Search results for "knowledge graph"
showing 6 items of 6 documents
Structured query construction via knowledge graph embedding
2020
In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our…
Measuring Semantic Coherence of a Conversation
2018
Conversational systems have become increasingly popular as a way for humans to interact with computers. To be able to provide intelligent responses, conversational systems must correctly model the structure and semantics of a conversation. We introduce the task of measuring semantic (in)coherence in a conversation with respect to background knowledge, which relies on the identification of semantic relations between concepts introduced during a conversation. We propose and evaluate graph-based and machine learning-based approaches for measuring semantic coherence using knowledge graphs, their vector space embeddings and word embedding models, as sources of background knowledge. We demonstrat…
A First Experiment on Including Text Literals in KGloVe
2018
Graph embedding models produce embedding vectors for entities and relations in Knowledge Graphs, often without taking literal properties into account. We show an initial idea based on the combination of global graph structure with additional information provided by textual information in properties. Our initial experiment shows that this approach might be useful, but does not clearly outperform earlier approaches when evaluated on machine learning tasks.
Conceptual Characterization of Cybersecurity Ontologies
2020
Part 7:Risk and Security Modeling; International audience; Cybersecurity is known as the practice of protecting systems from digital attacks. Organizations are seeking efficient solutions for the management and protection of their assets. It is a complex issue, especially for great enterprises, because it requires an interdisciplinary approach. The kinds of problems enterprises must deal with and this domain complexity induces misinterpretations and misunderstandings about the concepts and relations in question. This article focus on dealing with Cybersecurity from an ontological perspective. The first contribution is a search of previously existing works that have defined Cybersecurity Ont…
Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases
2018
Industry is evolving towards Industry 4.0, which holds the promise of increased exibility in manufacturing, better quality and improved productivity. A core actor of this growth is using sensors, which must capture data that can used in unforeseen ways to achieve a performance not achievable without them. However, the complexity of this improved setting is much greater than what is currently used in practice. Hence, it is imperative that the management cannot only be performed by human labor force, but part of that will be done by automated algorithms instead. A natural way to represent the data generated by this large amount of sensors, which are not acting measuring independent variables,…
Understanding the Study Experiences of Students in Low Agency Profile: Towards a Smart Education Approach
2020
In this paper, we use student agency analytics to examine how university students who assessed to have low agency resources describe their study experiences. Students ( n=292 ) completed the Agency of University Students (AUS) questionnaire. Furthermore, they reported what kinds of restrictions they experienced during the university course they attended. Four different agency profiles were identified using robust clustering. We then conducted a thematic analysis of the open-ended answers of students who assessed to have low agency resources. Issues relating to competence beliefs, self-efficacy, student-teacher relations, time as a resource, student well-being, and course contents seemed to …