María G. Buey

Nacira Abbas, Kholoud Alghamdi, Mortaza Alinam et al.

One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is that of a: "Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of entities. [...] This grand challenge extends this further by asking if we can create a knowledge graph of "everything" ranging from common sense concepts to location based entities. This knowledge graph should be "open to the public" in a FAIR manner democratizing this mass amount of knowledge." Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides a unique testbed for experimenting and evaluating research hypotheses on open and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing evolution and long term preservation. We want to investigate this problem, that is to understand what preserving and supporting the evolution of KGs means and how these problems can be addressed. Clearly, the problem can be approached from different perspectives and may require the development of different approaches, including new theories, ontologies, metrics, strategies, procedures, etc. This document reports a collaborative effort performed by 9 teams of students, each guided by a senior researcher as their mentor, attending the International Semantic Web Research School (ISWS 2019). Each team provides a different perspective to the problem of knowledge graph evolution substantiated by a set of research questions as the main subject of their investigation. In addition, they provide their working definition for KG preservation and evolution.

María G. Buey, Carlos Bobed, Jorge Gracia et al.

Understanding the meaning of words is crucial for many tasks that involve human-machine interaction. This has been tackled by research in Word Sense Disambiguation (WSD) in the Natural Language Processing (NLP) field. Recently, WSD and many other NLP tasks have taken advantage of embeddings-based representation of words, sentences, and documents. However, when it comes to WSD, most embeddings models suffer from ambiguity as they do not capture the different possible meanings of the words. Even when they do, the list of possible meanings for a word (sense inventory) has to be known in advance at training time to be included in the embeddings space. Unfortunately, there are situations in which such a sense inventory is not known in advance (e.g., an ontology selected at run-time), or it evolves with time and its status diverges from the one at training time. This hampers the use of embeddings models for WSD. Furthermore, traditional WSD techniques do not perform well in situations in which the available linguistic information is very scarce, such as the case of keyword-based queries. In this paper, we propose an approach to keyword disambiguation which grounds on a semantic relatedness between words and senses provided by an external inventory (ontology) that is not known at training time. Building on previous works, we present a semantic relatedness measure that uses word embeddings, and explore different disambiguation algorithms to also exploit both word and sentence representations. Experimental results show that this approach achieves results comparable with the state of the art when applied for WSD, without training for a particular domain.