Improving Retrieval Results with discipline-specific Query Expansion

Choosing the right terms to describe an information need is becoming more difficult as the amount of available information increases. Search-Term-Recommendation (STR) systems can help to overcome these problems. This paper evaluates the benefits that may be gained from the use of STRs in Query Expansion (QE). We create 17 STRs, 16 based on specific disciplines and one giving general recommendations, and compare the retrieval performance of these STRs. The main findings are: (1) QE with specific STRs leads to significantly better results than QE with a general STR, (2) QE with specific STRs selected by a heuristic mechanism of topic classification leads to better results than the general STR, however (3) selecting the best matching specific STR in an automatic way is a major challenge of this process.Comment: 6 pages; to be published in Proceedings of Theory and Practice of Digital Libraries 2012 (TPDL 2012

Recognizing Topic Change in Search Sessions of Digital Libraries based on Thesaurus and Classification System

Log analysis in Web search showed that user sessions often contain several different topics. This means sessions need to be segmented into parts which handle the same topic in order to give appropriate user support based on the topic, and not on a mixture of topics. Different methods have been proposed to segment a user session to different topics based on timeouts, lexical analysis, query similarity or external knowledge sources. In this paper, we study the problem in a digital library for the social sciences. We present a method based on a thesaurus and a classification system which are typical knowledge organization systems in digital libraries. Five experts evaluated our approach and rated it as good for the segmentation of search sessions into parts that treat the same topic

The best of both worlds: highlighting the synergies of combining manual and automatic knowledge organization methods to improve information search and discovery.

Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the 'findability' of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a knowledge organization system (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry was used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the 'manual' versus 'automatic' debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what arc often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research

Enterprise search and discovery capability: the factors and generative mechanisms for user satisfaction.

Many organizations are re-creating the 'Google-like' experience behind their firewall to exploit their information. However, surveys show dissatisfaction with enterprise search is commonplace. No prior study has investigated unsolicited user feedback from an enterprise search user interface to understand the underlying reasons for dissatisfaction. A mixed methods longitudinal study was undertaken analysing feedback from over 1,000 users and interviewing search service staff in a multinational corporation. Results show that 62% of dissatisfaction events were due to human (information & search literacy) rather than technology factors. Cognitive biases and the 'Google Habitus' influence expectations and information behaviour, and are postulated as deep underlying generative mechanisms. The current literature focuses on 'structure' (technology and information quality) as the reason for enterprise search satisfaction, agency (search literacy) appears downplayed. Organizations which emphasise 'systems thinking' and bimodal approaches towards search strategy and information behaviour may improve capabilities

Re-examining and re-conceptualising enterprise search and discovery capability: towards a model for the factors and generative mechanisms for search task outcomes.

Many organizations are trying to re-create the Google experience, to find and exploit their own corporate information. However, there is evidence that finding information in the workplace using search engine technology has remained difficult, with socio-technical elements largely neglected in the literature. Explication of the factors and generative mechanisms (ultimate causes) to effective search task outcomes (user satisfaction, search task performance and serendipitous encountering) may provide a first step in making improvements. A transdisciplinary (holistic) lens was applied to Enterprise Search and Discovery capability, combining critical realism and activity theory with complexity theories to one of the worlds largest corporations. Data collection included an in-situ exploratory search experiment with 26 participants, focus groups with 53 participants and interviews with 87 business professionals. Thousands of user feedback comments and search transactions were analysed. Transferability of findings was assessed through interviews with eight industry informants and ten organizations from a range of industries. A wide range of informational needs were identified for search filters, including a need to be intrigued. Search term word co-occurrence algorithms facilitated serendipity to a greater extent than existing methods deployed in the organization surveyed. No association was found between user satisfaction (or self assessed search expertise) with search task performance and overall performance was poor, although most participants had been satisfied with their performance. Eighteen factors were identified that influence search task outcomes ranging from user and task factors, informational and technological artefacts, through to a wide range of organizational norms. Modality Theory (Cybersearch culture, Simplicity and Loss Aversion bias) was developed to explain the study observations. This proposes that at all organizational levels there are tendencies for reductionist (unimodal) mind-sets towards search capability leading to fixes that fail. The factors and mechanisms were identified in other industry organizations suggesting some theory generalizability. This is the first socio-technical analysis of Enterprise Search and Discovery capability. The findings challenge existing orthodoxy, such as the criticality of search literacy (agency) which has been neglected in the practitioner literature in favour of structure. The resulting multifactorial causal model and strategic framework for improvement present opportunities to update existing academic models in the IR, LIS and IS literature, such as the DeLone and McLean model for information system success. There are encouraging signs that Modality Theory may enable a reconfiguration of organizational mind-sets that could transform search task outcomes and ultimately business performance

Evolutionary Design of Search and Triage Interfaces for Large Document Sets

This dissertation is concerned with the design of visual interfaces for searching and triaging large document sets. Data proliferation has generated new and challenging information-based tasks across various domains. Yet, as the document sets of these tasks grow, it has become increasingly difficult for users to remain active participants in the information-seeking process, such as when searching and triaging large document sets. During information search, users seek to understand their document set, align domain knowledge, formulate effective queries, and use those queries to develop document set mappings which help generate encounters with valued documents. During information triage, users encounter the documents mapped by information search to judge relevance to information-seeking objectives. Yet, information search and triage can be challenging for users. Studies have found that when using traditional design strategies in tool interfaces for search and triage, users routinely struggle to understand the domain being searched, apply their expertise, communicate their objectives during query building, and assess the relevance of search results during information triage. Users must understand and apply domain- specific vocabulary when communicating information-seeking objectives. Yet, task vocabularies typically do not align with those of users, especially in tasks of complex domains. Ontologies can be valuable mediating resources for bridging between the vocabularies of users and tasks. They are created by domain experts to provide a standardized mapping of knowledge that can be leveraged both by computational- as well as human-facing systems. We believe that the activation of ontologies within user-facing interfaces has a potential to help users when searching and triaging large document sets, however more research is required