Enhancing cultural recommendations through social and linked open data

This paper proposes information visualization system for exploratory LOD (Linked Open Data) Analysis. The LOD is a framework to make data open to the public. Recently, it has been widely used to publish various kinds of data such as statistical data, geographical data, and academic data. The RDF (Resource Description Framework), which describes data as a set of triples consisting of subject, predicate and object, is commonly used to publish data as LOD. When we want to use LOD, it is necessary to understand its structure, such as graph structure of RDF data, used vocabularies and resources. Therefore, we often have to conduct exploratory analysis of LOD. In order to support the analysis, the proposed system analyzes the structure of LOD written with RDF, and visualizes the result of analysis. As most of currently available LOD have table structure, the proposed system identifies whether target dataset contains table structure or not using resource sampling with SPARQL queries. Graph structure of resources obtained by the resource sampling is visualized by assigning different colors to different tables. A user cannot only examine the visualized structure, but also conduct exploratory search by selecting a resource in the visualized result. Effectiveness of the proposed system is evaluated by applying it to several LOD resources. Experiments with test participants are also conducted, of which results show even users who are not familiar with RDF can perform exploratory analysis effectively.

Open data is becoming increasingly popular in a wide range of service domains; however, most open datasets in Taiwan remain separate. The lack of linked open data (LOD) makes it difficult to locate and combine open datasets for the creation of innovative applications. In this study, we sought to facilitate the spread of open data in Taiwan using a novel approach referred to as define–produce–invoke (DPI). The proposed scheme employs a newly defined data query language, called LODQL (LOD query language), to allow the definition of rules for the generation of LOD by data experts. We also developed an LOD engine, which is able to produce linked open datasets and allow application developers to access LOD by invoking RESTful services. This scheme also allows data visualizations indicating the relevance of open datasets and the associations among open data items. Experiments demonstrate the feasibility and effectiveness of the proposed DPI approach.

Linked open data in authoring virtual exhibitions

Bridging the gap between linked open data-based recommender systems and distributed representations

The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We present an overview on recommender systems and we sketch how to use Linked Open Data to build a new generation of semantics-aware recommendation engines.

The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are connected with each other to form the so called Linked Open Data cloud. As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power. In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data. We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users. We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics.

Companies and institutions now realize the potential of Linked Open Data (LOD) and they start publishing their own data as LOD. However, publishing LOD is still a challenging task. One of the main reasons is a lack of user friendly tooling which would properly support the whole LOD publishing process. The process typically consists of source data extraction, transformation to RDF, alignment with commonly used vocabularies, linking to other datasets, computing metadata, publishing on the web as a dump, loading into a triplestore and recording the dataset in a data catalog such as CKAN. In this paper we present LinkedPipes ETL, a tool for ETL-like LOD publishing, which mainly focuses on supporting such LOD publishing workflows in a user friendly way. In addition, the tool also eases consumption of already existing LOD data sources as it addresses some of the practical issues associated with it. Finally, the tool itself uses Linked Data technologies for representation of the ETL processes. We describe LinkedPipes ETL and its main distinguishing features in context of the use cases in which the tool has already been deployed. They include an institution of public administration, a municipality, a university, a software company and an open data initiative.

Linked Open Data (LOD) has gained widespread adoption by large industries as well as non-profit organizations and governmental organizations. One of the early adopters of LOD technologies are libraries. Since the “early years”, libraries have been key use case and innovation driver for LOD and significantly contributed to the adoption of semantic technologies. The first part of this paper presents selected success stories of current activities in the Linked Data Library community. In a nutshell, these studies include (1) a conceptualization of the Linked Data Value chain, (2) a case study for consumption of Linked Data in a digital journal environment, and (3) an approach to publish metadata on the Semantic Web from an Open Access repository. These stories reveal a strong relationship between LOD in libraries and research topics addressed in traditional fields of computer science such as artificial intelligence, databases, and knowledge discovery. Thus, in the second part of this paper we systematically review the relation of LOD in digital libraries from a computer science perspective. We discuss current LOD research topics such as data integration and schema integration, distributed data management, and others. These challenges have been discussed with computer scientists at a German national database meetup as well as with librarians from ZBW—Leibniz Information Center for Economics and at international librarians meetup.

The web of Linked Open Data (LOD) has a prominent and rapid evolution recently. Over the last few years, LOD had developed to involve a wide range of various domains. Due to these facts, and the great interconnections among linked open datasets, linked data integration task had gained a huge attention and became a focal point of research. LOD applications aim to incorporate data from different LOD sources. Unfortunately, these sources of data are heterogeneous in schema and/or in vocabularies. Due to this heterogeneity, numerous challenges are emerging that have to be overcome. In this paper, a LOD integration framework is proposed, which aims to tackle these challenges. It works on integrating academic LOD datasets that reside in different LOD repositories with intrinsic schema and vocabularies heterogeneity. An automatic mapping technique in the integration processes is proposed in this paper. Consequently, an obvious decrease in execution time for the entire integration process, as well as, a great progress in the integrated data quality assessment metrics has been achieved.

Publication and Usage of Official Czech Pension Statistics Linked Open Data

Nowadays, Linked Open Data (LOD) has grown rapidly to become large open datasets defined by RDF standards. Thanks to development of Data Web, the information on LOD is increasingly deeper, larger and easier to link in multi-domains, constituting the Linked Open Data cloud. Recently end-users applications using linked data sources as background knowledge appeared [15]. Thus, the exploitation of information on LOD effectively brings tremendous values as well as challenges. Meanwhile, Exploratory Search (ES) describes information-seeking processes that are opportunistic, iterative, and multi-tactical [7]. Furthermore, systems based on ES capitalize on new technological capabilities and interface paradigms that facilitate an increased level of interaction with information. In this paper, we present a method on searching and recommending information to empower exploratory search with semantic technologies. Our aim is to use algorithms with incorporation of structured semantics in search to give users the best related-semantic results and enhance users' interactions. We have exploited the data within LinkedMDB1 to support users in finding some information in the movie domain.

The key significant worldview of the Semantic Web is Linked Open Data, another period of the World Wide Web that capacities to carry suggestions to information. An enormous number of both public and private foundations have dis-tributed their information following the Linked Open Data philosophies, or have done as such with information from different associations. To this degree, since the generation and production of Linked Open Data are thorough designing procedures that require high consideration so as to achieve high caliber, and since experience has uncovered that current general guidance is not constantly adequate to be applied to each area, this paper presents a lot of guidance system for creating and distributing Linked Open Data with regards to ethnic groups in Thailand to outside (TEG-LOD Framework). This framework offers an exhaustive depiction of the undertakings to perform, including a rundown of steps, tools that help in accomplishing the errand, different alternatives for achievement of the assignment, and best practices and proposals. Also, this paper exhibits a pilot model on the generation and distribution of Linked Open Data about ethnic groups in Thai-land, adhering to the available guidance, where the ethnic groups in Thailand are the property of the Princess Maha Chakri Sirindhorn Anthropology Center (SAC) have been made and distributed as Linked Open Data.

Linked Open Data (LOD) is a huge effort in the direction of making the Web of Data a reality. The LOD cloud consists of about 200 datasets contributed by several independent data providers from various domains such as Publications, Geography, Government, Media, Biology and Drugs etc. The data is represented in RDF and SPARQL is the query language. Due to the open nature of the data and its heterogeneous origin, it is often the case that a real-world entity appears in different datasets and with different names. When such entities are to be reported in the query results, a mechanism of rank ordering them becomes essential. In this paper, we propose a new framework for calculating the importance scores of datasets and also resources inside the datasets. As consensus is a key element that adds value to the data in the Web of Data, we base our ranking framework on constructs that are used to express sameness or equivalence among the entities in RDF data. We also make use of the underlying graph structure of LOD in the framework. The framework is experimentally verified on the Billion Triple Challenge (BTC-2010) dataset. The results indicate that the framework is successful in giving entities from the most relevant dataset a higher score compared to other datasets.

With the popularity of Linked Open Data (LOD) and the associated rise in freely accessible knowledge that can be accessed via LOD, exploiting LOD for recommender systems has been widely studied based on various approaches such as graph-based or using different machine learning models with LOD-enabled features. Many of the previous approaches require construction of an additional graph to run graph-based algorithms or to extract path-based features by combining user-item interactions (e.g., likes, dislikes) and background knowledge from LOD. In this paper, we investigate Factorization Machines (FMs) based on particularly lightweight LOD-enabled features which can be directly obtained via a public SPARQL Endpoint without any additional effort to construct a graph. Firstly, we aim to study whether using FM with these lightweight LOD-enabled features can provide competitive performance compared to a learning-to-rank approach leveraging LOD as well as other well-established approaches such as kNN-item and BPRMF. Secondly, we are interested in finding out to what extent each set of LOD-enabled features contributes to the recommendation performance. Experimental evaluation on a standard dataset shows that our proposed approach using FM with lightweight LOD-enabled features provides the best performance compared to other approaches in terms of five evaluation metrics. In addition, the study of the recommendation performance based on different sets of LOD-enabled features indicate that property-object lists and PageRank scores of items are useful for improving the performance, and can provide the best performance through using them together for FM. We observe that subject-property lists of items does not contribute to the recommendation performance but rather decreases the performance.

In cultural heritage, many projects execute Named Entity Linking (NEL) through global Linked Open Data (LOD) references in order to identify and disambiguate entities in their local datasets. It allows users to obtain extra information and contextualise the data with it. Thus, the aggregation and integration of heterogeneous LOD are expected. However, such development is still limited partly due to data quality issues. In addition, analysis on the LOD quality has not sufficiently been conducted for cultural heritage. Moreover, most research on data quality concentrates on ontology and corpus level observations. This paper examines the quality of the eleven major LOD sources used for NEL in cultural heritage with an emphasis on instance-level connectivity and graph traversals. Standardised linking properties are inspected for 100 instances/entities in order to create traversal route maps. Other properties are also assessed for quantity and quality. The outcomes suggest that the LOD is not fully interconnected and centrally condensed; the quantity and quality are unbalanced. Therefore, they cast doubt on the possibility of automatically identifying, accessing, and integrating known and unknown datasets. This implies the need for LOD improvement, as well as the NEL strategies to maximise the data integration.