Exploratory Web Searching with Dynamic Taxonomies and Results Clustering

Fundamenta Informaticae

Papadakos

2013

Self Cite

Current proposals for preference-based information access seem to ignore that users should be acquainted with the information space and the available choices for describing effectively their preferences. Furthermore users rarely formulate complex (preference or plain) queries. The interaction paradigm of Faceted Dynamic Taxonomies (FDT) allows users to explore an information space and to restrict their focus without having to formulate queries. Instead the users can restrict their focus (object set, or set of choices in general) gradually through a simple set of actions, each corresponding to a more refined query (formulated on-the-fly) which can be enacted by a simple click. In this paper we extend this interaction paradigm with actions that allow users to dynamically express their preferences. The proposed model supports progressive preference elicitation, inherited preferences and scope-based resolution of conflicts over single or multi-valued attributes with hierarchically organized values. Finally we elaborate on the algorithmic perspective and the applicability of the model over large information bases.

Section: Information Exploration Through Fdtmentioning

confidence: 95%

“…[18,31]), general purpose WSE (e.g. Mitos [36]), collaborative enviroments (e.g. mSpace [49]), and booking applications (e.g.…”

Section: Information Exploration Through Fdtmentioning

confidence: 99%

Section: Information Exploration Through Fdtmentioning

confidence: 99%

See 1 more Smart Citation

Interactive Exploration of Multi-Dimensional and Hierarchical Information Spaces with Real-Time Preference Elicitation

Fundamenta Informaticae

Papadakos

2013

Self Cite

“…Implementation The algorithms have been implemented over Mitos [15,14,13] 6 . The snippets in our experiments were quite small: up to two sentences, each one consisting of 11 words maximum.…”

Section: Experimental Evaluationmentioning

confidence: 99%

STC+ and NM-STC: Two Novel Online Results Clustering Methods for Web Searching

Kopidaki

Papadakos

Web Information Systems Engineering - WISE 2009

2009

Self Cite

Abstract. Results clustering in Web Searching is useful for providing users with overviews of the results and thus allowing them to restrict their focus to the desired parts. However, the task of deriving singleword or multiple-word names for the clusters (usually referred as cluster labeling) is difficult, because they have to be syntactically correct and predictive. Moreover efficiency is an important requirement since results clustering is an online task. Suffix Tree Clustering (STC) is a clustering technique where search results (mainly snippets) can be clustered fast (in linear time), incrementally, and each cluster is labeled with a phrase. In this paper we introduce: (a) a variation of the STC, called STC+, with a scoring formula that favors phrases that occur in document titles and differs in the way base clusters are merged, and (b) a novel algorithm called NM-STC that results in hierarchically organized clusters. The comparative user evaluation showed that both STC+ and NM-STC are significantly more preferred than STC, and that NM-STC is about two times faster than STC and STC+.

“…In the context of a standalone WSE, the engine can pre-compute and store not only the cluster label tree, but also the facets (metadata-based groupings) of the top-k results of each logged query. For example, in [12] the engine characterizes the top-k results according to five facets: by clustering, by domain, by date, by filetype and by language. The provision of such information during typing can accelerate the search process.…”

Section: Applications Of Iosmentioning

confidence: 99%

Exploiting Available Memory and Disk for Scalable Instant Overview Search

Fafalios

Lecture Notes in Computer Science

2011

Self Cite

Abstract. Search-As-You-Type (or Instant Search) is a recently introduced functionality which shows predictive results while the user types a query letter by letter. In this paper we generalize and propose an extension of this technique which apart from showing on-the-fly the first page of results, it shows various other kinds of information, e.g. the outcome of results clustering techniques, or metadata-based groupings of the results. Although this functionality is more informative than the classic search-as-you type, since it combines Autocompletion, Search-AsYou-Type, and Results Clustering, the provision of real-time interaction is more challenging. To tackle this issue we propose an approach based on pre-computed information and we comparatively evaluate various index structures for making real-time interaction feasible, even if the size of the available memory space is limited. This comparison reveals the memory/performance trade-off and allows deciding which index structure to use according to the available main memory and desired performance. Furthermore we show that an incremental algorithm can be used to keep the index structure fresh.