Maintaining with recent research is becoming increasingly difficult as a result of the rise of scientific publications. As an illustration, greater than 8 million scientific articles were recorded in 2022 alone. Researchers use various techniques, from search interfaces to suggestion systems, to research connected mental entities, comparable to authors and institutions. Modeling the underlying academic data as an RDF knowledge graph (KG) is one efficient method. This makes standardization, visualization, and interlinking with Linked Data resources easier. In consequence, scholarly KGs are essential for converting document-centric academic material into linked and automatable knowledge structures. 

Nevertheless, a number of of the next are limitations of the present academic KGs:

1. They seldom include a comprehensive list of works from every subject.
2. They incessantly solely cover particular fields, like computer science.
3. They get updated infrequently, making a variety of studies and business models outdated.
4. They often have use limitations.
5. They don’t comply with W3C standards like RDF, even in the event that they meet these criteria.

These problems prevent the widespread deployment of scientific KGs, comparable to in thorough search and recommender systems or for quantifying scientific impact. As an illustration, the Microsoft Academic Knowledge Graph (MAKG), its RDF descendant, can’t be updated since the Microsoft Academic Graph was terminated in 2021. 

The progressive OpenAlex dataset seeks to shut this gap. OpenAlex’s data, nonetheless, doesn’t adhere to the Linked Data Principles and shouldn’t be accessible in RDF. In consequence, OpenAlex can’t be considered a KG, making semantic inquiries, application integration, and connecting to latest resources difficult. At first appearance, it could appear to be a simple strategy to include academic details about scientific articles into Wikidata, and so support the WikiCite movement. Aside from the precise schema, the quantity of information is already so vast that the Wikidata Query Service’s Blazegraph triplestore approaches its capability limit, blocking any integration. 

SemOpenAlex, a really sizable RDF dataset of the educational landscape with its publications, authors, sources, institutions, ideas, and publishers, is introduced by researchers from Karlsruhe Institute of Technology and Metaphacts GmbH on this work. SemOpenAlex has about 249 million papers from all academic areas and greater than 26 billion semantic triples. It’s built on their comprehensive ontology and references additional LOD sources, including Wikidata, Wikipedia, and the MAKG. They provide a public SPARQL interface to facilitate quick and effective usage of SemOpenAlex’s integration with the LOD cloud. Moreover, they supply a complicated semantic search interface that allows users to retrieve information in real-time about entities contained within the database and their semantic relationships (for instance, by displaying co-authors or an writer’s most significant concepts, that are inferred through semantic reasoning fairly than being directly contained within the database). 

Additionally they offer the entire RDF data snapshots to facilitate large data evaluation. They’ve created a pipeline utilizing AWS for routinely updating SemOpenAlex completely with none service disruptions as a result of the dimensions of SemOpenAlex and the growing variety of scientific articles being integrated into SemOpenAlex. Moreover, they trained cutting-edge knowledge graph entity embeddings for usage with SemOpenAlex in downstream applications. They guarantee system interoperability according to FAIR principles by employing pre-existing ontologies each time possible, and so they open the door for integrating SemOpenAlex into the Linked Open Data Cloud. By offering monthly updates that enable continuing monitoring of an writer’s scientific impact, tracking of award-winning research, and other use cases employing their data, they fill the void left by the termination of MAKG. They allow research groups from many disciplinary backgrounds to access the information it provides and incorporate it into their studies by making SemOpenAlex free and unconstrained. Initial SemOpenAlex application cases and production systems currently exist. 

Overall, they contribute the next: 

1. They use popular vocabulary to develop an ontology for SemOpenAlex. 

2. At https://semopenalex.org, they produce the SemOpenAlex knowledge graph in RDF, which covers 26 billion triples, and make all SemOpenAlex data, code, and services available to the general public.

3. They allow SemOpenAlex to take part in the Linked Open Data cloud by making all its URIs resolvable. Using a SPARQL endpoint, they index all the information in a triple store and make it accessible to most of the people. 

4. They provide a semantic search interface with entity disambiguation in order that users may access, search, and immediately view the knowledge graph and its essential statistical data. 

5. Using high-performance computation, they provide cutting-edge knowledge graph embeddings for the entities represented in SemOpenAlex. 

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