Building user interest profiles from wikipedia clusters

Users of search systems are often reluctant to explicitly build profiles to indicate their search interests. Thus automatically building user profiles is an important research area for personalized search. One difficult component of doing this is accessing a knowledge system which provides broad coverage of user search interests. In this work, we describe a method to build category id based user profiles from a user's historical search data. Our approach makes significant use of Wikipedia as an external knowledge resource

External query reformulation for text-based image retrieval

In text-based image retrieval, the Incomplete Annotation Problem (IAP) can greatly degrade retrieval effectiveness. A standard method used to address this problem is pseudo relevance feedback (PRF) which updates user queries by adding feedback terms selected automatically from top ranked documents in a prior retrieval run. PRF assumes that the target collection provides enough feedback information to select effective expansion terms. This is often not the case in image retrieval since images often only have short metadata annotations leading to the IAP. Our work proposes the use of an external knowledge resource (Wikipedia) in the process of refining user queries. In our method, Wikipedia documents strongly related to the terms in user query (" definition documents") are first identified by title matching between the query and titles of Wikipedia articles. These definition documents are used as indicators to re-weight the feedback documents from an initial search run on a Wikipedia abstract collection using the Jaccard coefficient. The new weights of the feedback documents are combined with the scores rated by different indicators. Query-expansion terms are then selected based on these new weights for the feedback documents. Our method is evaluated on the ImageCLEF WikipediaMM image retrieval task using text-based retrieval on the document metadata fields. The results show significant improvement compared to standard PRF methods

Document expansion for text-based image retrieval at WikipediaMM 2010

We describe and analyze our participation in the Wikipedi- aMM task at ImageCLEF 2010. Our approach is based on text-based image retrieval using information retrieval techniques on the metadata documents of the images. We submitted two English monolingual runs and one multilingual run. The monolingual runs used the query to retrieve the metadata document with the query and document in the same language; the multilingual run used queries in one language to search the metadata provided in three languages. The main focus of our work was using the English query to retrieve images based on the English meta-data. For these experiments the English metadata data was expanded using an external resource - DBpedia. This study expanded on our application of document expansion in our previous participation in Image-CLEF 2009. In 2010 we combined document expansion with a document reduction technique which aimed to include only topically important words to the metadata. Our experiments used the Okapi feedback algorithm for document expansion and Okapi BM25 model for retrieval. Experimental results show that combining document expansion with the document reduction method give the best overall retrieval results

A text-based approach to the ImageCLEF 2010 photo annotation task

The challenges of searching the increasingly large collections of digital images which are appearing in many places mean that automated annotation of images is becoming an important task. We describe our participation in the ImageCLEF 2010 Visual Concept Detection and Annotation Task. Our approach used only the textual features (Flickr user tags and EXIF information) to perform the automatic annotation. Our approach was to explore the use of different techniques to improve the results of textual annotation. We identify the drawbacks of our approach and how these might be addressed and optimized in further work

Building a domain-specific document collection for evaluating metadata effects on information retrieval

This paper describes the development of a structured document collection containing user-generated text and numerical metadata for exploring the exploitation of metadata in information retrieval (IR). The collection consists of more than 61,000 documents extracted from YouTube video pages on basketball in general and NBA (National Basketball Association) in particular, together with a set of 40 topics and their relevance judgements. In addition, a collection of nearly 250,000 user profiles related to the NBA collection is available. Several baseline IR experiments report the effect of using video-associated metadata on retrieval effectiveness. The results surprisingly show that searching the videos titles only performs significantly better than searching additional metadata text fields of the videos such as the tags or the description

Document expansion for image retrieval

Successful information retrieval requires e�ective matching between the user's search request and the contents of relevant documents. Often the request entered by a user may not use the same topic relevant terms as the authors' of the documents. One potential approach to address problems of query-document term mismatch is document expansion to include additional topically relevant indexing terms in a document which may encourage its retrieval when relevant to queries which do not match its original contents well. We propose and evaluate a new document expansion method using external resources. While results of previous research have been inconclusive in determining the impact of document expansion on retrieval e�ectiveness, our method is shown to work e�ectively for text-based image retrieval of short image annotation documents. Our approach uses the Okapi query expansion algorithm as a method for document expansion. We further show improved performance can be achieved by using a \document reduction" approach to include only the signi�cant terms in a document in the expansion process. Our experiments on the WikipediaMM task at ImageCLEF 2008 show an increase of 16.5% in mean average precision (MAP) compared to a variation of Okapi BM25 retrieval model. To compare document expansion with query expansion, we also test query expansion from an external resource which leads an improvement by 9.84% in MAP over our baseline. Our conclusion is that the document expansion with document reduction and in combination with query expansion produces the overall best retrieval results for shortlength document retrieval. For this image retrieval task, we also concluded that query expansion from external resource does not outperform the document expansion method

Utilizing external resources for enriching information retrieval

Information retrieval (IR) seeks to support users in finding information relevant to their information needs. One obstacle for many IR algorithms to achieve better results in many IR tasks is that there is insufficient information available to enable relevant content to be identified. For example, users typically enter very short queries, in text-based image retrieval where textual annotations often describe the content of the images inadequately, or there is insufficient user log data for personalization of the search process. This thesis explores the problem of inadequate data in IR tasks. We propose methods for Enriching Information Retrieval (ENIR) which address various challenges relating to insufficient data in IR. Applying standard methods to address these problems can face unexpected challenges. For example, standard query expansion methods assume that the target collection contains sufficient data to be able to identify relevant terms to add to the original query to improve retrieval effectiveness. In the case of short documents, this assumption is not valid. One strategy to address this problem is document side expansion which has been largely overlooked in the past research. Similarly, topic modeling in personalized search often lacks the knowledge required to form adequate models leading to mismatch problems when trying to apply these models improve search. This thesis focuses on methods of ENIR for tasks affected by problems of insufficient data. To achieve ENIR, our overall solution is to include external resources for ENIR. This research focuses on developing methods for two typical ENIR tasks: text-based image retrieval and personalized web data search. In this research, the main relevant areas within existing IR research are relevance feedback and personalized modeling. ENIR is shown to be effective to augment existing knowledge in these classical areas. The areas of relevance feedback and personalized modeling are strongly correlated since user modeling and document modeling in personalized retrieval enrich the data from both sides of the query and document, which is similar to query and document expansion in relevance feedback. Enriching IR is the key challenge in these areas for IR. By addressing these two research areas, this thesis provides a prototype for an external resource based search solution. The experimental results show external resources can play a key role in enriching IR

DCU at WikipediaMM 2009: Document expansion from Wikipedia abstracts

In this paper, we describe our participation in the WikipediaMM task at CLEF 2009. Our main efforts concern the expansion of the image metadata from the Wikipedia abstracts collection DBpedia. Since the metadata is short for retrieval by query words, we decided to expand the metadata using a typical query expansion method. In our experiments, we use the Rocchio algorithm for document expansion. Our best run is in the 26th rank of all 57 runs which is under our expectation, and we think that the main reason is that our document expansion method uses all the words from the metadata documents which contain words which are unrelated to the content of the images. Compared with our text retrieval baseline, our best document expansion run improves MAP by 11.17%. As one of our conclusions, we think that the document expansion can play an effective factor in the image metadata retrieval task. Our content-based image retrieval uses the same approach as in our participation in ImageCLEF 2008

Synthetic rabbit-human antibody conjugate as a control in immunoassays for immunoglobulin M specific to hepatitis E virus

<p>Abstract</p> <p>Background</p> <p>In assays for anti-hepatitis E virus (HEV) immunoglobulin M (IgM), large volumes of the patient's sera cannot be easily obtained for use as a positive control. In this study, we investigated an alternative chemical method in which rabbit anti-HEV IgG was conjugated with human IgM and was used as a positive control in the anti-HEV IgM assay. Rabbit anti-HEV IgG was isolated from immune sera by chromatography on protein A-Sepharose and was conjugated with human IgM by using 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) as a crosslinker.</p> <p>Results</p> <p>The specific anti-HEV IgG antibody titer was 100,000 times that of the negative control, i.e., prebleed rabbit serum. The results of anti-HEV IgM enzyme-linked immunosobent assay showed that the antibody conjugate was similar to anti-HEV IgM antibodies produced in humans. The results of a stability experiment showed that the antibody conjugate was stable for use in external quality assessment or internal quality control trials.</p> <p>Conclusions</p> <p>We concluded that the chemically conjugated rabbit-human antibody could be used instead of the traditional serum control as a positive control in the anti-HEV IgM assay.</p

DCU's experiments in NTCIR-8 IR4QA task

We describe DCU's participation in the NTCIR-8 IR4QA task [16]. This task is a cross-language information retrieval(CLIR) task from English to Simplified Chinese which seeks to provide relevant documents for later cross language question answering (CLQA) tasks. For the IR4QA task, we submitted 5 official runs including two monolingual runs and three CLIR runs. For the monolingual retrieval we tested two information retrieval models. The results show that the KL-Divergence language model method performs better than the Okapi BM25 model for the Simplified Chinese retrieval task. This agrees with our previous CLIR experimental results at NTCIR-5. For the CLIR task, we compare query translation and document translation methods. In the query translation based runs, we tested a method for query expansion from external resource (QEE) before query translation. Our result for this run is slightly lower than the run without QEE. Our results show that the document translation method achieves 68.24% MAP performance compared to our best query translation run. For the document translation method, we found that the main issue is the lack of named entity translation in the documents since we do not have a suitable parallel corpus for training data for the statistical machine translation system. Our best CLIR run comes from the combination of query translation using Google translate and the KL-Divergence language model retrieval method. It achieves 79.94% MAP relative to our best monolingual run