Overview

AllegroGraph is a database and application framework for building Semantic Web applications. It can store data and meta-data as triples; query these triples through various query APIs like SPARQL (the standard W3C query language) and Prolog; and apply RDFS++ reasoning with its built-in reasoner. AllegroGraph includes support for Federation, Social Network Analysis, Geospatial capabilities and Temporal reasoning. All of these are described in more detail below.

The Semantic Web

Since AllegroGraph is a database, we'll start by looking at the kind of data it is designed to store. The figure below represents some information about Jans and his pets. Like much of the data on the web, there are explicit relations like Robbie is the petOf Jans and implicit or common-sense relations such as petOf is an inverse relation to hasPet and Dog is a subClassOf Mammal

Though there are many ways to store this information, the W3C has standardized on the Resource Description Framework (RDF). RDF breaks knowledge into assertions of subject predicate object (like the three sentences above). For obvious reasons, these assertions are called triples. If we have many triples from different contexts, we can append an additional slot to each assertion; we call this slot a named graph. Even though these assertions are now quads, we'll still call them triples. Here are the assertions from above rewritten slightly to fit them into the triple-framework:

subject  predicate   object   graph  
jans     Type        Human    jans's home page  
robbie   petOf       jans     jans's home page  
petOf    inverseOf   hasPet   english grammar  
Dog      subClassOf  Mammal   science 

The Semantic Web vision is one where web pages contain enough self-describing data that machines will be able to navigate them as easily as humans do now. This will let computers better assist us in answering questions and managing our ever more complicated world. AllegroGraph is a high-performance database built to hold this information, query it, and reason with it. For more information on the Semantic Web, RDF and all the rest, see the following resources:

• RDF: http://www.w3.org/TR/rdf-primer/,
• RDFS: http://www.w3.org/TR/rdf-schema/, and
• OWL: http://www.w3.org/TR/owl-guide/.

For more information on the above topics, see the Suggested Reading for recommended introductory texts.

One thing to note is that AllegroGraph doesn't restrict the contents of its triples to pure RDF. In fact, we can represent any graph data-structure by treating its nodes as subjects and objects, its edges as predicates and creating a triple for every edge. The named-graph slot can be used to hold additional, application-specific, information. Used this way, AllegroGraph becomes a powerful graph-oriented database.

A Block diagram of an AllegroGraph database

Now that we've seen what kind of data AllegroGraph can manage, we can take a look at how it keeps track of it in a bit more detail:

• In RDF-land, an assertion is a statement that subject predicate object (in the context of graph).

• The bulk of an AllegroGraph triple-store is composed of assertions. Though called triples for historical reasons, each assertion has five fields:

  • subject (s)
  • predicate (p)
  • object (o)
  • graph (g)
  • triple-id (i)

• All of s, p, o, and g are strings of arbitrary size. Of course, it would be very inefficient to store all of the duplicated strings directly so we associate a special number (called a Unique Part Identifier or UPI) with each unique string. The string dictionary manages these strings and UPIs and prevents duplication.

• To speed queries, AllegroGraph creates indices which contain the assertions plus additional information.

• AllegroGraph can also perform freetext searching in the assertions using its freetext indices.

• and Finally, AllegroGraph keeps track of deleted triples

Triple-data generally comes into AllegroGraph as strings either from pure RDF/XML (see example) or as the more verbose but simpler N-Triple format (see example). The programmer API also makes it easy to import data from RDBMSs, CSV or any other custom data format

<http://www.franz.com/simple#Animal> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2002/07/owl#Class> .  
<http://www.franz.com/simple#Mammal> <http://www.w3.org/2000/01/rdf-schema#subClassOf> <http://www.franz.com/simple#Animal> .  
<http://www.franz.com/simple#Mammal> <http://www.franz.com/simple#eyes> "two" . 

<?xml version="1.0"?>  
<RDF xmlns="http://www.w3.org/1999/02/22-rdf-syntax-ns#"  
     xml:base="http://www.w3.org/1999/02/22-rdf-syntax-ns#"  
     xmlns:xsd="http://www.w3.org/2001/XMLSchema#"  
     xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"  
     xmlns:owl="http://www.w3.org/2002/07/owl#">  
  <Description rdf:about="http://www.franz.com/simple#Animal">  
    <rdf:type rdf:resource="http://www.w3.org/2002/07/owl#Class"/>  
  </Description>  
  <Description rdf:about="http://www.franz.com/simple#Mammal">  
    <rdfs:subClassOf rdf:resource="http://www.franz.com/simple#Animal"/>  
    <ns1:eyes>two</ns1:eyes>  
  </Description>  
</RDF> 

You should notice that although the above diagram shows a triple-store through an RDF-lens, there is nothing that constrains AllegroGraph to the RDF world. In fact, AllegroGraph contains many features that are outside of pure RDF and make it a true graph database.

Graph databases

We've already seen how AllegroGraph can help manage Semantic Web data and noticed how any graph can be viewed as a collection of triples representing its edges. Because AllegroGraph can store anything in the subject, predicate, object and graph fields of its triples we can add one more quality to the list above:

• In graph-database land, an assertion says that node s is connected to node o via edge p with additional data g.

For many applications, graph databases can be both more flexible and faster than RDBMSs because

• You add new predicates without changing any schema

• One-to-many relations are directly encoded without the indirection of tables

• You never think about what to index because everything is indexed

AllegroGraph has the ability to encode values directly into its triples (thus bypassing the string dictionary completely). This allows for both more efficient data retrieval and extremely efficient range queries. We take advantage of this data representation in the add-on libraries for geospatial reasoning, temporal reasoning and social network analysis.

Atomicity

The atomicity property defines that all updates within one transaction are persisted together. A transaction either completely fails or completely succeeds.

With AllegroGraph, when an application calls rollback, the changes that it has made to the triple store since the last commit or rollback are discarded. Likewise, when an application calls commit, all changes since the last commit or rollback are persisted. No partial transaction results will ever be seen by other clients of the database.

Consistency

The consistency property defines that every transaction takes the database as a whole from one consistent state to another. The database itself will never be inconsistent, according to its own consistency rules.

AllegroGraph does not allow for user-defined consistency rules (like, say, foreign key constraints in a relational database). That means that the database has no influence on the application-level consistency of the data stored in a triple store. It is up to the user to make sure that transactions create and maintain a consistent application-level state. At the same time, AllegroGraph guarantees that every commit operation will take the database from one consistent internal state to another consistent internal state.

It is possible for two concurrent transactions to attempt contradictory changes to triple-store metadata, such as mapping the same predicate to two different datatypes, or defining the same free-text index using different parameters. The first transaction to commit will succeed normally. Those changes become visible to the second transaction when it attempts to commit. (No rollback is necessary.) AllegroGraph will signal metadata errors at that point, preventing the second transaction from committing.

Isolation

The isolation property defines that every transaction only sees data of other completed transactions, and not partial results of transactions running concurrently.

AllegroGraph implements the snapshot isolation model. In this model, every transaction sees a snapshot of the persistent database state as of the time when the transaction has been started by a commit or rollback operation. During the course of executing transaction code, other transactions can commit and change the persistent state without affecting the snapshot of this transaction. Once the transaction commits, any updates made by concurrent transactions will be made visible as part of the snapshot of the next transaction.

As no triple locking is performed by AllegroGraph, it is possible that a triple that is being read in a transaction could be deleted in a concurrent transaction. Developers need to be aware of this and similar possibilities and make sure that transactions are properly sequenced if such concurrent updates could have an impact on application-level consistency.

Durability

The durability property defines that once the database system signals the successful completion of a transaction to the application, the changes made by the transaction will persist even in the presence of hardware and software failures. (Obviously, the durability property cannot guarantee the persistence of data after a hard disk failure that destroyed data.)

When the commit operation of AllegroGraph returns, the database server will have written the updates made by the transaction to the transaction log and waited for the log I/O operation to finish. Therefore, the application can be sure that every committed transaction will have a permanent effect on the persistent database state.

Triple-store operations

You can manipulate data in triple-stores via many different interfaces and languages including Java, HTTP and Lisp. Each language provides mechanisms to create and open triple-stores; load them with data in bulk-mode or programmatically; enable RDFS++ reasoning; query for triples that match simple or complex constraints; serialize triples in many formats; and understand and manage server performance.

Adding triples

AllegroGraph has facilities to bulk load from both N-Triples, and RDF/XML files. ¹ You can create freetext-indices while loading triples by specifying which predicates should be indexed. Additionally, AllegroGraph supports a wide array of encoded data-types such as numbers, dates, and geospatial coordinates. Using these data-types not only shrinks the size of your triple-store (because the string data need not be saved) but also allows super-fast range queries and geospatial queries.

Of course, you can also load triples into AllegroGraph programmatically. This can be used to import custom data formats, or to build a triple-store incrementally. Triples can be added using RDF syntax or AllegroGraph's special encoded data-types. Programmatically added triples can also make use of the AllegroGraph's triple-id to perform super-efficient reification.

Getting Triples back

Query Patterns

AllegroGraph provides numerous methods for getting triples back out of a triple-store. The simplest is to ask for triples matching a pattern of subject, predicate, object and graph. Each part of the pattern can be an exact match, a range specifier, or a wild card (don't care). For example, the pattern

subject  : <http://www.example.com/people/gwking>  
predicate: wild  
object   : wild  
graph    : <http://www.example.com/context/initial> 

would retrieve all triples about the person gwking from the graph named initial. We could learn about all of someone's phone numbers using:

subject  : <http://www.example.com/people/gwking>  
predicate: <http://www.example.com/telephone/>  
object   : wild  
graph    : wild 

and learn about everyone born in the first half of 1964 with:

subject      : wild  
predicate    : <http://www.example.com/birthm/>  
object-start : "1964-01-01"^^<http://www.w3.org/2000/01/XMLSchema#date>  
object-end   : "1964-06-30"^^<http://www.w3.org/2000/01/XMLSchema#date>  
graph        : wild 

You can use these pattern-based queries in your own programs to query triple-stores at the bare-metal. In fact, AllegroGraph's other query interfaces such as SPARQL, Prolog and the RDFS++ reasoner all ground out in patterns exactly like these.

Range Queries

In addition to strings, AllegroGraph can store many data-types directly in its triples. This lets it perform range queries in a single operation. Suppose, for example, that weights were stored as strings like

"158"^^<http://www.w3.org/2000/01/XMLSchema#long> 

If you wanted to find all people whose weight was greater than 200, then you would need to scan every triple in the store, lookup the string, parse it and then do your comparison. Ouch! With AllegroGraph, the value is encoded directly in the raw triple data (using a special kind of UPI). A range query involves immediate data lookup and comparison and is therefore as fast as a search for an individual triple.

Cursors

When AllegroGraph is given a query pattern, it responds with a cursor that iterates over the triples that match the pattern. Programs can use functions like Lisp's cursor-next to move through a cursor or use higher-level constructs like map-cursor.

Querying Quickly: Indices

AllegroGraph builds indices automatically so that any query can find its first match in a single I/O operation. We can abbreviate the index flavors using s for subject, p for predicate and so on. What matters with an index is the sort order of the triples. For example, the spogi index first sorts on subject, then predicate, object, graph, and finally, id. If we ignore the triple ID, there are 24 different index flavors running from spogi through gopsi. Fortunately, we don't need every possible flavor in order to produce fast queries. Suppose, for example, that one of the triples we saw above is in our triple-store as

id    subject  predicate  object  graph  
21445 jans     Type       Human   jans's home page 

There are many queries that will return this triple but the best flavor to use is the same for many of them. For example, we can use the spogi flavor for any of these queries:

• subject = <jans>
• subject = <jans> and predicate = <Type>
• subject = <jans> and predicate = <Type> and object = <Human>

Out of the box, AllegroGraph builds six index flavors in the background as triples are added.

Duplicate triples

It is possible that duplicate triples will be stored. This can, for example, occur because two users independently create and commit identical triples. Duplicate triples reduce the efficiency of the system. To remove these duplicate triples, use the function delete-duplicate-triples. It lets you specify what duplicate means: Two triples can be the same if they have the same subject, predicate, object, and graph (the :spog option, which is the initial value); or they can be the same if they have the same subject, predicate, and object, ignoring the graph (the :spo option).

Query APIs

AllegroGraph and SPARQL

SPARQL is the query language of choice for modern triple-stores. AllegroGraph's SPARQL sub-system is twinql; it adheres to W3C standard; includes a query optimizer; and has full support for named-graphs. For more information on using SPARQL with AllegroGraph, see the tutorial and SPARQL reference guide.

RDFS++ Reasoning

Description logic or OWL reasoners are good at handling (complex) ontologies, they are usually complete (give all the possible answers to a query) but have completely unpredictable execution times when the number of individuals increases beyond millions.

AllegroGraph's RDFS++ reasoning supports all the RDFS predicates and some of OWL's. It is not complete but it has predictable and fast performance. Here are the supported predicates:

• rdf:type and rdfs:subClassOf
• rdfs:range and rdfs:domain
• rdfs:subPropertyOf
• owl:sameAs
• owl:inverseOf
• owl:TransitiveProperty

The reasoner tutorial provides a quick introduction of how each predicate behaves and describes the reasoner in more detail. AllegroGraph also includes an optional OWL restriction reasoning module that supports owl:hasValue, owl:someValuesFrom and owl:allValuesFrom.

Prolog

Prolog is an alternative query mechanism for AllegroGraph. With Prolog, you can specify queries declaratively. The Prolog tutorial provides an introduction to using Prolog and AllegroGraph together. Prolog is an integral part of Lisp. So for Lispers the combination of Lisp, Prolog, and AllegroGraph are a natural triad. In Java mode and with the HTTP-interface you can send Prolog select queries to the server. You send them as a string and you get the bindings back as a list of values. See the HTTP protocol and Javadocs for the specifics.

Managing Massive Data - Federation

The block diagram we saw above is abstract: it can be implemented in many different ways. AllegroGraph 4.7 uses that same programming API to connect to local triple-stores (either on-disk or in-memory), remote-triple-stores and federated triple-stores. A federated store collects multiple triple-stores of any kind into a single virtual store that can be manipulated as if it were a simple local-store. Federation provides three big benefits:

1. it scales,
2. it makes triple-stores more manageable, and
3. it makes data archive almost trivial.

Federation: Scalable triple-stores

Since federation provides a natural mechanism to join disparate triple-stores, we can use separate instances of AllegroGraph to load data on multiple CPUs and then combine them at query time. ² Loading triples is an extremely parallelizable task in that using N CPUs decreases the total time by a factor of N. Experiments on the LUBM-8000 dataset show that we can easily load and index a billion triples on a four CPU machine in less than 10-hours.

Federation: Data Management

AllegroGraph's federation mechanism and flexible triple-store architecture combine to make it easy to connect multiple stores together and treat them as one. For example, we can combine the dbPedia, the USGS Geonames database and Census information into a single virtual store and explore the interconnections between these datasets without worrying about where the triples originate. Even better, we can keep different kinds of triples separate and combine them as needed. E.g., we can keep known facts, inferred triples, provenance information, ontologies, metadata and deleted triples in separate, easily manageable stores and combine and re-combine the data as necessary.

Federation: Data warehousing

Enterprise data volumes are growing without bound making it essential to enable the accumulation and archiving of multi-billions of triples. Federation lets you segment your data into usable chunks that can be swapped in and out as needed.

The figure illustrates how an enterprise data center can use federation to easily work with the three most current months of data. Since federated data stores can be built easily and easily changed, it is just as simple to look at historical data whenever that is necessary. }

AllegroGraph and the Network

Sesame 2.0 HTTP interface

AllegroGraph's Sesame 2.0 HTTP Server runs in either the Java server or in any Lisp image with AllegroGraph loaded. The parameters to the Java server are described in the Server Installation document. Once running, clients interact with the server via HTTP requests. The Sesame libraries make it easy for user's to create requests and interpret the results.

We have extended the Sesame HTTP protocol (as described on the OpenRDF website) to expose additional AllegroGraph features such as indexing and encoded data-types. These extensions are described in the HTTP protocol documentation.

Specialized Datatypes

AllegroGraph supports several specialized datatypes for efficient storage, manipulation, and search of Social Network, Geospatial and Temporal information.

Social Network Analysis

By viewing interactions as connections in a graph, we can treat a multitude of different situations using the tools of Social Network Analysis (SNA). SNA lets us answer questions like:

• How closely connected are any two individuals?

• What are the core groups or clusters within the data?

• How important is this person (or company) to the flow of information

• How likely is it that this person and that person know one another

The field is full of rich mathematical techniques and powerful algorithms. AllegroGraph's SNA toolkit includes an array of search methods, tools for measuring centrality and importance, and the building blocks for creating more specialized measures.

Geospatial Primitives

AllegroGraph provides a novel mechanism for efficient storage and retrieval of geospatial data. ³ Support is provided both for Cartesian coordinate systems (i.e., a flat plane) and for spherical coordinate systems (e.g., the surface of the earth or the celestial sphere).

Coordinates in two dimensions are encoded into a single UPI. Once data has been encoded this way, AllegroGraph can perform the following sorts of queries in either Cartesian or spherical coordinates very quickly:

• bounding-box: return a cursor that iterates over all triples within given a rectangular region.

• center/radius: return a cursor that iterates over all triples within a circular region.

AllegroGraph's geospatial application also has support for defining polygons and quickly determining:

• whether a point lies inside or outside a given polygon.
• whether two polygons overlap.
• retrieving all triples that lie inside of a given polygon.

Temporal Primitives

AllegroGraph supports efficient storage and retrieval of temporal data including datetimes, time points, and time intervals:

• datetimes in ISO8601 format: "2008-02-01T00:00:00-08:00"
• time points: ex:point1, ex:h-hour, ex:when-the-meeting-began, etc
• time intervals: ex:delay-interval (say, from point ex:point1 to ex:h-hour)

Once data has been encoded, applications can perform queries involving a broad range of temporal constraints on data, including relations between :

• points and datetimes
• intervals and datetimes
• two points
• two intervals
• points and intervals

Freetext Indexing

AllegroGraph can build freetext indexes of the strings of the objects associated with a set of predicates that you specify. Given a freetext index, you can search for text using:

• boolean expressions ("market" AND "housing")
• wild cards ("science*" OR "math*")
• phrases ("Semantic Web search")

Of course, freetext indexing slows the rate at which you can insert triples. Our experiments suggest that you'll see a decrease somewhere between 5 and 25% depending on the number of predicates involved and the kinds of string data in your application.

Programming with AllegroGraph

AllegroGraph comes in multiple flavors and works with multiple programming languages and environments.

Java

The Java client interface implements most of the Sesame and Jena interfaces for accessing remote RDF repositories. Because AllegroGraph provides functionality not found in other triple-stores, we have implemented extensions where applicable. See the pre-release Jena page for information on our Jena support. (See the tutorial and Javadocs for more information).

HTTP

It is now possible for web developers and programmers alike to interact with AllegroGraph 4.7 completely using a RESTful HTTP protocol (using GET, PUT, POST) to add and delete triples, to query for individual triples and to do SPARQL and Prolog selects using the Sesame 2.0 HTTP-interface with some extensions

Python

The Python API offers convenient and efficient access to an AllegroGraph server from a Python-based application. This API provides methods for creating, querying and maintaining RDF data, and for managing the stored triples. The Python tutorial provides an good overview of the API.

Lisp

Lisp programmers can open and use triple-stores from within Lisp. Lispers can create applications in the same image that the AllegroGraph server is running or use a remote-triple-store to access data in client/server mode.

Footnotes