Our societies produce knowledge and data at an ever increasing pace. These knowledge and data are generated in an independent manner by autonomous individuals or companies. They are heterogeneous and their joint exploitation requires connecting them.
However, data and knowledge have to evolve, facing changes in what they represent, changes in the context in which they are used and connections to new data and knowledge sources. These sources are currently mostly maintained by hand. As they grow and get more interconnected, this becomes less sustainable. But if knowledge does not evolve, it will freeze leading to sure obsolescence.
Beyond the production of knowledge on the semantic web and linked data, this problem applies to any domain in which knowledge is produced in a way usable by computers. For instance, smart cities or the internet of things produce a wealth of changing data. The knowledge about this data has to evolve continuously to remain up-to-date as new data sources are encountered and conditions are changing. Knowledge must evolve organically with the life of its users.
This problem lies in the lack of autonomous evolution of heterogeneous knowledge. No one waits for knowledge to be perfect before using it and agents and societies cannot be interrupted for upgrading their knowledge. Hence, knowledge has to be situated, i.e., considered with respect to its use (called situation), and evolve continuously, i.e., without interruption.
mOeX addresses the seamless evolution of knowledge representations in individuals and populations. The question at the core of our proposal is to understand how to make knowledge representation continuously evolve in presence of environment changes and new knowledge sources. Currently, no satisfactory solution to this problem exists.
To tackle this problem, we start from two specific hypotheses:
Based on such hypotheses, we study populations of agents sharing knowledge through interaction. The interactions may be carried out through precisely specified modalities (which may involve direct knowledge exchange, talking, acting together or in presence). After interacting, when they discover that constraints have changed, agents will not relearn knowledge from scratch. Instead, adaptation operators, taking into account the current knowledge and other constraints, will adapt it to the new constraints. We study how knowledge evolve when these populations:
The highly difficult problem is not to have procedures allowing such agents to converge towards a common state of knowledge, but to characterise this state by the properties satisfied by the resulting knowledge. Such properties may, for instance, be:
What is radically new here is that these problems are approached from the standpoint of the resulting knowledge representations. mOeX work will contribute to answer the following questions:
Our ambition is to spark a new approach to knowledge evolution that we call cultural knowledge evolution. It designs, studies, and experiments with mechanisms for making knowledge representations serendipitously evolve through their use. This should enable developing and sharing complex knowledge in a more robust way.
Now is the right time to start such a research programme: on the one hand, developments on the semantic web provide us with proven knowledge representation formalisms and tools which have been designed for sharing knowledge; on the other hand, work on experimental cultural evolution provides a solid methodology for carrying out this type of research. This approach has not been applied yet to knowledge representation directly. Both fields are mature enough to be associated.
To investigate the foundations of situated knowledge evolution we need an approach that:
Thus, mOeX will develop the unique combination of knowledge representation and experimental cultural evolution methods. Knowledge representation provides formal models of knowledge; experimental cultural evolution provides a well-defined framework for studying situated evolution. We do not intend to replace symbolic representation, but to complement it.
The reasons why these approaches are well adapted are the following:
Our methodology involves the following three tasks interacting together in a constant feedback:
Finally, in order to ensure the repeatability and reusability of experiments we aim at developing a software platform to support this approach.
This work has been carried out within Exmo, but is a good illustration of our work here.
Alignments between ontologies may be established through agents holding such ontologies attempting at communicating and taking appropriate actions when communication fails. This approach, that we call cultural knowledge evolution, has the advantage of not assuming that everything should be set correctly before trying to communicate and of being able to overcome failures. We have tested this approach on alignment repair, i.e., the improvement of incorrect alignments. For that purpose, we performed a series of experiments in which agents react to mistakes in alignments. Agents only know about their ontologies and alignments with others and they act in a fully decentralised way. We showed that cultural repair is able to converge towards successful communication through improving the objective correctness of alignments. The obtained results are on par with a baseline of state-of-the-art alignment repair algorithms [euzenat2014c].The benchmarks, results and software are available at http://lazylav.gforge.inria.fr.
We are continuing our work on link keys for data interlinking in two specific directions:
More about it on the Exmo web site.
Initial project proposal (2016) (pdf)
Publications: our paper section (from which references are taken)
mOeX is building on top of the results of the Exmo project whose pages may provide some background information on previous work.