Web NDL Authorities SPARQL API Specication
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1 Web NDL Authorities SPARQL API Specication National Diet Library of Japan Created: March 31th, 2014 Revised: March 31th, 2018
2 Contents 1 The Outline of the Web NDLA SPARQL API SPARQL query API Authority Record RDF Graph and SPARQL SPARQL RDF Query Language Basic Syntax Groups of Graph Patterns Optional Pattern Matching UNION for Matching Alternatives RDF Dataset and Graph Restricting Values with FILTER Solution Sequences and Modiers Query Forms and Results API Parameters and Result Formats XML Result Format JSON Result Format RDF Graph of the Authority Records and Applied Examples Authority Records of Personal Name, Family Name and Corporate Name Authority Records of Geographical Name, Uniform Title, Subject Heading and Subject Subdivision SPARQL Request URI and parameters New Functionalities of SPARQL 1.1, and Sample Queries The Dierences between SPARQL 1.1 endpoint and exsiting ARC2 (1.0 endpoint) Revision History 35 1
3 1 The Outline of the Web NDLA SPARQL API The Web NDL Authorities (Web NDLA) stores the name authority information as RDF (Resource Description Framework) data. RDF data can be queried by SPARQL (SPARQL RDF Query Language), and the Web NDLA has the function to respond the SPARQL query. This document describes the RDF data structure of the Web NDLA, and explains how to query it with SPARQL. 1.1 SPARQL query API Basic SPARQL Query A user can search any authority data in Web NDLA with SPARQL Query. The next example is a query to nd the authority URI of the subject heading " " (library). PREFIX rdfs: < rdfs:label " " The search result will be returned by sending this query to NDLA though API using the following procedure Request URI and Parameters A SPARQL query to the Web NDLA is to be requested against the following URI (endpoint). There are two parameters as shown in Table1. parameter query output Table 1: Web NDLA API Parameters value URL encoded SPARQL query string result format (xml json turtle*) *turtle is for DESCRIBE, CONSTRUCT only A request to get results as XML is of the following form. encoded query&output=xml With the previous query being URL encoded, the entire request will be the following %2F01%2Frdf-schema%23%3E%0D%0ASELECT+*+WHERE+%7B%0D%0A%09%3Fsubj+rdfs%3Alabel 2
4 +%22%E5%9B%B3%E6%9B%B8%E9%A4%A8%22%0D%0A%7D%0D%0A&output=xml There are four SPARQL query forms: SELECT to nd values, ASK to see if the matching data exists, CONSTRUCT to create new RDF graphs with the matching value, and DESCRIBE to obtain an explanation graph for resources. For DESCRIBE or CONSTRUCT queries, value turtle can be specied for the result format parameter (output). SPARQL query will be discussed in chapter 2, and four query forms will be explained in Result Format As the result of a request, a list of bound variables (for SELECT), an RDF graph (for DESCRIBE and CONSTRUCT) or a truth value (for ASK) will be returned, according to the type of the query. The format of the binding list and the truth value is varied by the output parameter as shown in Table 2. output parameter xml json Table 2: Web NDLA API output Parameters format XML as specified in SPARQL Query Results XML Format JSON as specified in SPARQL 1.1 Query Results JSON Format The resulting XML for the previous query (output=xml) will be: <sparql xmlns=" <head> <variable name="subj"/> </head> <results> <result> <binding name="subj"> <uri> </binding> </result> </results> </sparql> The detail of XML format and JSON format will be shown in 3.1 and 3.2 respectively. The result format of RDF graph is shown in Table Authority Record RDF Graph and SPARQL Authority records in the Web NDLA are expressed as RDF graphs. A SPARQL query is constructed with "patterns to nd in a graph". This section explains the outline of how to write patterns in an RDF graph as SPARQL query. 3
5 output parameter xml json turtle Table 3: Web NDLA API RDF Graph Formats Format RDF/XML format RDF/JSON format Turtle format The RDF Graph of the Authority Record RDF describes a basic information in a way that a thing (e.g. an authority record = subject) has a property or characteristic (predicate) which has a value (object). This subject - predicate - object relation is called an RDF Triple. Each element of a triple is named (identied) by a URI 1. An object can be literal instead of a URI. A set of RDF triples is called an RDF Graph. In an RDF graph, triples that share the same URI is connected, resulting a large network of information (Figure 1) 2. Figure 1: Triples are connected via a common URI. Authority records in the Web NDLA are represented by RDF. Each authority record is identied by a URI, and the relationship between records (e.g. broader or alternative term) is described as RDF triple. Thus authority records are connected, and form a large RDF graph (Figure 2). Moreover, because the Web NDLA references external authorities such as VIAF or LC authorities, the RDF graph of the Web NDLA goes beyond the National Diet Library of Japan, and becomes a part of LOD (Linked Open Data) cloud (The whole model of an authority record will be discussed in chapter 4). 1 Actually, it is IRI (Internationalized Resource Identifier, the extension of URI so that non ASCII characters can be appear in an identifier string) that is used for naming. This document uses more familiar URI as the term of identifier, but it should be read as IRI. 2 Some nodes might be blank nodes without URIs, which are connected via internal blank node IDs. 4
6 Figure 2: The subject URI of (Semantic Web, ) is also the object of skos:narrower property of (Internet, ), hence these triples are connected Partial Graph Pattern and Search With SPARQL, a user will write a partial pattern of an RDF graph that contains unknown data (variables), nd values that match this pattern from the target graph, and retrieve the result as sets of values. For example, in order to nd authorities whose label (rdfs:label) are the narrower terms (skos:narrower) of " ", the partial graph pattern will be as Figure 3. Figure 3: A partial graph pattern where the variables subj2 and label represent the narrower term and its label. The search will nd the matching sub graphs from the Web NDLA, and returns values of the corresponding variables as in Table Writing a SPARQL Query In a SPARQL query, partial graph patterns are expressed with Turtle-like syntax. Unknown elements are written as variables, whose name begin with a letter?. The partial graph pattern of Fig. 3 will be written as follows. 5
7 Table 4: The results retrieved by the partial graph pattern subj2 label rdfs:label " " ; skos:narrower?subj2.?subj2 rdfs:label?label. # Internet The steps to search the graph with this pattern are: 1. If graph patterns use prexed names, map those prexes to URI by keyword PREFIX 3, 2. put keyword SELECT followed by space separated variables to retrieve (similar to columns in SQL), 3. then put keyword WHERE followed by patterns enclosed by {. PREFIX rdfs: < PREFIX skos: < SELECT?subj2?label WHERE {?subj1 rdfs:label " " ; skos:narrower?subj2.?subj2 rdfs:label?label. In this example, two variables?subj2?label are listed after SELECT in order to get the values shown in Table 4. By writing * instead of variables list, all values of the variables used in the graph pattern will be retrieved For ASK, CONSTRUCT and DESCRIBE queries, SELECT clause is replaced by each corresponding construct while PREFIX clause and WHERE clause parts are the same (See 2.8 for detail). 3 Although some of following examples may omit PREFIX clause for simplicity, all prefixes must be mapped to URIs by PREFIX. 4 In these examples, first one has a new line before WHERE, while the second one does not. Since a new line character is regarded as a white space in SPARQL syntax, it does not make any difference. 6
8 2 SPARQL RDF Query Language The Web NDLA uses ARC2 library 5 which supports SPARQL Query Language for RDF This chapter explains SPARQL query that can be used with the Web NDLA. 2.1 Basic Syntax Most forms of SPARQL query contain a set of triple patterns called a basic graph pattern. These patterns are expressed by Turtle-like syntax Triple Patterns A triple pattern is like RDF triple except that each of the subject, predicate and object may be a variable. A triple pattern consists of the following components: A term enclosed by <> is a URI 7. It can be an absolute URI, or a relative URI combined with BASE clause. A URI can be expressed as a prexed name in the form of prefix:localname. The prex label must be associated with a URI by PREFIX clause. A value enclosed by "" or is a literal, with either an optional language tag (introduced or an optional datatype IRI / prexed name (introduced by ^^). If a prexed style name starts with _:, it is a blank node in a graph. A blank node can also be expressed by [] form as in Turtle 8. A variable is an alphanumeric string 9 prexed by either? or $. A variable name may contain an under bar (_), but not a hyphen (-). Query variables in SPARQL queries have global scope. A special single character 'a' may be used as an abbreviation of predicate rdf:type which relates a subject to a class. A user may nd it convenient because PREFIX clause for rdf: is not necessary if there is no other term from that namespace. The next is an example of abbreviation 'a' which relates the subject (variable) to foaf:person class. This query will nd person entity resources (see 4.1) in the authority records. PREFIX foaf: < a foaf:person Without using abbreviation 'a', the above query will be as follows It is actually an IRI in SPARQL, too. This document uses more familiar term URI in place of IRI. 8 Blank nodes in graph patterns act as non-distinguished variables, not as references to specific blank nodes in the data. Therefore, they will match even URIs or literals in RDF graph, though those values cannot be retrieved. 9 The Web NDLA does not support non alphanumeric variable names, although SPARQL specification permits them. 7
9 PREFIX foaf: < PREFIX rdf: < rdf:type foaf:person A character # is a comment marker if presents outside literals and URIs. After the maker up to the end of line will be a comment. New line and TAB characters are treated as white spaces Datatype and Language Tag in Literal A literal may be a plain literal, or may have a datatype (e.g. date, integer etc.) or a language tag. A care should be taken because a plain literal and a typed literal or a language tagged literal are treated as dierent values even if their lexical forms (string parts) are the same. The Web NDLA does not use datatypes. For example, a record creation date is always in the form of " " in the Web NDLA, while it might be expressed as " "^^xsd:date in some other datasets. The Web NDLA uses language tags only for the transcriptions of structured labels. For example, the subject heading " " (library) has two transcriptions " "@ja-kana and "Toshokan"@ja-Latn, so that users can distinguish Japanese-kana and Japanese-romaji transcriptions Graph Patterns A graph pattern is a set of triple patterns. Each triple pattern is delimited by a period (.) 10. There are abbreviated ways of writing some common triple pattern constructs, same as in Turtle: The common subject can be abbreviated by ; so that the rest are written as predicate-object list. The common predicate is abbreviated by, and the rest are just object list.?subj rdfs:label?label ; skos:relatedmatch < < The above example contains three triple patterns which share the common subject?subj. Also, the common predicate skos:relatedmatch is omitted in the last line with, at the end of previous line. Without shortcut, those triple patterns will be written as follows.?subj rdfs:label?label.?subj skos:relatedmatch < skos:relatedmatch < 10 A period is just a delimiter, and not required at the end of a triple pattern (different from Turtle), hence it is not necessary to place a period at the end of a graph pattern. 8
10 2.1.4 Filters SPARQL provides FILTER to test or restrict the values. For example, the following query matches records whose labels contain " " (FILTER will be further explained in the section 2.6).?uri rdfs:label?label. FILTER regex(?label, " ") # Natsume 2.2 Groups of Graph Patterns SPARQL graph pattern matching is dened in terms of combining the results from matching basic graph patterns. A sequence of triple patterns (with optional lters) comprises a single basic graph pattern. Any other graph pattern terminates the basic graph pattern. A set of one or more graph patterns delimited by { is called group graph pattern. The WHERE clause of a query consists of the keyword followed by one group graph pattern. Group graph patterns can be nested. WHERE {?subj1 rdfs:label " " ; skos:narrower?subj2. {?subj2 rdfs:label?label # Internet In the above example, the WHERE clause has one group graph pattern, within which there are one basic graph pattern and one group graph pattern. The inner group graph pattern consists of one basic graph pattern Graph Patterns and Filters A FILTER restricts solutions over the whole group graph pattern in which the lter appears. Within the same group graph pattern, the lter has the same scope regardless its position. WHERE {?subj1 rdfs:label " " ; skos:narrower?subj2.?subj2 rdfs:label?label. FILTER regex(?label, " ") # net The above example has the same solutions as bellow. WHERE { FILTER regex(?label, " ") 9
11 ?subj1 rdfs:label " " ; skos:narrower?subj2.?subj2 rdfs:label?label Graph Patterns and Blank Node ID Labels for blank nodes (blank node IDs) are scoped to the basic graph pattern. A label can be used in only a single basic graph pattern in any query 11. Therefore, the following example is valid: WHERE {?subj1 rdfs:label " " ; skos:narrower _:s2. _:s2 rdfs:label?label. while the next one is an error. WHERE {?subj1 rdfs:label " " ; skos:narrower _:s2. {_:s2 rdfs:label?label In the second example, the inner { divides the query into two basic graph patterns, which cannot share the same blank node id (_:s2 in this case). 2.3 Optional Pattern Matching In a query with basic graph patterns, all variables in the query must have matches to have a solution. For example, in the next query, narrower terms of " " will not be retrieved unless they have their related terms. SELECT?subj2?label?subj3?rels WHERE {?subj1 rdfs:label " " ; skos:narrower?subj2.?subj2 rdfs:label?label ; skos:related?subj3.?subj3 rdfs:label?rels. 11 Although blank nodes act like variables, their scope is different from that of variable which is global. 10
12 In order to make non-required variables optional, enclose the partial graph that contain those variables as a group graph pattern, then concatenate it to the required pattern by OPTIONAL keyword. SELECT?subj2?label?subj3?rels WHERE {?subj1 rdfs:label " " ; skos:narrower?subj2.?subj2 rdfs:label?label. OPTIONAL {?subj2 skos:related?subj3.?subj3 rdfs:label?rels. The above query will nd all narrower terms of " ", as well as the related terms of them if they have any Multiple Optional Patterns A query can have multiple OPTIONAL patterns. pattern 1 OPTIONAL {pattern 2 OPTIONAL {pattern 3 Those patterns are left-associative. The above one is the same as the next: {pattern 1 OPTIONAL {pattern 2 OPTIONAL {pattern OPTIONAL and FILTER A FILTER can be applied to an optional pattern. The next example is a query that will nd the narrower terms of " " and also nd their related terms which contain " " (information). SELECT?subj2?label?subj3?rels WHERE {?subj1 rdfs:label " " ; skos:narrower?subj2.?subj2 rdfs:label?label. OPTIONAL {?subj2 skos:related?subj3.?subj3 rdfs:label?rels. FILTER regex(?rels, " ") 11
13 Note that the scope of a FILTER is the group graph pattern. If the FILTER keyword is placed outside the OPTIONAL pattern as in the next example, it will aect the entire WHERE clause. SELECT?subj2?label?subj3?rels WHERE {?subj1 rdfs:label " " ; skos:narrower?subj2.?subj2 rdfs:label?label. OPTIONAL {?subj2 skos:related?subj3.?subj3 rdfs:label?rels. FILTER regex(?rels, " ") Because this FILTER examines whether?rels contains " " for all solutions, any solution where the narrower term does not have related term (no binding for?rels) will be excluded from the results set. 2.4 UNION for Matching Alternatives To nd results that match any of alternative patterns, join group graph patterns by UNION keyword. For example, the next query will nd subject headings that have classication code ND633 from National Diet Library Classication (NDLC) or from Japan Decimal Classication 9th edition (NDC9). PREFIX rdfs: < PREFIX skos: < prefix xl: < prefix ndl: < SELECT * WHERE { {?subj skos:relatedmatch < ; rdfs:label?dcndl. UNION {?subj skos:relatedmatch < ; rdfs:label?ndc9. 12
14 2.5 RDF Dataset and Graph A SPARQL query is executed against an RDF Dataset which represents a collection of graphs. An RDF Dataset comprises one default graph, which does not have a name, and zero or more named graphs, each of which is identied by an URI. A query involves any of graphs in a dataset. The graph that is used for matching a basic graph pattern is the active graph. GRAPH keyword (see 2.5.1) species the active graph. If it does not present, the default graph is used as the active graph. The dataset in the Web NDLA consists of two named graphs shown in Table 5. data type Subject Headings Name Authorities Table 5: Graphs in Web NDLA dataset graph URI Also, the merge of those two graphs is the default graph in the Web NDLA GRAPH Keyword In a WHERE clause, a GRAPH followed by a graph URI will set the active graph. After the URI, place the group graph pattern to search against this active graph. GRAPH < {?s rdfs:label " " The above example will nd " " in the Name Authority graph (" " as a corporate name will match). If a variable follows GRAPH keyword, the result will have the graph URI where the group graph pattern has match. GRAPH?g {?s rdfs:label " " The above query returns the result shown in Table 6, which tells that each of Subject Heading and Name Authority has a record " ". With multiple GRAPH keywords, a query will be executed against each graph. The same variable can be used across those graphs. The next example will nd the records that share the same labels in Subject Heading and Name Authority by using the same variable?label for both graphs. 13
15 Table 6: The search result of with graph name as variable g s GRAPH < {?sh rdfs:label?label GRAPH < {?na rdfs:label?label FROM Clause A clause FROM <graph URI> before WHERE clause indicates the graph to be used to form the default graph. FROM NAMED <graph URI> will introduce that graph as a named graph. Those keywords can be used multiple times (i.e. the query will be executed against multiple graphs). In the Web NDLA, FROM clause acts to restrict the target graph 12. The next example will nd " " in the Name Authority, and have the same results as the query with GRAPH keyword. SELECT * FROM < WHERE {?s rdfs:label " " 2.6 Restricting Values with FILTER FILTER restricts solutions that match the graph pattern, by excluding solutions where any FILTER expression evaluates to FALSE.?book ex:price?price. FILTER (?price < 2000) In the above example, only solutions whose?price value is less than 2000 will be returned, others being excluded from the matching sets. 12 Some services introduce external dataset by FROM, although the Web NDLA does not. FROM and FROM NAMED behave identically in the Web NDLA: specified graph will be incorporated to both default graph and named graph. 14
16 2.6.1 Comparison and Logical Operators Like many programming languages, FILTER expression can have comparison and logical operators shown in Tables 7 and 8 respectively. An expression has to be enclosed by (). Operator A = B A!= B A > B A < B A >= B A <= B Table 7: SPARQL Comparison Operators TRUE condition A is equal to B A is not equal to B A is greater than B A is less than B A is greater than or equal to B A is less than or equal to B Table 8: SPARQL Logical Operators Operator TRUE condition A B Either A or B is TRUE (OR) A && B Both A and B are TRUE (AND)! A Not A (NOT) Expressions are evaluated based on the data types of A and B. Suppose A is 10 and B is 5, the expression A > B evaluates to TRUE if those are numeric values, and to FALSE if string values. A and B must have the same types to be compared. In the Web NDLA, all literal values are strings (no data type), however, they are converted to numbers automatically if both A and B can be treated as numerical values 13. For numerical values, arithmetic operators can be applied. The next example restricts the solutions to have 10 or more dierence between max and min values.?what ex:height?max ex:low?min. FILTER (?max - 10 >=?min) Test Functions Functions in Table 9 are provided to test values, e.g. whether they are literals, blank nodes etc. The combination of OPTIONAL pattern and negation of bound() can be used to nd solutions that do NOT have a value of particular property. The next example will nd any living person (who has birth date but does not have death date). 13 In general, use accessors such as STR() in to align data types when operands have different types. 15
17 Function bound(a) isiri(a) isuri(a) isblank(a) isliteral(a) Table 9: SPARQL Test Functions TRUE condition A is bound to a value A is an IRI A is a URI A is a blank node A is a literal PREFIX foaf: < PREFIX rda: < WHERE {?who a foaf:person; foaf:name?name ; rda:dateofbirth?bdate. OPTIONAL {?who rda:dateofdeath?ddate. FILTER (!bound(?ddate)) Note that dateofdeath has to be OPTIONAL. Otherwise, the graph pattern requires?ddate while FILTER excludes results that have matching?ddate, resulting empty solution set Accessors Operators in Table 10 are accessors to obtain values other than truth value. Operator str(a) lang(a) datatype(a) Table 10: SPARQL Accessors Return Value the lexical form of A (simple string) language tag value of A datatype URI of A For example, with lang() accessor, a user can retrieve Romaji transcription of headings that match a pattern. PREFIX xl: < PREFIX ndl: < xl:preflabel [ ndl:transcription?yomi ] ; 14 SPARQL 1.1 provides more intuitive negation, e.g. FILTER NOT EXISTS?who rda:dateofdeath?ddate. 16
18 #any graph pattern FILTER (lang(?yomi) = "ja-latn") Regular Expressions The regular expression, introduced by regex() function, is a method to evaluate a string with exible pattern. Partial matching to a string is also executed by a regular expression function. The Web NDLA supports regular expression notations in Table Table 11: The Web NDLA Regular Expression Notation Functionality. matches any single character * matches zero or more times of the pattern immediately before + matches one or more times of the pattern immediately before? matches zero or one of the pattern immediately before ^ matches the head of a string $ matches the tail of a string () grouping the enclosed patterns choice of a pattern in a group (OR) { specifies the number of repetition of the pattern immediately before [...] character class (set or range of characters) [^...] negation of the character class The regular expression function is used in a form of regex(text, pattern). For partial match, the pattern is the desired partial string. For example, following query will nd headings that contain " ".?uri rdfs:label?label. FILTER regex(?label, " ") The next query will nd headings that consist of three capital letters.?uri rdfs:label?label. FILTER regex(?label, "^[A-Z]{3$") 15 SPARQL Regular Expression follows the definition in XQuery/XPath specification, however, the Web NDLA does not support escape and meta characters with backslash at this moment. 17
19 The regex() function may have a ag as the third argument that controls matching behavior. The Web NDLA supports "i" ag that makes the search case-insensitive. In the next query,?label will match either "internet", "Internet" or "INTERNET".?uri rdfs:label?label. FILTER regex(?label, "internet", "i") A FILTER can have multiple expressions combined by a logical operator. The next query will nd headings that contain either " " or "Internet".?uri rdfs:label?label. FILTER ( regex(?label, " ") regex(?label, "Internet") ) Note concatenation of two expressions by && is equivalent to writing two FILTER clauses. 2.7 Solution Sequences and Modifiers Sequence modiers canbe used to make solutions an ordered sequence or to restrict numbers of solutions LIMIT and OFFSET LIMIT clause after WHERE clause restricts the maximum number of solutions to be returned. OFFSET clause controls the starting solution to be returned in whole sequence. The next example will return the rst 10 headings.?uri rdfs:label?label LIMIT 10 The next example will return headings from 6 to 10 (OFFSET species how many solutions to be skipped from the top). The order of LIMIT and OFFSET clauses is not signicant.?uri rdfs:label?label LIMIT 10 OFFSET 5 At this moment, the Web NDLA is congured to return at most 100 results. Therefore, the maximum number of results per request is 100, even if LIMIT clause tells more. Please use OFFSET clause in order to get results after
20 ?uri rdfs:label?label OFFSET Sorting ORDER BY keyword followed by space separated sort key variables establishes the order of the solution sequence. DESC() / ASC() modiers indicate the enclosed variable is a descending / ascending key, respectively. Without enclosing modier, a variable is treated as an ascending key.?uri rdfs:label?label ; dct:modified?moddate. ORDER BY?moddate Order modiers can be combined with LIMIT and OFFSET clause to retrieve the sorted slice of the solutions. In this case, write ORDER BY rst, then LIMIT and OFFSET Eliminating Duplications In some graph patterns, it is possible that some solutions share the same set of bindings from variables to values. DISTINCT modier after SELECT ensures that those duplications are eliminated from the solution set. SELECT DISTINCT?type WHERE {?s a?type. REDUCED modier also eliminates duplications, but does not guarantee uniqueness. While both modiers generate the same solution set in most cases, REDUCED would return the results faster for large data sets, since the heavy computational burden required by complete elimination would be obviated Aggregates Aggregates such as grouping the solutions and counting the number of results are introduced in SPARQL 1.1. Although the Web NDLA (ARC2 library) implements SPARQL 1.0, there are some aggregate functions available. In order to count the number of the results, use set function COUNT() with the target variable as its argument, and assign the counted number to a new variable by AS keyword, in SELECT clause. For example, the next query will nd the number of authority records of people who were born in 1960, in the Web NDLA. 19
21 SELECT (COUNT(?who) AS?howmany) WHERE {?who rda:dateofbirth "1960". GROUP BY keyword followed by variables, after WHERE clause, divides results into groups. Then the aggregate value is calculated for each group. The next example will nd the year-by-year number of authority records of people who were born in 1900 or later. SELECT?byear (COUNT(?who) AS?howmany) WHERE {?who rda:dateofbirth?byear. FILTER (?byear >= 1900) GROUP BY?byear ORDER BY?byear Set functions for aggregates are MAX(), MIN(), AVG() and SUM() as well as COUNT() (AVG() and SUM() are applicable only for numerical values). The next example will nd the latest and the oldest year of birth in authority records of personal name. SELECT (MIN(?byear) AS?past) (MAX(?byear) AS?recent) WHERE {?who rdf:dateofbirth?byear. 2.8 Query Forms and Results SPARQL has four query forms: SELECT, ASK, CONSTRUCT and DESCRIBE SELECT To nd the values that match the pattern, use SELECT query. PREFIX rdfs: < PREFIX skos: < SELECT?subj2?label WHERE {?subj1 rdfs:label " " ; skos:narrower?subj2.?subj2 rdfs:label?label. With the above SELECT query, the Web NDLA will return the following results (for XML format). 20
22 <sparql xmlns=" <head> <variable name="subj2"/> <variable name="label"/> </head> <results> <result> <binding name="subj2"> <uri> </binding> <binding name="label"> <literal> </literal> </binding> </result> <result> <binding name="subj2"> <uri> </binding> <binding name="label"> <literal> </literal> </binding> </result> <result> <binding name="subj2"> <uri> </binding> <binding name="label"> <literal> </literal> </binding> </result> </results> </sparql> The <head> element enumerates the variable names in the results set, and the <results> element contains <result> elements for each matching variable set. See 3.1 for the detail of the result format ASK To determine whether there are any partial graphs that match the pattern (without retrieving the values), use ASK query. Because this query ignores variable values, the keyword is immediately followed by the WHERE clause. PREFIX rdfs: < ASK WHERE { 21
23 ?subj1 rdfs:label " " With the above ASK query, the Web NDLA will return the following results (for XML format). <sparql xmlns=" <head></head> <boolean>true</boolean> </sparql> If no matching partial graph is found, the content of <boolean> is false CONSTRUCT The matching variable values can be used to construct another RDF graph. Write the pattern of the new graph with similar syntax, put it in { after the keyword CONSTRUCT, then place the WHERE clause. PREFIX rdfs: < PREFIX skos: < PREFIX ex: < CONSTRUCT {?subj1 ex:?label. # means narrower term WHERE {?subj1 rdfs:label " " ; skos:narrower?subj2.?subj2 rdfs:label?label. With the above CONSTRUCT query, the Web NDLA will return the following graph (for Turtle rdf: ns0: < < ns0: " ", " ", " ". # virtual private network # Intranet # Semantic Web Note the prexes in the returned Turtle might be dierent from those specied in the query. If output parameter is xml, the graph is returned in RDF/XML format. 22
24 2.8.4 DESCRIBE With DESCRIBE query, a user can retrieve an RDF graph which is about the resources (variable values) that match the condition. In the Web NDLA, this will be a "description graph" that consists of triples whose subjects are the value URIs, plus triples connected to them via blank nodes. DESCRIBE < With the above DESCRIBE query, the Web NDLA will return the same RDF graph as to be retrieved from the URI with sux.ttl or.rdf. A description graph can be obtained for the resources that match a graph pattern in WHERE clause. If there are multiple matches or multiple variables are specied, the merge of the "description graphs" will be returned. PREFIX rdfs: < PREFIX skos: < DESCRIBE?subj2 WHERE {?subj1 rdfs:label " " ; skos:narrower?subj2.?subj2 rdfs:label?label. The "description graph" will be empty if the value of the variable is a literal. 23
25 3 API Parameters and Result Formats An API request is to be sent to the following URI, with URL encoded query string as query parameter value and result format as output parameter value, as shown in The variables binding list and TRUTH value, as the result of SELECT and ASK query respectively, will be returned with XML or JSON result format according to output parameter value. 3.1 XML Result Format When the output parameter value is xml, the result set is returned in SPARQL Query Results XML Format 16. This format is, as shown in the example in 2.8.1, an XML document in namespace whose root sparql element has head and results (boolean for ASK query) elements. The head element has variable elements in query order, where name attribute is the name of each variable. The head element is empty for ASK query. <head> <variable name="subj2"/> <variable name="label"/> </head> The results element contains zero or more result elements which have binding elements for each bound variable. A binding element has a name attribute corresponding to the name of variable, and has one child element <uri>, <literal> or <bnode> (depending on the value type) whose content is the value of the variable. If the value is literal and has language tag, <literal> has xml:lang attribute. If it is a typed literal, the element has datatype attribute whose value is the datatype URI. <results> <result> <binding name="subj2"> <uri> </binding> <binding name="label"> <literal> </literal> </binding> </result>... </results>
26 For ASK query, there is a <boolean> element instead of <results>, whose content is true or false, as shown in the example in JSON Result Format When the output parameter value is json, the result set is returned in SPARQL 1.1 Query Results JSON Format 17. This is a JSON document whose top most object has head and results (boolean for ASK query) property. The result in JSON format for the query in will be as follows. { "head": { "vars": [ "subj2", "label" ], "results": { "bindings": [ { "subj2": { "type": "uri", "value": " "label": { "type": "literal", "value": "\u30d0\u30fc\u30c1\u30e3\u30eb\u30d7\u30e9...",... ] The head property value is an object which has a vars property, whose value is an array of variable names. The results property is an object which has a bindings property, whose value is an array of objects of result sets, where bound variable names are properties. Each property value is an object, which has a type property to tell whether the value is URI, blank node or literal, and a value property to show the value. Note that value property value will be escaped as \u + Unicode number for non-ascii characters, as shown in the above example
27 4 RDF Graph of the Authority Records and Applied Examples This chapter explains the model (graph structure) of the authority records in the Web NDLA, and presents some examples to apply SPARQL constructs discussed in the previous chapters against the graph. 4.1 Authority Records of Personal Name, Family Name and Corporate Name The authority record resource is distinguished from the entity resource that corresponds to the real world thing (person etc.) in the graph of personal name, family name and corporate name. An authority record has such properties as preferred label, alternative label, source and related link, to name the few. Preferred label and alternative label have structures via blank nodes in order to provide literal forms and yomi (transcriptions) together. An entity resource describes such real world attributes as birth year of a person, establish year or history of a corporate. An authority record resource and its corresponding entity resource are related by foaf:primarytopic (Figure 4) To Find Birth Year, Preferred Label and Kana Transcription of a Person Provided that a person's name is known, compose a SELECT query where the name is the object of foaf:name and other values in question are variables. PREFIX rda: < PREFIX foaf: < PREFIX xl: < PREFIX ndl: < foaf:primarytopic?entity ; xl:preflabel [ xl:literalform?preflabel ; ndl:transcription?yomi ].?entity rda:dateofbirth?birth ; rda:dateofdeath?death ; foaf:name " ". # Natsume, Soseki FILTER (lang(?yomi) = "ja-kana") Note that preferred label and rdfs:label value are normalized in the form of family name, rst name and birth-death year, e.g. ",, ". The above example uses foaf:name of the entity resource to search with a common name format such as concatenated name (for Japanese) or rst name then last name form. 26
28 Figure 4: Name authority graph of. A node.../ndlna/ in the left of the figure represents the authority record, and.../entity/ at the top represents the entity resource. 27
29 Since the preferred label has both Kana and Romaji yomi (ndl:transcription), use FILTER to restrict the language tag To Search Person Born in 11th Century, Find VIAF Link and Sort by Birth Year To nd person born in 11th century, express the condition "born between 1001 and 1100" with the logical operator in the FILTER clause. An authority record links to VIAF with skos:exactmatch. Variable?birth is used to sort the result in addition to FILTER expressions. PREFIX rda: < PREFIX foaf: < PREFIX skos: < foaf:primarytopic [ rda:dateofbirth?birth ; foaf:name?name ] ; skos:exactmatch?viaf. FILTER (?birth >= 1001 &&?birth <= 1100) ORDER BY?birth Note that the Web NDLA does not allow more than 100 results to be retrieved for a single request. Use OFFSET clause repeatedly to get more than 100 results (See 2.7.1). 4.2 Authority Records of Geographical Name, Uniform Title, Subject Heading and Subject Subdivision These authority records do not have corresponding entity resources, hence they have at structure graphs (though preferred label and alternative label have their substructures to provide literal form and transcriptions together). Subject headings have more properties than name authorities in order to describe broader, narrower, related terms or classications (Figure 5) To Find Broader or Narrower Terms of Subject Heading Since subject headings are not normalized as name authorities, simply use rdfs:label as the property of the known heading, and let broader or narrower terms be variables. The next example will nd the broader term of " ". PREFIX skos: < PREFIX rdfs: < rdfs:label " " ; skos:broader?broader. 28
30 Figure 5: RDF graph of subject heading. Some narrower and related terms are omitted for simplicity. 29
31 ?broader rdfs:label?label To Find Subject Headings with Some Classifications In order to nd subject headings that have classication of NDLC "DM225" which is identied by a URI, construct a query with that URI as the object of skos:relatedmatch. PREFIX skos: < PREFIX rdfs: < skos:relatedmatch < ; rdfs:label?label. To nd subject headings classied as both NDLC "DK341" and NDC9 "694.5", let the graph pattern have two objects of skos:relatedmatch (NDC9 is also identied by a URI, with dierent base URI). PREFIX skos: < PREFIX rdfs: < skos:relatedmatch < < ; rdfs:label?label. 30
32 5 SPARQL 1.1 A SPARQL 1.1 endpoint (based on Virtuoso) was introduced in FY2017 as a trial viersion. The existing SPARQL 1.0 endpoint (explained in the previous sections) is still available, too. 5.1 Request URI and parameters The endpoint URI of the new service is as follows: It uses two parameters shown in table 12. parametaer query format Table 12: Web NDLA API parameters value SPARQL 1.1 query (should be URL encoded) result format (xml json turtle csv html) In case of SELECT and ASK query, the results set for xml, json, csv will be in SPARQL Query Results Formats as in each specication. The results set for turtle will be RDF graph serialized by Virtuoso. In case of DESCRIBE, CONSTRUCT query, the results set is RDF/XML for xml, JSON-LD Expanded Form 18 for json, and CSV (each line corresponds to a triple) for csv. Normal Turtle will be returned for turtle. The maximum number of results per request is New Functionalities of SPARQL 1.1, and Sample Queries This section explains some major functionalities introdued by SPARQL 1.1. See the SPARQL 1.1 Query Language specication 19 (SPARQL 1.1 spec) for details Negation Filtering of query solutions is done within a FILTER expression using NOT EXISTS (SPARQL 1.1 spec 8) The following is an example to query the authority records which do not have transcriptions of the preferred label. 18 All URIs, including properties, are expanded to full absolute form
33 PREFIX xl: < PREFIX ndl: < xl:preflabel?xl.?xl xl:literalform?label. FILTER NOT EXISTS {?xl ndl:transcription?yomi LIMIT Property Paths Property paths allow for more concise expressions for some SPARQL basic graph patterns (SPARQL 1.1 spec 9). The following is an example of the * operator to query all 'narrower' subject headings of " ". PREFIX skos: < PREFIX rdfs: < SELECT?sub?label WHERE {?top rdfs:label " " ; skos:narrower*?sub.?sub rdfs:label?label Assignment The BIND form allows a value to be assigned to a variable from a basic graph pattern or property path expression (SPARQL 1.1 spec 10). The follwing example selects the narrower subject headings of " " and their classications, extracts classication numbers from the URIs, then assigns a variable (?ndc) to them and sort the results by NDC order using the variable. PREFIX skos: < PREFIX rdfs: < SELECT?sub?label?ndc WHERE {?top rdfs:label " " ; skos:narrower*?sub.?sub rdfs:label?label ; skos:relatedmatch?rel. FILTER(regex(?rel, "^ BIND (strafter(str(?rel), "ndc9/") as?ndc) ORDER BY?ndc 32
34 5.2.4 Aggregates Aggregates apply expressions over groups of solutions (SPARQL 1.1 spec 11). The GROUP BY and some set functions e.g. COUNT() are already available in the existing ARC2 endpoint (see 2.7.4). The combination of the Aggregates and Assignment makes more exible queries possible. The following example lists the number of NDC classications assigned to subject headings, aggregated by the NDC Classes (top level categories). PREFIX skos: < SELECT?ndc (count(?id) as?count) WHERE {?id skos:relatedmatch?rel. FILTER(regex(?rel, "^ BIND (substr(str(?rel), 32, 1) as?ndc) GROUP BY?ndc ORDER BY?ndc Subqueries Subqueries are a way to embed SPARQL queries within other queries, normally to achieve results which cannot otherwise be achieved (SPARQL 1.1 spec 12). The following example queries the narrower subject headings of " ", counts further narrower headings of each result, and returns them with corresponding labels. PREFIX skos: < PREFIX rdfs: < SELECT?sub?label?count WHERE {?top rdfs:label " " ; skos:narrower?sub.?sub rdfs:label?label. { SELECT?sub (count(?ssub) as?count) WHERE{?sub skos:narrower?ssub. 33
35 5.3 The Differences between SPARQL 1.1 endpoint and exsiting ARC2 (1.0 endpoint) Due to the limitation of Virtuoso, language tags of transcriptions are all lower-cased. While the query gateway maps the normal lang tags to lower-cased ones in query, the returned results sets will contain lower-cased language tags. For the following query: PREFIX xl: < PREFIX ndl: < SELECT?yomi WHERE {?id xl:preflabel [ xl:literalform " " ; ndl:transcription?yomi] LIMIT 1 { { in the existing ARC2 endpoint, the returned value of bindings in JSON result format is: "yomi": { "type": "literal", "value": "Sosai", "xml:lang": "ja-latn" while it will be as follows in SPARQL 1.1: "yomi": { "type": "literal", "value": "Sosai", "xml:lang": "ja-latn" Notice the dierence of xml:lang values. 34
36 6 Revision History Added 5 according to the SPARQL 1.1 endpoint test service. Also minor changes in the description of the SPARQL 1.1 in the document. 35
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