We present our ongoing work on upgrading the Amsterdam Public Library's book database search capabilities. So far, users have had to input the exact book title and/or author name without any typos or misspellings in order to retrieve any results. This is in sharp contrast with the manner in which users typically use the interface: they frequently search for books on a particular topic, input the names of the characters, or even ask fully-fledged questions. The aim of this project is therefore to enable smart search in natural language based on book content. The initial focus is on the Dutch language, with the possibility of including English and other languages later. In the first phase of the project, we built a proof-of-concept knowledge graph from a sample of the existing tabular database and enriched the data with named entities extracted from book summaries. Based on this first step, a user query like "Heeft u boeken over de Tweede Wereldoorlog in Amsterdam?" would yield all books that mention both WW2 and Amsterdam. We are currently working on augmenting the knowledge graph with embeddings, which will enable us to retrieve semantically similar results. The final step of the research involves integrating our knowledge graph with a pre-trained large language model.
A considerable amount of literature has been published on Corporate Reputation, Branding and Brand Image. These studies are extensive and focus particularly on questionnaires and statistical analysis. Although extensive research has been carried out, no single study was found which attempted to predict corporate reputation performance based on data collected from media sources. To perform this task, a biLSTM Neural Network extended with attention mechanism was utilized. The advantages of this architecture are that it obtains excellent performance for NLP tasks. The state-of-the-art designed model achieves highly competitive results, F1 scores around 72%, accuracy of 92% and loss around 20%.
