Programming & Development!

We are fascinated by the potential of deep semantics and artificial intelligence to solve humanity's knowledge utilization issues. We develop state-of-art technologies, combining the benefits of A.I., machine learning and semantic interpretation.

Artificial Neural Networks Semantic Web Technologies
We build autonomous intelligent agents that can react to changes in their environment, that can plan and make decisions autonomously, and that can interact with each other to achieve local or global ends. We train entire neural networks to extract knowledge or to recognize patterns. Semantic technologies work with the huge amount information and sources available on the web by using ontologies. We use such triplets and vocabularies for advanced entity recognition, link building and matching.

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Our approach combines the benefits of Artificial Neural Networks with those of the symbolic applications of Semantic Web Technologies. We are unique in that we integrate agent-based technologies and ANN models with semantics for intelligent knowledge discovery. If you are interested in understanding how this will solve your individual problems ...

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Why combining INTELLLIGENT AGENTS and SEMANTIC algorithms works?

Intelligent systems based on symbolic knowledge processing, on the one hand, and artificial neural networks, on the other, differ substantially. Nevertheless, they are both standard approaches to artificial intelligence and it is very desirable to combine the robustness provided by neural networks, especially when data are noisy, with the expressivity of symbolic knowledge representation.

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Ultimately, hybrid neural-symbolic systems concern the use of problem-specific symbolic knowledge within the neurocomputing paradigm. Neural nets tend to be especially good at recognition of patterns in highdimensional quantitative data, among other things. Symbolic systems tend to be good at abstract reasoning and syntax processing, among other things. Both kinds of systems can be good at generalization and analogy, and at credit assignment, but in different contexts and in different ways. What we achieve by interweaving both is faster real-world modelling, combined with meaningful expression of results and links, automated link-building, analytics and reporting.

Better Sourcing

Apply trainable deep learning architectures based on the use of convolutional neural networks to extract both local contextual features and global contextual features from text. The higher layers in the overall deep architecture will make highly effective use of the extracted context - sensitive features to perform semantic matching between documents and queries, both in the form of text, for Web search applications.

Faster Extraction

Many current solutions are complicated, consist of several stages and hand-built features, and are too slow to be applied as part of real applications that require such semantic labels, partly because of their use of a syntactic parser. Our ANN-based algorithms instead can do direct mapping from source sentence to semantic tags for a given predicate without the aid of a parser or a chunker. The resulting system obtains accuracies comparable to the current state-of-the-art at a fraction of the computational cost.

Query Improvement

Think of an Ontology Driven Agent Based Focused Crawler that attempts to improve the efficiency of queries by introducing semantics in which a keyword is searched. It follows a context-based approach that analyzes content of web page thereby reducing the redundant information and hence deduces the relevant information from a page. Multiagent neural network systems are an alternative effective solution to large scale Web mining. Such a neural network based framework for mining content of semantic web can provide query relevant knowledge using clustering technique. Both methods allow us to provide context based knowledge oriented results to the user.

Improved Classification

Neural Network techniques can be useful for tasks like text routing applied to different architectures and different corpora. By combining ANNs and Semantic technologies, better recall and precision results are achieved because the influence of misleading word representations is limited. In addition recurrent neural networks can learn to process messages in a faster and more robust manner. Moreover, neural networks can be used effectively for better and faster ontology alignment.