Collaborating Authors

Wembedder: Wikidata entity embedding web service Machine Learning

I present a web service for querying an embedding of entities in the Wikidata knowledge graph. The embedding is trained on the Wikidata dump using Gensim's Word2Vec implementation and a simple graph walk. A REST API is implemented. Together with the Wikidata API the web service exposes a multilingual resource for over 600'000 Wikidata items and properties.


AAAI Conferences

Littar was the second-prize winning entry in an app competition. It implemented a system for visualizing places mentioned in individual literary works. Wikidata acted as the backend for the system. Here I describe the Littar system and also some of the issues I encountered while developing the system: How locations and literature can be related, what types of location-literature relations are possible within Wikidata, what limitations there are and what questions we may ask once we have enough data in Wikidata.

Admissible and Restrained Revision

Journal of Artificial Intelligence Research

As partial justification of their framework for iterated belief revision Darwiche and Pearl convincingly argued against Boutilier's natural revision and provided a prototypical revision operator that fits into their scheme. We show that the Darwiche-Pearl arguments lead naturally to the acceptance of a smaller class of operators which we refer to as admissible. Admissible revision ensures that the penultimate input is not ignored completely, thereby eliminating natural revision, but includes the Darwiche-Pearl operator, Nayak's lexicographic revision operator, and a newly introduced operator called restrained revision. We demonstrate that restrained revision is the most conservative of admissible revision operators, effecting as few changes as possible, while lexicographic revision is the least conservative, and point out that restrained revision can also be viewed as a composite operator, consisting of natural revision preceded by an application of a "backwards revision" operator previously studied by Papini. Finally, we propose the establishment of a principled approach for choosing an appropriate revision operator in different contexts and discuss future work.

Commonsense Knowledge in Wikidata Artificial Intelligence

Wikidata and Wikipedia have been proven useful for reason-ing in natural language applications, like question answering or entitylinking. Yet, no existing work has studied the potential of Wikidata for commonsense reasoning. This paper investigates whether Wikidata con-tains commonsense knowledge which is complementary to existing commonsense sources. Starting from a definition of common sense, we devise three guiding principles, and apply them to generate a commonsense subgraph of Wikidata (Wikidata-CS). Within our approach, we map the relations of Wikidata to ConceptNet, which we also leverage to integrate Wikidata-CS into an existing consolidated commonsense graph. Our experiments reveal that: 1) albeit Wikidata-CS represents a small portion of Wikidata, it is an indicator that Wikidata contains relevant commonsense knowledge, which can be mapped to 15 ConceptNet relations; 2) the overlap between Wikidata-CS and other commonsense sources is low, motivating the value of knowledge integration; 3) Wikidata-CS has been evolving over time at a slightly slower rate compared to the overall Wikidata, indicating a possible lack of focus on commonsense knowledge. Based on these findings, we propose three recommended actions to improve the coverage and quality of Wikidata-CS further.

Approximating Model-Based ABox Revision in DL-Lite: Theory and Practice

AAAI Conferences

Model-based approaches provide a semantically well justified way to revise ontologies. However, in general, model-based revision operators are limited due to lack of efficient algorithms and inexpressibility of the revision results. In this paper, we make both theoretical and practical contribution to efficient computation of model-based revisions in DL-Lite. Specifically, we show that maximal approximations of two well-known model-based revisions for DL-Lite_R can be computed using a syntactic algorithm. However, such a coincidence of model-based and syntactic approaches does not hold when role functionality axioms are allowed. As a result, we identify conditions that guarantee such a coincidence for DL-Lite_FR. Our result shows that both model-based and syntactic revisions can co-exist seamlessly and the advantages of both approaches can be taken in one revision operator. Based on our theoretical results, we develop a graph-based algorithm for the revision operat