-
Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005)
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- Abstract
- Ontologien werden eingesetzt, um durch semantische Fundierung insbesondere für das Dokumentenretrieval eine grundlegend bessere Basis zu haben, als dies gegenwärtiger Stand der Technik ist. Vorgestellt wird eine an der FH Darmstadt entwickelte und eingesetzte Ontologie, die den Gegenstandsbereich Hochschule sowohl breit abdecken und gleichzeitig differenziert semantisch beschreiben soll. Das Problem der semantischen Suche besteht nun darin, dass sie für Informationssuchende so einfach wie bei gängigen Suchmaschinen zu nutzen sein soll, und gleichzeitig auf der Grundlage des aufwendigen Informationsmodells hochwertige Ergebnisse liefern muss. Es wird beschrieben, welche Möglichkeiten die verwendete Software K-Infinity bereitstellt und mit welchem Konzept diese Möglichkeiten für eine semantische Suche nach Dokumenten und anderen Informationseinheiten (Personen, Veranstaltungen, Projekte etc.) eingesetzt werden.
- Date
- 11. 2.2011 18:22:58
-
Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005)
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- Abstract
- Ontologien werden eingesetzt, um durch semantische Fundierung insbesondere für das Dokumentenretrieval eine grundlegend bessere Basis zu haben, als dies gegenwärtiger Stand der Technik ist. Vorgestellt wird eine an der FH Darmstadt entwickelte und eingesetzte Ontologie, die den Gegenstandsbereich Hochschule sowohl breit abdecken und gleichzeitig differenziert semantisch beschreiben soll. Das Problem der semantischen Suche besteht nun darin, dass sie für Informationssuchende so einfach wie bei gängigen Suchmaschinen zu nutzen sein soll, und gleichzeitig auf der Grundlage des aufwendigen Informationsmodells hochwertige Ergebnisse liefern muss. Es wird beschrieben, welche Möglichkeiten die verwendete Software K-Infinity bereitstellt und mit welchem Konzept diese Möglichkeiten für eine semantische Suche nach Dokumenten und anderen Informationseinheiten (Personen, Veranstaltungen, Projekte etc.) eingesetzt werden.
- Date
- 11. 2.2011 18:22:25
-
Vechtomova, O.; Robertson, S.E.: ¬A domain-independent approach to finding related entities (2012)
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- Abstract
- We propose an approach to the retrieval of entities that have a specific relationship with the entity given in a query. Our research goal is to investigate whether related entity finding problem can be addressed by combining a measure of relatedness of candidate answer entities to the query, and likelihood that the candidate answer entity belongs to the target entity category specified in the query. An initial list of candidate entities, extracted from top ranked documents retrieved for the query, is refined using a number of statistical and linguistic methods. The proposed method extracts the category of the target entity from the query, identifies instances of this category as seed entities, and computes similarity between candidate and seed entities. The evaluation was conducted on the Related Entity Finding task of the Entity Track of TREC 2010, as well as the QA list questions from TREC 2005 and 2006. Evaluation results demonstrate that the proposed methods are effective in finding related entities.
- Date
- 27. 1.2016 18:44:29
-
Bando, L.L.; Scholer, F.; Turpin, A.: Query-biased summary generation assisted by query expansion : temporality (2015)
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- Abstract
- Query-biased summaries help users to identify which items returned by a search system should be read in full. In this article, we study the generation of query-biased summaries as a sentence ranking approach, and methods to evaluate their effectiveness. Using sentence-level relevance assessments from the TREC Novelty track, we gauge the benefits of query expansion to minimize the vocabulary mismatch problem between informational requests and sentence ranking methods. Our results from an intrinsic evaluation show that query expansion significantly improves the selection of short relevant sentences (5-13 words) between 7% and 11%. However, query expansion does not lead to improvements for sentences of medium (14-20 words) and long (21-29 words) lengths. In a separate crowdsourcing study, we analyze whether a summary composed of sentences ranked using query expansion was preferred over summaries not assisted by query expansion, rather than assessing sentences individually. We found that participants chose summaries aided by query expansion around 60% of the time over summaries using an unexpanded query. We conclude that query expansion techniques can benefit the selection of sentences for the construction of query-biased summaries at the summary level rather than at the sentence ranking level.
-
Gnoli, C.; Santis, R. de; Pusterla, L.: Commerce, see also Rhetoric : cross-discipline relationships as authority data for enhanced retrieval (2015)
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- Abstract
- Subjects in a classification scheme are often related to other subjects belonging to different hierarchies. This problem was identified already by Hugh of Saint Victor (1096?-1141). Still with present-time bibliographic classifications, a user browsing the class of architecture under the hierarchy of arts may miss relevant items classified in building or in civil engineering under the hierarchy of applied sciences. To face these limitations we have developed SciGator, a browsable interface to explore the collections of all scientific libraries at the University of Pavia. Besides showing subclasses of a given class, the interface points users to related classes in the Dewey Decimal Classification, or in other local schemes, and allows for expanded queries that include them. This is made possible by using a special field for related classes in the database structure which models classification authority data. Ontologically, many relationships between classes in different hierarchies are cases of existential dependence. Dependence can occur between disciplines in such disciplinary classifications as Dewey (e.g. architecture existentially depends on building), or between phenomena in such phenomenon-based classifications as the Integrative Levels Classification (e.g. fishing as a human activity existentially depends on fish as a class of organisms). We provide an example of its representation in OWL and discuss some details of it.
- Source
- Classification and authority control: expanding resource discovery: proceedings of the International UDC Seminar 2015, 29-30 October 2015, Lisbon, Portugal. Eds.: Slavic, A. u. M.I. Cordeiro
-
Brandão, W.C.; Santos, R.L.T.; Ziviani, N.; Moura, E.S. de; Silva, A.S. da: Learning to expand queries using entities (2014)
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- Abstract
- A substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. Recently, methods that exploit named entities have been shown to be more effective for query expansion than traditional pseudorelevance feedback methods. In this article, we introduce a supervised learning approach that exploits named entities for query expansion using Wikipedia as a repository of high-quality feedback documents. In contrast with existing entity-oriented pseudorelevance feedback approaches, we tackle query expansion as a learning-to-rank problem. As a result, not only do we select effective expansion terms but we also weigh these terms according to their predicted effectiveness. To this end, we exploit the rich structure of Wikipedia articles to devise discriminative term features, including each candidate term's proximity to the original query terms, as well as its frequency across multiple article fields and in category and infobox descriptors. Experiments on three Text REtrieval Conference web test collections attest the effectiveness of our approach, with gains of up to 23.32% in terms of mean average precision, 19.49% in terms of precision at 10, and 7.86% in terms of normalized discounted cumulative gain compared with a state-of-the-art approach for entity-oriented query expansion.
- Date
- 22. 8.2014 17:07:50
-
Jun, W.: ¬A knowledge network constructed by integrating classification, thesaurus and metadata in a digital library (2003)
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- Abstract
- Knowledge management in digital libraries is a universal problem. Keyword-based searching is applied everywhere no matter whether the resources are indexed databases or full-text Web pages. In keyword matching, the valuable content description and indexing of the metadata, such as the subject descriptors and the classification notations, are merely treated as common keywords to be matched with the user query. Without the support of vocabulary control tools, such as classification systems and thesauri, the intelligent labor of content analysis, description and indexing in metadata production are seriously wasted. New retrieval paradigms are needed to exploit the potential of the metadata resources. Could classification and thesauri, which contain the condensed intelligence of generations of librarians, be used in a digital library to organize the networked information, especially metadata, to facilitate their usability and change the digital library into a knowledge management environment? To examine that question, we designed and implemented a new paradigm that incorporates a classification system, a thesaurus and metadata. The classification and the thesaurus are merged into a concept network, and the metadata are distributed into the nodes of the concept network according to their subjects. The abstract concept node instantiated with the related metadata records becomes a knowledge node. A coherent and consistent knowledge network is thus formed. It is not only a framework for resource organization but also a structure for knowledge navigation, retrieval and learning. We have built an experimental system based on the Chinese Classification and Thesaurus, which is the most comprehensive and authoritative in China, and we have incorporated more than 5000 bibliographic records in the computing domain from the Peking University Library. The result is encouraging. In this article, we review the tools, the architecture and the implementation of our experimental system, which is called Vision.
- Source
- Bulletin of the American Society for Information Science. 29(2003) no.2, S.24-28
-
Layfield, C.; Azzopardi, J,; Staff, C.: Experiments with document retrieval from small text collections using Latent Semantic Analysis or term similarity with query coordination and automatic relevance feedback (2017)
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- Abstract
- One of the problems faced by users of databases containing textual documents is the difficulty in retrieving relevant results due to the diverse vocabulary used in queries and contained in relevant documents, especially when there are only a small number of relevant documents. This problem is known as the Vocabulary Gap. The PIKES team have constructed a small test collection of 331 articles extracted from a blog and a Gold Standard for 35 queries selected from the blog's search log so the results of different approaches to semantic search can be compared. So far, prior approaches include recognising Named Entities in documents and queries, and relations including temporal relations, and represent them as `semantic layers' in a retrieval system index. In this work, we take two different approaches that do not involve Named Entity Recognition. In the first approach, we process an unannotated version of the PIKES document collection using Latent Semantic Analysis and use a combination of query coordination and automatic relevance feedback with which we outperform prior work. However, this approach is highly dependent on the underlying collection, and is not necessarily scalable to massive collections. In our second approach, we use an LSA Model generated by SEMILAR from a Wikipedia dump to generate a Term Similarity Matrix (TSM). We automatically expand the queries in the PIKES test collection with related terms from the TSM and submit them to a term-by-document matrix derived by indexing the PIKES collection using the Vector Space Model. Coupled with a combination of query coordination and automatic relevance feedback we also outperform prior work with this approach. The advantage of the second approach is that it is independent of the underlying document collection.
- Date
- 10. 3.2017 13:29:57
-
Bradford, R.B.: Relationship discovery in large text collections using Latent Semantic Indexing (2006)
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- Abstract
- This paper addresses the problem of information discovery in large collections of text. For users, one of the key problems in working with such collections is determining where to focus their attention. In selecting documents for examination, users must be able to formulate reasonably precise queries. Queries that are too broad will greatly reduce the efficiency of information discovery efforts by overwhelming the users with peripheral information. In order to formulate efficient queries, a mechanism is needed to automatically alert users regarding potentially interesting information contained within the collection. This paper presents the results of an experiment designed to test one approach to generation of such alerts. The technique of latent semantic indexing (LSI) is used to identify relationships among entities of interest. Entity extraction software is used to pre-process the text of the collection so that the LSI space contains representation vectors for named entities in addition to those for individual terms. In the LSI space, the cosine of the angle between the representation vectors for two entities captures important information regarding the degree of association of those two entities. For appropriate choices of entities, determining the entity pairs with the highest mutual cosine values yields valuable information regarding the contents of the text collection. The test database used for the experiment consists of 150,000 news articles. The proposed approach for alert generation is tested using a counterterrorism analysis example. The approach is shown to have significant potential for aiding users in rapidly focusing on information of potential importance in large text collections. The approach also has value in identifying possible use of aliases.
- Source
- Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism, and Security, SIAM Data Mining Conference, Bethesda, MD, 20-22 April, 2006. [http://www.siam.org/meetings/sdm06/workproceed/Link%20Analysis/15.pdf]
-
Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002)
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- Date
- 22. 2.2003 17:25:39
22. 2.2003 18:17:40
-
Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
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- Date
- 30. 3.2001 13:32:22
-
Ross, J.: ¬A new way of information retrieval : 3-D indexing and concept mapping (2000)
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- Date
- 25. 2.1997 10:29:16
-
Shiri, A.A.; Revie, C.; Chowdhury, G.: Thesaurus-enhanced search interfaces (2002)
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- Date
- 18. 5.2002 17:29:00
-
Shiri, A.A.; Revie, C.: ¬The effects of topic complexity and familiarity on cognitive and physical moves in a thesaurus-enhanced search environment (2003)
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- Source
- Journal of information science. 29(2003) no.6, S.517-
-
Celik, I.; Abel, F.; Siehndel, P.: Adaptive faceted search on Twitter (2011)
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- Abstract
- In the last few years, Twitter has become a powerful tool for publishing and discussing information. Yet, content exploration in Twitter requires substantial efforts and users often have to scan information streams by hand. In this paper, we approach this problem by means of faceted search. We propose strategies for inferring facets and facet values on Twitter by enriching the semantics of individual Twitter messages and present di erent methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective.
-
Vo, D.-T.; Bagheri, E.: Feature-enriched matrix factorization for relation extraction (2019)
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- Abstract
- Relation extraction aims at finding meaningful relationships between two named entities from within unstructured textual content. In this paper, we define the problem of information extraction as a matrix completion problem where we employ the notion of universal schemas formed as a collection of patterns derived from open information extraction systems as well as additional features derived from grammatical clause patterns and statistical topic models. One of the challenges with earlier work that employ matrix completion methods is that such approaches require a sufficient number of observed relation instances to be able to make predictions. However, in practice there is often insufficient number of explicit evidence supporting each relation type that could be used within the matrix model. Hence, existing work suffer from a low recall. In our work, we extend the work in the state of the art by proposing novel ways of integrating two sets of features, i.e., topic models and grammatical clause structures, for alleviating the low recall problem. More specifically, we propose that it is possible to (1) employ grammatical clause information from textual sentences to serve as an implicit indication of relation type and argument similarity. The basis for this is that it is likely that similar relation types and arguments are observed within similar grammatical structures, and (2) benefit from statistical topic models to determine similarity between relation types and arguments. We employ statistical topic models to determine relation type and argument similarity based on their co-occurrence within the same topics. We have performed extensive experiments based on both gold standard and silver standard datasets. The experiments show that our approach has been able to address the low recall problem in existing methods, by showing an improvement of 21% on recall and 8% on f-measure over the state of the art baseline.
-
Liu, X.; Zheng, W.; Fang, H.: ¬An exploration of ranking models and feedback method for related entity finding (2013)
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- Abstract
- Most existing search engines focus on document retrieval. However, information needs are certainly not limited to finding relevant documents. Instead, a user may want to find relevant entities such as persons and organizations. In this paper, we study the problem of related entity finding. Our goal is to rank entities based on their relevance to a structured query, which specifies an input entity, the type of related entities and the relation between the input and related entities. We first discuss a general probabilistic framework, derive six possible retrieval models to rank the related entities, and then compare these models both analytically and empirically. To further improve performance, we study the problem of feedback in the context of related entity finding. Specifically, we propose a mixture model based feedback method that can utilize the pseudo feedback entities to estimate an enriched model for the relation between the input and related entities. Experimental results over two standard TREC collections show that the derived relation generation model combined with a relation feedback method performs better than other models.
-
Nagao, M.: Knowledge and inference (1990)
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- Abstract
- Knowledge and Inference discusses an important problem for software systems: How do we treat knowledge and ideas on a computer and how do we use inference to solve problems on a computer? The book talks about the problems of knowledge and inference for the purpose of merging artificial intelligence and library science. The book begins by clarifying the concept of ""knowledge"" from many points of view, followed by a chapter on the current state of library science and the place of artificial intelligence in library science. Subsequent chapters cover central topics in the artificial intelligence: search and problem solving, methods of making proofs, and the use of knowledge in looking for a proof. There is also a discussion of how to use the knowledge system. The final chapter describes a popular expert system. It describes tools for building expert systems using an example based on Expert Systems-A Practical Introduction by P. Sell (Macmillian, 1985). This type of software is called an ""expert system shell."" This book was written as a textbook for undergraduate students covering only the basics but explaining as much detail as possible.
-
Stojanovic, N.: On the query refinement in the ontology-based searching for information (2005)
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- Date
- 5. 4.1996 15:29:15
-
Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016)
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- Date
- 1. 2.2016 18:25:22
