User driven information extraction with LODIE

Information Extraction (IE) is the technique for transforming unstructured or semi-structured data into structured representation that can be understood by machines. In this paper we use a user-driven Information Extraction technique to wrap entity-centric Web pages. The user can select concepts and properties of interest from available Linked Data. Given a number of websites containing pages about the concepts of interest, the method will exploit (i) recurrent structures in the Web pages and (ii) available knowledge in Linked data to extract the information of interest from the Web pages

Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval

Tables contain valuable knowledge in a structured form. We employ neural language modeling approaches to embed tabular data into vector spaces. Specifically, we consider different table elements, such caption, column headings, and cells, for training word and entity embeddings. These embeddings are then utilized in three particular table-related tasks, row population, column population, and table retrieval, by incorporating them into existing retrieval models as additional semantic similarity signals. Evaluation results show that table embeddings can significantly improve upon the performance of state-of-the-art baselines.Comment: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '19), 201

WibNED: Wikipedia based Named Entity Disambiguation

Natural Language is a mean to express and discuss concepts, which are taken to be abstractions from perceptions of the experienced real world: what texts describe consist of objects and events. Objects of the real world are identified by proper names, which are words, thus raising the problem of proper linkage between the textual reference and the real object. This work addresses the problem of automatically association of meanings to words within an unstructured text and focuses the attention on words representing Named Entities. The proposed solution consists of a Knowledge based algorithm for Named Entity Disambiguation: we used an ad hoc built corpus, extracted form Wikipedia’s articles to prove the soundness of the algorithm