Web and Semantic Web Query Languages: A Survey

3.1.7 The Query Language XQuery

An activity towards specifying an XML query language, now XQuery, has been launched by W3C shortly before the publication of the final XPath 1.0 and XSLT 1.0 recommendations.

Requirements and use cases have been given in [76, 77, 192]. Proposals, e.g., XQL and Quilt, have been made that have influenced XQuery [42]:

• XQL [244, 246] has influenced XPath. XQL does not have the full range of XPath axes. XQL has existential and universal quantifiers and set operations that are under reconsideration for XPath 2.0.
• Quilt [79] is very close to the current version of XQuery, mainly lacking its extensive type system. Quilt can be considered the predecessor of XQuery.

XQuery's developement is not completed. It represents the state-of-the-art of navigational XML query languages.

XQuery Principles: XQuery is an extension of XPath 2.0 adding:

• Sequences. Where in XPath 1.0 the results of path expressions are node sets, XQuery and XPath 2.0 use sequences. In contrast to XPath 1.0, sequences cannot only be composed of nodes but also atomic values.
• Strong typing. Like XPath 2.0, XQuery is a strongly typed language supporting most of the (simple and complex) data types of XML Schema, cf. [104]. Furthermore, many XQuery implementations provide (as an optional feature) static type checking.
• Construction, Grouping, and Ordering. Where XPath is limited to selecting parts of the input data, XQuery provides ample support for constructing new data. Constructors for all node types as well as the simple data types from XML Schema are provided. New elements can be created either by direct element constructors (that look just like XML elements) or computed element constructors (allowing the name of a newly constructed element to be the result of a part of the query), cf. Query 1 and 3 below.
• Variables. Like XPath 2.0, XQuery has variables defined in so-called FLWOR expressions.
  A FLWOR expression usually consists in one or more for clauses, an optional where clause, an optional order by, and a return clause.
  The for clause iterates over the items in the sequence returned by the path expression in its in part:

  for $book in //book

  iterates over all books selected by the path expression //book.
  The where clause specifies conditions on the selected data items.
  The order by clause allows the items to be processed in a certain order.
  The return clause specifies the result of the entire FLWOR expression (often using constructors as shown above).
  Additionally, FLWOR expressions may contain, after the for clauses, let clauses that also bind variables but without iterating over the individual data items in the sequence bound to the variable.
  FLWOR expressions resemble very much XSLT's explicit iteration, selection, and assignment constructs.
• User-defined functions. XQuery allows to define new, possibly recursive, functions.
• Unordered sequences. XQuery provides the unordered keyword indicating that the order of elements in sequences that are constructed or returned as result of XQuery expressions is not relevant. E.g.,

  unordered{for $book in //book return $book/name} 

  indicates that the nodes selected by //book may be processed in any order in the for clause and the order of the resulting name nodes also can be arbitrary (i.e. implementation dependent). Within unordered query parts, the result of expressions querying the order of elements in sequences such as fn:position or fn:last is non-deterministic.
• Universal and existential quantification. Both XPath 2.0 and XQuery 1.0 provide some and all for expressing existentially or universally quantified conditions (cf. Query 9)
• Schema validation. XQuery implementations may provide support for schema validation, both of input and constructed data, using the validate expression.
• Full host language. XQuery completes XPath with defining contexts for path expressions, e.g., declaring namespace prefixes and default namespace, importing function libraries and modules (optional), and (optionally) providing flexible means for serialization (shared with XSLT 2.0) [168].

Not all of XPath's axes are mandatory in XQuery implementations: ancestor, ancestor-or-self, following, following-sibling, preceding, and preceding-sibling do not have to be supported. This is no restriction to XQuery's expressiveness, as expressions using reverse axes can be rewritten [225], and the "horizontal axes", e.g., following and following-sibling, can be replaced by FLWOR expressions using the << and >> operators that compare two nodes with respect to their position in a sequence.

Sample Queries: All nine sample queries can be expressed in XQuery. Query 2 is omitted because it can be expressed as a simplification of of Query 1. Query 5 can be expressed in XPath. Query 8 and 9 are similar. Sincve Query 9 in XQuery exhibits an interesting anomaly, it is given below and Query 8 is not given.

Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed in XQuery as follows (interpreting the phrase an essay as a book with type attribute equal to the id of the category Essay or one of its sub-categories represented as descendants in the XML structure):

<results> {
  let $doc := doc("http://example.org/books")/bookdata
  let $sub-of-essay := 
        $doc//category[@id="Essay"]/descendant-or-self::category
  for $book in $doc//book 
  where $book/@type = $sub-of-essay/@id
  return
    <result>
      { $book }
      { $book/author }
      { $book/author/name }
   </result> }
</results>  

Note the use of the let clause in line 2: the sequence of all sub-categories of the category with id Essay including that category itself (we use the reflexive transitive axis descendant-or-self) is bound to the variable. However, in contrast to a for expression, this sequence is not iterated over. Instead of the where clause in line 4 a predicate could be added to the path expression in line 3 resulting in the expression $doc//book[@type = $sub-of-essay/@id].

Query 3 ("Select all data items except ontology information and translators.") requires structural recursion while constructing new elements that are identical to the ones encountered, except omitting translator and category nodes. The following implementation shows the use of a user-defined, recursive function that copies the tree rooted at its first parameter $e, except all nodes in the sequence given as second parameter.

declare function 
  local:tree-except($e as element(), 
                    $exceptions as node()*) as element()*
{
    element {fn:node-name($e)} {
      $e/@* except $exceptions, (: copy the attributes :)
      for $child in $element/node() except $exceptions
      return
        if $child instance of element() 
          (: for elements process them recursively :) 
          local:tree-except($section) 
        else (: others (text, comments, etc. copy  :)
          $child
    }                 
};

document {
     let $doc :=  doc("http://example.org/books")/bookdata
     let $exceptions := $doc//translator union $doc//category
     local:tree-except($doc, $exceptions)
   }

Note the typing of the parameters: the first parameter is a single element, the second, a sequence of nodes and the function returns a sequence of elements. In the main part of the query, the document constructor is used to indicate that its content is to be the document element of the constructed tree.

Query 4 ("Invert the relation author (from a book to an author) into a relation authored (from an author to a book).")

<bookdata> {
  let $a := doc("http://example.org/books")//author
  for $name in distinct-values($a/name)
  return
    <person>
      <name> { $name } </name>
      <authored
      {
         for $b in doc("http://example.org/books")//book
         where some $ba in $b/author 
               satisfies $ba/name = $name
         return 
           <book> { $b/@*, $b/* except $b/author } </book>
      }
      </authored
    </person>
  }
</bookdata>

This implementation is in fact similar to the implementation of use case XMP-Q4 in [76]. It exhibits two noteworthy functionalities:

• The use of distinct-value in line 3 to avoid duplication in the result, if an author occurs several times in the document.
• The use of an existentially quantified condition in lines 10-11, to find books where some (i.e. at least one) of the authors have the same name as the currently considered author.

Using aggregation expressions (see lines 8 and 10), Query 6 ("Return each of the subclasses of 'Writing', together with the average number of authors per publication of that subclass.") can be expressed in XQuery as follows:

<results> {
  let $doc := doc("http://example.org/books")/bookdata
  for $category in $doc//category[@id="Essay"]//category 
  return 
    <category>
      { $category/@id }
      <average-number-of-authors>{ 
        fn:avg(for $book in $doc//book
               where @type = $category/@id
               return fn:count($book/author))
      }
      </average-number-of-authors>
    </category>
  }
</results>

Combining data can be expressed in a very compact manner in XQuery, as the following expression of Query 7 ("Combine the information about the book titled 'The Civil War' and authored by 'Julius Caesar' with the information about the book with identifier 'bellum_civile'.") shows:

<book>
  { for $book in doc("http://example.org/books")//book
    where title="Bellum Civile" and author/name="Julius Caesar"
    return ($book/@*, $book/*)
  }
  {
    for $book in doc("http://example.org/books")//book
    where @id="bellum_civile"
    return ($book/@*, $book/*)
  } 
</book>

Query 9 ("Return the co-author relationship between two persons that stand in author relationships with the same book.") can be expressed in XQuery as follows:

<results>
  { let $doc := doc("http://example.org/books")
    for $book in doc("http://example.org/books")//book
      for $author in $book/author
        for $co-author in $book/author
        where $author << $co-author
        return 
          <co-authors>
            <name> { $author/name } </name>
            <name> { $co-author/name } </name>
          </co-authors>
  }
</results>

This implementation does not treat the case where two authors co-authored multiple books. In this case, duplicates are created by the above solution. To avoid this the following refinement uses the before operator << in combination with a negated condition, for specifying that only such pairs of authors should be considered, where there is no book that occurs prior to the currently considered one and which is also co-authored by the current pair of authors:

<results>
  { let $doc := doc("http://example.org/books")
    for $book in doc("http://example.org/books")//book
      for $author in $book/author
        for $co-author in $book/author
        where $author << $co-author and not(
          some $pb in doc("http://example.org/books")//book 
          satisfies ($pb << $book and  
                     $pb//author/name = $author/name and
                     $pb//author/name = $co-author/name))
        return 
          <co-authors>
            <name> { $author/name } </name>
            <name> { $co-author/name } </name>
          </co-authors>
  }
</results>

It is intuitively clear that XQuery is Turing complete since it provides recursive functions and conditional expressions. A proof of the Turing-completeness of XQuery is given in [169].

3.2 Research Prototypes: The Positional Approach to XML Querying

3.2.1 Characteristics of the Positional Approach

The positional approach consists in using patterns to specify the position of data within larger structures.

Positional queries mimic the data to be queried. Languages using this query-by-example style for queries mostly fall into two categories:

• query languages influenced by logic or functional programming (UnQL, XML-QL, XMAS, XML-RL, TQL, Xcerpt)
• visual query languages or visual interfaces to textual query languages (XML-GL, BBQ, visXcerpt, and X2's visual query interface).

3.2.2 UnQL

UnQL [64, 66], the Unstructured Query Language, has been originally developed for querying semistructured data and nested relational databases with cyclic structures. UnQL has later been adapted to querying XML, but the origins are still apparent. For example, UnQL has a non-XML syntax very similar to OEM's syntax and does not support querying or construction of ordered data.

The evaluation model and core language of UnQL is based upon structural recursion over labeled trees.

The following expression uses functional style pattern matching for selecting all books in a tree.

fun f1(T1 ∪ T2)   = f1(T1) ∪ f1(T2)
  | f1({ L => T }) = if L = book 
                                then {result => book => T} 
                                else f1(T)
  | f1({})         = {}
  | f1(V)          = {}

UnQL's surface syntax uses query patterns and construction patterns: a query consists of a single select ... where ... or traverse rule that separate construction from querying.

Queries may be nested, in which case the separation of querying and construction is abandoned.

Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed in UnQL as:

select { results => { 
  select { result => { Book, 
    select { author => { 
               author => Author,
               authorName => Name 
             } }
    where { author => \Author } ← Book, 
          { name => \Name } ← Author
  where { book => \Book } ← Bib 
where bookdata => Bib ← DB

A ← scopes a query pattern, i.e. it specifies that the left-hand query pattern is to be found in bindings for the right-hand variable.

The => operator is the direct edge traversal operator. E.g.

book => author

specifies that author is a direct child of book.

Recursive traversals can be specified using regular path expressions including regular expressions over labels. E.g.

_*

traverses over arbitrary many elements with any label,

[^book]**

over arbitrary many elements with any label except book.

UnQL also provides traverse clauses for reduction and restructuring queries like Query 3 ("Select all data items except ontology information and translators."):

traverse DB given X
  case translator => _ then X := {}
  case category => _   then X := {}
  case \L => _         then X := {l => X}

This query is evaluated by traversing the tree in the database and matching recursively each element against the three case expressions. All elements except translators and categories are copied to the newly constructed tree, structured as in the input data.

UnQL s evaluation is founded in graph simulation [66].

[64] shows that all queries expressible in UnQL can be evaluated in PTIME.

3.2.3 XML-QL

XML-QL [98, 99] is a pattern-based and rule-based query language for XML developed specifically to address the W3C's call for an XML query language (that resulted in the development of XQuery).

Like UnQL, XML-QL uses query patterns called element patterns in [98] in a WHERE clause.

Such patterns can be augmented by variables for selecting data.

The result of a query is specified as a construction pattern in the CONSTRUCT clause.

An XML-QL query always consists of a single WHERE-CONSTRUCT rule which may be divided into several nested subqueries.

Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed in XML-QL as follows:

WHERE
   <bookdata>
     <book>
     </> ELEMENT_AS $b
   </>
CONSTRUCT 
   <results>
     <result>
       $b
       WHERE <author>
               <name> $n </>
             </> ELEMENT_AS $a 
       CONSTRUCT $a
                 $n
     </>
   </>

Variables are preceded in XML-QL by $. Note how the grouping of authors with their books is expressed using a nested query.

Also note the tag minimization (end tags abbreviated by </> as in SGML).

In line 4, the variable $b is restricted to data matching the pattern in lines 3 and 4. Such pattern restrictions are indicated in XML-QL using the ELEMENT AS keyword.

A characteristics of XML-QL is that it uses query patterns containing several variables that may select several data items at a time instead of path selections that may only select one data item at a time.

XML-QL's variables are similar to the variables of logic programming, i.e. "joins" can be expressed by variable name equality.

Since XML-QL does not allow one to use more than one rule, one often has to employ nested subqueries to express complex queries.

Query 6 ("Return each of the subclasses of 'Writing', together with the average number of authors per publication of that subclass.") cannot be expressed in XML-QL due to lack of aggregation, in particular structural aggregation (e.g., counting the number of children of an element).

The following query returns all books classified in a sub-category of Novel:

WHERE
  <book type=$Sub>  
  </> ELEMENT_AS $b,
  <category id='Novel'>  
    <category* id=$Sub>
    </>
  </>
CONSTRUCT $b

Joins are expressed by repeated occurrences of the same variable (lines 2 and 5). In line 5 a further feature of XML-QL is shown: regular path expressions for element labels in patterns.

Transformation queries such as Query 2, except for rather local changes (e.g., omission of elements or changing labels), cannot be expressed in XML-QL.

XML-QL has no means for testing the non-existence of elements and therefore cannot express queries such as "Return all books that have no translator.".

No results on the complexity or expressiveness of XML-QL have been published.

3.2.4 XMAS

XMAS [189], the XML Matching And Structuring language, is an XML query language developed as part of MIX [18] and builds upon XML-QL.

Like XML-QL, XMAS uses query patterns, construction patterns, and rules of the form CONSTRUCT ... WHERE ....

XMAS extends XML-QL in that it provides a powerful grouping construct, instead of relying on subqueries for grouping data items within an element.

Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed in XMAS as follows:

WHERE
  <bookdata>
    $B: <book>
          $A: <author>
                <name> $N </name>
              </>
         </>
  </> 
CONSTRUCT 
  <results>
    <result>
      $B
      <book-author>
        $A
        <name> $N </name>
      </> {$A,$N}
    </> {$B}
  </>

Here, one can observe the two main syntactic differences to XML-QL:

• In XMAS, grouping is expressed by enclosing the variables on whose bindings the grouping is performed in curly braces and attaching them to the end of the subpattern that specifies the structure of the resulting instances.
  In the above example, a result element is created for every instance of $B (indicated by {$B} after the closing tag of the element result). Within every such result element, all authors of a book (indicated by {$A}) are collected nested in book-author elements (the book-author element is necessary because grouped variables are allowed only after closing tags or single variables in XMAS).
• XMAS provides a compact syntax for pattern restrictions that allows one to restrict the admissible bindings of a variable as seen in line 3 ($B in front of the subpattern instead of XML-QL's ELEMENT_AS $B at the end).

Grouping queries can be specified more concisely by using implicit collection labels: instead of specifying the grouping variables explicitly, all variables nested inside square brackets are considered grouping variables for that grouping, unless there is another grouping (i.e., block enclosed by square brackets) closer to the variable occurrence. Using implicit collection labels, Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed as:

WHERE
  <bookdata>
    $B: <book>
          $A: <author>
                <name> $N </name>
              </>
        </>
  </> 
CONSTRUCT 
  <results>
    [<result>
      $B
       [<book-author>
         $A
         <name> $N </name>
       </book-author>]
    </>]
  </>

No results on complexity or expressiveness of XMAS have been published.

BBQ [215] is a visual interface for XMAS that allows browsing of XML data as well as authoring of XMAS queries based on a DTD of the data to be queried.

3.2.5 Xcerpt

Xcerpt http://xcerpt.org [28, 60, 61, 247, 248] is a query language designed after principles given in [57] for querying both data on the "standard Web" (e.g., XML and HTML data) and data on the Semantic Web (e.g., RDF, Topic Maps, etc. data). This Section addresses using Xcerpt on the standard Web, Section 4.5.?, on the Semantic Web.

Xcerpt's Principles:

• Xcerpt is data versatile, i.e. the same Xcerpt query can access and generate, as answers, data in different Web formats.
• Xcerpt is strongly answer-closed, ie. it not only allows one to construct answers in the same data formats as the data queries like, e.g., XQuery, but also allows further processing of the data generated by this same query program.
• Xcerpt's queries are pattern-based and allow to incompletely specify the data to retrieve, by
  • specifying only some of the children of an element,
  • specifying descendant elements at indefinite depths (restrictions in the form of regular path expressions being possible),
  • specifying optional query parts.
• Xcerpt's evaluation of incomplete queries is based on a novel unification algorithm called simulation unification [62, 63].
• Xcerpt s processing of XML documents is graph-oriented, i.e., Xcerpt is aware of the reference mechanisms (e.g., ID/IDREF attributes and links) of XML.
• Xcerpt is rule-based. An Xcerpt rule expresses how data queried can be re-assembled into new data items. An Xcerpt rule corresponds to an SQL view.
• Xcerpt allows both traversal of cyclic documents and recursive rules, termination being ensured by memoing.
• Xcerpt rules can be chained forward or backward, backward chaining being the processing of choice on the Web.
• Xcerpt is inspired from Logic Programming. Since it does not offer backtracking as a programming concept, Xcerpt can also be seen set-oriented functional.

All sample queries can be expressed in Xcerpt. In the following, Query 2, 5, 7, and 8 are omitted as they are similar to other solutions shown.

Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed in Xcerpt as follows:

GOAL
results [ 
  all result [
    var Book, 
    all var Author, 
    all var AuthorName
  ]
]
FROM
bookdata {{
  var Book → book {{
    var Author → author {{ 
      name [ var AuthorName ] }} 
    }} 
  }}
END

In the query part (enclosed by FROM and END), a pattern of the requested data is specified: a bookdata element with a book child (associated with the variable Book using the pattern restriction operator →) that in turn has an author child (bound to the variable Author) with a name child whose content is bound to the Variable AuthorName.

Double curly braces (line 10) indicate an incomplete, unordered query pattern. A matching bookdata element may have additional children not specified in the query and the order among the children is irrelevant for the query. Incomplete query patterns might result in several alternative variable bindings.

Square brackets (e.g. line 13) and in the construct part (between GOAL and FROM) specify that the order of the children matters. Single brackets specify that the pattern is complete.

Similar to XMAS, Xcerpt allows to group answers using the constructs all and some: all t collects all possible different instances of the subexpression t that might result from alternative variable bindings. Grouping may be nested.

The construct term creates a result subterm for each alternative binding of Book, and within each such result subterm, it groups all authors and authornames associated with that particular book.

An Xcerpt program may contain several rules, as shown in the following solution for Query 3 ("Select all data items except ontology information and translators."):

GOAL
   var Result
FROM
transform [ bookdata {{ }}, result [ var Result ] ]
END

CONSTRUCT
transform [ var Element, result [ ] ]
FROM
desc var Element → /translator|category/
END

CONSTRUCT
transform [ var Element, result [ var Label [ all var Child ] ] ]
FROM
and {
  desc var Element → var Label [[ var Child ]]
  where { 
         and { var Label != "translator", var Label != "category }
        }, 
  transform [ var Child, result [ var ChildTransformed ] ]
}
END

Xcerpt rules come in two flavors: GOAL ... FROM ... END and CONSTRUCT ... FROM ... END. The first may only occur once in a program, specifies the ultimate result of the entire program. The latter form is used for all other rules.

Here, the two lower rules transform (recursively) an input element as specified in the query: if it is a translator or a category the result of the transformation is empty, otherwise the children of the element are recursively transformed and the result of these transformations is used to reconstruct the structure of the input data.

The desc operator (lines 10 and 17) indicates a pattern that is incomplete in depth.

The where clause (line 18) restricts matches to elements that are neither translators nor categorys.

In line 17, a label variable is used: whereas the variable Element is bound to the entire element matched by the pattern, Label is bound to the label of the element, i.e. a string such as "book".

Query 4 ("Invert the relation author (from a book to an author) into a relation authored (from an author to a book).") can be expressed in Xcerpt as follows:

GOAL
bookdata [
  all person [
    name [ var Name ],
    authored [
      all book [
        all var NonAuthorChildren
      ] group by { var Book } 
    ]
  ]
]
FROM
bookdata {{
  desc var Book → book [[
    author {{ name [ var Name ] }}, 
    var NonAuthorChildren → !/author/ {{ }}
  ]]
}}
END

All books (at any depth) are selected together with the names of their authors and non-author children (a negated regular expression on the label is used for the non-author children).

For each name of an author, a person element is constructed (note the position of the all in line 3) containing the name and an authored element. In the author element all books for that author are nested again using all with a group by clause naming the grouping variable.

Query 6 ("Return each of the subclasses of 'Writing', together with the average number of authors per publication of that subclass.") can be expressed in Xcerpt as follows:

GOAL
results [
  all category [
    attributes [ id [ var ID ] ], 
    average-number-of-authors [
      div( count( all var Author ), count( all var Book ) )
    ]
  ]  
]
FROM
bookdata {{
  desc category {{ attributes {{ id [ var ID ] }} }},
  desc var Book → book {{ 
    attributes {{ type [ var ID ] }},
    desc var Author → author {{ }} 
  }}
}}
END

The average number of authors is computed in line 6 using the structural aggregation function count over all books and authors for a category. The join between the id attribute of categorys and the type attribute of books is expressed by repeating the same variable.

Query 9 ("Return the co-author relationship between two persons that stand in author relationships with the same book.") can be expressed in Xcerpt as follows:

GOAL
results [
  all co-authors [
    name [ var Author ], 
    name [ var CoAuthor ]
  ]
]
FROM
bookdata {{
  desc book {{
    author {{ name {{ var Author }} }},
    author {{ name {{ var CoAuthor }} }}
}}
END

This query benefits from two features of Xcerpt:

• Xcerpt's simulation unification is injective. This ensures that the two children of the book element in line 10 are different without requiring to explicit state that the author and the co-author must be different.
• Xcerpt's grouping is set-based and uses unification for equality, i.e., two terms with same structure and values are considered equal even if they represent distinct elements in the input. Therefore the above program does not generate duplicates (as does e.g the first XQuery solution for Query 9 in Section).

A visual language, called visXcerpt [29, 30], has been conceived as a visual rendering of textual Xcerpt programs.

Static type checking methods have been developed for Xcerpt [55, 283].

A declarative semantics for Xcerpt has been proposed in [63, 247].

A procedural semantics for Xcerpt has been proposed in [63] in the form of a a proof procedure. An implementation of this semantic in Haskell has been realized using Constraint Programming techniques [247].

The XQuery use case [76] has been worked out in Xcerpt (cf. [174] (in German) and [45]).

Based on Xcerpt and extending it, a reactive language called XChange [58, 59] for updates and events on the Web is currently being developed.

4 RDF Query Languages

Figure 3: History of the RDF Query Languages

4.1 The SPARQL Family

The SPARQL family consists of the four query languages SquishQL, RDQL, SPARQL, and TriQL. Common to all four languages in this family is that they "regard RDF as triple data without schema or ontology information unless explicitly included in the RDF source".

4.1.1 Basic RDF Access: SquishQL and RDQL

SquishQL [212, 213] aims at ease-of-use and similarity to SQL.

SquishQL relies on a query model for RDF influenced by [141].

SquishQL offers so-called triple patterns and conjunctions between triple patterns for specifying parts of RDF graphs to retrieved. "This results in quite a weak pattern language but it does ensure that in a result all variables are bound." [213].

SquishQL queries have the following form:

SELECT ?essay, ?author, ?authorName
>FROM   http://example.org/books
WHERE  (?essay, <rdf:type>, <books:Essay>),
       (?essay, <books:author>, ?author),
       (?author, <books:name>, ?authorName)
USING  books FOR http://example.org/books#,
       rdf FOR http://www.w3.org/1999/02/22-rdf-syntax-ns#

In SquishQL, Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed as follows:

SELECT ?property, ?propertyValue
>FROM   http://example.org/books
WHERE  (?essay, <books:book-title>, "Bellum Civile")
       (?essay, ?property, ?propertyValue),
USING  books FOR http://example.org/books#

In SquishQL, Query 2 ("Select all data items with any relation to the book titled 'Bellum Civile'.") can (almost) be expressed as follows:

SELECT ?property, ?propertyValue
>FROM   http://example.org/books
WHERE  (?essay, <books:book-title>, "Bellum Civile")
       (?essay, ?property, ?propertyValue),
USING  books FOR http://example.org/books#

A property value can be a node with other properties that an answer to Query 2 should return. Since SquishQL has no means to express recursion, such indirect properties cannot be returned by the above query if the schema of the data is unknown or recursive.

Other sample queries cannot be expressed in SquishQL.

In a SquishQL query, the AND clause serves to express constraints on variable values. E.g. the following query returns the URIs of persons that have authored a book with title 'Bellum Civile':

SELECT ?person
>FROM   http://example.org/books
WHERE  (?book, <books:author>, ?person)
       (?book, <books:title>, ?title)
AND    ?title = 'Bellum Civile'

An answer to an SquishQL query is a set of bindings for the variables occurring in the query.

SquishQL does not support RDFS.

RDQL, a "RDF Data Query Language", is an evolution of the SquishQL versions SquishQL [213], and Inkling [212] influenced by rdfDB [140].

RDQL has been recently submitted to the W3C for standardisation [213, 251, 253].

RDQL queries have the same form as SquishQL queries. As with SquishQL, an answer to an RDQL query is a set of bindings for the variables occurring in the query. Like SquishQL, RDQL supports only selection and extraction queries.

RDQL's authors see inferencing as a possible feature of an "RDF implementation", not of the query language RDQL: "if a graph implementation provides inferencing to appear as virtual triples (i.e. triples that appear in the graph but are not in the ground facts), then an RDQL query will include those triples as possible matches in triple patterns." [251]. As a consequence, queries referring to RDF/S relations such as type, set or class are cumbersome and/or complex.

The RDQLPlus (http://rdqlplus.sourceforge.net/) implementation of RDQL provides a language extension, called RIDIQL [284]. RIDIQL supports updates and a transparent use of the inference abilities of the Jena Toolkit [136].

SquishQL and RDQL queries cannot be composed.

Negation can be used in filters, or AND clauses but not in WHERE clauses.

Disjunctions and optional matching cannot be expressed.

Although a variable in SquishQL and RDQL queries can be bound to blank nodes, there is no way to specify blank nodes in SquishQL's and RDQL's triple patterns. As a consequence, a query returning the blank nodes of a graph cannot be expressed in SquishQL and RDQL.

SquishQL and RDQL have no recursion and no iteration.

Only selection and extraction queries can be expressed in SquishQL and RDQL i.e. of the sample queries only Query 1 and (an approximation of) Query 2.

Like SquishQL, RDQL does not support RDFS concepts, although its implementation in the Jena Toolkit [136] supports the transitive closures of the RDFS relations rdfs:subClassOf and rdfs:subPropertyOf.

No formal semantics has been defined for SquishQL or RDQL.

The complexity of SquishQL and RDQL has not been investigated so far.

4.1.2 SPARQL

SPARQL [239], a "Query Language for RDF" formerly called SBrQL [238], has been developed by members of the W3C RDF Data Access Working Group. SPARQL is an extension of RDQL designed according to requirements and use cases [87] and is still under development.

SPARQL extends RDQL with facilities to:

• Extract RDF subgraphs.
• Construct, using CONSTRUCT clauses, one new RDF graph with data from the RDF graph queried. Like RDQL queries, the new graph can be specified with triple patterns or graph patterns.
• Return, using DESCRIBE clauses, "descriptions" of the resources matching the query part. The exact meaning of "description" is not yet defined.
• Specify OPTIONAL triple or graph query patterns.
• Testing the absence, or non-existence, of triples.

SPARQL queries have the following form:

PREFIX	Specification of a name for a URI (like RDQL's USING)
SELECT	Returns all or some of the variables bound in the WHERE clause.
CONSTRUCT	Returns a RDF graph with all or some of the variable bindings.
DESCRIBE	Returns a description of the resources found.
ASK	Returns whether a query pattern matches or not
WHERE	list, i.e. conjunction, of query (triple or graph) patterns
OPTIONAL	list, i.e. conjunction, of optional (triple or graph) patterns
AND	boolean expression (the filter to be applied to the result)

An extension of Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") returning the translators of a book, if there are some, can be expressed in SPARQL as follows:

PREFIX   books: http://example.org/books#
PREFIX   rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#
SELECT   ?essay, ?author, ?authorName, ?translator
>FROM     http://example.org/books
WHERE    (?essay  books:author     ?author),
         (?author books:authorName ?authorName)
OPTIONAL (?essay  books:translator ?translator)

Using the CONSTRUCT clause, restructuring and non-recursive inference queries can be expressed in SPARQL.

Query 4 ("Invert the relation author (from a book to an author) into a relation authored (from an author to a book).") can be expressed in SPARQL as follows:

PREFIX   books: http://example.org/books#
CONSTRUCT (?y books:authored ?x)
>FROM      http://example.org/books
WHERE     (?x books:author ?y)

Query 9 ("Return the co-author relationship between two persons that stand in author relationships with the same book.") can be expressed in SPARQL as follows:

PREFIX    books: http://example.org/books#
CONSTRUCT (?x books:co-author ?y)
>FROM      http://example.org/books
WHERE     (?book books:author ?x)
          (?book books:author ?y)
AND       (?x neq ?y)

4.1.3 TriQL

TriQL extends RDQL by constructs supporting querying of named graphs [72], as introduced in TriG [40]. Named graphs allow one to filter RDF statements after their sources or authors, like in the following query: "Return the books with rating above a threshold of 5, using only information asserted by Marcus Tullius Cicero." This can be expressed in TriQL as follows:

SELECT ?books
WHERE  ?graph ( ?books books:rating ?rating )
      (?graph   swp:assertedBy ?warrant)
       (?warrant swp:authority <http://people.net/cicero>)
USING  books FOR http://example.org/books#,
       swp   FOR <http://www.w3.org/2004/03/trix/swp-1/>

4.2 The RQL Family

The RQL family consists of RQL, SeRQL, and eRQL. Common to these languages is that they support combining data and schema querying. Furthermore, their RDF data model deviates from the standard RDF(S) data model:

• cycles in the subclass-class relation are forbidden,
• for each property, both a domain and a range must be defined.

This ensure a clear separation of the 3 abstraction layers of RDF and RDFS:

1. Data, i.e. description of resources such as persons.
2. Schemas, i.e. classifications for such resources.
3. Meta-schemas specifying meta-classes such as rdfs:Class and rdfs:Property.

4.2.1 RQL

RQL, the "RDF Query Language" [84, 158, 161] is the basis for SeRQL and eRQL.

Basic schema queries. A salient feature of RQL is the use of the types from RDFS schemas. The query

subClassOf(books:Writing)

returns the sub-classes of the class books:Writing (assuming: USING NAMESPACE books = &http://example.org/books-rdfs#).

A similar query, using subPropertyOf instead of subClassOf, returns the the sub-properties of a property .

The following query returns the domain ($C1) and range ($C2) of the property author defined at the URI named book (The prefix $ indicates a class variable, i.e., a variable ranging on schema classes). It can be expressed in RQL in three different manners:

• Using class variables:

  SELECT $C1, $C2 FROM {$C1}books:author{$C2}
  USING NAMESPACE books = &http://example.org/books#

• Using a type constraint:

  SELECT C1, C2 FROM Class{C1}, Class{C2}, {;C1}books:author{;C2}
  USING NAMESPACE books = &http://example.org/books#

• without class variables or type constraints:

  SELECT C1, C2 FROM subClassOf(domain(book:author)){C1},
                     subClassOf(range(books:author)){C2}
  USING NAMESPACE books = &http://example.org/books#

The query

topclass(books:Historical Essay)

returns the top of the subclass-class relation, i.e. books:Writing.

The query

nca(books:Historical Essay, books:Historical Novel)

returns the nearest common ancestor of the classes of 'historical essays' and 'historical novels', i.e., the class books:Essay of 'essays'.

RQL has property variables prefixed by @ using which RDF properties can be queried (like classes using class variables). The following query returns the properties together with their ranges that can be assigned to resources classified as books:Writing:

SELECT @P, $V FROM {;books:Writing}@P{$V}
USING NAMESPACE books = &http://example.org/books#

Combining these facilities, Query 8 ("Return the transitive closure of the subClassOf relation.") is expressible as follows:

SELECT X, Y FROM Class{X}, subClassOf(X){Y}

Data queries. With RQL, data can be retrieved by its types , by navigating to the appropriate position in the RDF graph. Restrictions can be expressed using filters. Classes, as well as properties, can be queried for their (direct and indirect12) extent. The query:

books:Writing

returns the resources classified books:Writing or one of its sub-classes. This query can also be expressed as follows:

SELECT X FROM books:Writing{X}

Prefixing the variable X yields queries returning only resources directly classified as books:Writing i.e. for which (X, rdf:type, books:Writing) "exists". The extent of a property can be similarly retrieved.

The query

^books:author

returns the pairs of resources X, Y that stand in the books:author relation, i.e. for which (X, books:author, Y) exists.

RQL offers the extended dot notation, like OQL [73], for navigation in data and schema graphs. This is convenient for expressing Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names."):

SELECT X, Y, Z FROM {X;books:Essay}books:author{Y}.books:authorName{Z}
USING NAMESPACE books = &http://example.org/books#

Mixed schema and data queries. Access to data and schema can be combined, e.g.:

X;books:Essay

restricts bindings for variable X to resources with type books:Essay.

Type information often needs to be queried:

SELECT $C, ( SELECT @P, Y FROM {Z ; ^$D} ^@P {Y}
             WHERE Z = X and $D = $C )
>FROM  ^$C {X}, {X}books:title{T} WHERE T = "Bellum Civile"
USING  NAMESPACE books = &http://example.org/books#

This query returns the classes under which the resource with title 'Bellum Civile' is directly classified; ^$C{X} selects the values in the direct extent of any class.

Further features of RQL are support for containers,   aggregation, schema discovery.

RQL has no concept of view. The extension RVL [191] of RQL has a facility for specifying views, e.g, the inverse relation of books:author:

CREATE NAMESPACE mybooks = &http://example.org/books-rdfs-extension#
VIEW   authored(Y, X) FROM {X}books:author{Y}
USING NAMESPACE books = &http://example.org/books#

RQL has been criticised for

• its large number of features
• its choice of syntactic constructs (like the prefixes ^ for calls and @ for property variables).

This has resulted in the simplifications SeRQL and eRQL of RDF.

RQL is far more expressive than most other RDF query languages.

Most sample queries, except those queries referring to the transitive closures of arbitrary relations, can be expressed in RQL: RDF supports only the transitive closures of rdfs:subClassOf and rdfs:subPropertyOf. Query 1 in RQL is given above. Query 2 ("Select all data items with any relation to the book titled 'Bellum Civile'.") cannot be expressed in RQL exactly, since RQL has no means to select everything "related to some resource". However, a modified version of Query 2, where a resource is described by its schema, is given above. Reduction queries, e.g. Query 3 ("Select all data items except ontology information and translators.") can often be concisely expressed in RQL, in particular if types are available:

SELECT S, @P, O
>FROM   (Resources minus (SELECT T FROM {B}books:translator{T})){S},
       (Resources minus (SELECT T FROM {B}books:translator{T})){O},
       {S}@P{O}
USING  NAMESPACE books = &http://example.org/books#

An implementation in RQL of the restructuring Query 4 is given above in the extension RVL of RQL.

RQL is convenient for expressing aggregation queries such as Query 5 ("Return each of the subclasses of 'Writing', together with the average number of authors per publication of that subclass."):

max(SELECT Y
    FROM {B;books:Writing}books:author.books:authorName{A},
         {B}books:pubYear{Y}
    WHERE A = "Julius Caesar")

Inference queries that do not need recursion, e.g., Query 9 ("Return the co-author relationship between two persons that stand in author relationships with the same book."), can be expressed in RQL as follows:

SELECT A1, A2  FROM {Z}books:author{A1}, {Z}books:author{A2}
WHERE  A1 != A2
USING  NAMESPACE books = &http://example.org/books#

Both typing rules and a semantics for RQL have been specified [161].

No formal complexity study of RDF has been published yet. An implementation of RDF is given with the so-called ICS-FORTH RDFSuite.

RQL has influenced several other RDF query languages, e.g., BrQL and SPARQL.

4.2.2 SeRQL

SeRQL [52, 67] is derived from RQL. It differs from RQL as follows:

• SeRQL does not support RDF and RDFS types, except literal types.
• SeRQL modifies and extends RQL's path expressions. SeRQL compound paths use an empty node {} for path concatenation.
  SeRQL provides a shorthand notation for retrieving several values of a property in a single path expression, simplifying e.g. Query 9 in

  FROM {Book} <books:author> {X, Y}

  instead of

  FROM {Book} <books:author> {X}, {Book} <books:author> {Y}

  SeRQL supports optional path expressions (using square brackets), e.g.:

  SELECT * FROM {Book} <books:title> {Title}; 
                [ <books:translator> {Translator} [ <books:age> {Age} ] ].

• SeRQL provides a shorthand notation for several properties of a resource in a FROM clause. The following SeRQL query returns the authors of books entitled 'Bellum Civile' having a translator named 'J.M. Carter' (note the ; separating the different properties):

  SELECT Author  FROM {Book} <books:title> {"Bellum Gallicum"};
              <books:translator> {} <books:translatorName> {"J.M. Carter"};
              <books:author> {Author}
  USING NAMESPACE books = <!http://example.org/books#>

• SeRQL eases querying a reified statement by enclosing the non-reified version of the statement in curly brackets.

SeRQL cannot express all sample queries. Selection and extraction queries, e.g. Queries 1 and 2, can be expressed in SeRQL (with the same limitation as with RQL). In contrast to RQL, SeRQL has neither set operations, nor existential or universal quantification. As a consequence, Query 3 cannot be expressed in SeRQL. Thanks to the CONSTRUCT clause, SeRQL, like RQL, can express restructuring and simple inference queries, e.g., Query 4 ("Invert the relation author (from a book to an author) into a relation authored (from an author to a book)."):

CONSTRUCT       {Author} <mybooks:authored> {Book}
>FROM            {Book} <books:author> {Author}
USING NAMESPACE books   = <!http://example.org/books#>
                mybooks = <!http://example.org/books-rdfs-extension#>

4.3 Query Languages Inspired from XPath, XSLT or XQuery

4.3.1 XQuery for RDF

The Syntactic Web Approach [242, 245] consists of

• a preliminary normalisation of the RDF data being queried essentially by
  • serialising RDF data in XML,
  • grouping the statements by their subjects,
• defining in XQuery, functions conveying the semantics of RDFS e.g. a function rdf:instance-of-class returning the (sequence of the) resources (represented by their description elements) that are (direct or indirect) instances of a class:

  define function rdf:instance-of-class($t as element(description)*, 
                                        $base-name as xs:string) 
      as element(description)*
  {
      $t[rdf:type = $base-name]
      ,
      for $i in $t[rdfs:subClassOf = $base-name]
      return rdf:instance-of-class($t, string($i/@rdf:about))
  }

Using the function rdf:instance-of-class and assuming a convenient normalisation of the RDF data Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed as follows:

let $t := document("http://example.org/books")//description
for $essay in rdf:instance-of-class($t, "books:Essay"),
    $author in $t[rdf:about = $essay/books:author]
return  <result>  {$essay, $author}  </result>

The "Syntactic Web" approach also proposes a normalisation and XQuery functions for Topic Map.

The "Syntactic Web" approach makes it possible

• to return answers in any possible XML format
• to query both standard Web and Semantic Web data with the same query language as advocated in [231].

4.3.2 XSLT for RDF: TreeHugger and RDF Twig

Similar in spirit to the Syntactic Web Approach, TreeHugger [266] proposes to rely on XSLT for querying and transforming RDF data.

RDF Twig [277] is another extension of XSLT 1.0 with functions for querying RDF. It is based on redundant and non-redundant depth or breadth first traversals of the RDF graph, i.e. traversals that repeat or do not repeat elements in the XML-based representation of RDF. Two query mechanisms are provided: A small set of logical operations on the RDF graph, and an interface to RDQL.

4.3.3 Versa

Developed as part of the Python-based 4Suite XML and RDF toolkit15, Versa [219, 220, 223] is a query language for RDF inspired from, but significantly different from, XPath.

Versa can be used in lieu of XPath in the XSLT version of 4Suite. Like the Syntactic Web Approach, TreeHugger, and RDF Twig, Versa is aligned with XML.

Like XPath, Versa can be extended by externally defined functions.

4.4 Metalog: Querying in Controlled English

Metalog differs from other RDF query languages for two reasons:

• Metalog combines querying with reasoning.
• Metalog's syntax is a controlled natural language (English).

Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed in Metalog as follows:

comment: some definitions of variables (or representations)
ESSAY  represents the term "Essay" 
         from the ontology "http://example.org/books#".
AUTHORED-BY represents the verb "author" 
         from the ontology "http://example.org/books#".
IS     represents the verb "rdf:type" 
         from RDF "http://www.w3.org/1999/02/22-rdf-syntax-ns#".
BELLUM_CIVILE represents the book "Bellum_Civile" 
         from the collection of books "http://example.org/books#".

comment: RDF triples written as Metalog statements.
BELLUM_CIVILE IS an ESSAY.
BELLUM_CIVILE is AUTHORED-BY "Julius Caesar".
BELLUM_CIVILE is AUTHORED-BY "Aulus Hirtius".

comment: a Metalog query
do you know SOMETHING that IS an ESSAY and that is AUTHORED-BY SOMEONE?

4.5 Algae: A Query Language with Reactive Rules

The reactive language Algae, or Algae2, is based on two concepts:

• Actions are the directives ask, assert, and fwrule that determine whether an expression is used to query RDF data, insert data into the graph, or to specify ECA-like rules.
• Answers to Algae queries are bindings for query variables as well as triples from the RDF graph as "proofs" of the answers.

Algae queries can be composed. Syntactically, Algae is based on the RDF syntax N-triples [133] extended with the above-mentioned actions and with constraints written between curly brackets and specifying arithmetic or string comparisons.

Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed as follows:

ns rdf   = <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
ns books = <http://example.org/books#>
read <http://example.org/books> ()
ask (    ?essay  rdf:type         <http://example.org/books#Essay> 
         ?essay  books:author     ?author .
         ?author books:authorName ?authorName )
collect( ?essay, ?author, ?authorName )

This query becomes more interesting if we are not only interested in the titles of essays written by 'Julius Caesar' but also want the translators of such books returned, if there are any:

ns rdf   = <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
ns books = <http://example.org/books#>
read <http://example.org/books> ()
ask (    ?essay rdf:type          <http://example.org/books#Essay> 
         ?essay books:author      ?author .
         ?author books:authorName  ``Julius Caesar'' .
         ?essay books:title       ?title .
         ~?essay books:translator ?translator .
    )
collect( ?title, ?translatorName )

~ declare translator an optional. This query returns the following answer and "proofs":

?title	?translator	Proof
"Bellum Civile"	"J. M. Carter"	_:1 rdf:type <http://exam...ks-rdfs#Essay>.
		_:1 books:author _:2.
		_:2 books:authorName "Julius Caesar".
		_:1 books:title "Bellum Civile".
		_:1 books:translator "J. M. Carter".

Query 2 and Query 4 cannot be expressed in Algae due to the lack of closure,   recursion, and negation. Queries 5 and 6 cannot be expressed in Algae due to the lack of aggregation. All other queries can be expressed in Algae, most of them requiring "extended action directives" [236].

No formal semantics has been published for Algae.

4.6 Deductive Query Languages

4.6.1 N3QL

N3QL [34], is derived from Notation 3, shortN3, [35], a syntax for and extension of RDF with variables, rules, and quoting for expressing of statements about statements.

An N3QL query is an N3 expression. All N3QL reserved words are the RDF properties of an RDF (usually, but not necessarily blank) node representing the query.

Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed as follows:

@prefix books: <http://example.org/books#>.
@prefix n3ql:  <http://www.w3.org/2004/ql#>.
@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#>.
[] n3ql:select { n3ql:result n3ql:is (?book ?author ?authorName) };
   n3ql:where  { ?book   rdf:type         books:Essay;
                 ?book   books:author     ?author;
                 ?author books:authorName ?authorName }.

The answer to this query is the RDF graph specified in the n3ql:select clause, a set of RDF collections (indicated by the collection constructor ()) of bindings for the variables.

The following N3 rule specifies the transitive closure of a RDF property:

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>.
{?x rdfs:subClassOf ?z; ?z rdfs:subClassOf ?y} 
                    => {?x rdfs:subClassOf ?y}

The N3 syntax for anonymous nodes, for navigating in the RDF graph using path expressions, and for quantified variables gives rise to concise expressions of queries such as "Return the books written by the author named 'Julius Caesar'." :

@prefix books: <http://example.org/books#>.
@prefix n3ql:  <http://www.w3.org/2004/ql#>.
[] n3ql:select { n3ql:result n3ql:is (?book) };
   n3ql:where  { ?book!books:author!books:authorName ``Julius Caesar'' }.

4.6.2 TRIPLE

TRIPLE [147, 259, 260] is a rule-based query, inference, and transformation language for RDF.

TRIPLE's syntax is close to F-Logic [170].

TRIPLE has been designed to address two weaknesses of previous approaches to querying RDF:

• Predefined constructs expressing RDFS' semantics that restrain a query language's extensibility
• Lack of formal semantics.

Instead of predefined RDFS-related language constructs, TRIPLE offers Horn logic rules (in F-Logic syntax) [170]. Using TRIPLE rules, one can implement features of, e.g., RDFS. Where Horn logic is not su cient, as is the case of OWL, TRIPLE is designed to be extended by external modules implementing, e.g., an OWL reasoner.

TRIPLE differs from Horn logic and Logic Programming as follows [260]:

• TRIPLE supports resources identified by URIs.
• RDF statements are represented in TRIPLE by slots, allowing the grouping and nesting of statements; like in F-Logic, path expressions can be used for traversing several properties.
• TRIPLE provides concise support for reified statements.
• TRIPLE has a notion of module allowing specification of the "model" in which a statement, or an atom, is true.
• TRIPLE requires an explicit quantification of all variables.

Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.")   can be approximated as follows:

rdf   := 'http://www.w3.org/1999/02/22-rdf-syntax-ns#'.
books := 'http://example.org/books#'.
booksModel := 'http://example.org/books'.
FORALL B, A, AN  result(B, A, AN) <- 
     B[rdf:type -> books:Essay; 
       books:author -> A[books:authorName -> AN]]@booksModel.

This query selects only resources directly classified as books:Essay. Query 1 is properly expressed below. TRIPLE's rules give rise to specify RDFS' semantics [260]:

rdf     := 'http://www.w3.org/1999/02/22-rdf-syntax-ns#'. 
rdfs    := 'http://www.w3.org/2000/01/rdf-schema#'. 
type    := rdf:type. 
subPropertyOf := rdfs:subPropertyOf. 
subClassOf    := rdfs:subClassOf. 

FORALL Mdl @rdfschema(Mdl) {
  transitive(subPropertyOf). 
  transitive(subClassOf). 
  FORALL O,P,V O[P->V] <- 
               O[P->V]@Mdl. 
  FORALL O,P,V O[P->V] <- 
               EXISTS S S[subPropertyOf->P] AND O[S->V]. 
  FORALL O,P,V O[P->V] <- 
               transitive(P) AND EXISTS W (O[P->W] AND W[P->V]). 
  FORALL O,T   O[type->T] <- 
               EXISTS S (S[subClassOf->T] AND O[type->S]).
}

The following rules provides Iinference from range and domain restrictions of properties:

  FORALL S,T  S[type-$>$T] <-
              EXISTS P, O (S[P-$>$O] AND P[rdfs:domain-$>$T]).
  FORALL O,T  O[type->T] <-
              EXISTS P, S (S[P-$>$O] AND P[rdfs:range-$>$T]).

With the rules given above, the approximation of Query 1 given above only needs to be modified so as to express the "model" it is evaluated against: instead of @booksModel alone, @booksModel, @rdfschema(booksModel) should be used.

Most sample queries can be expressed in TRIPLE. Aggregation queries cannot be expressed in TRIPLE.

4.6.3 Xcerpt

Xcerpt http://xcerpt.org [28, 60, 61, 247, 248] is a language for querying both data on the "standard Web" (e.g., XML and HTML data) and data on the Semantic Web (e.g., RDF, Topic Maps, etc. data).

Three features of Xcerpt are convenient for querying RDF data:

• Xcerpt's pattern-based incomplete queries are convenient for
  • collecting related resources a resource's neighbourhood,
  • traversals of RDF graphs of indefinite lengths.
• Xcerpt chaining of (possibly recursive rules) is convenient for expressing
  • RDFS's semantics e.g. the transitive closure of the subClassOf relation,
  • all kinds of graph traversals.
• Xcerpt's optional construct is convenient   for collecting properties of resources.

All sample queries can be expressed in Xcerpt both on the XML serialization and on the RDF serialization of the sample data. The following Xcerpt programs refer to the RDF serialization.

[44] gives two views on RDF data:

• as plain triples,
• as a graph.

On the plain triple view, Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be expressed as follows:

DECLARE ns-prefix rdf = "http://www.w3.org/1999/02/22-rdf-syntax-ns#"
DECLARE ns-prefix books = "http://example.org/books#"

GOAL
  result [
    all essay [
      id [ var Essay ], 
      all author [ 
        id [ var Author ],
        all name [ var AuthorName ]
      ]
    ]
  ]
FROM
  and{
    RDFS-TRIPLE [ var Essay:uri{},  "rdf:type":uri{},         "books:Essay":uri{} ],
    RDF-TRIPLE  [ var Essay:uri{},  "books:author":uri{},     var Author:uri{} ],
    RDF-TRIPLE  [ var Author:uri{}, "books:authorName":uri{}, var AuthorName ]
  }
END

Using the prefixes declared in line 1 and 2, the query pattern (between FROM and END) is a conjunction of three queries. The first conjunct refers to RDFS. This view of the RDF data contains all basic triples as well as the triple entailed by the RDFS semantics [148]. Using RDFS-TRIPLE instead of RDF-TRIPLE ensures that resources from a sub-class of books:Essay are also returned.

Xcerpt shares with [242] and a few other languages the ability to construct arbitrary XML data as answer.

On Xcerpt's graph view of RDF, Query 1 can be expressed as follows:

DECLARE ns-prefix rdf = "http://www.w3.org/1999/02/22-rdf-syntax-ns#"
DECLARE ns-prefix books = "http://example.org/books#"

GOAL
  result [
    all essay [
      id [ var Essay ], 
      all author [ 
        id [ var Author ],
        all name [ var AuthorName ]
      ]
    ]
  ]
FROM
  RDFS-GRAPH {{ 
    var Essay:uri {{
      rdf:type {{ "books:Essay":uri {{ }} }},
      books:author {{
        var Author:uri {{ 
          books:name {{ var AuthorName }}
        }}
      }}
    }}
  }}
END

The RDF graph view is represented by RDF-GRAPH. The RDFS-GRAPH view is used that extends RDF-GRAPH, i.e. RDF-TRIPLE, with RDFS-TRIPLE.

Triples are represented like in striped RDF/XML: each resource is a direct child element in RDF-GRAPH with a sub-element for each statement having this resource as object. The sub-element is labeled with the URI of the predicate and contains the object of the statement. As Xcerpt's data model is a rooted graph an not tree this can be represented without duplication of resources.

In contrast to the previous query, no conjunction is used but instead a nested pattern reflecting the structure of the RDF graph except that labeled edges are represented as nodes with edges to the elements representing their source and sink.

The following rule specifies the graph view from the triple view introduced above:

CONSTRUCT
  RDF-GRAPH {
    all var Subject @ var Subject:var SubjectType {
      all optional var Predicate {
        ^var Object
      },
      all optional var Predicate {
        var Literal
      }
    } }
FROM
  or{
    RDF-TRIPLE[
      var Subject:var SubjectType{},
      var Predicate:uri{},
      optional var Literal → literal{{}},
      optional var Object:/uri|blank/{{}}
    ],
    RDF-TRIPLE[ /.*/:/.*/{{}}, /.*/:/.*/{{}}, var Subject:var SubjectType{{}} ]  
  }
END

optional at lines 16 and 17 indicates that a part of the pattern does not have to occur in the data, but if it does, variables are bound appropriately. optional allows queries with alternatives to be expressed concisely. It is crucial for RDF where all properties are optional by default.

In lines 3 and 5 the operators @ and ^ are used to build a link, possibly yielding cycles.

Xcerpt's rules make it "serialisation transparent": For each RDF serialisation, a set of Xcerpt rules expresses a translation from or into this serialisation.

The rules for RDF/XML [25], the offcial XML serialisation, are complex and lengthy due to the high degree of flexibility RDF/XML allows. They can be found in [44]. (They are similar to functions expressing RDF/XML in XQuery given in [245]).

The following rule extracts all triples from an RXR document, i.e. a document in the Regular XML RDF format [11],. Since different types (such as URI, blank node, or literal) of subjects and objects of RDF triples are represented differently in RXR, the conversion of the RXR representation into the plain triples is performed in separate rules cf. [44].

DECLARE ns-prefix rxr = "http://ilrt.org/discovery/2004/03/rxr/"

CONSTRUCT
  RDF-TRIPLE[ 
    var Subject, var Predicate:uri{}, var Object
  ] 
FROM
  and[
    rxr:graph {{
      rxr:triple {
        var S → rxr:subject{{}},
        rxr:predicate{ attributes{ rxr:uri{ var Predicate } } },
        var O → rxr:object{{}}
      }
    }},
    RXR-RDFNODE[ var S, var Subject ],
    RXR-RDFNODE[ var O, var Object ]
  ]
END

5 Topic Maps Query Languages

Figure 3: History of the Topic Map Query Languages

5.1 tolog: Logic Programming for Topic Maps

tolog [121, 124] has been selected in April 2004 as an initial straw-man for the ISO Topic Maps Query Language.

tolog is inspired from Logic Programming and has SQL-style constructs.

tolog provides a means for identifying a topic by its (internal) identifier and its subject indicator. E.g., the topic (type) Novel of the sample data can be accessed either by its identifier Novel, or its subject indicator i"http://example.org/books#Novel"

URI prefixes can be used: using books for i"http://example.org/books#" gives rise to the short form books:Novel.

tolog URI prefixes contain indicators and therefore differ from XML namespaces.

In tolog, all occurrences of variables must be prefixed with $.

The original version of tolog [123]) has two kinds of Prolog-like predicates:

• Built-in predicates. Two built-in predicates, instance-of($INSTANCE, $CLASS) and direct-instance-of($INSTANCE, $CLASS) convey the semantics of the subclass-class relation.
• Dynamic association predicates. There is a dynamic association predicate forquerying the extent of each association type e.g.

  authors-for-book(b1, $AUTHOR: author)

  selects the authors of book b1. Note the association role identifying the topic author.

The current version of tolog [122, 124] has further built-in predicates, e.g., role-player and association-role, returning the associations, association roles, occurrences, and topics. These allow querying arbitrary topic maps without a-priori knowledge of the types used in the topic maps.

Query 2 ("Select all data items with any relation to the book titled 'Bellum Civile'.") can be implemented as follows using only these built-in predicates:

select $RELATED from
  title($BOOK, "Bellum Civile"), 
  related($BOOK, $RELATED)?
related($X, $Y) :- {
   role-player($R1, $X), association-role($A, $R1), 
   association-role($A, $R2), role-player($R2, $Y) |
   related($X, $Z), related($Z, $Y) 
}.

Conjunctions are expressed, as in Prolog, by commas. Disjunctions are in curly braces the disjuncts being separated by |.

The built-in predicates instance-of and direct-instance-of can indeed be implemented using tolog rules as follows [123]:

direct-instance-of($INSTANCE, $CLASS) :- 
   i"http://psi.topicmaps.org/sam/1.0/#type-instance"( 
      $INSTANCE : i"http://psi.topicmaps.org/sam/1.0/#instance", 
      $CLASS : i"http://psi.topicmaps.org/sam/1.0/#class").  
super-sub($SUB, $SUPER) :-
  i"http://www.topicmaps.org/xtm/1.0/core.xtm#superclass-subclass"(
    $SUB   : i"http://www.topicmaps.org/xtm/1.0/core.xtm#subclass",
    $SUPER : i"http://www.topicmaps.org/xtm/1.0/core.xtm#superclass").
descendant-of($DESC, $ANC) :- {
  super-sub($DESC, $ANC) |
  super-sub($DESC, $INT), descendant-of($INT, $ANC)
}.
instance-of($INSTANCE, $CLASS) :- {
  direct-instance-of($INSTANCE, $CLASS) |
  direct-instance-of($INSTANCE, $DCLASS), descendant-of($DCLASS, $CLASS)
}.

Negation is available, however, its semantics is not yet specified [122].

tolog has constructs for aggregation and sorting (although deemed insufficient [122]), paged queries using limit and offset as in SQL, and modules.

Thanks to tolog's (possibly recursive) rules, Query 7 ("Combine the information about the book titled 'The Civil War' and authored by 'Julius Caesar' with the information about the book with identifier 'bellum_civile'.") and Query 8 ("Return the transitive closure of the subClassOf relation.") can be implemented in tolog.

Neither tolog's semantics nor complexity have been investigated.

5.2 AsTMA?: Functional Style Querying of Topic Maps

AsTMa? [14, 16] is a functional query language in the style of XQuery [41].

AsTMa? offers several path languages for accessing data in topic maps.

With AsTMa?, answers can be re-structured yielding new XML documents.

Query 1 ("Select all Essays together with their authors, i.e. author items and corresponding names.") can be implemented as follows:

{  forall [$book (Writing)] in http://example.org/books
    return 
    >book<
      {$book,
      forall $author in ($book -< author / author-for-book) return
      >author<
        {$author}
        >name<{$author/bn}>/name<
      >/author<
    >/book<  }

Query 1 can also be implemented as follows using path expressions for accessing topics and associations:

<books>
{  forall [$book (Writing)] in http://example.org/books
    return 
    <book>
      {$book,
      forall [ (author-for-book)
               Writing : $book
               author:   $author ]
             in http://example.org/books return
      <author>
        {$author}
        <name>{$author/bn}</name>
      </author>
    </book>  }    
</books>

5.3 Path-based Access to Topic Maps

A number of path-based query languages have been proposed for Topic Maps, cf. [15] for an overview and a plea for the inclusion of path navigation in the upcoming ISO Topic Maps query language.

XTMPath [17] is a path-based query language relying on the XTM [232] serialisation of topic maps in XML.

The following path selects all topics that are (directly) typed Historical Novel:

topic[instanceOf/topicRef/@href = "\#Historical\_Novel"]} 

This path expression reflects the XTM serialisation:

<topic id="b1"> 
  <instanceOf> <topicRef xlink:href="#Historical_Novel"/> </instanceOf> 
</topic>  

Note the following:

• Only a limited subset of the XPath constructs is supported by XTMPath, mostly the child and descendant axis and some simple predicates (in XPath s abbreviated syntax).
• XTMPath operates on data conforming to a single DTD, the DTD of XTM DTD [17]. This leads to treating the axis child like the axis descendant with a few exceptions, e.g., instanceOf.

6 Conclusion: Salient Aspects of the Query Languages Considered

Path vs. Logic or Navigational vs. Positional. Semantic Web query languages express basic queries using one of two paradigms, paths à la XPath, or Logic, la Logic Programming. These two paradigms can be named navigational and positional , respectively. Path-oriented navigations inherently conflict with referential transparency.

Logical Variables When Web and Semantic Web query languages have variables, they almost always are logical variables.

Referential Transparency and (Weak or Strong) Answer-Closure. Referential Transparency (i.e., within the same scope, an expression always means the same), the trait of declarative languages, is, if not fully achieved, obviously striven for by both positional and logic, query languages, especially in Semantic Web query languages. Some query languages are weakly answer-closed or answer-closed in the sense of [76], i.e., they deliver answers in the formalism of the data queried. A few query languages are strongly answer-closed, i.e., they make query programs possible that can further process data generated by these very programs.

Backtracking-free Logic Programming or Set-Oriented Functional Query Evaluation. Positional, or logic query languages that offer construct similar to rules or views, are, with a few exceptions, backtracking-free. Equivalently, they can be called set-oriented functional.

Incomplete Queries and Answers. Many query languages offer means for incomplete specifications of queries, paying tribute to the semi-structured [2] nature of data on the Web. Incomplete query specifications are extremely useful on the Semantic Web, too.

Versatile vs. Data Format Specific Query Languages. Most RDF query languages are RDF-specific, and even specifically designed for one serialisation. Data format versatile languages capable of easily accommodating XML, RDF, Topic Maps, OWL, etc. should be striven for.

Reasoning Capabilities. Not all XML query languages have views, rules, or similar concepts allowing the specification of other forms of reasoning. The same holds true of RDF query languages. Query languages capable of reasoning are needed with RDF data.

Language engineering. Abstract data types and static type checking, modules, polymorphism, and abstract machines, have clearly not yet made their way in the Web query languages.

Acknowledgments

The authors are thankful to Renzo Orsini, Ian Horrocks, Michael Kraus, and Oliver Bolzer for stimulating discussions and useful suggestions during the preparation of the report [56] that has been an important input for this survey.

This research has been funded by the European Commission and by the Swiss Federal Office for Education and Science within the 6th Framework Programme project REWERSE number 506779.

Bibliography

All references can be found in the article

James Bailey, François Bry, Tim Furche, and Sebastian Schaffert:
"Web and Semantic Web Query Languages: A Survey" (in PDF, 870KB).
In: Reasoning Web, First International Summer School 2005, Norbert Eisinger and Jan Małuszynski, editors. Springer-Verlag, LNCS 3564, 2005.
© Springer-Verlag

upon which this presentation is based.

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Last modified: Thu Jun 30 13:11:15 CEST 2005