Posts Tagged ‘cognitive media’
15 Apr
Socio-Semantic Conversational Information Access
We develop an innovative approach to delivering relevant information using a combination of socio-semantic search and filtering approaches. The goal is to facilitate timely and relevant information access through the medium of conversations by mixing past community specific conversational knowledge and web information access to recommend and connect users and information together. Conversational Information Access is a socio-semantic search and recommendation activity with the goal to interactively engage people in conversations by receiving agent supported recommendations. It is useful because people engage in online social discussions unlike solitary search; the agent brings in relevant information as well as identifies relevant users; participants provide feedback during the conversation that the agent uses to improve its recommendations.
Socio-Semantic Conversational Information Access
by Saurav Sahay, Ashwin Ram
WWW-2012 Workshop on Community Question Answering on the Web (CQA-12).15 Nov
Socio-Semantic Conversational Information Access
The main contributions of this thesis revolve around development of an integrated conversational recommendation system, combining data and information models with community network and interactions to leverage multi-modal information access. We have developed a real time conversational information access community agent that leverages community knowledge by pushing relevant recommendations to users of the community. The recommendations are delivered in the form of web resources, past conversation and people to connect to. The information agent (cobot, for community/ collaborative bot) monitors the community conversations, and is ‘aware’ of users’ preferences by implicitly capturing their short term and long term knowledge models from conversations. The agent leverages from health and medical domain knowledge to extract concepts, associations and relationships between concepts; formulates queries for semantic search and provides socio-semantic recommendations in the conversation after applying various relevance filters to the candidate results. The agent also takes into account users’ verbal intentions in conversations while making recommendation decision.
One of the goals of this thesis is to develop an innovative approach to delivering relevant information using a combination of social networking, information aggregation, semantic search and recommendation techniques. The idea is to facilitate timely and relevant social information access by mixing past community specific conversational knowledge and web information access to recommend and connect users with relevant information. Language and interaction creates usable memories, useful for making decisions about what actions to take and what information to retain.
Cobot leverages these interactions to maintain users’ episodic and long term semantic models. The agent analyzes these memory structures to match and recommend users in conversations by matching with the contextual information need. The social feedback on the recommendations is registered in the system for the algorithms to promote community preferred, contextually relevant resources. The nodes of the semantic memory are frequent concepts extracted from user’s interactions. The concepts are connected with associations that develop when concepts co-occur frequently. Over a period of time when the user participates in more interactions, new concepts are added to the semantic memory. Different conversational facets are matched with episodic memories and a spreading activation search on the semantic net is performed for generating the top candidate user recommendations for the conversation.
The tying themes in this thesis revolve around informational and social aspects of a unified information access architecture that integrates semantic extraction and indexing with user modeling and recommendations.
Read the dissertation:
Socio-Semantic Conversational Information Access
by Saurav Sahay
PhD dissertation, College of Computing, Georgia Institute of Technology, November 2011.23 Sep
Social Media for Health and Wellness 2.0
The Internet has surpassed physicians as the leading source of health information. With the advent of the social web, Health 2.0 is emerging as a strong segment with 34% of consumers using social resources such as blogs and forums to locate health information. Yet information overload leads to “search engine fatigue” that discourages users.
We advocate a consumer-centric approach to healthcare information access that increases engagement and improves health literacy. Artificial Intelligence (AI) techniques can be used to support human effort, creating a new generation of “intelligent web” technologies. These technologies can combine the benefits of the “information web” (timely, relevant health information) with those of the “social web” (human interaction, support, comfort). Our vision is to promote well-being and prevention before illness, support and information during illness, and comfort to family and friends in a natural, social, yet private manner.
Invited talk at Humana Innovation Conference: Connect, Collaborate, Create (C3), Louisville, KY, September 23, 2011.22 Mar
CBArch: A Case-Based Reasoning Framework for Conceptual Design of Commercial Buildings
The paper describes the first phase of development of a Case-Base Reasoning (CBR) system to support early conceptual design of buildings. As specific context of application, the research focuses on energy performance of commercial buildings, and the early identification of energy-related features that contribute to its outcomes. The hypothesis is that bringing knowledge from relevant precedents may facilitate this identification process, thus offering a significant contribution for early analysis and decision-making.
The paper introduces a proof-of-concept for such a system, proposing a novel integration of Case-Based Reasoning, Parametric Modeling (Building Information Modeling), and Ontology Classification. While CBR provides a framework to store and retrieve cases at an instance level, Parametric Modeling offers a framework for rule-based geometric adaptation and evaluation. The ontology is intended to provide a semantic representation, so that new design concepts can be created, classified and retained for further reuse. Potential advantages and limitations of this three-level integration approach are discussed along with recommendations for future development.
CBArch: A Case-Based Reasoning Framework for Conceptual Design of Commercial Buildings
by Andrés Cavieres, Urjit Bhatia, Preetam Joshi, Fei Zhao, Ashwin Ram
AAAI-11 Spring Symposium on Artificial Intelligence and Sustainable Designwww.cc.gatech.edu/faculty/ashwin/papers/er-11-07.pdf