Knowledge Management and Semantic Reasoning: Ontology and Information Theory Enable the Construction of Knowledge Bases and Knowledge Graphs

FAIR (Findable, Accessible, Interoperable, Reusable) principles are guidelines Wilkinson, et. al. (2016) proposed for data governance and stewardship. Ontology is a powerful tool that can achieve many aspects of all four FAIR principles. Unfortunately, there is a misconception about ontology that it is only useful for establishing FAIR data. We need to think beyond data to answer the question “So what?” after an ontology is developed. It is critical to apply FAIR principles to results, analysis, and models, which is where the concept of digital thread comes in. FAIRified results, analysis, and models can be stored in a knowledge base and represented in a knowledge graph (KG), a flexible and extensible representation of knowledge, capable of inductive and deductive reasoning via the inherent structure that allows semantic reasoning, as well as the semantics applied by an ontology as the underlying schema layer. This versatile data structure can also be combined with principles of information theory that can refine the patterns and relationships by minimizing the uncertainties and randomness of the data. In essence, we supply a KG with a knowledge base and a semantic reasoning engine to infer new patterns and relationships as new knowledge, which can be imported back into the knowledge base

Mining Scholarly Publications for Scientific Knowledge Graph Construction

In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, and iii) analyse an automatically generated Knowledge Graph including 10,425 entities and 25,655 relationships in the field of Semantic Web

Exploring the Historical Context of Graphic Symbols: the NOTAE Knowledge Graph and its Visual Interface

Graphic symbols i.e. graphic entities drawn as a visual unit in a written text and representing something other or something more than a word of that text are the research object of the NOTAE project, which investigates them in the documentary practice of the late Roman State and Post-Roman Kingdoms (400-800 AD). While research results from the project are stored by filling forms resulting from the analysis of ancient documents, we argue that the availability of a navigable knowledge graph can ease the work of researchers at finding non trivial implications in data. In this paper, we propose a first version of the NOTAE Knowledge Graph, and we outline future works and possible synergies

Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space

The trends of open science have enabled several open scholarly datasets which include millions of papers and authors. Managing, exploring, and utilizing such large and complicated datasets effectively are challenging. In recent years, the knowledge graph has emerged as a universal data format for representing knowledge about heterogeneous entities and their relationships. The knowledge graph can be modeled by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between these embedding vectors. However, the semantic structures in the knowledge graph embedding space are not well-studied, thus knowledge graph embedding methods are usually only used for knowledge graph completion but not data representation and analysis. In this paper, we propose to analyze these semantic structures based on the well-studied word embedding space and use them to support data exploration. We also define the semantic queries, which are algebraic operations between the embedding vectors in the knowledge graph embedding space, to solve queries such as similarity and analogy between the entities on the original datasets. We then design a general framework for data exploration by semantic queries and discuss the solution to some traditional scholarly data exploration tasks. We also propose some new interesting tasks that can be solved based on the uncanny semantic structures of the embedding space.Comment: TPDL 2019; add appendix for the KG20C scholarly knowledge graph benchmark datase

Understanding information resources for college student mental health: A knowledge graph approach

Many universities and colleges have not provided well-organized and easy to use mental health related information resources to their students although mental illness has become a significant barrier to college student success. This study aims to understand the information resources important to college student mental health (CSMH). We conducted a content analysis of two CSMH websites as the first step to build a knowledge graph for CSMH. Two site maps are developed based on the analysis. Seven types of information are therefore identified and considered important for colleges to provide to their students: Appointment, Mental Disorders, Self-help Resources, Information for Parents, Local Referral Sources, Substance Abuse Prevention, and University Policies on Mental Disorders. The next step of this study is to develop ontology by verifying the seven types of information and establishing their relationships. More CSMH websites will be examined to achieve reliable results