243 research outputs found
Deriving query suggestions for site search
Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T
Professional Search in Pharmaceutical Research
In the mid 90s, visiting libraries â as means of retrieving the latest literature â was still a common necessity among professionals. Nowadays, professionals simply access information by âgooglingâ. Indeed, the name of the Web search engine market leader âGoogleâ became a synonym for searching and retrieving information. Despite the increased popularity of search as a method for retrieving relevant information, at the workplace search engines still do not deliver satisfying results to professionals.
Search engines for instance ignore that the relevance of answers (the satisfaction of a searcherâs needs) depends not only on the query (the information request) and the document corpus, but also on the working context (the userâs personal needs, education, etc.). In effect, an answer which might be appropriate to one user might not be appropriate to the other user, even though the query and the document corpus are the same for both. Personalization services addressing the context become therefore more and more popular and are an active field of research.
This is only one of several challenges encountered in âprofessional searchâ: How can the working context of the searcher be incorporated in the ranking process; how can unstructured free-text documents be enriched with semantic information so that the information need can be expressed precisely at query time; how and to which extent can a companyâs knowledge be exploited for search purposes; how should data from distributed sources be accessed from into one-single-entry-point.
This thesis is devoted to âprofessional searchâ, i.e. search at the workplace, especially in industrial research and development. We contribute by compiling and developing several approaches for facing the challenges mentioned above. The approaches are implemented into the prototype YASA (Your Adaptive Search Agent) which provides meta-search, adaptive ranking of search results, guided navigation, and which uses domain knowledge to drive the search processes. YASA is deployed in the pharmaceutical research department of Roche in Penzberg â a major pharmaceutical company â in which the applied methods were empirically evaluated.
Being confronted with mostly unstructured free-text documents and having barely explicit metadata at hand, we faced a serious challenge. Incorporating semantics (i.e. formal knowledge representation) into the search process can only be as good as the underlying data. Nonetheless, we are able to demonstrate that this issue can be largely compensated by incorporating automatic metadata extraction techniques. The metadata we were able to extract automatically was not perfectly accurate, nor did the ontology we applied contain considerably ârich semanticsâ. Nonetheless, our results show that already the little semantics incorporated into the search process, suffices to achieve a significant improvement in search and retrieval.
We thus contribute to the research field of context-based search by incorporating the working context into the search process â an area which so far has not yet been well studied
Personalised Query Suggestion for Intranet Search with Temporal User Profiling
Recent research has shown the usefulness of using collective user interaction data (e.g., query logs) to recommend query modification suggestions for Intranet search. However, most of the query suggestion approaches for Intranet search follow an ``one size fits all'' strategy, whereby different users who submit an identical query would get the same query suggestion list. This is problematic, as even with the same query, different users may have different topics of interest, which may change over time in response to the user's interaction with the system.
In this paper, we address the problem by proposing a personalised query suggestion framework for Intranet search. For each search session, we construct two temporal user profiles: a click user profile using the user's clicked documents and a query user profile using the user's submitted queries. We then use the two profiles to re-rank the non-personalised query suggestion list returned by a state-of-the-art query suggestion method for Intranet search. Experimental results on a large-scale query logs collection show that our personalised framework significantly improves the quality of suggested queries
Personalizing Interactions with Information Systems
Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation tailored to the individual. In this chapter, we study personalization from the viewpoint of personalizing interaction. The survey covers mechanisms for information-finding on the web, advanced information retrieval systems, dialog-based applications, and mobile access paradigms. Specific emphasis is placed on studying how users interact with an information system and how the system can encourage and foster interaction. This helps bring out the role of the personalization system as a facilitator which reconciles the userâs mental model with the underlying information systemâs organization. Three tiers of personalization systems are presented, paying careful attention to interaction considerations. These tiers show how progressive levels of sophistication in interaction can be achieved. The chapter also surveys systems support technologies and niche application domains
Biologically Motivated Distributed Designs for Adaptive Knowledge Management
We discuss how distributed designs that draw from biological network
metaphors can largely improve the current state of information retrieval and
knowledge management of distributed information systems. In particular, two
adaptive recommendation systems named TalkMine and @ApWeb are discussed in more
detail. TalkMine operates at the semantic level of keywords. It leads different
databases to learn new and adapt existing keywords to the categories recognized
by its communities of users using distributed algorithms. @ApWeb operates at
the structural level of information resources, namely citation or hyperlink
structure. It relies on collective behavior to adapt such structure to the
expectations of users. TalkMine and @ApWeb are currently being implemented for
the research library of the Los Alamos National Laboratory under the Active
Recommendation Project. Together they define a biologically motivated
information retrieval system, recommending simultaneously at the level of user
knowledge categories expressed in keywords, and at the level of individual
documents and their associations to other documents. Rather than passive
information retrieval, with this system, users obtain an active, evolving
interaction with information resources.Comment: To appear in Design Principles for the Immune System and Other
Distributed Autonomous Systems. i. Cohen and L. Segel (Eds.). Oxford
University Pres
Multi-Agents Corporate Memory Management System
International audienceThis paper presents an approach to design a multi-agent system managing a corporate memory in the form of a distributed semantic web and describes the resulting architecture. The system was designed during the CoMMA European project (Corporate MemoryManagement through Agents) and aims at helping users in the management of a corporate memory, facilitating the creation, dissemination, transmission and reuse of knowledge in an organisation. The implementation integrated several emerging technologies: multi-agents system technology (using the JADE FIPA-compliant platform), knowledge modelling and XML technology for information retrieval (using the CORESE semantic search engine) and machine learning techniques. Here, we describe the agent roles and interactions, we explain the design rationale for the agent societies and we discuss the configuration and implementation issues
Closing Information Gaps with Need-driven Knowledge Sharing
InformationslĂŒcken schlieĂen durch bedarfsgetriebenen Wissensaustausch
Systeme zum asynchronen Wissensaustausch â wie Intranets, Wikis oder Dateiserver â leiden hĂ€ufig unter mangelnden NutzerbeitrĂ€gen. Ein Hauptgrund dafĂŒr ist, dass Informationsanbieter von Informationsuchenden entkoppelt, und deshalb nur wenig ĂŒber deren Informationsbedarf gewahr sind. Zentrale Fragen des Wissensmanagements sind daher, welches Wissen besonders wertvoll ist und mit welchen Mitteln WissenstrĂ€ger dazu motiviert werden können, es zu teilen.
Diese Arbeit entwirft dazu den Ansatz des bedarfsgetriebenen Wissensaustauschs (NKS), der aus drei Elementen besteht. ZunĂ€chst werden dabei Indikatoren fĂŒr den Informationsbedarf erhoben â insbesondere Suchanfragen â ĂŒber deren Aggregation eine fortlaufende Prognose des organisationalen Informationsbedarfs (OIN) abgeleitet wird. Durch den Abgleich mit vorhandenen Informationen in persönlichen und geteilten InformationsrĂ€umen werden daraus organisationale InformationslĂŒcken (OIG) ermittelt, die auf fehlende Informationen hindeuten. Diese LĂŒcken werden mit Hilfe so genannter Mediationsdienste und MediationsrĂ€ume transparent gemacht. Diese helfen Aufmerksamkeit fĂŒr organisationale InformationsbedĂŒrfnisse zu schaffen und den Wissensaustausch zu steuern. Die konkrete Umsetzung von NKS wird durch drei unterschiedliche Anwendungen illustriert, die allesamt auf bewĂ€hrten Wissensmanagementsystemen aufbauen.
Bei der Inversen Suche handelt es sich um ein Werkzeug das WissenstrĂ€gern vorschlĂ€gt Dokumente aus ihrem persönlichen Informationsraum zu teilen, um damit organisationale InformationslĂŒcken zu schlieĂen. Woogle erweitert herkömmliche Wiki-Systeme um Steuerungsinstrumente zur Erkennung und Priorisierung fehlender Informationen, so dass die Weiterentwicklung der Wiki-Inhalte nachfrageorientiert gestaltet werden kann. Auf Ă€hnliche Weise steuert Semantic Need, eine Erweiterung fĂŒr Semantic MediaWiki, die Erfassung von strukturierten, semantischen Daten basierend auf Informationsbedarf der in Form strukturierter Anfragen vorliegt.
Die Umsetzung und Evaluation der drei Werkzeuge zeigt, dass bedarfsgetriebener Wissensaustausch technisch realisierbar ist und eine wichtige ErgĂ€nzung fĂŒr das Wissensmanagement sein kann. DarĂŒber hinaus bietet das Konzept der Mediationsdienste und MediationsrĂ€ume einen Rahmen fĂŒr die Analyse und Gestaltung von Werkzeugen gemÀà der NKS-Prinzipien. SchlieĂlich liefert der hier vorstellte Ansatz auch Impulse fĂŒr die Weiterentwicklung von Internetdiensten und -Infrastrukturen wie der Wikipedia oder dem Semantic Web
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Dynamic User Profiling for Search Personalisation
The performance of a personalised search system largely depends upon the ability to build user profiles which accurately capture the user's search interests. However, many approaches to user profiling have neglected the dynamic nature of the user's search interests. That is, a user's search interests typically change in response to their interactions with the search system during the search period. Therefore, a profile built for previous searches might not reflect that user's current search interests.
A widely used type of profile represents the topical interests of the user. In these cases, a typical approach is to build a user profile using topics discussed in documents which the user has found relevant, and where the topics are obtained from a human-generated ontology or directory. However, a key limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the human-generated ontology requires manual effort to determine the correct categories for each document.
In this research, we address these problems by proposing novel techniques for dynamically building user profiles which capture the user's search interests changing over time. Instead of using a human-generated ontology, we use a topic modelling technique (Latent Dirichlet Allocation) for unsupervised extraction of the topics from documents. To dynamically build user profiles, we make two important assumptions. First, that the group of users with whom a user shares a set of common interests may be different depending upon the particular topic of interest. Second, the more recently clicked/relevant documents tell us more about the user's current search interests.
To test these assumptions, we develop and implement dynamic user profiles, and then evaluate them on two search personalisation tasks. Our first chosen task is personalising search results returned by a Web search engine, and the second is the task of personalising query suggestions made by an Intranet search engine. We found that dynamic user profiles can significantly improve the ranking quality over well-established baselines
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