Posts Tagged ‘information retrieval’

Conversational Framework for Web Search and Recommendations

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 Ram

ICCBR-10 Workshop on Reasoning from Experiences on the Web (WebCBR-10), Alessandria, Italy, 2010.

Collaborative Information Access: A Conversational Search Approach

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 Ram

ICCBR-09 Workshop on Reasoning from Experiences on the Web (WebCBR-09), Seattle, July 2009

Using Content Analysis to Investigate The Research Paths Chosen by Scientists over Time

We present an application of a clustering technique to a large original dataset of SCI publications which is capable at disentangling the different research lines followed by a scientist, their duration over time and the intensity of effort devoted to each of them. Information is obtained by means of software-assisted content analysis, based on the co-occurrence of words in the full abstract and title of a set of SCI publications authored by 650 American star-physicists across 17 years. We estimated that scientists in our dataset over the time span contributed on average to 16 different research lines lasting on average 3.5 years and published nearly 5 publications in each single line of research. The technique is potentially useful for scholars studying science and the research community, as well as for research agencies, to evaluate if the scientist is new to the topic and for librarians, to collect timely biographic information.

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Using Content Analysis to Investigate The Research Paths Chosen by Scientists over Time

by Chiara Franzoni, Chris Simpkins, Baoli Li, Ashwin Ram

Scientometrics journal 83(1):321-335, April 2010. (Earlier version in 1th International Conference on Scientometrics and Infometrics (ISSI-07), Madrid, Spain, June 2007.)

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 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 Ram

9th European Conference on Case-Based Reasoning (ECCBR-08), Trier, Germany

Subjectivity Analysis for Questions in QA Communities

In this paper we investigate how to automatically determine the subjectivity orientation of questions posted by real users in community question answering (CQA) portals. Subjective questions seek answers containing private states, such as personal opinion and experience. In contrast, objective questions request objective, verifiable information, often with support from reliable sources. Knowing the question orientation would be helpful not only for evaluating answers provided by users, but also for guiding the CQA engine to process questions more intelligently. Our experiments on Yahoo! Answers data show that our method exhibits promising performance.

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Subjectivity Analysis for Questions in QA Communities

by Baoli Li, Yandong Liu, Ashwin Ram, Ernie Garcia, Eugene Agichtein

31st Annual International ACM SIGIR Conference (ACM-SIGIR-08), Singapore, July 2008

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 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 Ram

ACM Transactions on Knowledge Discovery from Data, 2(1):3, 2008

Adapting Associative Classification to Text Categorization

Associative classification, which originates from numerical data mining, has been applied to deal with text data recently. Text data is firstly digitalized to database of transactions, and then training and prediction is actually conducted on the derived numerical dataset. This intuitive strategy has demonstrated quite good performance. However, it doesn’t take into consideration the inherent characteristics of text data as much as possible, although it has to deal with some specific problems of text data such as lemmatizing and stemming during digitalization. In this paper, we propose a bottom-up strategy to adapt associative classification to text categorization, in which we take into account structure information of text. Experiments on Reuters-21578 dataset show that the proposed strategy can make use of text structure information and achieve better performance.

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Adapting Associative Classification to Text Categorization

by Baoli Li, Neha Sugandh, Ernie Garcia, Ashwin Ram

ACM Conference on Document Engineering (ACM-DocEng-07), Winnipeg, Canada, August 2007