Semantic Enrichment

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest in the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data's quality and discoverability while enabling a self-sustaining ecosystem, "semantic enrichment" becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data's discoverability, use-and reuse-ability, and their value in the mainstream of DH and Semantic Web.

In the last few years, Twitter has become a powerful tool for publishing and discussing information. Yet, content exploration in Twitter requires substantial effort. 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 (tweets) and present different methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective. We conduct a large-scale evaluation of faceted search strategies, show significant improvements over keyword search and reveal significant benefits of those strategies that (i) further enrich the semantics of tweets by exploiting links posted in tweets, and that (ii) support users in selecting facet value pairs by adapting the faceted search interface to the specific needs and preferences of a user.

With the widespread of social media websites in the internet, and the huge number of users participating and generating infinite number of contents in these websites, the need for personalisation increases dramatically to become a necessity. One of the major issues in personalisation is building users’ profiles, which depend on many elements; such as the used data, the application domain they aim to serve, the representation method and the construction methodology. Recently, this area of research has been a focus for many researchers, and hence, the proposed methods are increasing very quickly. This survey aims to discuss the available user modelling techniques for social media websites, and to highlight the weakness and strength of these methods and to provide a vision for future work in user modelling in social media websites.

There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new inf...

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest in the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data's quality and discoverability while enabling a self-sustaining ecosystem, "semantic enrichment" becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data's discoverability, use-and reuse-ability, and their value in the mainstream of DH and Semantic Web.

Multimedia reasoning, which is suitable for, among others, multimedia content analysis and high-level video scene interpretation, relies on the formal and comprehensive conceptualization of the represented knowledge domain. However, most multimedia ontologies are not exhaustive in terms of role definitions, and do not incorporate complex role inclusions and role interdependencies. In fact, most multimedia ontologies do not have a role box at all, and implement only a basic subset of the available logical constructors. Consequently, their application in multimedia reasoning is limited. To address the above issues, VidOnt, the very first multimedia ontology with SROIQ(D) expressivity and a DL-safe ruleset has been introduced for next-generation multimedia reasoning. In contrast to the common practice, the formal grounding has been set in one of the most expressive description logics, and the ontology validated with industry-leading reasoners, namely HermiT and FaCT++. This paper also presents best practices for developing multimedia ontologies, based on my ontology engineering approach.

The study uses the Database of Names and Biographies (DNB) as an example to explore how in the transformation of original data into linked data, semantic enrichment can enhance engagement in digital humanities. In the preliminary results, we have defined instance-based and schema-based categories of semantic enrichment. In the instance-based category, in which enrichment occurs by enhancing the content of entities, we further determined three types, including: 1) enriching the entities by linking to diverse external resources in order to provide additional data of multiple perspectives; 2) enriching the entities with missing data, which is needed to satisfy the semantic queries; and, 3) providing the entities with access to an extended knowledge base. In the schema-based categories that enrichment occurs by enhancing the relations between the properties, we have identified two types, including: 1) enriching the properties by defining the hierarchical relations between properties; and, 2) specifying properties' domain and range for data reasoning. In addition, the study implements the LOD dataset in a digital humanities platform to demonstrate how instances and entities can be applied in the full texts where the relationship between entities are highlighted in order to bring scholars more semantic details of the texts.

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 different methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective. We conduct a preliminary analysis that shows that semantic enrichment of tweets is essential for faceted search on Twitter and that there is essential need for adaptive faceted search on Twitter. Furthermore, we propose an evaluation methodology that allows us to automatically evaluate the quality of adaptive faceted search on Twitter without requiring expensive user studies.

Big optical Earth observation (EO) data analytics usually start from numerical, sub-symbolic reflectance values that lack inherent semantic information (meaning) and require interpretation. However, interpretation is an ill-posed problem that is difficult for many users to solve. Our semantic EO data cube architecture aims to implement computer vision in EO data cubes as an explainable artificial intelligence approach. Automatic semantic enrichment provides semi-symbolic spectral categories for all observations as an initial interpretation of color information. Users graphically create knowledge-based semantic models in a convergence-of-evidence approach, where color information is modelled a-priori as one property of semantic concepts, such as land cover entities. This differs from other approaches that do not use a-priori knowledge and assume a direct 1:1 relationship between reflectance values and land cover. The semantic models are explainable, transferable, reusable, and users ...

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 different methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective. We conduct a preliminary analysis that shows that semantic enrichment of tweets is essential for faceted search on Twitter and that there is essential need for adaptive faceted search on Twitter. Furthermore, we propose an evaluation methodology that allows us to automatically evaluate the quality of adaptive faceted search on Twitter without requiring expensive user studies.

By using the metadata for the fonds of "Chen Cheng-po's Paintings and Documents" (CCP) in the database of the Archives of the Institute of Taiwan History (IHT, Academia Sinica, Taiwan), we develop and enhance a semantic data model for converting the data into a linked data project, focusing on data modeling, data reconciliation, and data enrichment. The research questions are: 1) How can we keep the original rich and contextual information of the archival materials during a LOD task?; 2) How can we integrate heterogeneous datasets about the same real-world resources from libraries, archives, and museums, while keeping the different views distinct?; and, (3) How can we provide added value for semantic metadata of archives in terms of instance-based and schema-based types of enrichment? The project adopts the Europeana Data Model (EDM) as the main model and extends the properties to fit the contextual characteristics of archival materials. Various methods are explored to preserve the hierarchical structure and context of the archival materials, to enrich semantic data, and to connect data from different sources and institutions. We propose four approaches to enriching data semantics by: 1) directly using external vocabularies; 2) reconciling local links to other linked data sources; 3) introducing contextual classes for the appropriate contextual entities; and, 4) utilizing named entity extraction. The results can contribute to the best practice for developing linked data for art-related archival materials.

Data cubes seem to be a promising methodological development to deal with the big EO data challenge. The Sentinel-2 Semantic Data Cube Austria (Sen2Cube.at) goes beyond the current state-of-the-art and aims to build an Austrian data & information cube. The main goal is to show
that semantic content-based image and information retrieval is possible in big EO image databases, allowing users to query and analyse EO data on a higher semantic level (i.e. based on at least basic land cover units and encoded ontologies). This includes: (1) fully automatic semantic enrichment of Sentinel-2 images up to land cover types ready for semantic content-based analysis; (2) the use of suitable database technologies to develop spatio-temporal modelling and querying techniques using encoded ontologies to decrease the complexity of queries for user interaction; (3) a Web interface for human-like queries based on semantic models of the spatio-temporal 4D physical-world domain; and (4) the demonstration of the potential of the generic data &
information cube in future service developments based on different service types.

With the widespread of social media websites in the internet, and the huge number of users participating and generating infinite number of contents in these websites, the need for personalisation increases dramatically to become a necessity. One of the major issues in personalisation is building users' profiles, which depend on many elements; such as the used data, the application domain they aim to serve, the representation method and the construction methodology. Recently, this area of research has been a focus for many researchers, and hence, the proposed methods are increasing very quickly. This survey aims to discuss the available user modelling techniques for social media websites, and to highlight the weakness and strength of these methods and to provide a vision for future work in user modelling in social media websites.

In this paper, we investigate whether semantic relationships between entities can be learnt from analyzing microblog posts published on Twitter. We identify semantic links between persons, products, events and other entities. We develop a relation discovery framework that allows for the detection of typed relations that moreover may have temporal dynamics. Based on a large Twitter dataset, we evaluate different strategies and show that co-occurrence based strategies allow for high precision and perform particularly well for relations between persons and events achieving precisions of more than 80%. We further analyze the performance in learning relationships that are valid only for a certain time period and reveal that for those types of relationships Twitter is a suitable source as it allows for discovering trending topics with higher accuracy and with lower delay in time than traditional news media.

The study uses the Database of Names and Biographies (DNB) as an example to explore how in the transformation of original data into linked data, semantic enrichment can enhance engagement in digital humanities. In the preliminary results, we have defined instance-based and schema-based categories of semantic enrichment. In the instance-based category, in which enrichment occurs by enhancing the content of entities, we further determined three types, including: 1) enriching the entities by linking to diverse external resources in order to provide additional data of multiple perspectives; 2) enriching the entities with missing data, which is needed to satisfy the semantic queries; and, 3) providing the entities with access to an extended knowledge base. In the schema-based categories that enrichment occurs by enhancing the relations between the properties, we have identified two types, including: 1) enriching the properties by defining the hierarchical relations between properties; an...

There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new inf...