Keynote Lectures

Abstract
The Web contains vast amounts of structured data in the form of HTML tables, schema.org annotations, as well as datasets accessible via data repositories. The automated integration of data from larger numbers of Web data sources is a long-standing research challenge as the integration requires dealing with several tricky tasks such as schema matching, entity matching, and data indexing for retrieval. Most state-of-the-art methods for these tasks rely on variants of the BERT transformer model fine-tuned using significant amounts of task-specific training data. In the talk, Christian Bizer will critically review BERT-based data integration methods and question their robustness concerning out-of-distribution entities. He will compare the performance of BERT-based methods with results of GPT-4-based data integration methods and will argue that GPT-4-based methods are more training data efficient and more robust concerning unseen entities.

Abstract
The area of Recommendation Systems has matured after intensive theoretical studies by researchers and practical applications by large e-commerce companies. On the other hand, Scientometrics has become an independent field, focusing in the study of laws and statistics related to scholarly publications. Nowadays, the publishing industry has accumulated big bibliographic data. Thus, the need to provide recommendations when searching in the abundance of bibliographic data has arised.
• Journal recommenders are important tools for researchers as many journals belonging to different publishers have emerged.
• Conference recommenders are useful towards avoiding predatory ones.
• Citation recommenders play an important role to alleviate the dilemma that researchers spend a lot of time and experiences for literature survey.
• Reviewer recommenders for scientific research proposals are helpful tools for funding agencies.
• Article recommendation to best fit reviewers is crucial to achieve constructive reviews towards a strong conference program.
• Collaborator Recommenders learn from researchers’ publications and advice about persons which can give research directions.

These are some fundamental research questions in the intersection area between Recommendation Systems and Scientometrics. In this talk, key approaches for each question will be presented, discussed and compared.

Abstract
We live in the age of data, where everything that surrounds us is linked to a data source and many aspects of our lives are being more and more digitalized. Under the lens of data heterogeneity, it can be argued that the fervent search for the meaning of data constitutes the main challenge of data integration. Today, myriads of data are collected at every moment by various types of sensors and the trend is increasing with the availability of smaller, cheaper and more efficient sensors that are able to sense almost everything. Let’s think about this simple example that everyone is doing very often: what is the source of the data we read on the thermometer when we are taking the temperature of our body? The thermometer itself as many people reply to this question? But the thermometer itself is not able to create any heat or increase the temperature of our body. It is rather a phenomenon in our body that creates heat and increases its temperature which, in its turn, is captured by the thermometer if we apply it correctly and at the right point of our body. The above reflection can be done with any type of sensor that is used to capture the value of something we want to measure: temperature, vibration, acceleration, movement, etc. The real source of the captured data is not the sensor itself but a particular phenomenon of the object where the sensor is embedded on. The function of the sensor is to capture the value of a parameter allowing to assess the behavior of the observed phenomenon. The aforementioned example illustrates the significance of identifying the meaning of data. As a solution to this challenge, a semantic data model tries to explicitly define what are things like “temperature”, “heat”, “fever”, “thermometer,” “Celsius degrees” and so forth. In addition to the above, it has to be noted that the collected data are always interpreted within the boundaries of a well-defined “context”. In this keynote talk it will be shown how the above concepts are used in emerging I4.0 applications using semantic technologies and the so called Cognitive Digital Twins in a number of EU R&I projects.