Measuring the Influence of Expanded Knowledge Graphs on Machine Learning

Frederick Giasson

Mike Bergman and I will release a new version 1.40 of the KBpedia Knowledge Graph in the coming month. This new version of the knowledge graph will include roughly 15,000 new concepts and 150,000 new alternative labels and 5,000 new definitions for existing KBpedia reference concepts. This new release will substantially increase the size of the current KBpedia Knowledge Graph.

This extension is based on a new methodology that we began to cover in the Extending KBpedia With Wikipedia Categories Cognonto use case. The extension uses graph embeddings for each KBpedia reference concept and its linkage to the Wikipedia category structure to pre-select the Wikipedia categories that are most likely to be good candidates to fill [current gaps] in the KBpedia graph structure. The new reference concept candidates scored through this automated process were then reviewed for likely selection. These selections were then analyzed by re-generating the KBpedia Knowledge Graph, which includes routines for identifying, reporting and fixing consistency and coherency issues using the KBpedia Generator. Problematic assignments are either dropped or fixed. These steps reflect the general process Cognonto follows in mapping and incorporating new schema and ontologies.

In the coming month or two, I will write a series of blog posts that will analyze the impact of different important versions of KBpedia on different machine learning models that we have previously created for the Cognonto use cases. All of the current use cases have been created using version 1.20 of KBpedia. We are about to finalize the creation of an intermediate version 1.30 (for internal analysis only). We are separately identifying thousands of reference concepts that will be temporarily removed, since they are more properly characterized as ‘aspects‘ and not true sub-classes. This removal will allow us to then define a third variant for machine learning comparisons. Some of these ‘aspects’ will be re-introduced into the graph where proper parent-child relationships can be established. The next public release of KBpedia, tentatively identified as The version 1.40, will include all of these updates.

Each of these three variants (versions 1.20, 1.30 and 1.40) will enable us to analyze and report on the influence that different version of the KBpedia knowledge graph can have on different machine learning tasks. The following tasks will be covered:

  1. Creating graph embeddings to disambiguate tagged concepts
  2. Creating domain specific training corpuses to train word embeddings
  3. Creating domain specific training sets to classify text, and
  4. Checking relatedness between Knowledge Graph concepts and Wikipedia categories based on their graph embeddings.

Our goal at Cognonto is to make available the power of knowledge-based artificial intelligence (KBAI) to any organization. Whether if it is for help populating search or tagging indexes, for performing semantic query expansion, or for help with a broad series of machine learning tasks, knowledge graphs plus KBAI provide a nearly automated way for doing so. Our research and expertise is geared toward creating, linking, extending, and leveraging knowledge graphs and knowledge bases to empower new and existing systems. We will continue to report in specific detail how and with what impact knowledge graphs and knowledge bases lead to better machine learning results.

Extended KBpedia With Wikipedia Categories

Frederick Giasson

A knowledge graph is an ever evolving structure. It needs to be extended to be able to cope with new kinds of knowledge; it needs to be fixed and improved in all kinds of different ways. It also needs to be linked to other sources of data and to other knowledge representations such as schemas, ontologies and vocabularies. One of the core tasks related to knowledge graphs is to extend its scope. This idea seems simple enough, but how can we extend a general knowledge graph that has nearly 40,000 concepts with potentially multiple thousands more? How can we do this while keeping it consistent, coherent and meaningful? How can we do this without spending undue effort on such a task? These are the questions we will try to answer with the methods we cover in this article.

The methods we are presenting in this article are how we can extend Cognonto‘s KBpedia Knowledge Graph using an external source of knowledge, one which has a completely different structure than KBpedia and one which has been built completely differently with a different purpose in mind than KBpedia. In this use case, this external resource is the Wikipedia categories structure. What we will show in this article is how we may automatically select the right Wikipedia categories that could lead to new KBpedia concepts. These selections are made using a SVM classifier trained over graph embedding vectors generated by a DeepWalk model based on the KBpedia Knowledge Graph structure linked to the Wikipedia categories. Once appropriate candidate categories are selected using this model, the results are then inspected by a human to take the final selection decisions. This semi-automated process takes 5% of the time it would normally take to conduct this task by comparable manual means.

Continue reading “Extended KBpedia With Wikipedia Categories” →

Building and Maintaining the KBpedia Knowledge Graph

Frederick Giasson

The Cognonto demo is powered by an extensive knowledge graph called the KBpedia Knowledge Graph, as organized according to the KBpedia Knowledge Ontology (KKO). KBpedia is used for all kinds of tasks, some of which are demonstrated by the Cognonto use cases. KBpedia powers dataset linkage and mapping tools, machine learning training workflows, entity and concept extractions, category and topic tagging, etc.

The KBpedia Knowledge Graph is a structure of more than 39,000 reference concepts linked to 6 major knowledge bases and 20 popular ontologies in use across the Web. Unlike other knowledge graphs that analyze big corpuses of text to extract “concepts” (n-grams) and their co-occurrences, KBpedia has been created, is curated, is linked, and evolves using humans for the final vetting steps. KBpedia and its build process is thus a semi-automatic system.

The challenge with such a project is to be able to grow and refine (add or remove relations) within the structure without creating unknown conceptual issues. The sheer combinatorial scope of KBpedia means it is not possible for a human to fully understand the impact of adding or removing a relation on its entire structure. There is simply too much complexity in the interaction amongst the reference concepts (and their different kinds of relations) within the KBpedia Knowledge Graph.

What I discuss in this article is how Cognonto creates and then constantly evolves the KBpedia Knowledge Graph. In parallel with our creating KBpedia over the years, we also have needed to develop our own build processes and tools to make sure that every time something changes in KBpedia’s structure that it remains satisfiable and coherent.

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Winnipeg City’s NOW [Data] Portal

Frederick Giasson

The Winnipeg City’s NOW (Neighbourhoods Of Winnipeg) Portal is an initiative to create a complete neighbourhood web portal for its citizens. At the core of the project we have a set of about 47 fully linked, integrated and structured datasets of things of interests to Winnipegers. The focal point of the portal is Winnipeg’s 236 neighbourhoods, which define the main structure of the portal. The portal has six main sections: topics of interests, maps, history, census, images and economic development. The portal is meant to be used by citizens to find things of interest in their neibourhood, to learn their history, to see the images of the things of interest, to find tools to help economic development, etc.

The NOW portal is not new; Structured Dynamics was also its main technical contractor for its first release in 2013. However we just finished to help Winnipeg City’s NOW team to migrate their older NOW portal from OSF 1.x to OSF 3.x and from Drupal 6 to Drupal 7; we also trained them on the new system. Major improvements accompany this upgrade, but the user interface design is essentially the same.

The first thing I will do is to introduce each major section of the portal and I will explain the main features of each. Then I will discuss the new improvements of the portal.

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New UMBEL 1.50 Ships With 20 Linked Ontologies

Frederick Giasson

I am proud to announce the immediate release of UMBEL version 1.50. This is a major effort that took a year to release.

What is UMBEL?

Let’s start by explaining what is UMBEL for the ones that never encountered this project before. UMBEL stands for “Upper Mapping and Binding Exchange Layer“. It is a conceptual structure that is designed to help content interoperate between systems.

UMBEL is a coherent general structure of 34 000 reference concepts which provides a scaffolding to link and interoperate other datasets and domain vocabularies. The conceptual structure is organized in a structure of 31 mostly disjoint SuperType.

UMBEL is written in OWL 2 and SKOS.

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