Active knowledge graph completion

Knowledge graphs (KGs) proliferating on theWeb are known to be incomplete. Much research has been proposed for automatic com- pletion, sometimes by rule learning, that scales well. All existing methods learn closed rules. Here we introduce open path (OP) rules and present a novel algorithm, oprl, for learning them. While closed rules are used to complete a KG by answering given queries, OP rules identify the incom- pleteness of a KG by inducing such queries to ask. We use adaptations of Freebase, YAGO2, and a synthetic but complete Poker KG to evaluate oprl. We find that oprl mines hundreds of accurate rules from massive KGs with up to 1M facts. The learnt OP rules induce queries with preci- sion up to 98% and recall of 62% on a complete KG, demonstrating the first solution for active knowledge graph completion

Towards SHACL learning from knowledge graphs

Knowledge Graphs (KGs) are typically large data-first knowl- edge bases with weak inference rules and weakly-constraining data schemes. The SHACL Shapes Constraint Language is a W3C recommendation for the expression of shapes as constraints on graph data. SHACL shapes serve to validate a KG and can give informative insight into the structure of data. Here, we introduce Inverse Open Path (IOP) rules, a logical for- malism which acts as a building block for a restricted fragment of SHACL that can be used for schema-driven structural knowledge graph validation and completion. We define quality measures for IOP rules and propose a novel method to learn them, SHACLearner. SHACLearner adapts a state-of-the-art embedding-based open path rule learner (oprl) by modifying the efficient matrix-based evaluation module. We demonstrate SHACLearner performance on real-world massive KGs, YAGO2s (4M facts), DBpedia 3.8 (11M facts), and Wikidata (8M facts), finding that it can efficiently learn hundreds of high-quality rules

Active Knowledge Graph Completion

Knowledge Graphs (KGs) proliferating on the Web are well known to be incomplete. Much research has been proposed for automatic completion, sometimes by rule learning, that is known to scale well. All existing methods learn closed rules. In this paper, we introduce open path (OP) rules and present a novel algorithm, OPRL, for learning OP rules. While CP rules complete a KG by answering given queries, OP rules identify the incompleteness of a KG by generating such queries. For our learning to scale well, we propose a novel, efficient, embedding-based fitness function to estimate the quality of rules. We also introduce a novel, efficient vector computation to formally assess the quality of such rules against a KG. We use adaptations of Freebase, YAGO2, Wikidata, and a synthetic but complete Poker KG to evaluate OPRL. We find that OPRL mines hundreds of accurate rules from massive KGs with up to 8M facts. The learnt OP rules are used to generate queries with precision as high as 98% and recall of 62% on a complete KG, demonstrating the first solution for active knowledge graph completion

J2RM: An ontology-based JSON-to-RDF mapping tool

This manuscript introduces J2RM: a tool to process mappings from JSON data to RDF triples guided by an OWL2 ontology structure. The mappings are defined as annotation properties associated with each ontology entity of interest. They are embedded in an ontology file so that they can be readily deployed and shared to automate RDF-graph creation. In this paper, we motivate the need for such mappings, describe some of their definitions on a use case example, present the formal grammar of the mapping language, and explain how these mappings work. We conclude with a discussion of the key aspects, main contributions, and future improvements

On2ts - Typescript generation from OWL ontologies and SHACL

Ontologies expressed in OWL and their associated SHACL [4] constraints contain detailed metadata and assumptions on Knowledge Graphs (KGs). With this information comes the possibility of developing front-end applications that interact with these KGs. These opportunities are often unrealised as Web developers are required to interpret OWL and SHACL statements in order to write front-end code that conforms with the back-end data model. This paper introduces on2ts, a developer tool that automatically converts OWL definitions and SHACL constraints into Typescript [1] classes and interfaces. This enables developers to import these definitions directly into their application. The authors have developed this tool with the goal of reducing development time of linked-data applications, improving type-safety of linked-data applications and providing an appropriate level of abstraction for front-end development environments

Correcting Knowledge Base Assertions

The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB