The GraphDB Connectors provide extremely fast normal and facet (aggregation) searches that are typically implemented by an external component or service such as Lucene, but have the additional benefit to stay automatically up-to-date with the GraphDB repository data.
The Connectors provide synchronisation at the entity level, where an entity is defined as having a unique identifier (a URI) and a set of properties and property values. In terms of RDF, this corresponds to a set of triples that have the same subject. In addition to simple properties (defined by a single triple), the Connectors support property chains. A property chain is defined as a sequence of triples where each triple's object is the subject of the following triple.
The main features of the GraphDB Connectors are:
Each feature is described in detail below.
All examples use the following sample data. It describes five fictitious wines: Yoyowine, Franvino, Noirette, Blanquito and Rozova, as well as the grape varieties needed to make these wines. The minimum needed ruleset level in GraphDB is RDFS.
All interactions with the Lucene GraphDB Connector shall be done through SPARQL queries.
There are three types of SPARQL queries:
In general this corresponds to INSERT adds or modifies data and SELECT queries existing data.
Each connector implementation defines its own URI prefix to distinguish it from other connectors. For the Lucene GraphDB Connector this is *http://www.ontotext.com/connectors/lucene#*. Each command or predicate that is executed by the connector uses this prefix, e.g. <http://www.ontotext.com/connectors/lucene##createConnector> for creating a connector for Lucene.
Individual instances of a connector are distinguished by unique names that are also URIs. They have their own prefix to avoid clashing with any of the command predicates. For Lucene, the instance prefix is http://www.ontotext.com/connectors/lucene/instance#.
Creating a connector is should be done by sending a SPARQL query with the following configuration data:
The configuration data must be provided as a JSON string representation and passed together with the create command.
The create command is triggered by a SPARQL INSERT with the createConnector predicate, e.g. this will create a connector called my_index that will synchronise the wines from the sample data above:
Note that one of the fields has "sort": true. This is explained further under Sorting.
The above command will create a new Lucene connector.
The "types" key defines the RDF type of the entities to synchronise and in the example it is only entities of the type <http://www.ontotext.com/example/wine#Wine> (and its subtypes). The "fields" key defines the mapping from RDF to Lucene. The basic building block is the property chain, i.e. a sequence of RDF properties where the object of each property is the subject of the following property. In the example we map three bits of information - the wine's grape, sugar content, and year. Each chain is assigned a short and convenient field name: "grape", "sugar", and "year". The field names are later used in the queries.
Grape is an example of a property chain composed of more than one property. First we take the wine's madeFromGrape property, the object of which is an instance of type Grape, and then we take the rdfs:label of this instance. Sugar and year are both composed of a single property that links the value directly to the wine.
Dropping a connector removes all references to its external store from GraphDB as well as the SOLR core associated with it. Dropping a connector is achieved through a SPARQL INSERT query with the following parameter:
The drop command is triggered by a SPARQL INSERT with the dropConnector predicate, e.g. this will remove the connector :my_index:
Listing connectors returns all previously created connectors. It is a SELECT query with the listConnectors predicate:
?cntUri will be bound to the prefixed URI of the connector that was used during creation, e.g. <http://www.ontotext.com/connectors/lucene/instance#my_index>, while ?cntStr will be bound to a string, representing the part after the prefix, e.g. "my_index".
The internal state of each connector can be queried using a SELECT query and the connectorStatus predicate:
?cntUri will be bound to the connector prefixed URI, while ?cntStatus will be bound to a string representation of the status of the connector represented by this URI. The status is key-value based.
From the user's point of view all synchronisation will happen transparently without using any additional predicates or naming a specific store explicitly, i.e. the user should simply execute standard SPARQL INSERT/DELETE queries. This is achieved by intercepting all changes in the plugin and determining which abstract documents need to be updated.
Once a connector has been created it will be possible to query data from it through SPARQL. For each matching abstract document, the connector returns the document's subject. In its simplest form querying is achieved by using a SELECT and providing the Lucene query as the object of the :query predicate:
The result will bind ?entity to the two wines made from grapes that have "cabernet" in their name, namely :Yoyowine and :Franvino.
Note that you must use the field names you chose when you created the connector. It is perfectly valid to have field names identical to the property URIs but then you responsible for escaping any special characters according to what Lucene expects.
First we get an instance of the requested connector by using the RDF notation "X a Y" (= X rdf:type Y), where X is a variable and Y is a connector. X will be bound to an instance of this connector. Then we assign a query to that instance by using the system predicate :query. Finally we request the matching entities through the :entities predicate.
It is also possible to provide per query search options by using one or more option predicates. The option predicates are described in details below.
The bound ?entity can be used in other SPARQL triples in order to build complex queries that fetch additional data from GraphDB. For example to see the actual grapes in the matching wines as well as the year they were made:
The result will look like this:
Note that :Franvino is returned twice because it is made from two different grapes, both of which are returned.
It is possible to access the match score returned by Lucene with the :score predicate. As each entity has its own score, the predicate must come at the entity level. For example:
The result will look like this but the actual score might be different as it depends on the specific Lucene version:
Consider the sample wine data and the my_index connector described previously. We can use the same connector to query facets too:
It is important to specify the fields we want to facet by using the facetFields predicate. Its value must be a simple comma-delimited list of field names. In order to get the faceted results, we have to use the facets predicate and as each facet has three components (name, value and count), the facets predicate binds a blank node, which in turn can be used to access the individual values for each component through the predicates facetName, facetValue, and facetCount.
The resulting bindings will look like in the table below:
We can easily see that there are three wines produced in 2012 and two in 2013. We also see that three of the wines are dry, while two are medium. However, it is not necessarily true that the three wines produced in 2012 are the same as the three dry wines as each facet is computed independently.
It is possible to sort the entities returned by a connector query according to one or more fields. In order to be able to use a certain field for sorting, you have to specify this at the time of creating the connector instance. Sorting is achieved by the orderBy predicate the value of which must be a comma-delimited list of fields. Each field may be prefixed with a minus to indicate sorting in descending order. For example:
The result will contain wines produced in 2013 sorted according to their sugar content in descending order:
By default, entities are sorted according to their matching score in descending order.
Note that if you join the entity from the connector query to other triples stored in GraphDB, GraphDB might scramble the order. To remedy this, use ORDER BY from SPARQL.
Limit and offset are supported on the Lucene side of the query. This is achieved through the predicates limit and offset. Consider this example in which we specify an offset of 1 and a limit of 1:
The result will contain a single wine, Franvino, as it would be second in the list if we executed the query without the limit and offset:
Note that the specific order in which GraphDB returns the results, depends on both how Lucene returns the matches, unless you specified sorting.
Snippet extraction is used to extract highlighted snippets of text that match the query. The snippets are accessed through the dedicated predicate :snippets, which binds a blank node that in in turn provides the actual snippets via the predicates :snippetField and :snippetText. The predicate :snippets must be attached to the entity, as each entity has a different set of snippets. For example, in a search for Cabernet:
The query will return the two wines made from Cabernet Sauvignon or Cabernet Franc grapes as well as the respective matching fields and snippets:
Note that the actual snippets might be somewhat different as this depends on the specific Lucene implementation.
It is possible to tweak how the snippets are collected/composed by using the following option predicates:
The option predicates are set on the connector instance, much like the :query predicate.
You can get the total number of hits by using the :totalHits predicate, e.g. for the connector :my_index and a query that would retrieve all wines made in 2012:
As there are three wines made in 2012, the value 3 (of type xdd:long) will be bound to ?totalHits.
The creation parameters define how a connector instance is created by the :createConnector predicate. There are some required parameters and some that are optional. All parameters are provided together in a JSON object, where the parameter names are the object keys. Parameter values may be simple JSON values such as a string or a boolean, or they can be lists or objects.
All of the creation parameters can also be set conveniently from the Create Connector user interface in the GraphDB Workbench without any knowledge of JSON.
The compulsory parameters must be present in every connector. They are responsible for the core behaviour.
The RDF types of entities to sync are specified as a list of URIs. At least one type URI must be provided.
The fields define exactly what parts of each entity will be synchronised as well as the specific details on the connector side. The field is the smallest synchronisation unit and it maps a property chain from GraphDB to a field in Lucene. The fields are specified as a list of field objects. At least one field object must be provided. Each field object has further keys that specify details.
The name of the field defines the mapping on the connector side. It is specified by the key fieldName with a string value. The field name is used at query time to refer to the field. There are few restrictions on the allowed characters in a field name but to avoid unnecessary escaping (which depends on how Lucene parses its queries) we recommend to keep the field names simple.
The property chain (propertyChain) defines the mapping on the GraphDB side. A property chain is defined as a sequence of triples where the entity URI is the subject of the first triple, its object is the subject of the next triple and so on. In this model, a property chain with a single element corresponds to a direct property defined by a single triple. Property chains are specified as a list of URIs and at least one URI must be provided. If you need to store the entity URI in the connector, you may map it by defining a property chain with a single special URI: $self. Only one field per connector may use the $self notation.
The default value (defaultValue) provides means for specifying a default value for the field when the property chain has no matching values in GraphDB. It has no default value. Currently only literals are supported.
Fields are indexed by default but that can be changed by using the Boolean option "index". True by default. Fields that are not indexed will be unavailable for queries but may still be used for faceting or sorting, if these are enabled.
Fields are synchronised for faceting by default but that can be changed by using the Boolean option "facet". True by default. Fields that are not synchronised for faceting will not be available for faceted search.
Fields are not synchronised for sorting by default but that can be changed by using the Boolean option "sort". False by default. Fields that are not synchronised for sorting will not be available for ordering the results.
When literal fields are indexed in Lucene, they will be analysed according to the analyser settings. Should you require that a given field is not analysed you may use syncAsIs. False by default.
The Lucene Connector supports custom Analyzer implementations. They may be specified via the analyzer parameter whose value must be a fully qualified name of a class that extends org.apache.lucene.analysis.Analyzer. The class must have either a default constructor or a constructor with exactly one parameter of type org.apache.lucene.util.Version. For example, these two classes would be valid implementations:
FancyAnalyzer and SmartAnalyzer could then be used by specifying their fully qualified names, for example:
RDF data is often multilingual but you may want to map only some of the languages represented in the literal values. This can be done by specifying a list of language codes.
The entityFilter parameter is used to fine-tune the set of entities and/or individual values for the configured fields, based on the field value. Entities and field values will be synchronised to Lucene if, and only if, they pass the filter. The entity filter is similar to a FILTER() inside a SPARQL query but not exactly the same. Each configured field can be referred to in the entity filter by prefixing it with a "?", much like referring to a variable in SPARQL. Several operators are supported:
The atomic operators in, not in and bound accept either an operand that is a field name variable as in the examples above, or a special construction composed of a field name variable followed by a URI. The URI will be used as a property of the particular field value bound to the field name variable, fetched from GraphDB and then its value will be used to evaluate the entity filter expression. It can be illustrated with this SPARQL snippet:
Instead of using the values of ?fieldName, the values of ?evaluatedValue will be used.
In addition to full URIs within < > the filters support the shorthand form type, which stands for <http://www.w3.org/1999/02/22-rdf-syntax-ns#type>.
Entity filters can be combined with default values in order to get more flexible behaviour.
A typical use-case for an entity filter is having soft deletes, i.e. instead of deleting an entity it is marked as deleted by the presence of a specific value for a given property.
For example, if we create a connector like this:
and then insert some entities:
We could create the following index to specify a default value for city:
The default value will be used for entity:b as it has no value for city in the repository. As the value is "London", the entity will be synchronised.
Sometimes data represented in RDF is not well suited to map directly to non-RDF. For example, if we have news articles and they can be tagged with different concepts (locations, persons, events, etc.), one possible way to model that is a single property :taggedWith. Consider the following RDF data:
Now, if we want to map this data to Lucene such that the property :taggedWith x is mapped to separate fields taggedWithPerson and taggedWithLocation according to the type of x (we are not interested in events), we can map :taggedWith twice to different fields and then use an entity filter to get the desired values:
Note: type is the short way to write <http://www.w3.org/1999/02/22-rdf-syntax-ns#type>.
The six articles in the RDF data above will be mapped as such:
This can be checked by issuing a faceted search for taggedWithLocation and taggedWithPerson:
If the filter was applied you should get only :Berlin for taggedWithLocation and only :Einstein and :Mozart for taggedWithPerson:
The following diagram shows a summary of all predicates that can administer (create, drop, check status) connector instances or issue queries and retrieve results. It can be used as a quick reference of what a particular predicate needs to be attached to. For example, to retrieve entities you need to use :entities on a search instance and to retrieve snippets you need to use :snippets on an entity. Variables that are bound as a result of query are shown in green, blank helper nodes are shown in blue, literals in red, and URIs in orange. The predicates are represented by labelled arrows.
Even though SPARQL per se is not sensitive to the order of triple patterns, the connectors expect to receive certain predicates before others so that queries can be executed properly. In particular, predicates that specify the query or query options need to come before any predicates that fetch results.
The diagram in Overview of connector predicates provides a quick overview of the predicates.
You can easily migrate your existing lucene4 plugin setup to the new connectors interface.
We provide an automated migration tool for your create index queries. The tool is distributed with GraphDB 6.0 onward and can be found in the tools subdirectory. Here is how to use it:
where input-file is your old sparql file and output-file is the new sparql file
you can find possible options with
We changed the syntax for the search queries to be able to match our needs for new features and better design. Here is an example query using the lucene4 plugin:
and here is it's connectors variant:
note the following changes:
Look at Overview of connector predicates for more info on the new syntax and how everything is linked together.
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