Knowledge and user-generated content is proliferating on the web in scientific publications, information portals and online social media. This knowledge explosion has continued to outpace technological innovation in efficient information access technologies. In this paper, we describe methods and technologies for “Conversational Search” as an innovative solution to facilitate easier information access and reduce the [...]
Posts Tagged ‘semantic memory’
1 Jul
On Similarity Measures based on a Refinement Lattice
Retrieval of structured cases using similarity has been studied in CBR but there has been less activity on defining similarity on description logics (DL). We present an approach that allows us to present two similarity measures for feature logics, a subfamily of DLs, based on the concept of “refinement lattice”. The first one is based [...]
4 Sep
iReMedI – Intelligent Retrieval from Medical Information
Effective encoding of information is one of the keys to qualitative problem solving. Our aim is to explore Knowledge Representation techniques that capture meaningful word associations occurring in documents. We have developed iReMedI, a TCBR-based problem solving system as a prototype to demonstrate our idea. For representation we have used a combination of NLP and [...]
1 Mar
Discovering Semantic Biomedical Relations Utilizing The Web
To realize the vision of a Semantic Web for Life Sciences, discovering relations between resources is essential. It is very difficult to automatically extract relations from Web pages expressed in natural language formats. On the other hand, because of the explosive growth of information, it is difficult to manually extract the relations. In this paper [...]
10 Oct
Semantic Annotation and Inference for Medical Knowledge Discovery
We describe our vision for a new generation medical knowledge annotation and acquisition system called SENTIENT-MD (Semantic Annotation and Inference for Medical Knowledge Discovery). Key aspects of our vision include deep Natural Language Processing techniques to abstract the text into a more semantically meaningful representation guided by domain ontology. In particular, we introduce a notion [...]
27 Jun
Domain Ontology Construction from Biomedical Text
NLM’s Unified Medical Language System (UMLS) is a very large ontology of biomedical and health data. In order to be used effectively for knowledge processing, it needs to be customized to a specific domain. In this paper, we present techniques to automatically discover domain-specific concepts, discover relationships between these concepts, build a context map from [...]
27 Jul
A Synapse Plasticity Model for Conceptual Drift Problems
Traditional supervised learning techniques do not address online learning problems such as concept drift, due to the fact that learning is offine when using these methods. Associative neural networks using Hebbian learning rules show robust performance in classification tasks involving concept drift. Biologically plausible neural networks represent a set of computational models designed to be [...]
1 Mar
Scaling Spreading Activation for Information Retrieval
The Information Retrieval Intelligent Assistant (IRIA) project applies principles of memory retrieval from cognitive science to the problem of information retrieval from large heterogeneous databases. IRIA uses spreading activation over a semantic network for information retrieval, a technique which has proven effective in a variety of tasks. However, some of the very features which motivated [...]
1 Jun
IRIA: The Information Research Intelligent Assistant
The explosion of information in the modern environment demands the ability to collect, organize, manage, and search large amounts of information across a wide variety of real-world applications. The primary tools available for such tasks are large-scale database systems and keyword-based document search techniques. However, such tools are rapidly proving inadequate: traditional database systems do [...]
15 May
Context-Sensitive Asynchronous Memory
Retrieving useful answers from large knowledge bases given under-specified questions is an important problem in the construction of general intelligent agents. The core of this problem is how to get the information an agent needs when it doesn’t know how to ask the right question and doesn’t have the time to exhaustively search all available [...]
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