Search (16 results, page 1 of 1)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Jiang, Y.; Zhang, X.; Tang, Y.; Nie, R.: Feature-based approaches to semantic similarity assessment of concepts using Wikipedia (2015) 0.04 
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    Abstract
    Semantic similarity assessment between concepts is an important task in many language related applications. In the past, several approaches to assess similarity by evaluating the knowledge modeled in an (or multiple) ontology (or ontologies) have been proposed. However, there are some limitations such as the facts of relying on predefined ontologies and fitting non-dynamic domains in the existing measures. Wikipedia provides a very large domain-independent encyclopedic repository and semantic network for computing semantic similarity of concepts with more coverage than usual ontologies. In this paper, we propose some novel feature based similarity assessment methods that are fully dependent on Wikipedia and can avoid most of the limitations and drawbacks introduced above. To implement similarity assessment based on feature by making use of Wikipedia, firstly a formal representation of Wikipedia concepts is presented. We then give a framework for feature based similarity based on the formal representation of Wikipedia concepts. Lastly, we investigate several feature based approaches to semantic similarity measures resulting from instantiations of the framework. The evaluation, based on several widely used benchmarks and a benchmark developed in ourselves, sustains the intuitions with respect to human judgements. Overall, several methods proposed in this paper have good human correlation and constitute some effective ways of determining similarity between Wikipedia concepts.
  2. Buccio, E. Di; Melucci, M.; Moro, F.: Detecting verbose queries and improving information retrieval (2014) 0.02 
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    Abstract
    Although most of the queries submitted to search engines are composed of a few keywords and have a length that ranges from three to six words, more than 15% of the total volume of the queries are verbose, introduce ambiguity and cause topic drifts. We consider verbosity a different property of queries from length since a verbose query is not necessarily long, it might be succinct and a short query might be verbose. This paper proposes a methodology to automatically detect verbose queries and conditionally modify queries. The methodology proposed in this paper exploits state-of-the-art classification algorithms, combines concepts from a large linguistic database and uses a topic gisting algorithm we designed for verbose query modification purposes. Our experimental results have been obtained using the TREC Robust track collection, thirty topics classified by difficulty degree, four queries per topic classified by verbosity and length, and human assessment of query verbosity. Our results suggest that the methodology for query modification conditioned to query verbosity detection and topic gisting is significantly effective and that query modification should be refined when topic difficulty and query verbosity are considered since these two properties interact and query verbosity is not straightforwardly related to query length.
  3. Olmos, R.; Jorge-Botana, G.; Luzón, J.M.; Martín-Cordero, J.I.; León, J.A.: Transforming LSA space dimensions into a rubric for an automatic assessment and feedback system (2016) 0.02 
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  4. Xu, B.; Lin, H.; Lin, Y.: Assessment of learning to rank methods for query expansion (2016) 0.02 
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  5. Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016) 0.02 
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    Date
    1. 2.2016 18:25:22
  6. Kozikowski, P. et al.: Support of part-whole relations in query answering (2016) 0.02 
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    Date
    1. 2.2016 18:25:22
  7. Marx, E. et al.: Exploring term networks for semantic search over RDF knowledge graphs (2016) 0.02 
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    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  8. Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016) 0.02 
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    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  9. Koopman, B.; Zuccon, G.; Bruza, P.; Sitbon, L.; Lawley, M.: Information retrieval as semantic inference : a graph Inference model applied to medical search (2016) 0.02 
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    Abstract
    This paper presents a Graph Inference retrieval model that integrates structured knowledge resources, statistical information retrieval methods and inference in a unified framework. Key components of the model are a graph-based representation of the corpus and retrieval driven by an inference mechanism achieved as a traversal over the graph. The model is proposed to tackle the semantic gap problem-the mismatch between the raw data and the way a human being interprets it. We break down the semantic gap problem into five core issues, each requiring a specific type of inference in order to be overcome. Our model and evaluation is applied to the medical domain because search within this domain is particularly challenging and, as we show, often requires inference. In addition, this domain features both structured knowledge resources as well as unstructured text. Our evaluation shows that inference can be effective, retrieving many new relevant documents that are not retrieved by state-of-the-art information retrieval models. We show that many retrieved documents were not pooled by keyword-based search methods, prompting us to perform additional relevance assessment on these new documents. A third of the newly retrieved documents judged were found to be relevant. Our analysis provides a thorough understanding of when and how to apply inference for retrieval, including a categorisation of queries according to the effect of inference. The inference mechanism promoted recall by retrieving new relevant documents not found by previous keyword-based approaches. In addition, it promoted precision by an effective reranking of documents. When inference is used, performance gains can generally be expected on hard queries. However, inference should not be applied universally: for easy, unambiguous queries and queries with few relevant documents, inference did adversely affect effectiveness. These conclusions reflect the fact that for retrieval as inference to be effective, a careful balancing act is involved. Finally, although the Graph Inference model is developed and applied to medical search, it is a general retrieval model applicable to other areas such as web search, where an emerging research trend is to utilise structured knowledge resources for more effective semantic search.
  10. Salaba, A.; Zeng, M.L.: Extending the "Explore" user task beyond subject authority data into the linked data sphere (2014) 0.01 
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    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  11. Mlodzka-Stybel, A.: Towards continuous improvement of users' access to a library catalogue (2014) 0.01 
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    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  12. Zeng, M.L.; Gracy, K.F.; Zumer, M.: Using a semantic analysis tool to generate subject access points : a study using Panofsky's theory and two research samples (2014) 0.01 
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    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  13. Brandão, W.C.; Santos, R.L.T.; Ziviani, N.; Moura, E.S. de; Silva, A.S. da: Learning to expand queries using entities (2014) 0.01 
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    Date
    22. 8.2014 17:07:50
  14. Brunetti, J.M.; Roberto García, R.: User-centered design and evaluation of overview components for semantic data exploration (2014) 0.01 
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    Date
    20. 1.2015 18:30:22
  15. Gillitzer, B.: Yewno (2017) 0.01 
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    Date
    22. 2.2017 10:16:49
  16. Thenmalar, S.; Geetha, T.V.: Enhanced ontology-based indexing and searching (2014) 0.01 
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    Date
    20. 1.2015 18:30:22

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