Semi-Automatic Discovery of Meaningful Ontology from a Relational Database

Many legacy relational databases are hidden behind business layers containing semantic in- formation describing the data contained within the tables of the database. With the creation of the Semantic Web some databases have been exposed utilizing this technology, but with a cost. The process of exposing the database to the Semantic Web has not taken o_ because the manual mapping of the database to the ontology is improbable at a large scale, it is a time intensive process, and to create a domain ontology requires an Ontologist and/or domain expert. Many applications and approaches have been presented over the years to help expose these legacy databases to the Semantic Web. None of these solutions has become widely accepted because they translate all the data to Resource Description Framework (RDF). This does not work with legacy databases since other systems are still interacting with that data. In addition, systems that translate the data from legacy database to RDF triples do not scale for large databases because a statement or RDF triple is made for every cell within every table. Thus, the amount of information generated from a legacy system that has terabytes of data grows too large to be store in a triple store. Other systems generate an ontology that is a basic representation of the schema and lacking any type of hierarchy or semantic meaning. This thesis proposes an architecture that will semi-automatically extract a meaningful ontology in a timely manner that can scale to handle large database and expose the database as virtual RDF graph by mapping the extracted domain ontology to the database. This will be accomplish by utilizing mapping rules that will evaluate the schema along with the data within the database and utilize existing knowledge base, like DBpedia, in order to find similar ontology classes that match the structure and data within the database. This hybrid approach to ontology extraction and generation of a mapping between the database and extracted ontology does not require an Ontologist, manual mapping, or time intensive work to be done. In addition, the approach can be applied at a larger scale

Geotriples: a tool for publishing earth observation and geospatial data as rdf graphs using the r2rml mapping language

Τα τελευταία χρόνια ένας ολοένα αυξανόμενος όγκος δεδομένων παρατήρησης γης γίνεται διαθέσιμος στην Ευρώπη και την Αμερική. Τα συνδεδεμένα δεδομένα είναι ένα μοντέλο το οποίο μελετάει τον τρόπο με τον οποίο τα δεδομένα μπορούν να γίνουν διαθέσιμα στον παγκόσμιο ιστό και να διασυνδεθούν με άλλα δεδομένα, δημιουργώντας επομένως έναν "Ιστό Δεδομένων". Ωστόσο τα δεδομένα παρατήρησης γης που διατίθενται από υπηρεσίες όπως η ESA \gt και η NASA δεν ακολουθούν το μοντέλο των συνδεδεμένων δεδομένων. Κατά συνέπεια, προκειμένου κάποιος χρήστης κάποιος χρήστης να ικανοποιήσει διαφόρου τύπου ανάγκες για πληροφορίες, θα πρέπει να συλλέξει γεωχωρικά δεδομένα και δεδομένα παρατήρησης γης από διαφορετικά σιλό. Δημοσιεύοντας τα δεδομένα των σιλό αυτών ως γράφους RDF, καθίσταται δυνατή η ανάπτυξη εφαρμογών ανάλυσης δεδομένων με μεγάλη περιβαλλοντολογική και οικονομική αξία. Στην παρούσα διπλωματική, παρουσιάζεται το εργαλείο GeoTriples για το μετασχηματισμό δεδομένων παρατήρησης γης και γεωχωρικών δεδομένων σε γράφους RDF. To GeoTriples επεκτείνει τη γλώσσα αντιστοίχησης R2RML ώστε να λάβει υπόψιν και τις ιδιαιτερότητες που παρουσιάζουν τα γεωχωρικά δεδομένα. Αποτελεί μία ημι-αυτόματη εφαρμογή για μετατροπή γεωχωρικής πληροφορίας σε RDF χρησιμοποιώντας δημοφιλή λεξιλόγια όπως GeoSPARQL και stSPARQL, χωρίς ταυτόχρονα να δεσμεύεται αποκλειστικά με κάποιο από αυτά.A plethora of Earth Observation data that is becoming available at no charge in Europe and the US recently reflects the strong push for more open Earth Observation data. Linked Data is a paradigm which studies how one can make data available on the Web and interconnect it with other data with the aim of making the value of the resulting "Web of data" greater than the sum of its parts. Open Earth Observation data that are currently made available by space agencies such as ESA and NASA are not following the linked data paradigm. Therefore, Earth Observation data and other kinds of geospatial data that are necessary for a user to satisfy her information needs can only be found in different data silos, where each silo may contain only part of the needed data. Publishing the content of these silos as RDF graphs, enables the development of data analytics applications with great environmental and financial value. In this thesis, we present the tool GeoTriples that allows for the transformation of Earth Observation data and geospatial data into RDF graphs. GeoTriples goes beyond the state of the art by extending the R2RML mapping language to be able to deal with the specificities of geospatial data. GeoTriples is a semiautomated tool that allows the publication of geospatial information into an RDF graph using the state of the art vocabularies like GeoSPARQL and stSPARQL, but at the same time it is not tightly coupled to a specific vocabulary

Relational databases as semantic web endpoints

This proposal explores the promotion of existing relational databases to Semantic Web Endpoints. It presents the benefits of ontology-based read and write access to existing relational data as well as the need for specialized, scalable reasoning over that data. We introduce our approach for translating SPARQL/Update operations to SQL, describe how scalable reasoning can be realized by using the power of the database system, and outline two case studies for evaluating our approach