Inductive queries for a drug designing robot scientist

It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments

Benchmarking qualitative spatial calculi for video activity analysis

This paper presents a general way of addressing problems in video activity understanding using graph based relational learning. Video activities are described using relational spatio-temporal graphs, that represent qualitative spatio- temporal relations between interacting objects. A wide range of spatio-temporal relations are introduced, as being well suited for describing video activities. Then, a formulation is proposed, in which standard problems in video activity under- standing such as event detection, are naturally mapped to problems in graph based relational learning. Experiments on video understanding tasks, for a video dataset consisting of common outdoor verbs, validate the significance of the proposed approach

Assessing Inductive Logic Programming Classification Quality by Image Synthesis

International audienceOne of the major difficulties in classification is the assessment of classification quality. This paper builds on a recently developed classification technique, based on Inductive Logic Programming to propose a way of visually reconstructing the learnt classes in order to give a feedback to the user. The system translates positioning constraints into a linear programming problem, which can be solved with standard state-of-the- art approaches. The result can then be used for generating a synthetic image, representative of the class

Ontologies learn by searching

Dissertation to obtain the Master degree in Electrical Engineering and Computer ScienceDue to the worldwide diversity of communities, a high number of ontologies representing the same segment of reality which are not semantically coincident have appeared. To solve this problem, a possible solution is to use a reference ontology to be the intermediary in the communications between the community enterprises and to outside. Since semantic mappings between enterprise‘s ontologies are established, this solution allows each of the enterprises to keep internally its own ontology and semantics unchanged. However information systems are not static, thus established mappings become obsoletes with time. This dissertation‘s objective is to identify a suitable method that combines semantic mappings with user‘s feedback, providing an automatic learning to ontologies & enabling auto-adaptability and dynamism to the information system

Semantic adaptability for the systems interoperability

In the current global and competitive business context, it is essential that enterprises adapt their knowledge resources in order to smoothly interact and collaborate with others. However, due to the existent multiculturalism of people and enterprises, there are different representation views of business processes or products, even inside a same domain. Consequently, one of the main problems found in the interoperability between enterprise systems and applications is related to semantics. The integration and sharing of enterprises knowledge to build a common lexicon, plays an important role to the semantic adaptability of the information systems. The author proposes a framework to support the development of systems to manage dynamic semantic adaptability resolution. It allows different organisations to participate in a common knowledge base building, letting at the same time maintain their own views of the domain, without compromising the integration between them. Thus, systems are able to be aware of new knowledge, and have the capacity to learn from it and to manage its semantic interoperability in a dynamic and adaptable way. The author endorses the vision that in the near future, the semantic adaptability skills of the enterprise systems will be the booster to enterprises collaboration and the appearance of new business opportunities