Naming and necessity

One of the main challenges that the Semantic Web faces is the integration of a growing number of independently designed ontologies. In this work, we present PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance level cross-fertilize with alignments at the schema level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic, i.e., we measure degrees of matchings based on probability estimates. This allows PARIS to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with some of the world's largest ontologies.

PARIS: Probabilistic Alignment of Relations, Instances, and Schema

HISCO. Historical International Standard Classification of Occupations

Ontologies nowadays have become widely used for knowledge representation, and are considered as foundation for Semantic Web. However with their wide spread usage, a question of their evaluation increased even more. This paper addresses the issue of finding an efficient ontology evaluation method by presenting the existing ontology evaluation techniques, while discussing their advantages and drawbacks. The presented ontology evaluation techniques can be grouped into four categories: gold standard-based, corpus-based, task-based and criteria based approaches.

https://hal.archives-ouvertes.fr/hal-01274199/document

A Survey on Ontology Evaluation Methods

The increasingly pervasive nature of the Web, expanding to devices and things in everyday
life, along with new trends in Artificial Intelligence call for new paradigms and a new look on
Knowledge Representation and Processing at scale for the Semantic Web. The emerging, but still
to be concretely shaped concept of "Knowledge Graphs" provides an excellent unifying metaphor
for this current status of Semantic Web research. More than two decades of Semantic Web
research provides a solid basis and a promising technology and standards stack to interlink data,
ontologies and knowledge on the Web. However, neither are applications for Knowledge Graphs
as such limited to Linked Open Data, nor are instantiations of Knowledge Graphs in enterprises
– while often inspired by – limited to the core Semantic Web stack. This report documents the
program and the outcomes of Dagstuhl Seminar 18371 "Knowledge Graphs: New Directions for
Knowledge Representation on the Semantic Web", where a group of experts from academia and
industry discussed fundamental questions around these topics for a week in early September 2018,
including the following: what are knowledge graphs? Which applications do we see to emerge?
Which open research questions still need be addressed and which technology gaps still need to
be closed?

https://drops.dagstuhl.de/opus/volltexte/2019/10328/pdf/dagrep_v008_i009_p029_18371.pdf/

Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web (Dagstuhl Seminar 18371)

In the absence of a central naming authority on the Semantic Web, it is common for different datasets to refer to the same thing by different IRIs. Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Studies that date back as far as 2009, have observed that the owl:sameAs property is sometimes used incorrectly. In this paper, we show how network metrics such as the community structure of the owl:sameAs graph can be used in order to detect such possibly erroneous statements. One benefit of the here presented approach is that it can be applied to the network of owl:sameAs links itself, and does not rely on any additional knowledge. In order to illustrate its ability to scale, the approach is evaluated on the largest collection of identity links to date, containing over 558M owl:sameAs links scraped from the LOD Cloud.

/pdf/detecting-erroneous-identity-links-on-the-web-using-network-526yfgjuxm.pdf

Detecting erroneous identity links on the web using network metrics

The owl:sameAs predicate is an essential ingredient of the Semantic Web architecture. It allows parties to independently mint names, while at the same time ensuring that these parties are able to understand each other’s data. An online resource that collects all owl:sameAs statements on the Linked Open Data Cloud has therefore both practical impact (it helps data users and providers to find different names for the same entity) as well as analytical value (it reveals important aspects of the connectivity of the LOD Cloud).

/pdf/sameas-cc-the-closure-of-500m-owl-sameas-statements-7mlqttcwjc.pdf

sameAs.cc: The Closure of 500M owl:sameAs Statements

This paper focuses on the knowledge representation task for an interdisciplinary project called Delicious concerning the production and transformation processes in food science The originality of this project is to combine data from different disciplines like food composition, food structure, sensorial perception and nutrition Available data sets are described using different vocabularies and are stored in different formats Therefore there is a need to define an ontology, called \(PO^2\) (Process and Observation Ontology), as a common and standardized vocabulary for this project The scenario 6 of the NeON methodology was used for building \(PO^2\) and the core component is implemented in OWL By making use of \(PO^2\), data from the project were structured and an use case is presented here \(PO^2\) aims to play a key role as the representation layer of the querying and simulation systems of Delicious project

PO^2 - A Process and Observation Ontology in Food Science. Application to Dairy Gels

Most of the Linked Data applications currently rely on the use of owl: sameAs for linking ontology instances. However, several studies have noticed multiple misuses of this identity link. These misuses, which are mainly caused by the lack of other well-defined linking alternatives, can lead to erroneous statements or inconsistencies. We propose in this paper a new contextual identity link: identiConTo that could serve as a replacement for owl: sameAs in linking identical instances in a specified context. To detect these contextual links, we have defined an algorithm named DECIDE that has been tested on scientific knowledge bases describing transformation processes.

/pdf/detection-of-contextual-identity-links-in-a-knowledge-base-2fkxfjoix5.pdf

Joe Raad

Papers

A Survey on Ontology Evaluation Methods

Detecting erroneous identity links on the web using network metrics

sameAs.cc: The Closure of 500M owl:sameAs Statements

PO^2 - A Process and Observation Ontology in Food Science. Application to Dairy Gels

Detection of Contextual Identity Links in a Knowledge Base