Beagle + + : Semantically Enhanced Searching and Ranking on the Desktop

Chirita, Paul-Alexandru; Costache, Stefania; Nejdl, Wolfgang; Paiu, Raluca

doi:10.1007/11762256_27

Cited by 46 publications

(30 citation statements)

References 9 publications

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“…More specifically, we show how our approach can be used to perform a vertical ontology-based search on the Web pages of the Internet Movie Database (IMDB) 14 . To this end, the necessary semantic annotations are automatically constructed from the IMDB Web pages.…”

Section: Semantic Web Search On the Internet Movie Databasementioning

confidence: 99%

Semantic Web Search Based on Ontological Conjunctive Queries

Fazzinga

Gianforme

Gottlob

et al. 2010

Lecture Notes in Computer Science

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Abstract. Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present a novel approach to Semantic Web search, called Serene, which allows for a semantic processing of Web search queries, and for evaluating complex Web search queries that involve reasoning over the Web. More specifically, we first add ontological structure and semantics to Web pages, which then allows for both attaching a meaning to Web search queries and Web pages, and for formulating and processing ontology-based complex Web search queries (i.e., conjunctive queries) that involve reasoning over the Web. Here, we assume the existence of an underlying ontology (in a lightweight ontology language) relative to which Web pages are annotated and Web search queries are formulated. Depending on whether we use a general or a specialized ontology, we thus obtain a general or a vertical Semantic Web search interface, respectively. That is, we are actually mapping the Web into an ontological knowledge base, which then allows for Semantic Web search relative to the underlying ontology. The latter is then realized by reduction to standard Web search on standard Web pages and logically completed ontological annotations. That is, standard Web search engines are used as the main inference motor for ontologybased Semantic Web search. We develop the formal model behind this approach and also provide an implementation in desktop search. Furthermore, we report on extensive experiments, including an implemented Semantic Web search on the Internet Movie Database.

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Section: Semantic Web Search On the Internet Movie Databasementioning

confidence: 99%

Semantic Web Search Based on Ontological Conjunctive Queries

Fazzinga

Gianforme

Gottlob

et al. 2010

Lecture Notes in Computer Science

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“…Beagle++ [6] is a desktop search system that indexes all personal documents and generates additional metadata that describes these documents, other resources, as well as their relationships. Triggered by modification events, Beagle++ annotates the material that the user has read, used, written or commented upon.…”

Section: Integrated Search Infrastructuresmentioning

confidence: 99%

Workplace Learning: How We Keep Track of Relevant Information

Bischoff

Herder

Nejdl

2007

Creating New Learning Experiences on a Global Scale

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At the workplace, learning is often a by-product of working on complex projects, requiring self-steered, need-driven and goal-oriented retrieval of information just in time from documents or peers. The personal desktop provides one rich source for learning material and for adaptation of learning resources. Data within that personal information space enables learning from previous experience, sharing tacit and explicit knowledge, and allows for establishing context and context-aware delivery of learning material. Results from personal desktop studies and the corresponding technologies have therefore great potential to enhance TEL. Thus, this paper (1) provides a short overview of desktop organization and search studies as well as applications and (2) envisions tighter incorporation of desktop research for innovative TEL infrastructures.

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“…Finally, in prior work [9,10] we described our Beagle ++8 personal information system, a semantically enriched extension of the Beagle open sources desktop search engine. There, we proposed various heuristics to generate ample activity based metadata associated to each desktop item.…”

Section: Desktop Organization Systemsmentioning

confidence: 99%

“…Several projects have started to explore search and resource organization on the desktop, including Stuff I've Seen [11], Haystack [27], Gnowsis [30], and our Beagle ++ project [10]. Moreover, at a recent panel at WWW 2005, one of the panelists identified desktop search as one of the future search engine development directions, together with other "vertical" search areas, such as image or video search [26].…”

Section: Introductionmentioning

confidence: 99%

Analyzing User Behavior to Rank Desktop Items

Chirita

Nejdl

2006

String Processing and Information Retrieval

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Existing desktop search applications, trying to keep up with the rapidly increasing storage capacities of our hard disks, are an important step towards more efficient personal information management, yet they offer an incomplete solution. While their indexing functionalities in terms of different file types they are able to cope with are impressive, their ranking capabilities are basic, and rely only on TFxIDF measures, comparable to the first generation of web search engines. In this paper we propose to connect semantically related desktop items by exploiting usage information about single accesses or sequences of accesses to local resources. We investigate and evaluate in detail the possibilities to translate this information into a desktop linkage structure, and we propose several algorithms that exploit these newly created links in order to efficiently rank desktop items. Finally, we empirically show that the access based links lead to ranking results comparable with TFxIDF ranking, and clearly surpass TFxIDF when used in combination with it, making them a very valuable source of input to desktop search ranking algorithms.

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Beagle + + : Semantically Enhanced Searching and Ranking on the Desktop

Cited by 46 publications

References 9 publications

Semantic Web Search Based on Ontological Conjunctive Queries

Semantic Web Search Based on Ontological Conjunctive Queries

Workplace Learning: How We Keep Track of Relevant Information

Analyzing User Behavior to Rank Desktop Items

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