Search (101 results, page 1 of 6)

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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.

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Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

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Abstract

The purpose of this work is to develop an ontology-based framework for developing an information retrieval system to cater to specific queries of users. For creating such an ontology, information was obtained from a wide range of information sources involved with brain tumour study and research. The information thus obtained was compiled and analysed to provide a standard, reliable and relevant information base to aid our proposed system. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as identification of the terminology, analysis, synthesis, standardization and ordering. A vast majority of the ontologies being developed nowadays lack flexibility. This becomes a formidable constraint when it comes to interoperability. We found that a facet-based method provides a distinct guideline for the development of a robust and flexible model concerning the domain of brain tumours. Our attempt has been to bridge library and information science and computer science, which itself involved an experimental approach. It was discovered that a faceted approach is really enduring, as it helps in the achievement of properties like navigation, exploration and faceted browsing. Computer-based brain tumour ontology supports the work of researchers towards gathering information on brain tumour research and allows users across the world to intelligently access new scientific information quickly and efficiently.

Date

12. 3.2016 13:21:22

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Abstract

Knowledge organization of large-scale content on the Web requires substantial amounts of semantic metadata that is expensive to generate manually. Recent developments in Web technologies have enabled any user to tag documents and other forms of content thereby generating metadata that could help organize knowledge. However, merely adding one or more tags to a document is highly inadequate to capture the aboutness of the document and thereby to support powerful semantic functions such as automatic classification, question answering or true semantic search and retrieval. This is true even when the tags used are labels from a well-designed classification system such as a thesaurus or taxonomy. There is a strong need to develop a semantic tagging mechanism with sufficient expressive power to capture the aboutness of each part of a document or dataset or multimedia content in order to enable applications that can benefit from knowledge organization on the Web. This article proposes a highly expressive mechanism of using ontology snippets as semantic tags that map portions of a document or a part of a dataset or a segment of a multimedia content to concepts and relations in an ontology of the domain(s) of interest.

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

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Content

Introduction: envisioning semantic information spacesIndexing and knowledge organization -- Semantic technologies for knowledge representation -- Information retrieval and knowledge exploration -- Approaches to handle heterogeneity -- Problems with establishing semantic interoperability -- Formalization in indexing languages -- Typification of semantic relations -- Inferences in retrieval processes -- Semantic interoperability and inferences -- Remaining research questions.

Date

23. 7.2017 13:49:22

LCSH

Information retrieval

RSWK

Information Retrieval

Subject

Information retrieval
Information Retrieval

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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

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Abstract

In this thesis, we present an ontology-based information extraction and retrieval system and its application to soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of the system is improved considerably using domain-specific information extraction, inference and rules. Scalability is achieved by adapting a semantic indexing approach. The system is implemented using the state-of-the-art technologies in SemanticWeb and its performance is evaluated against traditional systems as well as the query expansion methods. Furthermore, a detailed evaluation is provided to observe the performance gain due to domain-specific information extraction and inference. Finally, we show how we use semantic indexing to solve simple structural ambiguities.

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Abstract

We present an Information Retrieval System for scientific publications that provides the possibility to filter results according to semantic facets. We use sentence-level semantic annotations that identify specific semantic relations in texts, such as methods, definitions, hypotheses, that correspond to common information needs related to scientific literature. The semantic annotations are obtained using a rule-based method that identifies linguistic clues organized into a linguistic ontology. The system is implemented using Solr Search Server and offers efficient search and navigation in scientific papers.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Abstract

Indexing plays a vital role in Information Retrieval. With the availability of huge volume of information, it has become necessary to index the information in such a way to make easier for the end users to find the information they want efficiently and accurately. Keyword-based indexing uses words as indexing terms. It is not capable of capturing the implicit relation among terms or the semantics of the words in the document. To eliminate this limitation, ontology-based indexing came into existence, which allows semantic based indexing to solve complex and indirect user queries. Ontologies are used for document indexing which allows semantic based information retrieval. Existing ontologies or the ones constructed from scratch are used presently for indexing. Constructing ontologies from scratch is a labor-intensive task and requires extensive domain knowledge whereas use of an existing ontology may leave some important concepts in documents un-annotated. Using multiple ontologies can overcome the problem of missing out concepts to a great extent, but it is difficult to manage (changes in ontologies over time by their developers) multiple ontologies and ontology heterogeneity also arises due to ontologies constructed by different ontology developers. One possible solution to managing multiple ontologies and build from scratch is to use modular ontologies for indexing.
Modular ontologies are built in modular manner by combining modules from multiple relevant ontologies. Ontology heterogeneity also arises during modular ontology construction because multiple ontologies are being dealt with, during this process. Ontologies need to be aligned before using them for modular ontology construction. The existing approaches for ontology alignment compare all the concepts of each ontology to be aligned, hence not optimized in terms of time and search space utilization. A new indexing technique is proposed based on modular ontology. An efficient ontology alignment technique is proposed to solve the heterogeneity problem during the construction of modular ontology. Results are satisfactory as Precision and Recall are improved by (8%) and (10%) respectively. The value of Pearsons Correlation Coefficient for degree of similarity, time, search space requirement, precision and recall are close to 1 which shows that the results are significant. Further research can be carried out for using modular ontology based indexing technique for Multimedia Information Retrieval and Bio-Medical information retrieval.

Date

20. 1.2015 18:30:22

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Abstract

Purpose Ontologies are used to formally describe the concepts within a domain in a machine-understandable way. Matching of heterogeneous ontologies is often essential for many applications like semantic annotation, query answering or ontology integration. Some ontologies may include a large number of entities which make the ontology matching process very complex in terms of the search space and execution time requirements. The purpose of this paper is to present a technique for finding degree of similarity between ontologies that trims down the search space by eliminating the ontology concepts that have less likelihood of being matched. Design/methodology/approach Algorithms are written for finding key concepts, concept matching and relationship matching. WordNet is used for solving synonym problems during the matching process. The technique is evaluated using the reference alignments between ontologies from ontology alignment evaluation initiative benchmark in terms of degree of similarity, Pearson's correlation coefficient and IR measures precision, recall and F-measure. Findings Positive correlation between the degree of similarity and degree of similarity (reference alignment) and computed values of precision, recall and F-measure showed that if only key concepts of ontologies are compared, a time and search space efficient ontology matching system can be developed. Originality/value On the basis of the present novel approach for ontology matching, it is concluded that using key concepts for ontology matching gives comparable results in reduced time and space.

Date

20. 1.2015 18:30:22

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Abstract

Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.

Date

16.11.2018 14:22:01

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Abstract

Considering the characteristics of hypertext systems and problems such as cognitive overload and the disorientation of users, this project studies subject hypertext documents that have undergone conceptual structuring using facets for content representation and improvement of information retrieval during navigation. The main objective was to assess the possibility of the application of topic map technology for automating the compatibilization process of these structures. For this purpose, two dissertations from the UFMG Information Science Post-Graduation Program were adopted as samples. Both dissertations had been duly analyzed and structured on the MHTX (Hypertextual Map) prototype database. The faceted structures of both dissertations, which had been represented in conceptual maps, were then converted into topic maps. It was then possible to use the merge property of the topic maps to promote the semantic interrelationship between the maps and, consequently, between the hypertextual information resources proper. The merge results were then analyzed in the light of theories dealing with the compatibilization of languages developed within the realm of information technology and librarianship from the 1960s on. The main goals accomplished were: (a) the detailed conceptualization of the merge process of the topic maps, considering the possible compatibilization levels and the applicability of this technology in the integration of faceted structures; and (b) the production of a detailed sequence of steps that may be used in the implementation of topic maps based on faceted structures.

Date

22. 2.2013 11:39:23

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Abstract

Knowledge organization faces the challenge of managing the amount of knowledge available on the Web. Published literature in biomedical sciences is a huge source of knowledge, which can only efficiently be managed through automatic methods. The conventional channel for reporting scientific results is Web electronic publishing. Despite its advances, scientific articles are still published in print formats such as portable document format (PDF). Semantic Web and Linked Data technologies provides new opportunities for communicating, sharing, and integrating scientific knowledge that can overcome the limitations of the current print format. Here is proposed a semantic model of scholarly electronic articles in biomedical sciences that can overcome the limitations of traditional flat records formats. Scientific knowledge consists of claims made throughout article texts, especially when semantic elements such as questions, hypotheses and conclusions are stated. These elements, although having different roles, express relationships between phenomena. Once such knowledge units are extracted and represented with technologies such as RDF (Resource Description Framework) and linked data, they may be integrated in reasoning chains. Thereby, the results of scientific research can be published and shared in structured formats, enabling crawling by software agents, semantic retrieval, knowledge reuse, validation of scientific results, and identification of traces of scientific discoveries.

Date

12. 3.2016 13:17:22

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Abstract

We scho how the beenfits of the view-based search method, developed within the information retrieval community, can be extended with ontology-based search, developed within the Semantic Web community, and with semantic recommendations. As a proof of the concept, we have implemented an ontology-and view-based search engine and recommendations system Ontogaotr for RDF(S) repositories. Ontogator is innovative in two ways. Firstly, the RDFS.based ontologies used for annotating metadata are used in the user interface to facilitate view-based information retrieval. The views provide the user with an overview of the repositorys contents and a vocabulary for expressing search queries. Secondlyy, a semantic browsing function is provided by a recommender system. This system enriches instance level metadata by ontologies and provides the user with links to semantically related relevant resources. The semantic linkage is specified in terms of logical rules. To illustrate and discuss the ideas, a deployed application of Ontogator to a photo repository of the Helsinki University Museum is presented.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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LCSH

Information storage and retrieval
Information Storage and Retrieval

RSWK

Wissensbasiertes System
Information Retrieval

Subject

Wissensbasiertes System
Information Retrieval
Information storage and retrieval
Information Storage and Retrieval

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Abstract

Background: Because of the increasing number of electronic resources, designing efficient tools to retrieve and exploit them is a major challenge. Some improvements have been offered by semantic Web technologies and applications based on domain ontologies. In life science, for instance, the Gene Ontology is widely exploited in genomic applications and the Medical Subject Headings is the basis of biomedical publications indexation and information retrieval process proposed by PubMed. However current search engines suffer from two main drawbacks: there is limited user interaction with the list of retrieved resources and no explanation for their adequacy to the query is provided. Users may thus be confused by the selection and have no idea on how to adapt their queries so that the results match their expectations. Results: This paper describes an information retrieval system that relies on domain ontology to widen the set of relevant documents that is retrieved and that uses a graphical rendering of query results to favor user interactions. Semantic proximities between ontology concepts and aggregating models are used to assess documents adequacy with respect to a query. The selection of documents is displayed in a semantic map to provide graphical indications that make explicit to what extent they match the user's query; this man/machine interface favors a more interactive and iterative exploration of data corpus, by facilitating query concepts weighting and visual explanation. We illustrate the benefit of using this information retrieval system on two case studies one of which aiming at collecting human genes related to transcription factors involved in hemopoiesis pathway. Conclusions: The ontology based information retrieval system described in this paper (OBIRS) is freely available at: http://www.ontotoolkit.mines-ales.fr/ObirsClient/. This environment is a first step towards a user centred application in which the system enlightens relevant information to provide decision help.

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Abstract

Purpose - This paper seeks to develop a specified typology of various levels of semantic interoperability, designed to provide semantically expressive and functional means to interconnect typologically different sub-systems in an international comprehensive knowledge organization system, supporting advanced information retrieval and exploration strategies. Design/methodology/approach - Taking the analysis of rudimentary forms of a functional interoperability based on simple pattern matching as a starting-point, more refined strategies to provide semantic interoperability, which is actually reaching the conceptual and even thematic level, are being developed. The paper also examines the potential benefits and perspectives of the selective transfer of modelling strategies from the field of semantic technologies for the refinement of relational structures of inter-system and inter-concept relations as a requirement for expressive and functional indexing languages supporting advanced types of semantic interoperability. Findings - As the principles and strategies of advanced information retrieval systems largely depend on semantic information, new concepts and strategies to achieve semantic interoperability have to be developed. Research limitations/implications - The approach has been developed in the functional and structural context of an international comprehensive system integrating several heterogeneous knowledge organization systems and indexing languages by interconnecting them to a central conceptual structure operating as a spine in an overall system designed to support retrieval and exploration of bibliographic records representing complex conceptual entities. Originality/value - Research and development aimed at providing technical and structural interoperability has to be complemented by a thorough and precise reflection and definition of various degrees and types of interoperability on the semantic level as well. The approach specifies these levels and reflects the implications and their potential for advanced strategies of retrieval and exploration.

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Abstract

Current retrieval and recommendation approaches rely on hard-wired data models. This hinders personalized cus-tomizations to meet information needs of users in a more flexible manner. Therefore, the paper investigates how similarity-basedretrieval strategies can be combined with graph queries to enable users or system providers to explore repositories in the LinkedOpen Data (LOD) cloud more thoroughly. For this purpose, we developed novel content-based recommendation approaches.They rely on concept annotations of Simple Knowledge Organization System (SKOS) vocabularies and a SPARQL-based querylanguage that facilitates advanced and personalized requests for openly available knowledge graphs. We have comprehensivelyevaluated the novel search strategies in several test cases and example application domains (i.e., travel search and multimediaretrieval). The results of the web-based online experiments showed that our approaches increase the recall and diversity of rec-ommendations or at least provide a competitive alternative strategy of resource access when conventional methods do not providehelpful suggestions. The findings may be of use for Linked Data-enabled recommender systems (LDRS) as well as for semanticsearch engines that can consume LOD resources. (PDF) Similarity-based knowledge graph queries for recommendation retrieval. Available from: https://www.researchgate.net/publication/333358714_Similarity-based_knowledge_graph_queries_for_recommendation_retrieval [accessed May 21 2020].

Content

Vgl.: https://www.researchgate.net/publication/333358714_Similarity-based_knowledge_graph_queries_for_recommendation_retrieval. Vgl. auch: http://semantic-web-journal.net/content/similarity-based-knowledge-graph-queries-recommendation-retrieval-1.

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Abstract

Purpose - Ontologies are prone to wide semantic variability due to subjective points of view of their composers. The purpose of this paper is to propose a new approach for maximal unification of diverse ontologies for controversial domains by their relations. Design/methodology/approach - Effective matching or unification of multiple ontologies for a specific domain is crucial for the success of many semantic web applications, such as semantic information retrieval and organization, document tagging, summarization and search. To this end, numerous automatic and semi-automatic techniques were proposed in the past decade that attempt to identify similar entities, mostly classes, in diverse ontologies for similar domains. Apparently, matching individual entities cannot result in full integration of ontologies' semantics without matching their inter-relations with all other-related classes (and instances). However, semantic matching of ontological relations still constitutes a major research challenge. Therefore, in this paper the authors propose a new paradigm for assessment of maximal possible matching and unification of ontological relations. To this end, several unification rules for ontological relations were devised based on ontological reference rules, and lexical and textual entailment. These rules were semi-automatically implemented to extend a given ontology with semantically matching relations from another ontology for a similar domain. Then, the ontologies were unified through these similar pairs of relations. The authors observe that these rules can be also facilitated to reveal the contradictory relations in different ontologies. Findings - To assess the feasibility of the approach two experiments were conducted with different sets of multiple personal ontologies on controversial domains constructed by trained subjects. The results for about 50 distinct ontology pairs demonstrate a good potential of the methodology for increasing inter-ontology agreement. Furthermore, the authors show that the presented methodology can lead to a complete unification of multiple semantically heterogeneous ontologies. Research limitations/implications - This is a conceptual study that presents a new approach for semantic unification of ontologies by a devised set of rules along with the initial experimental evidence of its feasibility and effectiveness. However, this methodology has to be fully automatically implemented and tested on a larger dataset in future research. Practical implications - This result has implication for semantic search, since a richer ontology, comprised of multiple aspects and viewpoints of the domain of knowledge, enhances discoverability and improves search results. Originality/value - To the best of the knowledge, this is the first study to examine and assess the maximal level of semantic relation-based ontology unification.

Date

20. 1.2015 18:30:22

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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.