A systematic literature review on Wikidata

To review the current status of research on Wikidata and, in particular, of articles that either describe applications of Wikidata or provide empirical evidence, in order to uncover the topics of interest, the fields that are benefiting from its applications and which researchers and institutions are leading the work

ARL White Paper on Wikidata: Opportunities and Recommendations

In this Association of Research Libraries white paper, a task force of expert Wikidata users recommend a variety of ways for librarians to use the open knowledge base in advancing global discovery of their collections, faculty, and institutions. Beyond the task force, many library professionals from within and outside the Wikimedia community contributed to the white paper in draft form, offering a productive mix of enthusiasm and skepticism that improved the final product. ARL convened the task force and wrote this white paper to inform its membership about GLAM (galleries, libraries, archives, and museums) activity in Wikidata and to highlight opportunities for research library involvement, particularly in community-based collections, community-owned infrastructure, and collective collections

ARL White Paper on Wikidata: Opportunities and Recommendations

This white paper highlights opportunities for research library involvement in Wikidata, particularly in community-based collections, community-owned infrastructure, and collective collections

Assessing the quality of Wikidata referencing

Wikidata is a versatile and broad-based Knowledge Graph (KG) that leverages the power of collaborative contributions via an open wiki, augmented by bot accounts, to curate the content. Wikidata represents over 102 million interlinked data entities, accompanied by over 1.4 billion statements about the items, accessible to the public via a SPARQL endpoint and diverse dump formats. The Wikidata data model enables assigning references to every single statement. While the quality of Wikidata statements has been assessed, the quality of references in this knowledge graph is not well covered in the literature. To cover the gap, we develop and implement a comprehensive referencing quality assessment framework based on Linked Data quality dimensions and criteria. We implement the objective metrics of the assessment framework as the Referencing Quality Scoring System - RQSS. RQSS provides quantified scores by which the referencing quality can be analyzed and compared. Due to the scale of Wikidata, we developed a subsetting approach to creating a comparison platform that systematically samples Wikidata. We have used both well-defined subsets and random samples to evaluate the quality of references in Wikidata using RQSS. Based on RQSS, the overall referencing quality in Wikidata subsets is 0.58 out of 1. Random subsets (representative of Wikidata) have higher overall scores than topical subsets by 0.05, with Gene Wiki having the highest scores amongst topical subsets. Regarding referencing quality dimensions, all subsets have high scores in accuracy, availability, security, and understandability, but have weaker scores in completeness, verifiability, objectivity, and versatility. RQSS scripts can be reused to monitor the referencing quality over time. The evaluation shows that RQSS is practical and provides valuable information, which can be used by Wikidata contributors and WikiProject owners to identify the referencing quality gaps. Although RQSS is developed based on the Wikidata RDF model, its referencing quality assessment framework can be generalized to any RDF KG.James Watt Scholarship fundin

MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities

In this paper, we introduce the MLM (Multiple Languages and Modalities) dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. A second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalising on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding

ARTIFICIAL INTELLIGENCE THE USE OF KNOWLEDGE GRAPHS

In this digital age, an overwhelming amount of unstructured data found in the web is increasing at an unprecedented rate. In response, information extraction techniques have been developed to automatically extract information from unstructured text and populate knowledge bases. This thesis will introduce the reader to the idea of Knowledge Graphs, the history of how they were first experimented on and later developed, and in particular, this thesis is going to elaborate how Diffbot, Wikidata and IBM Watson technologies are modeled, stored and queried. By having this information at our fingertips, we believe that we can make smarter decisions, keep up with business trends, and even find ways to further improve our overall operations regarding data analysis. Different businesses demand different types of Business Intelligence Software. To understand well which one suits us, through this study we have evaluated features of Diffbot, Wikidata and IBM Watson by taking into consideration their conditions, features and costs

Knowledge Organization Systems (KOS) in the Semantic Web: A Multi-Dimensional Review

Since the Simple Knowledge Organization System (SKOS) specification and its SKOS eXtension for Labels (SKOS-XL) became formal W3C recommendations in 2009 a significant number of conventional knowledge organization systems (KOS) (including thesauri, classification schemes, name authorities, and lists of codes and terms, produced before the arrival of the ontology-wave) have made their journeys to join the Semantic Web mainstream. This paper uses "LOD KOS" as an umbrella term to refer to all of the value vocabularies and lightweight ontologies within the Semantic Web framework. The paper provides an overview of what the LOD KOS movement has brought to various communities and users. These are not limited to the colonies of the value vocabulary constructors and providers, nor the catalogers and indexers who have a long history of applying the vocabularies to their products. The LOD dataset producers and LOD service providers, the information architects and interface designers, and researchers in sciences and humanities, are also direct beneficiaries of LOD KOS. The paper examines a set of the collected cases (experimental or in real applications) and aims to find the usages of LOD KOS in order to share the practices and ideas among communities and users. Through the viewpoints of a number of different user groups, the functions of LOD KOS are examined from multiple dimensions. This paper focuses on the LOD dataset producers, vocabulary producers, and researchers (as end-users of KOS).Comment: 31 pages, 12 figures, accepted paper in International Journal on Digital Librarie