R2RML映射下的高效SPARQL-to-SQL转换技术
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"本文探讨了如何高效地将SPARQL查询转换为SQL查询，尤其是在结合R2RML映射的情况下。作者提出了一种在ontop系统中实现的技术，该技术解决了现有SPARQL-to-SQL转换方法存在的效率、正确性和可靠性的局限性。"
在知识图谱领域，SPARQL（SPARQL Protocol and RDF Query Language）是一种标准的查询语言，用于检索和操作基于RDF（Resource Description Framework）的数据。而SQL（Structured Query Language）则是传统关系数据库管理系统中用于查询和管理数据的语言。R2RML（RDB to RDF Mapping Language）则是一种标准，用于将关系数据库的数据映射到RDF模型，从而实现RDF与关系数据库之间的桥梁。
现有的SPARQL-to-SQL转换技术存在诸多问题，包括生成低效甚至错误的SQL查询，缺乏正式的理论基础，以及实现上的不足。这些限制阻碍了其在复杂环境中的应用，特别是当涉及到任意数据库模式时，由于不支持如R2RML这样的RDB到RDF映射语言，使得转换变得更加困难。
本文作者提出了一种新的技术，它已在ontop系统中实现，以解决上述问题。首先，该技术采用逻辑编程和SQL优化领域的技术相结合的方式，生成高效的SQL查询，提高了查询效率。其次，它提供了SPARQL语义的明确定义，确保了翻译过程的准确性。最后，它支持对通用关系型数据库模式的R2RML映射，增强了系统的适应性。
通过ontop系统进行的广泛基准测试，证明了使用这种技术进行Ontology-Based Data Access (OBDA)时，性能显著提升。OBDA是一种将数据库与本体论相结合的方法，允许用户通过高级查询语言（如SPARQL）访问底层的结构化数据。结果表明，这些技术可以提高查询效率，增强系统可靠性，并扩展了SPARQL查询在各种数据库环境中的应用范围。
这篇论文对于理解和改进SPARQL到SQL的转换过程具有重要意义，特别是在R2RML映射的支持下，为知识图谱与关系数据库的交互提供了一种更有效的方法。这对于数据科学家、数据库管理员以及从事知识图谱开发和维护的IT专业人员来说，都是一个重要的研究进展。
146 M. Rodríguez-Muro, M. Rezk / Web Semantics: Science, Services and Agents on the World Wide Web 33 (2015) 141–169
the two relations must have the same set of attributes. The result
includes all tuples that are in r
1
but not in r
2
.
r
1
\ r
2
= {t | t ∈ r
1
and t ∈ r
2
}.
Selection (σ ): This operator is used to choose a subset of the tuples
(rows) from a relation that satisfies a selection condition, acting as
a filter to retain only tuples that fulfills a qualifying requirement.
σ
p
(r) = {t | t ∈ r and p(t)}.
Rename (ρ): This is a unary operation written as, ρ
c
1
/c
2
(r), where
the result is identical to r except that the c
1
attribute in all tuples
is renamed to a c
2
attribute.
Projection (Π): This operator is used to reorder, select and filter
out attributes from a table.
Π
c
1
...c
k
(r) = {v
1
. . . v
k
| v
k
. . . v
n
∈ r}
In order to ease the presentation, we will often mimic SQL and
include the renaming in the projection using AS statements. Thus,
we write Π
c
1
AS c
2
(r) to denote ρ
c
1
/c
2
(r). We will also overload
the projection with statements of the form Π
constant AS c
2
(r) where
constant is null, or an string, or a concatenation of an string and
an attribute. Observe that this second operation can be easily
encoded in relational algebra using auxiliary tables. For instance,
Π
constant AS c
2
(r), can be encoded as ρ
nullttr/c
2
Π
attr(r )\ c
2
r × NullTable
where NullTable is a table with a single attribute nullttr and a single
null record.
Natural join (on): This is a binary operator written as, r
1
on r
2
, where
the result is the set of all combinations of tuples in r
1
and r
2
that
are equal on their common attribute names.
r
1
on
jn
r
2
= Π
sc
(σ
jn
(r
1
× r
2
)).
Left join ( ): This is a binary operator written as, r
1
r
2
, where
the result is the set of all combinations of tuples in R and S that
are equal on their common attribute names, in addition (loosely
speaking) to tuples in r
1
that have no matching tuples in r
2
.
r
1 jn
r
2
= (r
1
on
jn
r
2
)
∪((r
1
\ Π
col(r
1
)
(r
1
on
jn
r
2
)) × NullTable
attr(r
2
)\attr(r
1
)
)
where NullTable
attr(r
2
)\attr(r
1
)
is a table with a attributes attr(r
2
) \
attr(r
1
) and a single record consisting only on null values.
Recall that every relational algebra expression is equivalent to
a SQL query. Further details can be found in [28].
3.3. SPARQL
For formal purposes we will use the algebraic syntax of SPARQL
similar to the ones in [10,11] and defined in the standard.
6
How-
ever, to ease the understanding, we will often use graph patterns
(the usual SPARQL syntax) in the examples. It is worth noticing,
that although in this paper we restrict ourselves to SELECT queries,
in -ontop- we also allow ASK, DESCRIBE and CONSTRUCT queries,
which can be reduced or implemented using SELECT queries.
The SPARQL language that we consider contains the following
pairwise disjoint countably infinite sets of symbols: I, denoting
the IRIs, B, denoting blank nodes, L, denoting RDF literals; and V,
denoting variables.
The SPARQL algebra is constituted by the following graph
pattern operators (written using prefix notation): BGP (basic graph
pattern), Join, LeftJoin, Filter, and Union. A basic graph pattern is a
statement of the form:
BGP(s, p, o)
6
http://www.w3.org/TR/rdf-sparql-query/#sparqlAlgebra.
where s ∈ I ∪ B ∪ V, p ∈ I ∪ V, and o ∈ I ∪ B ∪ L ∪ V. In the
standard, a BGP can contain several triples, but since we include
here the join operator, it suffices to view BGPs as the result of ◃▹
of its constituent triple patterns. Observe that the only difference
between blank nodes and variables in BGPs, is that the former do
not occur in solutions. So, to ease the presentation, we assume that
BGPs contain no blank nodes. The remaining algebra operators are:
• Join(pattern, pattern)
• LeftJoin(pattern, pattern, expression)
• Union(pattern, pattern)
• Filter(pattern, expression)
and can be nested freely. Each of these operators returns the result
of the sub-query it describes. Details on how to translate SPARQL
queries into SPARQL algebra can be found in the W3C specification,
and, in addition, several examples will be presented along the
paper.
Note. Converting Graph Patterns. It is critical to notice that
graph patterns are not translated straightforwardly into algebra
expressions. There is a pre-processing of the graph patterns where
filter expressions are either moved to the top of graph, or absorbed
by LeftJoin expressions. Details can be found in the SPARQL 1.0
specification.
7
A SPARQL query is a graph pattern P with a solution modifier,
which specifies the answer variables, that is, the variables in P
whose values should be in the output. In this work we ignore this
solution modifiers for simplicity.
Definition 16 (SPARQL Query). Let P be a SPARQL algebra expres-
sion, V a set of variables occurring in P, and G a set of RDF triples.
Then a query is a triple of the form (V , P, G).
We will often omit specifying V and G when they are not relevant
to the problem at hand.
Example 2. Consider the following SPARQL query Q :
This query is then translated into an SPARQL algebra expression
that has the following tree shape:
where T
1
, T
2
and T
3
represent (x,
′
knows
′
, y), (x,
′
email
′
, z), and
(x,
′
site
′
, w) respectively.
Semantics. Now we briefly introduce the formal set semantics of
SPARQL as specified in [10] with the difference that we updated
the definition of the LeftJoin to match the published standard
specifications. The result is a semantic which is more strict as the
one in [9] and the standard W3C semantics in the sense that:
1. We do not allow joins through null values.
2. We work with set semantics opposed to bag semantics.
7
http://www.w3.org/TR/rdf-sparql-query#convertGraphPattern.
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