Resource Description Framework Triples Research Articles

JQPro:Join Query Processing in a Distributed System for Big RDF Data Using the Hash-Merge Join Technique

In the last decade, the volume of semantic data has increased exponentially, with the number of Resource Description Framework (RDF) datasets exceeding trillions of triples in RDF repositories. Hence, the size of RDF datasets continues to grow. However, with the increasing number of RDF triples, complex multiple RDF queries are becoming a significant demand. Sometimes, such complex queries produce many common sub-expressions in a single query or over multiple queries running as a batch. In addition, it is also difficult to minimize the number of RDF queries and processing time for a large amount of related data in a typical distributed environment encounter. To address this complication, we introduce a join query processing model for big RDF data, called JQPro. By adopting a MapReduce framework in JQPro, we developed three new algorithms, which are hash-join, sort-merge, and enhanced MapReduce-join for join query processing of RDF data. Based on an experiment conducted, the result showed that the JQPro model outperformed the two popular algorithms, gStore and RDF-3X, with respect to the average execution time. Furthermore, the JQPro model was also tested against RDF-3X, RDFox, and PARJs using the LUBM benchmark. The result showed that the JQPro model had better performance in comparison with the other models. In conclusion, the findings showed that JQPro achieved improved performance with 87.77% in terms of execution time. Hence, in comparison with the selected models, JQPro performs better.

Mining Sematic Association Rules from RDF Data

Many fields rely heavily on the accurate and consistent portrayal of structured data. In order to effectively express and link information on the Semantic Web, RDF (Resource Description Framework) data is essential. Here, we present a process for extracting semantic association rules from RDF data. For our method, we employ the Apriori algorithm to mine the RDF triples for hidden connections between ideas and relationships. Using metrics such as confidence, support, and lift, we examine how well our model performs. We also give visual representations, like as scatter plots and clustered matrices, to make the correlations easier to understand and analyse. The findings validate our model's potential to unearth significant relationships, which in turn reveal important details about the RDF data's underlying semantics. Our findings are discussed, and suggestions for further study are provided.

FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model

BackgroundKnowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by the research and healthcare AI communities. From the standardization perspective, community-based standards such as the Fast Healthcare Interoperability Resources (FHIR) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) are increasingly used to represent and standardize EHR data for clinical data analytics, however, the potential of such a standard on building CKG has not been well investigated. ObjectiveTo develop and evaluate methods and tools that expose the OMOP CDM-based clinical data repositories into virtual clinical KGs that are compliant with FHIR Resource Description Framework (RDF) specification. MethodsWe developed a system called FHIR-Ontop-OMOP to generate virtual clinical KGs from the OMOP relational databases. We leveraged an OMOP CDM-based Medical Information Mart for Intensive Care (MIMIC-III) data repository to evaluate the FHIR-Ontop-OMOP system in terms of the faithfulness of data transformation and the conformance of the generated CKGs to the FHIR RDF specification. ResultsA beta version of the system has been released. A total of more than 100 data element mappings from 11 OMOP CDM clinical data, health system and vocabulary tables were implemented in the system, covering 11 FHIR resources. The generated virtual CKG from MIMIC-III contains 46,520 instances of FHIR Patient, 716,595 instances of Condition, 1,063,525 instances of Procedure, 24,934,751 instances of MedicationStatement, 365,181,104 instances of Observations, and 4,779,672 instances of CodeableConcept. Patient counts identified by five pairs of SQL (over the MIMIC database) and SPARQL (over the virtual CKG) queries were identical, ensuring the faithfulness of the data transformation. Generated CKG in RDF triples for 100 patients were fully conformant with the FHIR RDF specification. ConclusionThe FHIR-Ontop-OMOP system can expose OMOP database as a FHIR-compliant RDF graph. It provides a meaningful use case demonstrating the potentials that can be enabled by the interoperability between FHIR and OMOP CDM. Generated clinical KGs in FHIR RDF provide a semantic foundation to enable explainable AI applications in healthcare.

Exploiting lexical patterns for knowledge graph construction from unstructured text in Spanish

Knowledge graphs (KGs) are useful data structures for the integration, retrieval, dissemination, and inference of information in various information domains. One of the main challenges in building KGs is the extraction of named entities (nodes) and their relations (edges), particularly when processing unstructured text as it has no semantic descriptions. Generating KGs from texts written in Spanish represents a research challenge as the existing structures, models, and strategies designed for other languages are not compatible in this scenario. This paper proposes a method to design and construct KGs from unstructured text in Spanish. We defined lexical patterns to extract named entities and (non) taxonomic, equivalence, and composition relations. Next, named entities are linked and enriched with DBpedia resources through a strategy based on SPARQL queries. Finally, OWL properties are defined from the predicate relations for creating resource description framework (RDF) triples. We evaluated the performance of the proposed method to determine the degree of elements extracted from the input text and to assess their quality through standard information retrieval measures. The evaluation revealed the feasibility of the proposed method to extract RDF triples from datasets in general and computer science domains. Competitive results were observed by comparing our method regarding an existing approach from the literature.

A unified ontology-based data integration approach for the internet of things

<span lang="EN-US">Data integration enables combining data from various data sources in a standard format. Internet of things (IoT) applications use ontology approaches to provide a machine-understandable conceptualization of a domain. We propose a unified ontology schema approach to solve all IoT integration problems at once. The data unification layer maps data from different formats to data patterns based on the unified ontology model. This paper proposes a middleware consisting of an ontology-based approach that collects data from different devices. IoT middleware requires an additional semantic layer for cloud-based IoT platforms to build a schema for data generated from diverse sources. We tested the proposed model on real data consisting of approximately 160,000 readings from various sources in different formats like CSV, JSON, raw data, and XML. The data were collected through the file transfer protocol (FTP) and generated 960,000 resource description framework (RDF) triples. We evaluated the proposed approach by running different queries on different machines on SPARQL protocol and RDF query language (SPARQL) endpoints to check query processing time, validation of integration, and performance of the unified ontology model. The average response time for query execution on generated RDF triples on the three servers were approximately 0.144 seconds, 0.070 seconds, 0.062 seconds, respectively.</span>

K-LM: Knowledge Augmenting in Language Models Within the Scholarly Domain

The use of superior algorithms and complex architectures in language models have successfully imparted human-like abilities to machines for specific tasks. But two significant constraints, the available training data size and the understanding of domain-specific context, hamper the pre-trained language models from optimal and reliable performance. A potential solution to tackle these limitations is to equip the language models with domain knowledge. While the commonly adopted techniques use Knowledge Graphs Embeddings (KGEs) to inject domain knowledge, we provide a Knowledge Language Model (K-LM) to use the Resource Description Framework (RDF) triples directly, extracted from world knowledge bases. The proposed model works in conjunction with Generative Pretrained Transformer (GPT-2) and Bidirectional Encoder Representations from Transformers (BERT) and uses a well-defined pipeline to select, categorize, and filter the RDF triples. In addition, we introduce heuristic methods to inject domain-specific knowledge in K-LM, leveraging knowledge graphs (KGs). We tested our approaches on the classification task within the scholarly domain using two KGs, and our results show that our proposed language model has significantly outperformed the baselines and BERT for each KG. Our experimental findings also help us conclude the importance of relevance of KG used over the quantity of injected RDF triples. Also, each of our proposed methods for injecting the RDF triples has increased the overall model’s accuracy, demonstrating that K-LM is a potential choice for domain adaptation to solve knowledge-driven problems.

LODQuMa: A Free-ontology process for Linked (Open) Data quality management

For many years, data quality is among the most commonly discussed issue in Linked Open Data (LOD) due to the huge volume of integrated datasets that are usually heterogeneous. Several ontology-based approaches dealing with quality problems have been proposed. However, when datasets lack a well-defined schema, these approaches become ineffective because of the lack of metadata. Moreover, the detection of quality problems based on an analysis between RDF (Resource Description Framework) triples without requiring ontology statistical and semantical information is not addressed. Keeping in mind that ontologies are not always available and they may be incomplete or misused. In this paper, a novel free-ontology process called LODQuMa is proposed to assess and improve the quality of LOD. It is mainly based on profiling statistics, synonym relationships between predicates, QVCs (Quality Verification Cases), and SPARQL (SPARQL Protocol and RDF Query Language) query templates. Experiments on the DBpedia dataset demonstrate that the proposed process is effective for increasing the intrinsic quality dimensions, resulting in correct and compact datasets.

Assessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search

Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.

OPAL: An extensible framework for ontology‐based program analysis

SummaryThe syntactic information of a program can be represented as resource description framework (RDF) triples called program triples. We propose an extensible static analysis framework, called OPAL—Ontology‐based Program AnaLysis. The framework enables formal representation of external knowledge, such as usage knowledge of libraries and domain knowledge. Utilizing this knowledge and the program triples, we compute the semantic information, called static trace of the program. It is generated through path‐sensitive intraprocedural traversal of the program. We approximate information in case of loops by unrolling them a fixed number of times. The main contribution of the framework is to store the static trace as RDF triples called semantic triples. They are described using the Program Analysis ontology proposed in this article. The program triples and the semantic triples are together called consolidated program triples. These triples are stored and used to accelerate the execution of client‐analyses specified by the end user. In the framework, a client‐analysis is specified by a set of conjunctive expressions that use SPARQL (W3C RDF query language) queries. The framework is effective for the client‐analyses that warrant sound and approximate information. The effectiveness is assessed first, using two source‐code‐analyses that require only the program triples, and then 10 intraprocedural path‐sensitive analyses that require the consolidated program triples. Using NPB and SPEC 2006 benchmarks, we achieve an improvement in the conciseness of analysis specifications. Also, the execution time using OPAL is competitive to LLVM's clang for individual analysis and outperforms clang over a series of analyses because of the reuse of consolidated program triples.

MESRG: multi-entity summarisation in RDF graph

Entity summarisation has drawn a lot of attention in recent years. But there still exist some problems. Firstly, most of the previous works focus on individual entity summarisation while ignoring the effect of neighbours. Secondly, the external resources which may be unavailable in practice are frequently used to calculate the similarity between resource description framework (RDF) triples. To solve the above two problems, this paper focuses on multi-entity summarisation. A topic model-based model multi-entity summarisation in RDF graph (MESRG) is proposed for multi-entity summarisation, which is capable of extracting informative and diverse summaries involving a two-phase process: 1) to select more important RDF triples, we propose an improved topic model that ranks triples with probability values; 2) to select diverse RDF triples, we use a graph embedding method to calculate the similarity between triples and obtain top k distinctive triples. Experiments of our model with significant results on the benchmark datasets demonstrate the effectiveness.

An integration approach of multi-source heterogeneous fuzzy spatiotemporal data based on RDF

With the growing importance of the fuzzy spatiotemporal data in information application, there is an increasing need for researching on the integration method of multi-source heterogeneous fuzzy spatiotemporal data. In this paper, we first propose a fuzzy spatiotemporal RDF graph model based on RDF (Resource Description Framework) that proposed by the World Wide Web Consortium (W3C) to represent data in triples (subject, predicate, object). Secondly, we analyze and classify the related heterogeneous problems of multi-source heterogeneous fuzzy spatiotemporal data, and use the fuzzy spatiotemporal RDF graph model to define the corresponding rules to solve these heterogeneous problems. In addition, based on the characteristics of RDF triples, we analyze the heterogeneous problem of multi-source heterogeneous fuzzy spatiotemporal data integration in RDF triples, and provide the integration methods FRDFG in this paper. Finally, we report our experiments results to validate our approach and show its significant superiority.

Assisting Biologists in Editing Taxonomic Information by Confronting Multiple Data Sources using Linked Data Standards

During the last decade, Web APIs (Application Programming Interface) have gained significant traction to the extent that they have become a de-facto standard to enable HTTP-based, machine-processable data access. Despite this success, however, they still often fail in making data interoperable, insofar as they commonly rely on proprietary data models and vocabularies that lack formal semantic descriptions essential to ensure reliable data integration. In the biodiversity domain, multiple data aggregators, such as the Global Biodiversity Information Facility (GBIF) and the Encyclopedia of Life (EoL), maintain specialized Web APIs giving access to billions of records about taxonomies, occurrences, or life traits (Triebel et al. 2012). They publish data sets spanning complementary and often overlapping regions, epochs or domains, but may also report or rely on potentially conflicting perspectives, e.g. with respect to the circumscription of taxonomic concepts. It is therefore of utmost importance for biologists and collection curators to be able to confront the knowledge they have about taxa with related data coming from third-party data sources. To tackle this issue, the French National Museum of Natural History (MNHN) has developed an application to edit TAXREF, the French taxonomic register for fauna, flora and fungus (Gargominy et al. 2018). TAXREF registers all species recorded in metropolitan France and overseas territories, accounting for 260,000+ biological taxa (200,000+ species) along with 570,000+ scientific names. The TAXREF-Web application compares data available in TAXREF with corresponding data from third-party data sources, points out disagreements and allows biologists to add, remove or amend TAXREF accordingly. This requires that TAXREF-Web developers write a specific piece of code for each considered Web API to align TAXREF representation with the Web API counterpart. This task is time-consuming and makes maintenance of the web application cumbersome. In this presentation, we report on a new implementation of TAXREF-Web that harnesses the Linked Data standards: Resource Description Framework (RDF), the Semantic Web format to represent knowledge graphs, and SPARQL, the W3C standard to query RDF graphs. In addition, we leverage the SPARQL Micro-Service architecture (Michel et al. 2018), a lightweight approach to query Web APIs using SPARQL. A SPARQL micro-service is a SPARQL endpoint that wraps a Web API service; it typically produces a small, resource-centric RDF graph by invoking the Web API and transforming the response into RDF triples. We developed SPARQL micro-services to wrap the Web APIs of GBIF, World Register of Marine Species (WoRMS), FishBase, Index Fungorum, Pan-European Species directories Infrastructure (PESI), ZooBank, International Plant Names Index (IPNI), EoL, Tropicos and Sandre. These micro-services consistently translate Web APIs responses into RDF graphs utilizing mainly two well-adopted vocabularies: Schema.org (Guha et al. 2015) and Darwin Core (Baskauf et al. 2015). This approach brings about two major advantages. First, the large adoption of Schema.org and Darwin Core ensures that the services can be immediately understood and reused by a large audience within the biodiversity community. Second, wrapping all these Web APIs in SPARQL micro-services “suddenly” makes them technically and semantically interoperable, since they all represent resources (taxa, habitats, traits, etc.) in a common manner. Consequently, the integration task is simplified: confronting data from multiple sources essentially consists of writing the appropriate SPARQL queries, thus making easier web application development and maintenance. We present several concrete cases in which we use this approach to detect disagreements between TAXREF and the aforementioned data sources, with respect to taxonomic information (author, synonymy, vernacular names, classification, taxonomic rank), habitats, bibliographic references, species interactions and life traits.

Indexing temporal RDF graph

Time data can be found in various real-world applications and different models have been proposed to model temporal information. With the wide utilization of the Web and the availability of massive Web resources, temporal Resource Description Framework (RDF) model has attracted more and more attention. In this paper, we propose an index approach for temporal RDF graphs to effectively query massive temporal RDF data. We build the prefix path index for querying subjects of temporal RDF triples and the suffix index for querying objects of temporal RDF triples, respectively. Meanwhile, we use frequent elements to improve the efficiency of the index we proposed. We also adopt B-tree index to manage all elements of triples. Our index approach can support inserting and deleting temporal triples in temporal RDF graphs. Experimental results show that our index approach can efficiently process queries in temporal RDF graphs.

Citrus ontology development based on the eight-point charter of agriculture

Developing large-scale agricultural ontologies is a challenging and error-prone task that requires substantial effort and collaboration between domain experts and ontology developers due to the complexity of agricultural knowledge. Inspired by the Chinese Eight-Point Charter of Agriculture, i.e., Soil, Fertilization, Water, Variety, Density, Protection, Management and Tool, this paper presents an approach to modeling and integrating citrus production knowledge. Citrus domain knowledge is classified into eight categories based on the Eight-Point Charter of Agriculture, and the relationships in each category and among categories are established. The eight categories and the relationships are defined as the citrus production knowledge framework. Then, we propose mechanisms to develop citrus ontology based on the citrus production knowledge framework. The Fertilization ontology is created as an illustration of our approach, which contains 866 ontology entities and 12,583 Resource Description Framework triples. The structural evaluation results of the eight metrics for the Fertilization ontology are considerably better than the average and median values of 1413 Web ontologies. In addition, four antipatterns were used to evaluate the ontology, and no occurrence of the antipatterns was detected for the 866 ontology entities. The accuracy of the ontology is ensured by the competency evaluation of the 110 questions with 88% accuracy. Our approach provides an effective solution for modeling complex agricultural knowledge and transforming the agriculture domain knowledge into computable resources.

Transforming XML to RDF(S) with Temporal Information

The Resource Description Framework (RDF) is a model for representing resources on the Web. With the widespread acceptance of RDF in various applications (e.g., knowledge graph), a huge amount of RDF data is being proliferated. Therefore, transforming legacy data resources into RDF data is of increasing importance. In addition, time information widely exists in various real-world applications and temporal Web data has been represented and managed in the context of temporal XML. In this paper, we concentrate on transformation of temporal XML (eXtensible Markup Language) to temporal RDF data. We propose the mapping rules and mapping algorithms which can transform the temporal XML Schema and document into temporal RDF Schema and temporal RDF triples, respectively. We illustrate our mapping approach with an example and implement a prototype system. It is demonstrated that our mapping approach is valid.

Integration of Biodiversity Linked Data and Web APIs using SPARQL Micro-Services

Web APIs (Application Programming Interface) are a common means for Web portals and data producers to enable HTTP-based, machine-processable access to their data. They are a prominent source of information*1 pertaining to topics as diverse as scientific information, social networks, entertainment or finance. The methods of Linked Data (Heath and Bizer 2011) similarly aim to publish machine-readable data on the Web, while connecting related resources within and between datasets, thereby creating a large distributed knowledge graph. Today, the biodiversity community is increasingly adopting the Linked Data principles to publish data such as trait banks, museum collections and taxonomic registers (Parr et al. 2016, Baskauf et al. 2016). However, standard approaches are still missing to combine disparate representations coming from both Linked Data interfaces and the manifold Web APIs that were developed during the last two decades to expose legacy biodiversity databases on the Web.TheSPARQL Micro-Servicearchitecture (Michel et al. 2018) tackles the goal of reconciling Linked Data interfaces and Web APIs. It proposes a lightweight method to query a Web API using SPARQL (Harris and Seaborne 2013), the Semantic Web standard to query knowledge graphs expressed in the Resource Description Framework (RDF). A SPARQL micro-service provides access to a small RDF graph, typically resource-centric, that it builds at run-time by transforming a fraction of the whole dataset served by the Web API into RDF triples. Furthermore, Web APIs traditionally rely on internal, proprietary resource identifiers that are unsuited for use as Uniform Resource Identifiers (URIs). To address this concern, a SPARQL micro-service can assign a URI to a Web API resource, allowing an application to look up this URI and get a description of the resource in return (this process is referred to asdereferencing).In this demo, we wish to showcase the value of SPARQL micro-services in the biodiversity domain. We first query TAXREF-LD, a Linked Data representation of the French taxonomic register of living beings (Michel et al. 2017), to retrieve information about a given taxon. Then, we demonstrate how we can enrich our knowledge about this taxon with various types of data retrieved on-the-fly from multiple Web APIs:trait data from the Encyclopedia of Life trait bank (Parr et al. 2016),articles or books from the Biodiversity Heritage Library,audio recordings from the Macaulay scientific media archive,photos from the Flickr photography social network, andmusic tunes from MusicBrainz.Different visualizations are demonstrated, ranging from raw RDF triples to Web pages generated dynamically and integrating heterogeneous data, as suggested in Fig. 1. Depending on the audience’s interests, we shall touch upon the alignment of Web APIs’ proprietary vocabularies with well-adopted thesauri or ontologies, or more technical concernse.g.related to the effort required to deploy a new SPARQL micro-service.

Pragmatic thought as a philosophical foundation for collaborative tagging and the Semantic Web

PurposeThe purpose of this paper is to use ideas drawn from two founders of American pragmatism, William James and Charles Sanders Peirce, in order to propose a philosophical foundation that supports the value of collaborative tagging and reinforces the structure and goals of the Semantic Web.Design/methodology/approachThe study employs a close analysis of key literature by James and Peirce to answer recent calls for a philosophy of the Web and to respond to research in the LIS literature that has assessed the value and limitations of folksonomy. Moreover, pragmatic views are applied to illustrate the relationships among collaborative tagging, linked data, and the Semantic Web.FindingsWith a philosophical foundation in place, the study highlights the value of the minority tags that fall within the so-called “long tail” of the power law graph, and the importance of granting sufficient time for the full value of folksonomy to be revealed. The discussion goes further to explore how “collaborative tagging” could evolve into “collaborative knowledge” in the form of linked data. Specifically, Peirce’s triadic architectonic is shown to foster an understanding of the construction of linked data through the functional requirements for bibliographic records entity-relation model and resource description framework triples, and James’s image of the multiverse anticipates the goals Tim Berners-Lee has articulated for the Semantic Web.Originality/valueThis study is unique in using Jamesian and Peircean thinking to argue for the value of folksonomy and to suggest implications for the Semantic Web.

GrandBase: generating actionable knowledge from Big Data

Purpose This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase. Design/methodology/approach In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

Htab2RDF: Mapping HTML Tables to RDF Triples

The Web has become a tremendously huge data source hidden under linked documents. A significant number of Web documents include HTML tables generated dynamically from relational databases. Often, there is no direct public access to the databases themselves. On the other hand, RDF (Resource Description Framework) gives an efficient mechanism to represent directly data on the Web based on a Web-scalable architecture for identification and interpretation of terms. This leads to the concept of Linked Data on the Web. To allow direct access to data on the Web as Linked Data, we propose in this paper an approach to transform HTML tables into RDF triples. It consists of three main phases: refining, pre-treatment and mapping. The whole process is assisted by a domain ontology and the WordNet lexical database. A tool called Htab2RDF has been implemented. Experiments have been carried out to evaluate and show efficiency of the proposed approach.

A MapReduce-based Approach to Scale Big Semantic Data Compression with HDT

Data generation and publication on the Web has increased over the last years. This phenomenon, usually known as “Big Data”, poses new challenges related with Volume, Velocity, and Variety (“The three V's”) of data. The Semantic Web offers the means to deal with variety, where RDF (Resource Description Framework) is used to model data in the form of triples subject-predicate-object. In this way, it is possible to represent and interconnect RDF triples to build a true Web of Data. Nonetheless, a problem arises when big RDF collections must be stored, exchanges, and/or queried because the existing serialization formats are highly verbose, hence the remaining Big Semantic Data challenges (volume and variety) are aggravated when storing, exchanging, or querying big RDG collections. HDT addresses this issue by proposing a binary serialization format based on compact data structures that allows RDF to be compressed, but also to be queried without prior decompression. Thus, HDT reduces data volume and increases retrieval velocity. However, this achievement comes at the cost of and expensive RDF-to-HDT serialization in terms of computational resources and time. Therefore, HDT alleviates velocity and volume challenges for the end user, but moves Big Data challenges to the data publisher. In this work we show HDT-MR, a MapReduce-based algorithm that allows RDF datasets to be serialized to HDT in a distributed way, reducing processing resources and time, but also enabling larger datasets to be compressed.