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- 497 creator evaggelia-pitoura.
- 497 creator georgia-koloniari.
- 497 type InProceedings.
- 497 label "LCA-based Selection for XML Document Collections".
- 497 sameAs 497.
- 497 abstract "In this paper, we address the problem of database selection for XML document collections, that is, given a set of collections and a user query, how to rank the collections based on their goodness to the query. Goodness is determined by the relevance of the documents in the collection to the query. We consider keyword queries and support Lowest Common Ancestor (LCA) semantics for defining query results, so that the relevance of each document to a query is determined by properties of the LCA of those nodes in the XML document that contain the query keywords. To avoid evaluating queries against each document in a collection, we propose maintaining in a preprocessing phase, information about the LCAs of all pairs of keywords in a document and use it to approximate the properties of the LCA-based results of a query. To improve storage and processing efficiency, we use appropriate summaries of the LCA information based on Bloom filters. We address both a boolean and a weighted version of the database selection problem. Our experimental results show that our approach incurs low errors in the estimation of the goodness of a collection and provides rankings that are very close to the actual ones.".
- 497 hasAuthorList authorList.
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- 497 keyword "Ranking in semistructured repositories".
- 497 title "LCA-based Selection for XML Document Collections".
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- item-1 hasContent georgia-koloniari.
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- 504 creator changhu-wang.
- 504 creator lei-zhang-2.
- 504 creator qiang-hao.
- 504 creator rui-cai.
- 504 type InProceedings.
- 504 label "Equip Tourists with Knowledge Mined from Travelogues".
- 504 sameAs 504.
- 504 abstract "With the prosperity of tourism and the Web 2.0 technologies, more and more people have willingness to share their travel experiences on the Web (e.g., weblogs, forums, or Web 2.0 communities). These so-called travelogues contain rich information, particularly including location-representative knowledge such as attractions (e.g., Golden Gate Bridge), styles (e.g., beach, history), and activities (e.g., diving, surfing). The location-representative information in travelogues can greatly facilitate other tourists’ trip planning, if it can be correctly extracted and summarized. However, since most travelogues are unstructured and contain much noise, it is difficult for common users to digest utilize such knowledge effectively. In this paper, to mine location-representative knowledge from a large collection of travelogues, we propose a probabilistic generative model, named as Location-Topic model. This model has the advantages of (1) differentiability between two kinds of topics, i.e., local topics which characterize locations and global topics which represent other common themes shared by different locations, and (2) representation of locations in the local topic space to encode both location-representative knowledge and similarities between various locations. Some novel applications are developed based on the proposed model, including (1) destination recommendation based on flexible queries, (2) characteristic summarization for a given destination with representative tags and snippets, and (3) identification of informative parts of a travelogue and enriching such highlights with related images. Based on a large collection of travelogues, the proposed framework is evaluated using both objective and subjective evaluation methods and shows promising results.".
- 504 hasAuthorList authorList.
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- 504 keyword "Other novel Web data mining algorithms".
- 504 title "Equip Tourists with Knowledge Mined from Travelogues".
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- item-1 hasContent qiang-hao.
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- 512 creator ansgar-scherp.
- 512 creator carsten-saathoff.
- 512 type InProceedings.
- 512 label "Unlocking the Semantics of Multimedia Presentations in the Web with the Multimedia Metadata Ontology".
- 512 sameAs 512.
- 512 abstract "The semantics of rich multimedia presentations in the web such as SVG, SMIL, and Flash cannot or only to a very limited extend be understood by search engines today. This hampers the retrieval of such presentations and makes their archival and management a difficult task. Existing metadata models and metadata standards are either conceptually too narrow, focus on a specific media type only, cannot be used and combined together, or are not practically applicable for the semantic description of such rich multimedia presentations. In this paper, we propose the Multimedia Metadata Ontology (M3O) for annotating rich, structured multimedia presentations. The M3O provides a generic modeling framework for representing sophisticated multimedia metadata. It allows for integrating the features provided by the existing metadata models and metadata standards. Our approach bases on Semantic Web technologies and can be easily integrated with multimedia formats such as the W3C standards SMIL and SVG. With the M3O, we unlock the semantics of rich multimedia presentations in the web by making the semantics machine-readable and machine-understandable. The M3O is used with our SemanticMM4U framework for the multi-channel generation of semantically-rich multimedia presentations.".
- 512 hasAuthorList authorList.
- 512 isPartOf proceedings.
- 512 keyword "Rich media semantic computing".
- 512 title "Unlocking the Semantics of Multimedia Presentations in the Web with the Multimedia Metadata Ontology".
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- item-1 hasContent carsten-saathoff.
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- 525 creator falk-brauer.
- 525 creator felix-naumann.
- 525 creator gregor-hackenbroich.
- 525 creator michael-huber.
- 525 creator ulf-leser.
- 525 creator wojciech-barczynski.
- 525 type InProceedings.
- 525 label "Identification and Disambiguation of Graph-structured Concepts for Enterprise Search".
- 525 sameAs 525.
- 525 abstract "Enterprise Search (ES) is a major challenge due to a number of reasons, among which the high level of ambiguity and implicitly addressed concepts in query and document terms are the most important. What distinguishes ES from ordinary search problems most is the existence of graph-structured enterprise data (ontologies) that describe the concepts of interest and their relationships to each other. We present a method to leverage this type of information to improve the quality of query answers. Our method identifies concepts from the enterprise ontology in the query and in the corpus. Therefore, we propose a ranking scheme for top-k ontology sub-graphs on top of approximately matched token q-grams between text and ontology. The ranking scheme leverages the graph-structure of the ontology for identification and disambiguation of not explicitly mentioned concepts. It improves previous solutions by using a fine-grained ranking function that leverages relevance ratings derived from the enterprise data and a confidence rating which takes constituent match situations derived from the document into account. Query/document-specific subgraphs are used for ranking documents based on the similarity of those subgraphs. This method is able to capture much more of the semantics of queries and documents than previous techniques. We prove this claim by an evaluation of our method using three real-life document sets and consider two knowledge bases.".
- 525 hasAuthorList authorList.
- 525 isPartOf proceedings.
- 525 keyword "Semantic search".
- 525 keyword "entity retrieval".
- 525 keyword "geo/temporal search".
- 525 keyword "sub/super-documents".
- 525 title "Identification and Disambiguation of Graph-structured Concepts for Enterprise Search".
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