-
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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- Abstract
- More and more cultural heritage institutions publish their collections, vocabularies and metadata on the Web. The resulting Web of linked cultural data opens up exciting new possibilities for searching and browsing through these cultural heritage collections. We report on ongoing work in which we investigate the estimation of relevance in this Web of Culture. We study existing measures of semantic distance and how they apply to two use cases. The use cases relate to the structured, multilingual and multimodal nature of the Culture Web. We distinguish between measures using the Web, such as Google distance and PMI, and measures using the Linked Data Web, i.e. the semantic structure of metadata vocabularies. We perform a small study in which we compare these semantic distance measures to human judgements of relevance. Although it is too early to draw any definitive conclusions, the study provides new insights into the applicability of semantic distance measures to the Web of Culture, and clear starting points for further research.
- Date
- 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
-
Tudhope, D.; Hodge, G.: Terminology registries (2007)
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- Date
- 26.12.2011 13:22:07
-
Baker, T.; Bermès, E.; Coyle, K.; Dunsire, G.; Isaac, A.; Murray, P.; Panzer, M.; Schneider, J.; Singer, R.; Summers, E.; Waites, W.; Young, J.; Zeng, M.: Library Linked Data Incubator Group Final Report (2011)
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- Abstract
- The mission of the W3C Library Linked Data Incubator Group, chartered from May 2010 through August 2011, has been "to help increase global interoperability of library data on the Web, by bringing together people involved in Semantic Web activities - focusing on Linked Data - in the library community and beyond, building on existing initiatives, and identifying collaboration tracks for the future." In Linked Data [LINKEDDATA], data is expressed using standards such as Resource Description Framework (RDF) [RDF], which specifies relationships between things, and Uniform Resource Identifiers (URIs, or "Web addresses") [URI]. This final report of the Incubator Group examines how Semantic Web standards and Linked Data principles can be used to make the valuable information assets that library create and curate - resources such as bibliographic data, authorities, and concept schemes - more visible and re-usable outside of their original library context on the wider Web. The Incubator Group began by eliciting reports on relevant activities from parties ranging from small, independent projects to national library initiatives (see the separate report, Library Linked Data Incubator Group: Use Cases) [USECASE]. These use cases provided the starting point for the work summarized in the report: an analysis of the benefits of library Linked Data, a discussion of current issues with regard to traditional library data, existing library Linked Data initiatives, and legal rights over library data; and recommendations for next steps. The report also summarizes the results of a survey of current Linked Data technologies and an inventory of library Linked Data resources available today (see also the more detailed report, Library Linked Data Incubator Group: Datasets, Value Vocabularies, and Metadata Element Sets) [VOCABDATASET].
-
Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021)
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- Abstract
- We present an overview of the NTCIR Math Tasks organized during NTCIR-10, 11, and 12. These tasks are primarily dedicated to techniques for searching mathematical content with formula expressions. In this chapter, we first summarize the task design and introduce test collections generated in the tasks. We also describe the features and main challenges of mathematical information retrieval systems and discuss future perspectives in the field.
-
Davies, J.; Weeks, R.; Krohn, U.: QuizRDF: search technology for the Semantic Web (2004)
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- Abstract
- An information-seeking system is described which combines traditional keyword querying of WWW resources with the ability to browse and query against RDF annotations of those resources. RDF(S) and RDF are used to specify and populate an ontology and the resultant RDF annotations are then indexed along with the full text of the annotated resources. The resultant index allows both keyword querying against the full text of the document and the literal values occurring in the RDF annotations, along with the ability to browse and query the ontology. We motivate our approach as a key enabler for fully exploiting the Semantic Web in the area of knowledge management and argue that the ability to combine searching and browsing behaviours more fully supports a typical information-seeking task. The approach is characterised as "low threshold, high ceiling" in the sense that where RDF annotations exist they are exploited for an improved information-seeking experience but where they do not yet exist, a search capability is still available.
-
OWL Web Ontology Language Test Cases (2004)
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- Date
- 14. 8.2011 13:33:22
-
Hauff-Hartig, S.: Wissensrepräsentation durch RDF: Drei angewandte Forschungsbeispiele : Bitte recht vielfältig: Wie Wissensgraphen, Disco und FaBiO Struktur in Mangas und die Humanities bringen (2021)
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- Date
- 22. 5.2021 12:43:05
-
Putkey, T.: Using SKOS to express faceted classification on the Semantic Web (2011)
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- Abstract
- This paper looks at Simple Knowledge Organization System (SKOS) to investigate how a faceted classification can be expressed in RDF and shared on the Semantic Web. Statement of the Problem Faceted classification outlines facets as well as subfacets and facet values. Hierarchical relationships and associative relationships are established in a faceted classification. RDF is used to describe how a specific URI has a relationship to a facet value. Not only does RDF decompose "information into pieces," but by incorporating facet values RDF also given the URI the hierarchical and associative relationships expressed in the faceted classification. Combining faceted classification and RDF creates more knowledge than if the two stood alone. An application understands the subjectpredicate-object relationship in RDF and can display hierarchical and associative relationships based on the object (facet) value. This paper continues to investigate if the above idea is indeed useful, used, and applicable. If so, how can a faceted classification be expressed in RDF? What would this expression look like? Literature Review This paper used the same articles as the paper A Survey of Faceted Classification: History, Uses, Drawbacks and the Semantic Web (Putkey, 2010). In that paper, appropriate resources were discovered by searching in various databases for "faceted classification" and "faceted search," either in the descriptor or title fields. Citations were also followed to find more articles as well as searching the Internet for the same terms. To retrieve the documents about RDF, searches combined "faceted classification" and "RDF, " looking for these words in either the descriptor or title.
-
Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005)
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- Date
- 11. 2.2011 18:22:25
-
Mayfield, J.; Finin, T.: Information retrieval on the Semantic Web : integrating inference and retrieval
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- Date
- 12. 2.2011 17:35:22
-
Priss, U.: Faceted knowledge representation (1999)
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- Date
- 22. 1.2016 17:30:31
-
Davies, J.; Weeks, R.: QuizRDF: search technology for the Semantic Web (2004)
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- Abstract
- An information-seeking system is described which combines traditional keyword querying of WWW resources with the ability to browse and query against RD annotations of those resources. RDF(S) and RDF are used to specify and populate an ontology and the resultant RDF annotations are then indexed along with the full text of the annotated resources. The resultant index allows both keyword querying against the full text of the document and the literal values occurring in the RDF annotations, along with the ability to browse and query the ontology. We motivate our approach as a key enabler for fully exploiting the Semantic Web in the area of knowledge management and argue that the ability to combine searching and browsing behaviours more fully supports a typical information-seeking task. The approach is characterised as "low threshold, high ceiling" in the sense that where RDF annotations exist they are exploited for an improved information-seeking experience but where they do not yet exist, a search capability is still available.
-
Zhang, L.; Liu, Q.L.; Zhang, J.; Wang, H.F.; Pan, Y.; Yu, Y.: Semplore: an IR approach to scalable hybrid query of Semantic Web data (2007)
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- Abstract
- As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we briefy describe how Semplore is used for searching Wikipedia and an IBM customer's product information.
-
Miles, A.; Matthews, B.; Beckett, D.; Brickley, D.; Wilson, M.; Rogers, N.: SKOS: A language to describe simple knowledge structures for the web (2005)
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- Content
- "Textual content-based search engines for the web have a number of limitations. Firstly, many web resources have little or no textual content (images, audio or video streams etc.) Secondly, precision is low where natural language terms have overloaded meaning (e.g. 'bank', 'watch', 'chip' etc.) Thirdly, recall is incomplete where the search does not take account of synonyms or quasi-synonyms. Fourthly, there is no basis for assisting a user in modifying (expanding, refining, translating) a search based on the meaning of the original search. Fifthly, there is no basis for searching across natural languages, or framing search queries in terms of symbolic languages. The Semantic Web is a framework for creating, managing, publishing and searching semantically rich metadata for web resources. Annotating web resources with precise and meaningful statements about conceptual aspects of their content provides a basis for overcoming all of the limitations of textual content-based search engines listed above. Creating this type of metadata requires that metadata generators are able to refer to shared repositories of meaning: 'vocabularies' of concepts that are common to a community, and describe the domain of interest for that community.
-
Definition of the CIDOC Conceptual Reference Model (2003)
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- Date
- 6. 8.2010 14:22:28
-
Bittner, T.; Donnelly, M.; Winter, S.: Ontology and semantic interoperability (2006)
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- Date
- 3.12.2016 18:39:22
-
Priss, U.: Description logic and faceted knowledge representation (1999)
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- Date
- 22. 1.2016 17:30:31
-
Beppler, F.D.; Fonseca, F.T.; Pacheco, R.C.S.: Hermeneus: an architecture for an ontology-enabled information retrieval (2008)
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- Date
- 28.11.2016 12:43:22
-
Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018)
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- Date
- 16.11.2018 14:22:01
