WebVOWL is an online version of the VOWL ontology visualizer. You can add the URI of the ontology you want visualized as a query parameter, in the following way:

http://vowl.visualdataweb.org/webvowl/#iri=http://purl.org/net/po#

Oops (Ontology Pitfall Scanner)

As the name suggests, Oops is a service which checks ontologies for errors, which can be anything from minor suggestions (missing comments) to more serious design issues. The URI of the ontology you want tested can be added as a query string parameter as well:

http://oops.linkeddata.es/response.jsp?uri=http://purl.org/net/po#

Parrot

Parrot is an older, but very useful tool for generating documentation / technical report for an ontology. You can use it in various ways, and one of them is denoting your ontology as a query string parameter, just as the other tools:

http://ontorule-project.eu/parrot/parrot?&documentUri=http://purl.org/net/po#

A couple of weeks ago, the European Commission launched the EU Open Data Hub, currently in early beta. It currently contains 5815 datasets, most of which are from Eurostat, the statistical office of the European Union.

The EU Open Data Hub provides a SPARQL endpoint for access to linked data. It is a standard Virtuoso SPARQL endpoint, which means it can be used both from the browser, or from code, by providing the SPARQL query as query string in the request. An example query, tweeted by Kingsley Uyi Idehen, shows how you can get information about partner institutions in the EU FP7 Programme projects, and group them by country. The result in HTML can be seen here. Notice that the URL contains the SPARQL query and the answer format.

An interesting video by David Siegel, the author of the book "Pull: the Power of the Semantic Web to Transform Your Business", about how information has evolved since the beginning of man.

A new version of the Linking Open Data (LOD) cloud diagram is published. The Linking Open Data community project has a goal to extend the Web with data commons by publishing various open datasets in RDF format on the Web and by setting RDF links between data items from different data sources, i.e interconnecting semantic data from different datasets via dereferenceable URIs. The LOD cloud diagram shows datasets that have been published in Linked Data format by contributors to the Linking Open Data community project, as well as other individuals and organisations. The latest version of the diagram shows 203 datasets, which comprise around 395 million links between over 25 billion RDF statements. The datasets hold semantic data about media, geographical info, publications, government info, life sciences, etc.

Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

After sir Tim Berners-Lee called upon everyone to 'put their data on the Web' in his previous appearance on TED, he's back on TED to show us what has been done with the (semantic) linked data on the Web, so far.

In this TED talk, we can see some interesting applications built upon the linked data provided by the UK and US Governments, which help users to access various information, spanning from the way their money is spent, to the safe bike routes through the city they live in. There are also some other interesting community-driven projects, such as the Open Street Map project, which seems to have helped the rescue teams during the Haitian post-earthquake rescue operations.