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.
Posts Tagged ‘semantic memory’
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.
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Socio-Semantic Conversational Information Access
by Saurav SahayPhD dissertation, College of Computing, Georgia Institute of Technology, November 2011.
We introduce a Conversational Interaction framework as an innovative and natural approach to facilitate easier information access by combining web search and recommendations. This framework includes an intelligent information agent (Cobot) in the conversation to provide contextually relevant social and web search recommendations. Cobot supports the information discovery process by integrating web information retrieval along with proactive connections to relevant users who can participate in real-time conversations. We describe the conversational framework and report on some preliminary experiments in the system.
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Conversational Framework for Web Search and Recommendations
by Saurav Sahay, Ashwin RamICCBR-10 Workshop on Reasoning from Experiences on the Web (WebCBR-10), Alessandria, Italy, 2010.
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 information overload for users.
Conversational Search is an interactive and collaborative information finding interaction. The participants in this interaction engage in social conversations aided with an intelligent information agent (Cobot) that provides contextually relevant search recommendations. The collaborative and conversational search activity helps users make faster and more informed search and discovery. It also helps the agent learn about conversations with interactions and social feedback to make better recommendations. Conversational search leverages the social discovery process by integrating web information retrieval along with the social interactions.
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Collaborative Information Access: A Conversational Search Approach
by Saurav Sahay, Anu Venkatesh, Ashwin RamICCBR-09 Workshop on Reasoning from Experiences on the Web (WebCBR-09), Seattle, July 2009
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 on computing the anti-unification (AU) of two cases to assess the amount of shared information. The second measure decomposes the cases into a set of independent “properties”, and then assesses how many of these properties are shared between the two cases. Moreover, we show that the defined measures are applicable to any representation language for which a refinement lattice can be defined. We empirically evaluate our measures comparing them to other measures in the literature in a variety of relational data sets showing very good results.
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On Similarity Measures based on a Refinement Lattice
by Santi Ontañón and Enric Plazain ICCBR 2009, LNAI 5650, pp 240 – 255
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 graph based techniques which we call as Shallow Syntactic Triples, Dependency Parses and Semantic Word Chains. To test their effectiveness we have developed retrieval techniques based on PageRank, Shortest Distance and Spreading Activation methods. The various algorithms discussed in the paper and the comparative analysis of their results provides us with useful insight for creating an effective problem solving and reasoning system.
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iReMedI – Intelligent Retrieval from Medical Information
by Saurav Sahay, Bharat Ravisekar, Anu Venkatesh, Sundaresan Venkatasubramanian, Priyanka Prabhu, Ashwin Ram9th European Conference on Case-Based Reasoning (ECCBR-08), Trier, Germany
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 we present techniques to automatically discover relations between biomedical resources from the Web. For this purpose we retrieve relevant information from Web Search engines and Pubmed database using various lexico-syntactic patterns as queries over SOAP web services. The patterns are initially handcrafted but can be progressively learnt. The extracted relations can be used to construct and augment ontologies and knowledge bases. Experiments are presented for general biomedical relation discovery and domain specific search to show the usefulness of our technique.
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Discovering Semantic Biomedical Relations utilizing the Web
by Saurav Sahay, Sougata Mukherjea, Eugene Agichtein, Ernie Garcia, Sham Navathe, Ashwin RamACM Transactions on Knowledge Discovery from Data, 2(1):3, 2008
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 of semantic fitness to model an optimal level of abstract representation for a text fragment given a domain ontology. We apply this notion to appropriately condense and merge nodes in semantically annotated syntactic parse trees. These transformed semantically annotated trees are more amenable to analysis and inference for abstract knowledge discovery, such as for automatically inferring general medical rules for enhancing an expert system for nuclear cardiology. This work is a part of a long term research effort on continuously mining medical literature for automatic clinical decision support.
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Semantic Annotation and Inference for Medical Knowledge Discovery
by Saurav Sahay, Eugene Agichtein, Baoli Li, Ernie Garcia, Ashwin RamNSF Symposium on Next Generation of Data Mining (NGDM-07), Baltimore, MD, October 2007
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 these relationships, link these domain concepts with the best-matching concept identifiers in UMLS using our context map and UMLS concept trees, and finally assign categories to the discovered relationships. This specific domain ontology of terms and relationships using evidential information can serve as a basis for applications in analysis, reasoning and discovery of new relationships. We have automatically built an ontology for the Nuclear Cardiology domain as a testbed for our techniques.
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Domain Ontology Construction from Biomedical Text
by Saurav Sahay, Baoli Li, Ernie Garcia, Eugene Agichtein, Ashwin RamInternational Conference on Artificial Intelligence (ICAI-07), Las Vegas, NV, June 2007
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 more strongly related to biological neuron models. In this paper, we apply a biologically inspired plasticity model of synapse dynamics to a concept drift classification problem. The motivation for this method is to provide more biologically plausible networks in cognitive tasks.
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