-
Xiong, C.: Knowledge based text representations for information retrieval (2016)
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- Abstract
- The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.
This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines word based and entity based representations together with their uncertainties considered. At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations. This dissertation overcomes the limitation of word based representations with external and carefully curated information from knowledge bases. We believe this thesis research is a solid start towards the new generation of intelligent, semantic, and structured information retrieval.
- Content
- Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
-
Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019)
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- Content
- Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
-
Alvers, M.R.: Semantische wissensbasierte Suche in den Life Sciences am Beispiel von GoPubMed (2010)
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- Abstract
- Nie zuvor war der Zugriff auf Informationen so einfach und schnell wie heute. Die Suchmaschine Google ist dabei mit einem Marktanteil von 95 Prozent in Deutschland führend. Aber reicht der heutige Status Quo aus? Wir meinen nein - andere meinen ja. Die Verwendung von Stichworten für die Suche ist sehr begrenzt, nicht intelligent und der Algorithmus zum ranking der Suchergebnisse fragwürdig. Wir zeigen neue Wege der semantischen Suche mittels der Verwendung von Hintergrundwissen. Die (semi)automatische Generierung von Ontologien wird ebenfalls als zentraler Bestandteil einer universellen Wissensplattform vorgestellt und gezeigt, wie Anwender mit dieser Technologie signifikant Zeit sparen und deutlich relevantere Informationen finden.
-
Thenmalar, S.; Geetha, T.V.: Enhanced ontology-based indexing and searching (2014)
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- Abstract
- Purpose - The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts. Design/methodology/approach - In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query. Findings - The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology "with and without query expansion" is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42. Practical implications - When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved. Originality/value - In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.
- Date
- 20. 1.2015 18:30:22
-
Lee, J.; Min, J.-K.; Oh, A.; Chung, C.-W.: Effective ranking and search techniques for Web resources considering semantic relationships (2014)
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- Abstract
- On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods.
-
Alaya, N.; Yahia, S.B.; Lamolle, M.: Ranking with ties of OWL ontology reasoners based on learned performances (2016)
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- Abstract
- Over the last decade, several ontology reasoners have been proposed to overcome the computational complexity of inference tasks on expressive ontology languages such as OWL 2 DL. Nevertheless, it is well-accepted that there is no outstanding reasoner that can outperform in all input ontologies. Thus, deciding the most suitable reasoner for an ontology based application is still a time and effort consuming task. In this paper, we suggest to develop a new system to provide user support when looking for guidance over ontology reasoners. At first, we will be looking at automatically predict a single reasoner empirical performances, in particular its robustness and efficiency, over any given ontology. Later, we aim at ranking a set of candidate reasoners in a most preferred order by taking into account information regarding their predicted performances. We conducted extensive experiments covering over 2500 well selected real-world ontologies and six state-of-the-art of the most performing reasoners. Our primary prediction and ranking results are encouraging and witnessing the potential benefits of our approach.
-
Maheswari, J.U.; Karpagam, G.R.: ¬A conceptual framework for ontology based information retrieval (2010)
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- Abstract
- Improving Information retrieval by employing the use of ontologies to overcome the limitations of syntactic search has been one of the inspirations since its emergence. This paper proposes a conceptual framework to exploit ontology based Information retrieval. This framework constitutes of five phases namely Query parsing, word stemming, ontology matching, weight assignment, ranking and Information retrieval. In the first phase, the user query is parsed into sequence of words. The parsed contents are curtailed to identify the significant word by ignoring superfluous terms such as "to", "is","ed", "about" and the like in the stemming phase. The objective of the stemming phase is to throttle feature descriptors to root words, which in turn will increase efficiency; this reduces the time consumed for searching the superfluous terms, which may not significantly influence the effectiveness of the retrieval process. In the third phase ontology matching is carried out by matching the parsed words with the relevant terms in the existing ontology. If the ontology does not exist, it is recommended to generate the required ontology. In the fourth phase the weights are assigned based on the distance between the stemmed words and the terms in the ontology uses improved matchmaking algorithm. The range of weights varies from 0 to 1 based on the level of distance in the ontology (superclass-subclass). The aggregate weights are calculated for the all the combination of stemmed words. The combination with the highest score is ranked as the best and the corresponding information is retrieved. The conceptual workflow is illustrated with an e-governance case study Academic Information System.
-
Rajasurya, S.; Muralidharan, T.; Devi, S.; Swamynathan, S.: Semantic information retrieval using ontology in university domain (2012)
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- Abstract
- Today's conventional search engines hardly do provide the essential content relevant to the user's search query. This is because the context and semantics of the request made by the user is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is upcoming in the area of web search which combines Natural Language Processing and Artificial Intelligence. The objective of the work done here is to design, develop and implement a semantic search engine- SIEU(Semantic Information Extraction in University Domain) confined to the university domain. SIEU uses ontology as a knowledge base for the information retrieval process. It is not just a mere keyword search. It is one layer above what Google or any other search engines retrieve by analyzing just the keywords. Here the query is analyzed both syntactically and semantically. The developed system retrieves the web results more relevant to the user query through keyword expansion. The results obtained here will be accurate enough to satisfy the request made by the user. The level of accuracy will be enhanced since the query is analyzed semantically. The system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query.
-
Boteram, F.: Semantische Relationen in Dokumentationssprachen : vom Thesaurus zum semantischen Netz (2010)
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- Date
- 2. 3.2013 12:29:05
- Source
- Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly
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Hollink, L.; Assem, M. van: Estimating the relevance of search results in the Culture-Web : a study of semantic distance measures (2010)
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- Date
- 29. 7.2011 14:44:56
26.12.2011 13:40:22
-
Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017)
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- Date
- 13.12.2017 14:17:22
-
Nielsen, M.: Neuronale Netze : Alpha Go - Computer lernen Intuition (2018)
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- Source
- Spektrum der Wissenschaft. 2018, H.1, S.22-27
-
Clark, M.; Kim, Y.; Kruschwitz, U.; Song, D.; Albakour, D.; Dignum, S.; Beresi, U.C.; Fasli, M.; Roeck, A De: Automatically structuring domain knowledge from text : an overview of current research (2012)
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- Date
- 29. 1.2016 18:29:51
-
Börner, K.: Atlas of knowledge : anyone can map (2015)
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- 22. 1.2017 16:54:03
22. 1.2017 17:10:56
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Hoppe, T.: Semantische Filterung : ein Werkzeug zur Steigerung der Effizienz im Wissensmanagement (2013)
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- 29. 9.2015 18:56:44
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Almeida Campos, M.L. de; Espanha Gomes, H.: Ontology : several theories on the representation of knowledge domains (2017)
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Maculan, B.C.M. dos; Lima, G.A. de; Oliveira, E.D.: Conversion methods from thesaurus to ontologies : a review (2016)
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- Source
- Knowledge organization for a sustainable world: challenges and perspectives for cultural, scientific, and technological sharing in a connected society : proceedings of the Fourteenth International ISKO Conference 27-29 September 2016, Rio de Janeiro, Brazil / organized by International Society for Knowledge Organization (ISKO), ISKO-Brazil, São Paulo State University ; edited by José Augusto Chaves Guimarães, Suellen Oliveira Milani, Vera Dodebei
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Brumm, A.: Modellierung eines Informationssystems zum Bühnentanz als semantisches Wiki (2010)
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- 18.10.2010 21:05:29
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Gödert, W.: Facets and typed relations as tools for reasoning processes in information retrieval (2014)
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- Metadata and semantics research: 8th Research Conference, MTSR 2014, Karlsruhe, Germany, November 27-29, 2014, Proceedings. Eds.: S. Closs et al
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Atanassova, I.; Bertin, M.: Semantic facets for scientific information retrieval (2014)
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- Source
- Semantic Web Evaluation Challenge. SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers. Eds.: V. Presutti et al
