Jena Full Text Search

This extension to ARQ combines SPARQL and full text search via Lucene 6.4.1 or ElasticSearch 5.2.1 (which is built on Lucene). It gives applications the ability to perform indexed full text searches within SPARQL queries.

SPARQL allows the use of regex in FILTERs which is a test on a value retrieved earlier in the query so its use is not indexed. For example, if you're searching for occurrences of "printer" in the rdfs:label of a bunch of products:

PREFIX   ex: <>
PREFIX rdfs: <>

SELECT ?s ?lbl
    ?s a ex:Product ;
       rdfs:label ?lbl
    FILTER regex(?lbl, "printer", "i")

then the search will need to examine all selected rdfs:label statements and apply the regular expression to each label in turn. If there are many such statements and many such uses of regex, then it may be appropriate to consider using this extension to take advantage of the performance potential of full text indexing.

Text indexes provide additional information for accessing the RDF graph by allowing the application to have indexed access to the internal structure of string literals rather than treating such literals as opaque items. Unlike FILTER, an index can set the values of variables. Assuming appropriate configuration, the above query can use full text search via the ARQ property function extension, text:query:

PREFIX   ex: <>
PREFIX rdfs: <>
PREFIX text: <>

SELECT ?s ?lbl
    ?s a ex:Product ;
       text:query (rdfs:label 'printer') ;
       rdfs:label ?lbl

This query makes a text query for 'printer' on the rdfs:label property; and then looks in the RDF data and retrieves the complete label for each match.

The full text engine can be either Apache Lucene hosted with Jena on a single machine, or Elasticsearch for a large scale enterprise search application where the full text engine is potentially distributed across separate machines.

This example code illustrates creating an in-memory dataset with a Lucene index.

Table of Contents


In general, a text index engine (Lucene or Elasticsearch) indexes documents where each document is a collection of fields, the values of which are indexed so that searches matching contents of specified fields can return a reference to the document containing the fields with matching values.

The basic idea of the Jena text extension is to associate a triple with a document and the property of the triple with a field of a document and the object of the triple (which must be a literal) with the value of the field in the document. The subject of the triple then becomes another field of the document that is returned as the result of a search match to identify what was matched. (NB, the particular triple that matched is not identified. Only, its subject.)

In this manner, the text index provides an inverted index that maps query string matches to subject URIs.

A text-indexed dataset is configured with a description of which properties are to be indexed. When triples are added, any properties matching the description cause a document to be added to the index by analyzing the literal value of the triple object and mapping to the subject URI. On the other hand, it is necessary to specifically configure the text-indexed dataset to delete index entries when the corresponding triples are dropped from the RDF store.

The text index uses the native query language of the index: Lucene query language or Elasticsearch query language.

External content

It is also possible that the indexed text is content external to the RDF store with only additional triples (about the indexed text) in the RDF store. The subject URI returned as a search result may then be considered to refer via the indexed property to the external content.

There is no requirement that the text data indexed is present in the RDF data. As long as the index contains the index text documents to match the index description, then text search can be performed.

For example, if the content of a collection of documents is indexed and the URI naming the document is the result of the text search, then an RDF dataset with the document metadata can be combined with accessing the content by URI.

The maintenance of the index is external to the RDF data store.

External applications

By using Elasticsearch, other applications can share the text index with SPARQL search.

Query with SPARQL

The URI of the text extension property function is more conveniently written:

PREFIX text: <>

...   text:query ...

The following forms are all legal:

?s text:query 'word'                   # query
?s text:query (rdfs:label 'word')      # query specific property if multiple
?s text:query ('word' 10)              # with limit on results
(?s ?score) text:query 'word'          # query capturing also the score
(?s ?score ?literal) text:query 'word' # ... and original literal value

The most general form is:

 (?s ?score ?literal) text:query (property 'query string' limit)

Only the query string is required, and if it is the only argument the surrounding ( ) can be omitted.

Input arguments:

 Argument    Definition 
property (optional) URI (including prefix name form)
query string The native query string
limit (optional) int limit on the number of results

Output arguments:

 Argument    Definition 
indexed term The indexed RDF term.
score (optional) The score for the match.
hit (optional) The literal matched.

The property URI is only necessary if multiple properties have been indexed and the property being searched over is not the default field of the index. Also the property URI must not be used when the query string refers explicitly to one or more fields.

The results include the subject URI, ?s; the ?score assigned by the text search engine; and the entire matched ?literal (if the index has been configured to store literal values).

If the query string refers to more than one field, e.g.,

"label: printer AND description: \"large capacity cartridge\""

then the ?literal in the results will not be bound since there is no single field that contains the match – the match is separated over two fields.

If an output indexed term is already a known value, either as a constant in the query or variable already set, then the index lookup becomes a check that this is a match for the input arguments.

Good practice

The query engine does not have information about the selectivity of the text index and so effective query plans cannot be determined programmatically. It is helpful to be aware of the following two general query patterns.

Query pattern 1 – Find in the text index and refine results

Access to the text index is first in the query and used to find a number of items of interest; further information is obtained about these items from the RDF data.

{ ?s text:query (rdfs:label 'word' 10) ; 
     rdfs:label ?label ;
     rdf:type   ?type 

The text:query limit argument is useful when working with large indexes to limit results to the higher scoring results – results are returned in the order of scoring by the text search engine.

Query pattern 2 – Filter results via the text index

By finding items of interest first in the RDF data, the text search can be used to restrict the items found still further.

{ ?s rdf:type     :book ;
     dc:creator  "John" .
  ?s text:query   (dc:title 'word') ; 


The usual way to describe a text index is with a Jena assembler description. Configurations can also be built with code. The assembler describes a 'text dataset' which has an underlying RDF dataset and a text index. The text index describes the text index technology (Lucene or Elasticsearch) and the details needed for each.

A text index has an "entity map" which defines the properties to index, the name of the Lucene/Elasticsearch field and field used for storing the URI itself.

For simple RDF use, there will be one field, mapping a property to a text index field. More complex setups, with multiple properties per entity (URI) are possible.

Once configured, any data added to the text dataset is automatically indexed as well.

Text Dataset Assembler

The following is an example of a TDB dataset with a text index.

@prefix :        <http://localhost/jena_example/#> .
@prefix rdf:     <> .
@prefix rdfs:    <> .
@prefix tdb:     <> .
@prefix ja:      <> .
@prefix text:    <> .

## Example of a TDB dataset and text index
## Initialize TDB
[] ja:loadClass "org.apache.jena.tdb.TDB" .
tdb:DatasetTDB  rdfs:subClassOf  ja:RDFDataset .
tdb:GraphTDB    rdfs:subClassOf  ja:Model .

## Initialize text query
[] ja:loadClass       "org.apache.jena.query.text.TextQuery" .
# A TextDataset is a regular dataset with a text index.
text:TextDataset      rdfs:subClassOf   ja:RDFDataset .
# Lucene index
text:TextIndexLucene  rdfs:subClassOf   text:TextIndex .
# Elasticsearch index
text:TextIndexES    rdfs:subClassOf   text:TextIndex .

## ---------------------------------------------------------------
## This URI must be fixed - it's used to assemble the text dataset.

:text_dataset rdf:type     text:TextDataset ;
    text:dataset   <#dataset> ;
    text:index     <#indexLucene> ;

# A TDB datset used for RDF storage
<#dataset> rdf:type      tdb:DatasetTDB ;
    tdb:location "DB" ;
    tdb:unionDefaultGraph true ; # Optional

# Text index description
<#indexLucene> a text:TextIndexLucene ;
    text:directory <file:/some/path/lucene-index> ;
    text:entityMap <#entMap> ;
    text:storeValues true ; 
    text:analyzer [ a text:StandardAnalyzer ] ;
    text:queryAnalyzer [ a text:KeywordAnalyzer ] ;
    text:queryParser text:AnalyzingQueryParser ;
    text:multilingualSupport true ;

The text:TextDataset has two properties:

  • a text:dataset, e.g., a tdb:DatasetTDB, to contain the RDF triples; and

  • an index configured to use either text:TextIndexLucene or text:TextIndexES.

The <#indexLucene> instance of text:TextIndexLucene, above, has two required properties:

  • the text:directory file URI which specifies the directory that will contain the Lucene index files – if this has the value "mem" then the index resides in memory;

  • the text:entityMap, <#entMap> that will define what properties are to be indexed and other features of the index; and

and several optional properties:

  • text:storeValues controls the storing of literal values. It indicates whether values are stored or not – values must be stored for the ?literal return value to be available in text:query in SPARQL.

  • text:analyzer specifies the default analyzer configuration to used during indexing and querying. The default analyzer defaults to Lucene's StandardAnalyzer.

  • text:queryAnalyzer specifies an optional analyzer for query that will be used to analyze the query string. If not set the analyzer used to index a given field is used.

  • text:queryParser is optional and specifies an alternative query parser

  • text:multilingualSupport enables Multilingual Support

If using Elasticsearch then an index would be configured as follows:

<#indexES> a text:TextIndexES ;
      # A comma-separated list of Host:Port values of the ElasticSearch Cluster nodes.
    text:serverList "" ; 
      # Name of the ElasticSearch Cluster. If not specified defaults to 'elasticsearch'
    text:clusterName "elasticsearch" ; 
      # The number of shards for the index. Defaults to 1
    text:shards "1" ;
      # The number of replicas for the index. Defaults to 1
    text:replicas "1" ;         
      # Name of the Index. defaults to jena-text
    text:indexName "jena-text" ;
    text:entityMap <#entMap> ;

and text:index <#indexES> ; would be used in the configuration of :text_dataset.

To use a text index assembler configuration in Java code is it necessary to identify the dataset URI to be assembled, such as in:

Dataset ds = DatasetFactory.assemble(
    "http://localhost/jena_example/#text_dataset") ;

since the assembler contains two dataset definitions, one for the text dataset, one for the base data. Therefore, the application needs to identify the text dataset by it's URI http://localhost/jena_example/#text_dataset.

Entity Map definition

A text:EntityMap has several properties that condition what is indexed, what information is stored, and what analyzers are used.

<#entMap> a text:EntityMap ;
    text:defaultField     "label" ;
    text:entityField      "uri" ;
    text:uidField         "uid" ;
    text:langField        "lang" ;
    text:graphField       "graph" ;
    text:map (
         [ text:field "label" ; 
           text:predicate rdfs:label ]
         ) .

Default text field

The text:defaultField specifies the default field name that Lucene will use in a query that does not otherwise specify a field. For example,

?s text:query "\"bread and butter\""

will perform a search in the label field for the phrase "bread and butter"

Entity field

The text:entityField specifies the field name of the field that will contain the subject URI that is returned on a match. The value of the property is arbitrary so long as it is unique among the defined names.

Automatic document deletion

When the text:uidField is defined in the EntityMap then dropping a triple will result in the corresponding document, if any, being deleted from the text index. The value, "uid", is arbitrary and defines the name of a stored field in Lucene that holds a unique ID that represents the triple.

If you configure the index via Java code, you need to set this parameter to the EntityDefinition instance, e.g.

EntityDefinition docDef = new EntityDefinition(entityField, defaultField);

Note: If you migrate from an index without deletion support to an index with automatic deletion, you will need to rebuild the index to ensure that the uid information is stored.

Language Field

The text:langField is the name of the field that will store the language attribute of the literal in the case of an rdf:langString. This Entity Map property is a key element of the Linguistic support with Lucene index

Graph Field

Setting the text:graphField allows graph-specific indexing of the text index to limit searching to a specified graph when a SPARQL query targets a single named graph. The field value is arbitrary and serves to store the graph ID that a triple belongs to when the index is updated.

The Analyzer Map

The text:map is a list of analyzer specifications as described below.

Configuring an Analyzer

Text to be indexed is passed through a text analyzer that divides it into tokens and may perform other transformations such as eliminating stop words. If a Lucene or Elasticsearch text index is used, then by default the Lucene StandardAnalyzer is used.

In case of a TextIndexLucene the default analyzer can be replaced by another analyzer with the text:analyzer property on the text:TextIndexLucene resource in the text dataset assembler, for example with a SimpleAnalyzer:

<#indexLucene> a text:TextIndexLucene ;
        text:directory <file:Lucene> ;
        text:analyzer [
            a text:SimpleAnalyzer

It is possible to configure an alternative analyzer for each field indexed in a Lucene index. For example:

<#entMap> a text:EntityMap ;
    text:entityField      "uri" ;
    text:defaultField     "text" ;
    text:map (
         [ text:field "text" ; 
           text:predicate rdfs:label ;
           text:analyzer [
               a text:StandardAnalyzer ;
               text:stopWords ("a" "an" "and" "but")
         ) .

will configure the index to analyze values of the 'text' field using a StandardAnalyzer with the given list of stop words.

Other analyzer types that may be specified are SimpleAnalyzer and KeywordAnalyzer, neither of which has any configuration parameters. See the Lucene documentation for details of what these analyzers do. Jena also provides LowerCaseKeywordAnalyzer, which is a case-insensitive version of KeywordAnalyzer, and ConfigurableAnalyzer.

Support for the new LocalizedAnalyzer has been introduced in Jena 3.0.0 to deal with Lucene language specific analyzers. See Linguistic Support with Lucene Index part for details.

Support for GenericAnalyzers has been introduced in Jena 3.4.0 to allow the use of Analyzers that do not have built-in support, e.g., BrazilianAnalyzer; require constructor parameters not otherwise supported, e.g., a stop words FileReader or a stemExclusionSet; and finally use of Analyzers not included in the bundled Lucene distribution, e.g., a SanskritIASTAnalyzer. See Generic and Defined Analyzer Support


ConfigurableAnalyzer was introduced in Jena 3.0.1. It allows more detailed configuration of text analysis parameters by independently selecting a Tokenizer and zero or more TokenFilters which are applied in order after tokenization. See the Lucene documentation for details on what each tokenizer and token filter does.

The available Tokenizer implementations are:

  • StandardTokenizer
  • KeywordTokenizer
  • WhitespaceTokenizer
  • LetterTokenizer

The available TokenFilter implementations are:

  • StandardFilter
  • LowerCaseFilter
  • ASCIIFoldingFilter

Configuration is done using Jena assembler like this:

text:analyzer [
  a text:ConfigurableAnalyzer ;
  text:tokenizer text:KeywordTokenizer ;
  text:filters (text:ASCIIFoldingFilter, text:LowerCaseFilter)

Here, text:tokenizer must be one of the four tokenizers listed above and the optional text:filters property specifies a list of token filters.

Analyzer for Query

New in Jena 2.13.0.

There is an ability to specify an analyzer to be used for the query string itself. It will find terms in the query text. If not set, then the analyzer used for the document will be used. The query analyzer is specified on the TextIndexLucene resource:

<#indexLucene> a text:TextIndexLucene ;
    text:directory <file:Lucene> ;
    text:entityMap <#entMap> ;
    text:queryAnalyzer [
        a text:KeywordAnalyzer

Alternative Query Parsers

New in Jena 3.1.0.

It is possible to select a query parser other than the default QueryParser.

The available QueryParser implementations are:

  • AnalyzingQueryParser: Performs analysis for wildcard queries . This is useful in combination with accent-insensitive wildcard queries.

  • ComplexPhraseQueryParser: Permits complex phrase query syntax. Eg: "(john jon jonathan~) peters*". This is useful for performing wildcard or fuzzy queries on individual terms in a phrase.

The query parser is specified on the TextIndexLucene resource:

<#indexLucene> a text:TextIndexLucene ;
    text:directory <file:Lucene> ;
    text:entityMap <#entMap> ;
    text:queryParser text:AnalyzingQueryParser .

Elasticsearch currently doesn't support Analyzers beyond Standard Analyzer.

Configuration by Code

A text dataset can also be constructed in code as might be done for a purely in-memory setup:

    // Example of building a text dataset with code.
    // Example is in-memory.
    // Base dataset
    Dataset ds1 = DatasetFactory.createMem() ;

    EntityDefinition entDef = new EntityDefinition("uri", "text", RDFS.label) ;

    // Lucene, in memory.
    Directory dir =  new RAMDirectory();

    // Join together into a dataset
    Dataset ds = TextDatasetFactory.createLucene(ds1, dir, entDef) ;

Graph-specific Indexing

jena-text supports storing information about the source graph into the text index. This allows for more efficient text queries when the query targets only a single named graph. Without graph-specific indexing, text queries do not distinguish named graphs and will always return results from all graphs.

Support for graph-specific indexing is enabled by defining the name of the index field to use for storing the graph identifier.

If you use an assembler configuration, set the graph field using the text:graphField property on the EntityMap, e.g.

# Mapping in the index
# URI stored in field "uri"
# Graph stored in field "graph"
# rdfs:label is mapped to field "text"
<#entMap> a text:EntityMap ;
    text:entityField      "uri" ;
    text:graphField       "graph" ;
    text:defaultField     "text" ;
    text:map (
         [ text:field "text" ; text:predicate rdfs:label ]
         ) .

If you configure the index in Java code, you need to use one of the EntityDefinition constructors that support the graphField parameter, e.g.

    EntityDefinition entDef = new EntityDefinition("uri", "text", "graph", RDFS.label.asNode()) ;

Note: If you migrate from a global (non-graph-aware) index to a graph-aware index, you need to rebuild the index to ensure that the graph information is stored.

Linguistic support with Lucene index

It is now possible to take advantage of languages of triple literals to enhance index and queries. Sub-sections below detail different settings with the index, and use cases with SPARQL queries.

Explicit Language Field in the Index

Literals' languages of triples can be stored (during triple addition phase) into the index to extend query capabilities. For that, the new text:langField property must be set in the EntityMap assembler :

<#entMap> a text:EntityMap ;
    text:entityField      "uri" ;
    text:defaultField     "text" ;        
    text:langField        "lang" ;       

If you configure the index via Java code, you need to set this parameter to the EntityDefinition instance, e.g.

EntityDefinition docDef = new EntityDefinition(entityField, defaultField);

SPARQL Linguistic Clause Forms

Once the langField is set, you can use it directly inside SPARQL queries, for that the 'lang:xx' argument allows you to target specific localized values. For example:

//target english literals
?s text:query (rdfs:label 'word' 'lang:en' )

//target unlocalized literals
?s text:query (rdfs:label 'word' 'lang:none')

//ignore language field
?s text:query (rdfs:label 'word')


You can specify a LocalizedAnalyzer in order to benefit from Lucene language specific analyzers (stemming, stop words,...). Like any other analyzers, it can be done for default text indexing, for each different field or for query.

With an assembler configuration, the text:language property needs to be provided, e.g :

<#indexLucene> a text:TextIndexLucene ;
    text:directory <file:Lucene> ;
    text:entityMap <#entMap> ;
    text:analyzer [
        a text:LocalizedAnalyzer ;
        text:language "fr"

will configure the index to analyze values of the 'text' field using a FrenchAnalyzer.

To configure the same example via Java code, you need to provide the analyzer to the index configuration object:

    TextIndexConfig config = new TextIndexConfig(def);
    Analyzer analyzer = Util.getLocalizedAnalyzer("fr");
    Dataset ds = TextDatasetFactory.createLucene(ds1, dir, config) ;

Where def, ds1 and dir are instances of EntityDefinition, Dataset and Directory classes.

Note: You do not have to set the text:langField property with a single localized analyzer.

Multilingual Support

Let us suppose that we have many triples with many localized literals in many different languages. It is possible to take all these languages into account for future mixed localized queries. Just set the text:multilingualSupport property at true to automatically enable the localized indexing (and also the localized analyzer for query) :

<#indexLucene> a text:TextIndexLucene ;
    text:directory "mem" ;
    text:multilingualSupport true;     

Via Java code, set the multilingual support flag :

    TextIndexConfig config = new TextIndexConfig(def);
    Dataset ds = TextDatasetFactory.createLucene(ds1, dir, config) ;

Thus, this multilingual index combines dynamically all localized analyzers of existing languages and the storage of langField properties.

For example, it is possible to refer to different languages in the same text search query :

    { ?s text:query ( rdfs:label 'institut' 'lang:fr' ) }
    { ?s text:query ( rdfs:label 'institute' 'lang:en' ) }

Hence, the result set of the query will contain "institute" related subjects (institution, institutional,...) in French and in English.

Note: If the text:langField property is not set, the text:langField will default to"lang".

Generic and Defined Analyzer Support

There are many Analyzers that do not have built-in support, e.g., BrazilianAnalyzer; require constructor parameters not otherwise supported, e.g., a stop words FileReader or a stemExclusionSet; or make use of Analyzers not included in the bundled Lucene distribution, e.g., a SanskritIASTAnalyzer. Two features have been added to enhance the utility of jena-text: 1) text:GenericAnalyzer; and 2) text:DefinedAnalyzer.

Generic Analyzer

A text:GenericAnalyzer includes a text:class which is the fully qualified class name of an Analyzer that is accessible on the jena classpath. This is trivial for Analyzer classes that are included in the bundled Lucene distribution and for other custom Analyzers a simple matter of including a jar containing the custom Analyzer and any associated Tokenizer and Filters on the classpath.

In addition to the text:class it is generally useful to include an ordered list of text:params that will be used to select an appropriate constructor of the Analyzer class. If there are no text:params in the analyzer specification or if the text:params is an empty list then the nullary constructor is used to instantiate the analyzer. Each element of the list of text:params includes:

  • an optional text:paramName of type Literal that is useful to identify the purpose of a parameter in the assembler configuration
  • a required text:paramType which is one of:
 Type    Description 
text:TypeAnalyzer a subclass of org.apache.lucene.analysis.Analyzer
text:TypeBoolean a java boolean
text:TypeFile the String path to a file materialized as a
text:TypeInt a java int
text:TypeString a java String
text:TypeSet an org.apache.lucene.analysis.CharArraySet
  • a required text:paramValue with an object of the type corresponding to text:paramType

In the case of an analyzer parameter the text:paramValue is any text:analyzer resource as describe throughout this document.

An example of the use of text:GenericAnalyzer to configure an EnglishAnalyzer with stop words and stem exclusions is:

text:map (
     [ text:field "text" ; 
       text:predicate rdfs:label;
       text:analyzer [
           a text:GenericAnalyzer ;
           text:class "org.apache.lucene.analysis.en.EnglishAnalyzer" ;
           text:params (
                [ text:paramName "stopwords" ;
                  text:paramType text:TypeSet ;
                  text:paramValue ("the" "a" "an") ]
                [ text:paramName "stemExclusionSet" ;
                  text:paramType text:TypeSet ;
                  text:paramValue ("ing" "ed") ]
       ] .

Here is an example of defining an instance of ShingleAnalyzerWrapper:

text:map (
     [ text:field "text" ; 
       text:predicate rdfs:label;
       text:analyzer [
           a text:GenericAnalyzer ;
           text:class "org.apache.lucene.analysis.shingle.ShingleAnalyzerWrapper" ;
           text:params (
                [ text:paramName "defaultAnalyzer" ;
                  text:paramType text:TypeAnalyzer ;
                  text:paramValue [ a text:SimpleAnalyzer ] ]
                [ text:paramName "maxShingleSize" ;
                  text:paramType text:TypeInt ;
                  text:paramValue 3 ]
       ] .

If there is need of using an analyzer with constructor parameter types not included here then one approach is to define an AnalyzerWrapper that uses available parameter types, such as file, to collect the information needed to instantiate the desired analyzer. An example of such an analyzer is the Kuromoji morphological analyzer for Japanese text that uses constructor parameters of types: UserDictionary, JapaneseTokenizer.Mode, CharArraySet and Set<String>.

Defined Analyzers

The text:defineAnalyzers feature allows to extend the Multilingual Support defined above. Further, this feature can also be used to name analyzers defined via text:GenericAnalyzer so that a single (perhaps complex) analyzer configuration can be used is several places.

The text:defineAnalyzers is used with text:TextIndexLucene to provide a list of analyzer definitions:

<#indexLucene> a text:TextIndexLucene ;
    text:directory <file:Lucene> ;
    text:entityMap <#entMap> ;
    text:defineAnalyzers (
        [ text:addLang "sa-x-iast" ;
          text:analyzer [ . . . ] ]
        [ text:defineAnalyzer <#foo> ;
          text:analyzer [ . . . ] ]

References to a defined analyzer may be made in the entity map like:

text:analyzer [
    a text:DefinedAnalyzer
    text:useAnalyzer <#foo> ]
Extending multilingual support

The Multilingual Support described above allows for a limited set of ISO 2-letter codes to be used to select from among built-in analyzers using the nullary constructor associated with each analyzer. So if one is wanting to use:

  • a language not included, e.g., Brazilian; or
  • use additional constructors defining stop words, stem exclusions and so on; or
  • refer to custom analyzers that might be associated with generalized BCP-47 language tags, such as, sa-x-iast for Sanskrit in the IAST transliteration,

then text:defineAnalyzers with text:addLang will add the desired analyzers to the multilingual support so that fields with the appropriate language tags will use the appropriate custom analyzer.

When text:defineAnalyzers is used with text:addLang then text:multilingualSupport is implicitly added if not already specified and a warning is put in the log:

    text:defineAnalyzers (
        [ text:addLang "sa-x-iast" ;
          text:analyzer [ . . . ] ]

this adds an analyzer to be used when the text:langField has the value sa-x-iast during indexing and search.

Naming analyzers for later use

Repeating a text:GenericAnalyzer specification for use with multiple fields in an entity map may be cumbersome. The text:defineAnalyzer is used in an element of a text:defineAnalyzers list to associate a resource with an analyzer so that it may be referred to later in a text:analyzer object. Assuming that an analyzer definition such as the following has appeared among the text:defineAnalyzers list:

[ text:defineAnalyzer <#foo>
  text:analyzer [ . . . ] ]

then in a text:analyzer specification in an entity map, for example, a reference to analyzer <#foo> is made via:

text:map (
     [ text:field "text" ; 
       text:predicate rdfs:label;
       text:analyzer [
           a text:DefinedAnalyzer
           text:useAnalyzer <#foo> ]

This makes it straightforward to refer to the same (possibly complex) analyzer definition in multiple fields.

Storing Literal Values

New in Jena 3.0.0.

It is possible to configure the text index to store enough information in the text index to be able to access the original indexed literal values at query time. This is controlled by two configuration options. First, the text:storeValues property must be set to true for the text index:

<#indexLucene> a text:TextIndexLucene ;
    text:directory "mem" ;
    text:storeValues true;     

Or using Java code, used the setValueStored method of TextIndexConfig:

    TextIndexConfig config = new TextIndexConfig(def);

Additionally, setting the langField configuration option is recommended. See Linguistic Support with Lucene Index for details. Without the langField setting, the stored literals will not have language tag or datatype information.

At query time, the stored literals can be accessed by using a 3-element list of variables as the subject of the text:query property function. The literal value will be bound to the third variable:

(?s ?score ?literal) text:query 'word'

Working with Fuseki

The Fuseki configuration simply points to the text dataset as the fuseki:dataset of the service.

<#service_text_tdb> rdf:type fuseki:Service ;
    rdfs:label                      "TDB/text service" ;
    fuseki:name                     "ds" ;
    fuseki:serviceQuery             "query" ;
    fuseki:serviceQuery             "sparql" ;
    fuseki:serviceUpdate            "update" ;
    fuseki:serviceUpload            "upload" ;
    fuseki:serviceReadGraphStore    "get" ;
    fuseki:serviceReadWriteGraphStore    "data" ;
    fuseki:dataset                  :text_dataset ;

Building a Text Index

When working at scale, or when preparing a published, read-only, SPARQL service, creating the index by loading the text dataset is impractical.
The index and the dataset can be built using command line tools in two steps: first load the RDF data, second create an index from the existing RDF dataset.

Step 1 - Building a TDB dataset

Note: If you have an existing TDB dataset then you can skip this step

Build the TDB dataset:

java -cp $FUSEKI_HOME/fuseki-server.jar tdb.tdbloader --tdb=assembler_file data_file

using the copy of TDB included with Fuseki.

Alternatively, use one of the TDB utilities tdbloader or tdbloader2 which are better for bulk loading:

$JENA_HOME/bin/tdbloader --loc=directory  data_file

Step 2 - Build the Text Index

You can then build the text index with the jena.textindexer tool:

java -cp $FUSEKI_HOME/fuseki-server.jar jena.textindexer --desc=assembler_file

Because a Fuseki assembler description can have several datasets descriptions, and several text indexes, it may be necessary to extract a single dataset and index description into a separate assembler file for use in loading.

Updating the index

If you allow updates to the dataset through Fuseki, the configured index will automatically be updated on every modification. This means that you do not have to run the above mentioned jena.textindexer after updates, only when you want to rebuild the index from scratch.

Configuring Alternative TextDocProducers

The default behaviour when text indexing is to index a single property as a single field, generating a different Document for each indexed triple. To change this behaviour requires writing and configuring an alternative TextDocProducer.

To configure a TextDocProducer, say net.code.MyProducer in a dataset assembly, use the property textDocProducer, eg:

<#ds-with-lucene> rdf:type text:TextDataset;
    text:index <#indexLucene> ;
    text:dataset <#ds> ;
    text:textDocProducer <java:net.code.MyProducer> ;

where CLASSNAME is the full java class name. It must have either a single-argument constructor of type TextIndex, or a two-argument constructor (DatasetGraph, TextIndex). The TextIndex argument will be the configured text index, and the DatasetGraph argument will be the graph of the configured dataset.

For example, to explicitly create the default TextDocProducer use:

    text:textDocProducer <java:org.apache.jena.query.text.TextDocProducerTriples> ;

TextDocProducerTriples produces a new document for each subject/field added to the dataset, using TextIndex.addEntity(Entity).


The example class below is a TextDocProducer that only indexes ADDs of quads for which the subject already had at least one property-value. It uses the two-argument constructor to give it access to the dataset so that it count the (?G, S, P, ?O) quads with that subject and predicate, and delegates the indexing to TextDocProducerTriples if there are at least two values for that property (one of those values, of course, is the one that gives rise to this change()).

  public class Example extends TextDocProducerTriples {

      final DatasetGraph dg;

      public Example(DatasetGraph dg, TextIndex indexer) {
          this.dg = dg;

      public void change(QuadAction qaction, Node g, Node s, Node p, Node o) {
          if (qaction == QuadAction.ADD) {
              if (alreadyHasOne(s, p)) super.change(qaction, g, s, p, o);

      private boolean alreadyHasOne(Node s, Node p) {
          int count = 0;
          Iterator<Quad> quads = dg.find( null, s, p, null );
          while (quads.hasNext()) {; count += 1; }
          return count > 1;

Maven Dependency

The jena-text module is included in Fuseki. To use it within application code, then use the following maven dependency:


adjusting the version X.Y.Z as necessary. This will automatically include a compatible version of Lucene.

For Elasticsearch implementation, you can include the following Maven Dependency:


adjusting the version X.Y.Z as necessary.