Management of Vessel Information by using RDF and SPARQL

2-1 Semantic Web

Recently, the Web's Semantic data have increased, and the systems are designed to represent the real world in the data set as accurately as possible. Data symbols are organized linearly and hierarchically to give particular meanings as described later on in this study. Semantic data enables machines to communicate with real information without human interpretation by representing the real world in datasets [3].

RDF provides the framework for publishing, by linking these data since it is widely tested, and scalable technology has been well known for modeling data. The framework was originally a part of the Semantic Web Stack. As shown in Fig. 1, it is used in Querying a Rules stack, Schema & Ontologies stack, and also Data Model stack representing the focus of this paper. The integrated representation of data in RDF helps the identification, disambiguation, and interconnection of information by software agents and various systems to read, analyze, and act on [4].

Fig. 1.

Semantic Web Stack [5]

2-2 Linked Open Data

Linked Open Data (LOD) is a robust combination of Linked Data and Open Data where both are linked and open source. DBpedia, for example, is a community-driven initiative for extracting structured data from Wikipedia and making it available on the internet is a remarkable example of a LOD collection providing greater queries, enhancing untapped data exploration and effective results analytics.

The following, Fig 2 shows an illustration of Linked Data by DBpedia when converting Wikipedia content to RDF and linking the contents to other databases such as GeoNames. The data offered by DBpedia is complete and more reliable by incorporating other data. Since 2008, the LOD Cloud has an average amount of 50 published datasets. As of March 2019, the datasets increased rapidly with 1,239 datasets with 16,147 links [6]-[8].

Fig. 2.

The Linked Open Data Cloud [6]

Linked Data is one of the major elements of the Semantic Web. It is a set of design concepts to share interlinked machine-readable data on the Web [9][10].

2-3 RDF, RDF Schema, and RDF Triplestore

RDF is a data integration format developed and agreed upon by W3C. While there are many standard tools to handle data and relationships between data. RDF is the simplest, most efficient standard that has been designed as of now.

The two W3C standards, RDF and RDF Schema (RDFS), are designed to improve the Web with machine-processable semantic data. As a data modeling vocabulary for RDF data, schema data has been introduced. The first public draft of the scheme was published in 1998 [11] and adopted it in 2004 as a recommendation [12].

RDF is based on making statements in the form of subject, predicate and object expressions (i.e., triples) about resources, which is described as the Web. The subject denotes the resource and the predicate expresses the relation between the subject and the object. RDF is a conceptual data-modelling tool in general [13].

RDF Triplestore is a special database system for data management in RDF, storing interconnected data and gathering new facts out of the existing ones. Triplestore provides a standard SPARQL query language. By combining the provided primitives, users can define unique patterns and restrictions. A dataset is used to parse and transform SPARQL queries, while Apache Jena provides HTTP access to SPARQL.

Apache Jena is a component of a SPARQL server for other RDF query and storage systems with the integration of Jena TDB [16][17]. It provides access control at the level on the server, on datasets, on endpoints and also on specific graphs within a dataset.

2-4 SPARQL

SPARQL is originally used to query graph data represented as RDF triples. Together, SPARQL and RDF make it easier to merge results from multiple data sources. It is designed to enable Linked Data to the Semantic Web and enrich data by linking it to other global semantic resources. Thus, sharing, merging and reusing data are performed in a more meaningful way [14].

According to Cambridge Semantics, the differences between SPARQL and SQL is that both languages give the user access to create, combine, and consume structured data. SQL access tables in relational databases and SPARQL access a web of Linked Data. SPARQL can be used to access relational data as well but was designed to merge disparate sources of data. Moreover, queries can be expressed across a range of datasets such as documents or any relevant information that could be presented as a directed labeled graph. The results of SPARQL queries can be in the form of sets or RDF graphs where the data is stored locally or viewed as RDF by middleware [15].

Relational data made up of rows of data collected into tables, which are also called “relations” in formal relational literature. The rows in a table conform to a set data types and constraints called a schema [15].

3-1 Vessel Information Structure in General

In order to improve a new concept of searching through the Web of Data, the structure design of datasets focuses on the data retrieved based on the type of information the user wants to search for. However, the vessel structure is divided into few categories.

As shown in Fig. 3, the overall structure consists of six main categories, including historical data, machinery, performances, capacities, general, and specifications. Each category includes the details of the vessel information.

Fig. 3.

Overall structure of vessel information

In the first two categories mentioned in Fig. 4, the historical data are the name of the vessel, the name of the port, the call sign, the date of arrival, the date of departure, the MMSI and the flag containing the data from the previous location. The machinery shows the basic details of the vessel's engine, such as the mains, generators and the vessel's control.

Fig. 4.

Historical data and machinery

Next, Fig. 5 shows the vessel performances and capabilities where specifics such as fuel consumption, speed, weight and overall volume are defined along with size of the vessel.

Fig. 5.

Performances and capacities

The last two categories as shown in Fig. 6 include general information such as the MMSI number, type of vessel, year it was built, name, owner and etc. The specifications category defines the IMO number, port of registry, class, callsign and others.

Fig. 6.

General and specifications

3-2 Vessel Information Structure based on Port-MIS

Port Management Information System (Port-MIS) is an information system that has all administrative tasks such as the movement, the entry and departure of vessels, the entry cargo and transport of cargo, as well as the collection of taxes in thirty-one commercial ports across the country. The Port-MIS has been transformed into a Web-based system. In order to provide real-time information, several different types of civil complaint systems and advanced civil complaint services are provided through the wired and wireless internet [19].

As shown in Fig. 7, the diagram shows how the main subject vessel is connected to other related information, for example, its type, tonnage, related information, port, traffic flows and other information about the vessel.

Fig. 7.

Vessel information structure based on Port-MIS

5-1 System Architecture

An architecture of RDF storage and query system is shown in Fig. 11. As illustrated, the user constructs a query and access the SPARQL server to process it. The system then processes the query conditions to match the datasets stored in the RDF storage. Next, the matched datasets are retrieved and analyzed thoroughly. Lastly, the result is returned to the user.

Fig. 11.

Architecture of RDF storage and query system

The illustration in Fig. 12 describes the process of how raw uncategorized data are analyzed and converted into RDF format that is readable for both humans and machines.

Fig. 12.

Process of RDF storage

Fig. 13 shows the process of SPARQL query to retrieve the matched result. In order to start all the construction of queries, the SPARQL server must be running in the background. The construction of queries needs to be completed to retrieve data from RDF storage.

Fig. 13.

Process of RDF query

5-2 SPARQL Query

Fig. 14 and Fig. 15 show an example of SPARQL query and the result of retrieved data. Retrieving information from RDF storage is done within the SPARQL server and the triplestore.

Fig. 14.

SPARQL query for vessel information

Fig. 15.

Query result for vessel information