The Green AI Ontology: An Ontology for Modeling the Energy Consumption of AI Models

Modeling AI systems’ characteristics of energy consumption and their sustainability level as an extension of the FAIR data principles has been considered only rudimentarily. In this paper, we propose the Green AI Ontology for modeling the energy consumption and other environmental aspects of AI models. We evaluate our ontology based on competency questions. Our ontology is available at https://w3id.org/ Green-AI-Ontology and can be used in a variety of scenarios, ranging from comprehensive research data management to strategic controlling of institutions and environmental efforts in politics

The data set knowledge graph: Creating a linked open data source for data sets

Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. However, although the number of data sets has increased remarkably in recent years, these knowledge graphs do not primarily focus on data sets but rather on associated entities such as publications. Moreover, publicly available data set knowledge graphs do not systematically contain links to the publications in which the data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these mentioned criteria. Our data set knowledge graph, DSKG, is publicly available at http://dskg.org and contains metadata of data sets for all scientific disciplines. To ensure high data quality of the DSKG, we first identify suitable raw data set collections for creating the DSKG. We then establish links between the data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets. As the author names of data sets can be ambiguous, we develop and evaluate a method for author name disambiguation and enrich the knowledge graph with links to ORCID. Overall, our knowledge graph contains more than 2,000 data sets with associated properties, as well as 814,000 links to 635,000 scientific publications. It can be used for a variety of scenarios, facilitating advanced data set search systems and new ways of measuring and awarding the provisioning of data sets

A Blocking-Based Approach to Enhance Large-Scale Reference Linking

Analyses and applications based on bibliographic references are of ever increasing importance. However, reference linking methods described in the literature are only able to link around half of the references in papers. To improve the quality of reference linking in large scholarly data sets, we propose a blocking-based reference linking approach that utilizes a rich set of reference fields (title, author, journal, year, etc.) and is independent of a target collection of paper records to be linked to. We evaluate our approach on a corpus of 300,000 references. Relative to the original data, we achieve a 90% increase in papers linked through references, a five-fold increase in bibliographic coupling, and a nine-fold increase in in-text citations covered. The newly established links are of high quality (85% F1)

Recommending Datasets for Scientific Problem Descriptions

The steadily rising number of datasets is making it increasingly difficult for researchers and practitioners to be aware of all datasets, particularly of the most relevant datasets for a given research problem. To this end, dataset search engines have been proposed. However, they are based on user\u27s keywords and, thus, have difficulty determining precisely fitting datasets for complex research problems. In this paper, we propose a system that recommends suitable datasets based on a given research problem description. The recommendation task is designed as a domain-specific text classification task. As shown in a comprehensive offline evaluation using various state-of-the-art models, as well as 88,000 paper abstracts and 265,000 citation contexts as research problem descriptions, we obtain an F1-score of 0.75. In an additional user study, we show that users in real-world settings are 88% satisfied in all test cases. We therefore see promising future directions for dataset recommendation

Explaining Convolutional Neural Networks by Tagging Filters

Convolutional neural networks (CNNs) have achieved astonishing performance on various image classification tasks, but it is difficult for humans to understand how a classification comes about. Recent literature proposes methods to explain the classification process to humans. These focus mostly on visualizing feature maps and filter weights, which are not very intuitive for non-experts. In this paper, we propose FilTag, an approach to effectively explain CNNs even to non-experts. The idea is that if images of a class frequently activate a convolutional filter, that filter will be tagged with that class. Based on the tagging, individual image classifications can then be intuitively explained using the tags of the filters that the input image activates. Finally, we show that the tags are useful in analyzing classification errors caused by noisy input images and that the tags can be further processed by machines

Which Publications’ Metadata Are in Which Bibliographic Databases? A System for Exploration

The choice of databases containing publications’ metadata (i.e., bibliographic databases) determines the available publication list of any author and, thus, their public appearance and evaluation. Having all publications listed in the various bibliographic databases is therefore important for researchers. However, the average number of publications a researcher publishes per year is steadily rising, making it labor-intensive and time-consuming for authors to investigate whether all their publications are given in all bibliographic databases online. In this paper, we present RefBee, an online system that retrieves the metadata of all publications for a given author from the various bibliographic databases and indicates which publications are missing in which database. Our system is available online at http://refbee.org/ and supports Wikidata, ORCID, Google Scholar, VIAF, DBLP, Dimensions, Microsoft Academic, Semantic Scholar, and DNB/GNB. Our system not only can serve as assistance tool for more than 4.7 million researchers of any discipline and publication’s language, but also incentivizes the usage and population of Wikidata in the scholarly field