Chapter 16 Linked Data, Ontologies, and DBpedia

Transcription

1 Abstract Chapter 16 Linked Data, Ontologies, and DBpedia Alex Adamec The Semantic Web is a collaborative movement which promotes common data formats on the World Wide Web and aims to convert the currently unstructured and semi-structured documents on the web into a web of data by encouraging the inclusion of semantic content (metadata) in web pages. [7] Not only does the Semantic Web need access to data, but relationships among data should be made available, too. This collection of interrelated datasets on the web is referred to as Linked Data, and the relationships among the data are defined using vocabularies, or ontologies. [11] [13] Just as relational databases or XML need specific query languages, the Web of Data needs its own specific query language and facilities. This is provided by the SPARQL query language and the accompanying protocols. [14] To improve the quality of data integration on the web, inferences are used to automatically analyze the content of data and discover new relationships. [12] 1 Background Cognitive scientist Allan M. Collins, linguist M. Ross Quillian, and psychologist Elizabeth F. Loftus formed the concept of the Semantic Network Model in the early 1960s as a form to represent semantically structured knowledge. [7] By inserting machine-readable metadata about pages and how they are related to each other, the Semantic Network Model extends the network of hyperlinked human-readable web pages by enabling automated agents to access the Web more intelligently and perform tasks on behalf of users. [7] The term "Semantic Web" was coined by Tim Berners-Lee, the inventor of the World Wide Web and director of the World Wide Web Consortium ("W3C"). He defines the Semantic Web as "a web of data that can be processed directly and indirectly by machines." [7] 2 Purpose Today, humans can use the web to perform tasks which machines are incapable of accomplishing without human direction because web pages are designed to be read by people, not machines. The semantic web, however, is a system that requires that the relevant information sources be semantically structured so as to enable machines to "understand" and respond to complex human requests based on their meaning. [7] Tim Berners-Lee originally expressed the vision of the Semantic Web as follows: I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web the content, links, and transactions between people and computers. A "Semantic Web", which makes this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The "intelligent agents" people have touted for ages will finally materialize. [7]

2 3 Components Often, the term "Semantic Web" is used more precisely to refer to the formats and technologies that enable it. These technologies provide a formal description of concepts, terms, and relationships within a given knowledge domain and enable the collection, structuring, and recovery of linked data. [7] 3.1 Linked Data LOD Cloud Diagram as of September 2011 [5] To make the Web of Data a reality, it is important to have a standard format for data on the web. This empowers the data to be reachable and manageable by Semantic Web tools. Not only does the Semantic Web need access to data, but relationships among data should be made available, too. This collection of interrelated datasets on the web is referred to as Linked Data. The relationships among the data is what distinguishes it from a sheer collection of datasets. [11] Linked Data lies at the heart of what Semantic Web is all about: large scale integration of, and reasoning on, data on the Web. [11] RDF, the Resource Description Framework, is a common format which allows for either conversion or on-the-fly access to existing databases such as relational, XML, and HTML databases (RDF will be discussed in further detail later in Section 3.2.1). This common framework is necessary to achieve and create Linked Data. Also important is the ability to setup query endpoints to access that data more conveniently. [11]

3 3.2 Ontologies The Semantic Web is a Web of Data of people and titles and dates and product numbers and any other data one might think of. Semantic Web technologies such as RDF, OWL, and SPARQL combine to allow applications to query that data and draw inferences using ontologies. [11] Ontologies define the concepts and terms (relationships) used to describe and represent an area of concern. According to the World Wide Web Consortium, ontologies are used to classify the terms that can be used in a particular application, characterize possible relationships, and define possible constraints on using those terms. [13] The complexity of ontologies can vary greatly. Ontologies can be very simple (describing only one or two concepts) or very intricate (containing several thousand terms). [13] The terms ontology and vocabulary are used interchangeably, although their meanings differ slightly. The word ontology is typically reserved for more complex, formal collections of terms, whereas vocabulary is used when such strict formalism is unnecessary or used only in a very loose sense. [13] Vocabularies help data integration when ambiguities may exist on the terms used in the different data sets. Vocabularies also form the basic building blocks for inference techniques. W3C offers an example of this regarding the application of ontologies in the field of health care: Medical professionals use [ontologies] to represent knowledge about symptoms, diseases, and treatments. Pharmaceutical companies use [ontologies] to represent information about drugs, dosages, and allergies. Combining this knowledge from the medical and pharmaceutical communities with patient data enables a whole range of intelligent applications such as decision support tools that search for possible treatments; systems that monitor drug efficacy and possible side effects; and tools that support epidemiological research. [13] Another use of ontologies is to organize knowledge. Ontologies can be used as standard formalisms by museums, libraries, enterprises, newspapers, etc. to manage their large collections of historical artifacts, books, business glossaries, and news reports. [13] The complexity of the vocabularies used varies by application. Although some applications may decide to rely on the logic of the application program and not even use small vocabularies, other applications may need more complex ontologies with complex reasoning procedures. It all depends on the requirements and the goals of the applications. To satisfy these different needs, a large palette of techniques to describe and define different forms of vocabularies in a standard format are used: These include RDF and RDF Schemas, Simple Knowledge Organization System (SKOS), Web Ontology Language (OWL), and the Rule Interchange Format (RIF). [13] Ontology Languages Ontologies are constructed using formal languages known as ontology languages. These languages permit the encoding of knowledge about specific domains and often include reasoning rules that support the processing of that knowledge. [6] Usually declarative languages, ontology languages are almost always generalizations of frame languages, and they are commonly based on either description logic or first-order logic. [6]

4 Resource Description Framework One example of an ontology language is the Resource Description Framework (RDF). RDF is a general method to decompose any type of knowledge into small pieces using some rules about the semantics, or meaning, of those pieces. [8] The following is an example of some : < :JOHN A :PERSON. :JOHN :HASMOTHER :SUSAN. :JOHN :HASFATHER :RICHARD. :RICHARD :HASBROTHER :LUKE. RDF isn t concerned merely with how it is written. RDF is about representing networkor graph-structured information. [8] Like RDF, XML also is designed to be simple and generalpurpose, and RDF can be written in XML such as in the following example: <RDF:RDF XMLNS:RDF=" XMLNS:NS=" <NS:PERSON RDF:ABOUT=" <NS:HASMOTHER RDF:RESOURCE=" /> <NS:HASFATHER> <RDF:DESCRIPTION RDF:ABOUT=" <NS:HASBROTHER RDF:RESOURCE=" /> </RDF:DESCRIPTION> </NS:HASFATHER> </NS:PERSON> </RDF:RDF> RDF is designed to represent knowledge in a distributed world and thus is particularly concerned with meaning. Each element mentioned in RDF means something, whether a reference to an abstract concept, something concrete in the world, or a fact. [8] RDF is well suited for distributed knowledge. RDF applications are able to combine RDF files from different sources and easily learn from them new things by linking documents together by their common vocabularies and by allowing any document to use any vocabulary. This flexibility is fairly unique to RDF. : < :RICHARD :HASSISTER :REBECCA {?A :HASFATHER?B.?B :HASSISTER?C. } => {?A :HASAUNT?C }. In the RDF above, the document uses two other relations to define what it means to be an aunt. In RDF, names of entities are global. [8] This means when :JOHN and :HASFATHER are used in one document, applications can assume they have the same meaning in any other RDF document with the [8] Because of this, an application would be able to put this document together with the first RDF document to determine that :REBECCA is :JOHN's aunt. RDF can be used to integrate data from different sources without custom programming or to offer data for re-use by other parties. [8]

5 Web Ontology Language Another example of ontology languages is the Web Ontology Language (OWL). OWL is a family of knowledge representation languages for authoring ontologies or knowledge bases. The languages are characterized by formal semantics and RDF/XML-based serializations for the Semantic Web. OWL is endorsed by the World Wide Web Consortium (W3C) and has attracted academic, medical and commercial interest. [10] An important distinction between RDFS and OWL is that in RDFS, you can only subclass existing classes, whereas OWL allows the construction of classes from existing ones. [4] Examples An example of an OWL ontology is the Music Ontology Specification which provides main concepts and properties for describing music (i.e. artists, albums, tracks, performances, arrangements, etc.) on the Semantic Web. An Example of a Music Ontology Production Workflow [2] Above is an example of a music production workflow using the Music Ontology Specification, and below is an example of how the Music Ontology can be used in HTML to describe Bach's "The Art of Fugue" and one of its performances.

6 <span typeof="mo:musicartist" about=" h"> <span property="foaf:name">johann Sebastian Bach was involved in the <span rev="mo:composer"> <span typeof="mo:composition" about="#composition"> <span property="rdfs:label"> Composition of <span rel="mo:produced_work"> <span typeof="mo:musicalwork" about="#kunst-der-fuge"> <span property="dc:title">the Art of Fugue, BWV 1080: Contrapunctus IX, a 4, alla Duodecima which happened between <span rel="event:time"> <span property="tl:start" datatype="xsd:gyear">1742 and <span property="tl:end" datatype="xsd:gyear">1749. Describing Bach's "The Art of Fugue" in HTML [3] <span typeof="mo:performance" about="#performance"> <span rel="mo:performance_of" resource="#kunst-der-fuge">this work was performed and <a rel="mo:recorded_as" href=" beaeb2a#_"> recorded </a> by the <a rel="mo:performer" href=" 13#_"> Emerson String Quartet </a> in <span rel="event:time"><span property="tl:at" datatype="xsd:gyear">2003, with a <span rel="mo:instrument" typeof="mo:instrument"><span property="rdfs:label">first violin< /span>, a <span rel="mo:instrument" typeof="mo:instrument"><span property="rdfs:label">second violin, a <span rel="mo:instrument" typeof="mo:instrument"><span property="rdfs:label">viola< /span> and a <span rel="mo:instrument" typeof="mo:instrument"><span property="rdfs:label">cello</sp an>. Describing Bach's "The Art of Fugue" in HTML Contd. [3]

7 3.3 Queries The Web of Data, usually represented using RDF, needs its own, RDF-specific query language and facilities just as relational databases and XML need specific query languages such as SQL and XQuery, respectively. This is provided by the SPARQL query language and the accompanying protocols. SPARQL makes it possible to send queries and receive results, e.g., through HTTP or SOAP. [14] As W3C explains, SPARQL queries are based on (triple) patterns. RDF can be seen as a set of relationships among resources (i.e., RDF triples); SPARQL queries provide one or more patterns against such relationships. These triple patterns are similar to RDF triples, except that one or more of the constituent resource references are variables. A SPARQL engine would returns the resources for all triples that match these patterns. [14] Consumers are able to use SPARQL to extract possibly complex information like existing resource references and their relationships. This information can be returned, for example, in a table format, and this table can then be incorporated into another web page. This approach allows SPARQL to provide a powerful tool to build complex mash-up sites or search engines that include data stemming from the Semantic Web. [14] 3.4 Inferences Inference on the Semantic Web can be characterized, broadly speaking, by discovering new relationships or ontologies. Data is modeled as a set of named relationships between resources. Inference means that automatic procedures are able to generate new relationships based on the data and on some additional information in the form of a vocabulary or rule sets. [12] Both vocabularies (ontologies) and rule sets draw upon knowledge representation techniques. Generally speaking, ontologies concentrate on classification methods, and rules focus on defining a general mechanism for discovering and generating new relationships based on existing ones. Unlike rule sets, ontologies put an emphasis on defining 'classes' and 'subclasses', on defining how individual resources can be associated to such classes, and on characterizing the relationships among classes and their instances. [12] To improve the quality of data integration on the web, inferences are used to automatically analyze the content of data and discover new relationships. Inference based techniques are also important in discovering possible inconsistencies in the (integrated) data. [12] 4 Projects This section lists one of the many projects and tools that exist to create Semantic Web solutions: DBpedia.

8 4.1 DBpedia DBpedia Logo [9] Wikipedia has grown into one of the central knowledge sources of mankind, maintained by thousands of contributors. The DBpedia project leverages this gigantic source of knowledge by extracting structured information from Wikipedia and by making this information accessible on the Web under the terms of the Creative Commons Attribution-ShareAlike 3.0 License and the GNU Free Documentation License. [1] According to dbpedia.org, The English version of the DBpedia knowledge base currently describes 4.0 million things, out of which 3.22 million are classified in a consistent ontology, including 832,000 persons, 639,000 places (including 427,000 populated places), 372,000 creative works (including 116,000 music albums, 78,000 films and 18,500 video games), 209,000 organizations (including 49,000 companies and 45,000 educational institutions), 226,000 species and 5,600 diseases. [1] Localized versions of DBpedia are also available in 119 languages. Together, all of these versions describe 24.9 million things, out of which 16.8 million are interlinked with the concepts from the English Dbpedia. The full DBpedia data set features labels and abstracts for 12.6 million unique things in 119 different languages; 24.6 million links to images and 27.6 million links to external web pages; 45.0 million external links into other RDF datasets, 67.0 million links to Wikipedia categories, and 41.2 million YAGO categories. The dataset consists of 2.46 billion pieces of information (RDF triples) out of which 470 million were extracted from the English edition of Wikipedia, 1.98 billion were extracted from other language editions, and about 45 million are links to external datasets. [1] The DBpedia knowledge base has several advantages over existing knowledge bases. The DBpedia knowledge base covers many domains; it represents real community agreement; it automatically evolves as Wikipedia changes, and it is truly multilingual. [1] The DBpedia knowledge base allows you to ask quite surprising queries against Wikipedia, for instance Give me all cities in New Jersey with more than 10,000 inhabitants or Give me all Italian musicians from the 18th century. Altogether, the use cases of the DBpedia knowledge base are widespread and range from enterprise knowledge management, over Web search to revolutionizing Wikipedia search, dbpedia.org explains. [1] 5 Conclusion The Semantic Web is a collaborative movement which uses vocabularies, or ontologies, to define the relationships among data and create collections of interrelated datasets on the web, referred to as Linked Data. [11] [13] By promoting common data formats on the World Wide Web and by encouraging the inclusion of semantic content in web pages, the Semantic Web movement aims to convert the documents on the web into a web of data. [7] The SPARQL query language and the accompanying protocols provide a specific query language to extract possibly complex information like existing resource references and their relationships. [14]

9 Inferences are then used to automatically analyze the content of data and discover new relationships to improve the quality of data integration on the web. [12] 6 References [1] About. (2013, September 17). wiki.dbpedia.org : About. Retrieved April 30, 2014, from [2] An Example of a Music Ontology Production Workflow [Web Drawing]. Retrieved from [3] Getting Started. (n.d.). The Music Ontology. Retrieved April 29, 2014, from [4] Herman, Ivan. "Why OWL and not WOL?". Tutorial on Semantic Web Technologies. World Wide Web Consortium. Retrieved 18 April [5] Jentzsch, A. (Artist). (2011, September 19). LOD Cloud Diagram as of September 2011 [Web Graphic]. Retrieved from [6] Ontology language. (2014, April 26). Wikipedia. Retrieved April 29, 2014, from [7] Semantic Web. (2014, April 25). Wikipedia. Retrieved April 29, 2014, from [8] Tauberer, J. (2008, January 21). RDF About. GitHub. Retrieved April 29, 2014, from [9] [Web Graphic]. Retrieved from [10] Web Ontology Language. (2014, April 29). Wikipedia. Retrieved April 29, 2014, from [11] W3C. (n.d.). Data. Retrieved April 29, 2014, from [12] W3C. (n.d.). Inference. Retrieved April 29, 2014, from [13] W3C. (n.d.). Ontologies. Retrieved April 29, 2014, from [14] W3C. (n.d.). Query. Retrieved April 29, 2014, from