Information Inference Theory and Practice
This event took place on Tuesday 10 May 2005 at 14:00
Dr. Dawei Song KMi, The Open University, UK
In this talk, I will present a information inference framework we developed for mimicking human text based reasoning. The notion of "information inference" refers to the derivation of context sensitive implicit information carried by often short text fragments. Many of us transact information inference as part of our daily information processing tasks, e.g., when scanning subject headings of emails, or captions retrieved from search engines. Due to the information explosion, it is becoming increasingly difficult for humans to keep pace. Thus, the objective of our work is to investigate how to automatically conduct information inference in a way, which is compatible with human reasoning. In short, we aim to construct a computational reasoning system which can draw context-sensitive associations in relation to text which correlates with those we would make, but increasingly can't.
Instead of using traditional symbolic reasoning, we take a psychologistic stance. By drawing on theories from non-classical logic, information retrieval and applied cognition, we have proposed an information inference mechanism via computation of information flow through a high dimensional semantic space, which is automatically constructed from a text corpus. A concept combination heuristic has been developed for priming vector representations according to context. Information flow relationships between concepts or concept combinations are established by computing the degrees of inclusions between their underlying vector representations in the semantic space. Our framework is evaluated, using standard TREC text corpora, topics and the corresponding human relevance judgments, by measuring the effectiveness of query models derived by information flow computations. Experimental results have shown that information inference largely outperforms the co-occurrence and semantic similarity based query expansion.
From a broader point of view, information inference can be applied to many scientific and social knowledge discovery and management problems. In the end of this talk, I will discuss about a global motivation of our work and a list of on-going initiatives derived from our information inference research agenda.
This event took place on Tuesday 10 May 2005 at 14:00
In this talk, I will present a information inference framework we developed for mimicking human text based reasoning. The notion of "information inference" refers to the derivation of context sensitive implicit information carried by often short text fragments. Many of us transact information inference as part of our daily information processing tasks, e.g., when scanning subject headings of emails, or captions retrieved from search engines. Due to the information explosion, it is becoming increasingly difficult for humans to keep pace. Thus, the objective of our work is to investigate how to automatically conduct information inference in a way, which is compatible with human reasoning. In short, we aim to construct a computational reasoning system which can draw context-sensitive associations in relation to text which correlates with those we would make, but increasingly can't.
Instead of using traditional symbolic reasoning, we take a psychologistic stance. By drawing on theories from non-classical logic, information retrieval and applied cognition, we have proposed an information inference mechanism via computation of information flow through a high dimensional semantic space, which is automatically constructed from a text corpus. A concept combination heuristic has been developed for priming vector representations according to context. Information flow relationships between concepts or concept combinations are established by computing the degrees of inclusions between their underlying vector representations in the semantic space. Our framework is evaluated, using standard TREC text corpora, topics and the corresponding human relevance judgments, by measuring the effectiveness of query models derived by information flow computations. Experimental results have shown that information inference largely outperforms the co-occurrence and semantic similarity based query expansion.
From a broader point of view, information inference can be applied to many scientific and social knowledge discovery and management problems. In the end of this talk, I will discuss about a global motivation of our work and a list of on-going initiatives derived from our information inference research agenda.
Future Internet
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Semantic Web and Knowledge Services is...
Our research in the Semantic Web area looks at the potentials of fusing together advances in a range of disciplines, and applying them in a systemic way to simplify the development of intelligent, knowledge-based web services and to facilitate human access and use of knowledge available on the web. For instance, we are exploring ways in which tnatural language interfaces can be used to facilitate access to data distributed over different repositories. We are also developing infrastructures to support rapid development and deployment of semantic web services, which can be used to create web applications on-the-fly. We are also investigating ways in which semantic technology can support learning on the web, through a combination of knowledge representation support, pedagogical theories and intelligent content aggregation mechanisms. Finally, we are also investigating the Semantic Web itself as a domain of analysis and performing large scale empirical studies to uncover data about the concrete epistemologies which can be found on the Semantic Web. This exciting new area of research gives us concrete insights on the different conceptualizations that are present on the Semantic Web by giving us the possibility to discover which are the most common viewpoints, which viewpoints are mutually inconsistent, to what extent different models agree or disagree, etc...
Our aim is to be at the forefront of both theoretical and practical developments on the Semantic Web not only by developing theories and models, but also by building concrete applications, for a variety of domains and user communities, including KMi and the Open University itself.
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