Guilherme Lima

Guilherme Lima, João M. B. Rodrigues, Marcelo Machado et al.

We present a Wikidata-based framework, called KIF, for virtually integrating heterogeneous knowledge sources. KIF is written in Python and is released as open-source. It leverages Wikidata's data model and vocabulary plus user-defined mappings to construct a unified view of the underlying sources while keeping track of the context and provenance of their statements. The underlying sources can be triplestores, relational databases, CSV files, etc., which may or may not use the vocabulary and RDF encoding of Wikidata. The end result is a virtual knowledge base which behaves like an "extended Wikidata" and which can be queried using a simple but expressive pattern language, defined in terms of Wikidata's data model. In this paper, we present the design and implementation of KIF, discuss how we have used it to solve a real integration problem in the domain of chemistry (involving Wikidata, PubChem, and IBM CIRCA), and present experimental results on the performance and overhead of KIF

Marcio Ferreira Moreno, Guilherme Lima, Rodrigo Costa Mesquita Santos et al.

In this paper, we give an overview of the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the gap in terms of a classical architecture for multimedia processing and discuss a structured approach to bridge it. This approach combines machine learning (for mapping signals to objects) and symbolic AI (for linking objects to meanings). Our main goal is to raise awareness and discuss the challenges involved in this structured approach to multimedia understanding, especially in the view of the latest developments in machine learning and symbolic AI.

Guilherme Lima, Rodrigo Costa, Marcio Ferreira Moreno

In this chapter, we give an introduction to symbolic artificial intelligence (AI) and discuss its relation and application to multimedia. We begin by defining what symbolic AI is, what distinguishes it from non-symbolic approaches, such as machine learning, and how it can used in the construction of advanced multimedia applications. We then introduce description logic (DL) and use it to discuss symbolic representation and reasoning. DL is the logical underpinning of OWL, the most successful family of ontology languages. After discussing DL, we present OWL and related Semantic Web technologies, such as RDF and SPARQL. We conclude the chapter by discussing a hybrid model for multimedia representation, called Hyperknowledge. Throughout the text, we make references to technologies and extensions specifically designed to solve the kinds of problems that arise in multimedia representation.