The OpenCog (hyper-)graph database and graph rewriting system
The OpenCog AtomSpace is an in-RAM knowledge representation (KR) database, an associated query engine and graph-re-writing system, and a rule-driven inferencing engine that can apply and manipulate sequences of rules to perform reasoning. It is a kind of in-RAM generalized hypergraph (metagraph) database. Metagraphs offer more efficient, more flexible and more powerful ways of representing graphs: a metagraph store is literally just-plain better than a graph store. On top of this, the Atomspace provides a large variety of advanced features not available anywhere else.
The AtomSpace is a platform for building Artificial General Intelligence (AGI) systems. It provides the central knowledge representation component for OpenCog. As such, it is a fairly mature component, on which a lot of other systems are built, and which depend on it for stable, correct operation in a day-to-day production environment.
It is now commonplace to represent data as graphs; there are more graph databases than you can shake a stick at. What makes the AtomSpace different? A dozen features that no other graph DB does, or has even dreamed of doing.
But, first: five things everyone else does: * Perform graphical database queries, returning results that satisfy a provided search pattern. * Arbitrarily complex patterns with an arbitrary number of variable regions can be specified, by unifying multiple clauses. * Modify searches with conditionals, such as "greater than", and with user callbacks into scheme, python or Haskell. * Perform graph rewriting: use search results to create new graphs. * Trigger execution of user callbacks... or of executable graphs (as explained below).
A key difference: the AtomSpace is a metagraph store, not a graph store. Metagraphs can efficiently represent graphs, but not the other way around. This is carefully explained here, which also gives a precise definition of what a metagraph is, and how it is related to a graph. As a side-effect, metagraphs open up many possibilities not available to ordinary graph databasases. These are listed below. Things are things that no one else does: * Search queries are graphs. (The API to the pattern engine is a graph.) That is, every query, every search is also a graph. That means one can store a collection of searches in the database, and access them later. This allows a graph rule engine to be built up. * Inverted searches. (DualLink.) Normally, a search is like "asking a question" and "getting an answer". For the inverted search, one "has an answer" and is looking for all "questions" for which its a solution. This is pattern recognition, as opposed to pattern search. All chatbots do this as a matter of course, to handle chat dialog. No chatbot can host arbitrary graph data, or search it. The AtomSpace can. This is because queries are also graphs, and not just data. * Both "meet" and "join" searches are possible: One can perform a "fill in the blanks" search (a meet, with MeetLink) and one can perform a "what contains this?" search (a join, with JoinLink.) * Graphs are executable. Graph vertex types include "plus", "times", "greater than" and many other programming constructs. The resulting graphs encode "abstract syntax trees" and the resulting language is called Atomese. It resembles the intermediate representation commonly found in compilers, except that, here, its explicitly exposed to the user as a storable, queriable, manipulable, executable graph. * Graphs are typed (TypeNode and type constructors.) Graph elements have types, and there are half a dozen type constructors, including types for graphs that are functions. This resembles programming systems that have type constructors, such as CaML or Haskell. * Graphs specify flows (Values and DynamicFormulaLink.) Graph elements host dynamic, mutable key-value databases. That is, every graph element has an associated key-value database. Think of the graph is "pipes" or "plumbing"; the key-value data is the mutable, dynamically changing "water" that flows through those pipes. * Unordered sets (UnorderedLink.) A graph vertex can be an unordered set (Think of a list of edges, but they are not in any fixed order.) When searching for a matching pattern, one must consider all permutations of the set. This is easy, if the search has only one unordered set. This is hard, if they are nested and inter-linked: it becomes a constraint-satisfaction problem. The AtomSpace pattern engine handles all of these cases correctly. * Alternative sub-patterns (ChoiceLink.) A search query can include a menu of sub-patterns to be matched. Such sets of alternatives can be nested and composed arbitrarily. (i.e. they can contain variables, etc.) * Globby matching (GlobNode.) One can match zero, one or more subgraphs with globs This is similar to the idea of globbing in a regex. Thus, a variable need not be grounded by only one subgraph: a variable can be grounded by an indeterminate range of subgraphs. * Quotations (QuoteLink.) Executable graphs can be quoted. This is similar to quotations in functional programming languages. In this case, it allows queries to search for other queries, without triggering the query that was searched for. Handy for rule-engines that use rules to find other rules. * Negation as failure (AbsentLink.) Reject matches to subgraphs having particular sub-patterns in them. That is, find all graphs of some shape, except those having these other sub-shapes. * For-all predicate (AlwaysLink.) Require that all matches contain a particular subgraph or satisfy a particular predicate. For example: find all baskets that have only red balls in them. This requires not only finding the baskets, making sure they have balls in them, but also testing each and every ball in a basket to make sure they are all of the same color.
As it turns out, knowledge representation is hard, and so the AtomSpace has been (and continues to be) a platform for active scientific research on knowledge representation, knowledge discovery and knowledge manipulation. If you are comfortable with extremely complex mathematical theory, and just also happen to be extremely comfortable writing code, you are invited -- encouraged -- to join the project.
The AtomSpace is not an "app". Rather, it is a knowledge-base platform. It is probably easiest to think of it as kind-of-like an operating system kernel: you don't need to know how it works to use it. You probably don't need to tinker with it. It just works, and it's there when you need it.
End-users and application developers will want to use one of the existing "app" subsystems, or write their own. Most of the existing AtomSpace "apps" are focused on various aspects of "Artificial General Intelligence". This includes (unsupervised) natural-language learning, machine-learning, reasoning and induction, chatbots, robot control, perceptual subsystems (vision processing, sound input), genomic and proteomic data analysis, deep-learning neural-net interfaces. These can be found in other github repos, including:
The OpenCog Brainwave blog provides reading material for what this is all about, and why.
The AtomSpace is a mashup of a large variety of concepts from mathematical logic, theorem proving, graph theory, database theory, type theory, model theory and knowledge representation. Its hard to provide a coherent overview without throwing around a lot of "big words" and "big concepts". We're trying to get a lot of things done, here, and there's no particularly simple or effective way of explaining it without a lot of foundational theory.
There are pre-defined Atoms for many basic knowledge-representation and computer-science concepts. These include Atoms for relations, such as similarity, inheritance and subsets; for logic, such as Boolean and, or, for-all, there-exists; for Bayesian and other probabilistic relations; for intuitionist logic, such as absence and choice; for parallel (threaded) synchronous and asynchronous execution; for expressions with variables and for lambda expressions and for beta-reduction and mapping; for uniqueness constraints, state and a messaging "blackboard"; for searching and satisfiability and graph re-writing; for the specification of types and type signatures, including type polymorphism and type construction. See Atom types.
Because of these many and varied Atom types, constructing graphs to represent knowledge looks kind-of-like "programming"; the programming language is informally referred to as "Atomese". It vaguely resembles a strange mash-up of SQL (due to queriability), prolog/datalog (due to the logic and reasoning components), lisp/scheme (due to lambda expressions), Haskell/CaML (due to the type system) and rule engines (due to the graph rewriting and forward/backward chaining inference systems). This "programming language" is NOT designed for use by human programmers (it is too verbose and awkward for that); it is designed for automation and machine learning. That is, like any knowledge representation system, the data and procedures encoded in "Atomese" are meant to be accessed by other automated subsystems manipulating and querying and inferencing over the data/programs. See Atomese.
Aside from the various advanced features, Atomese also has some very basic and familiar atom types: atoms for arithmetic operations like "plus" and "times", conditional operators, like "greater-than" or "equals", control operations like "sequential and" and "cond", as well as settable state. This makes Atomese resemble a kind of intermediate language, something you might find inside of a compiler, a bit like CIL or Gimple. However, it is both far more flexible and powerful than these, and also far less efficient. Adventurous souls are invited to create a compiler to GNU Lighting, CIL, Java bytecode or the bytecode of your choice; or maybe to a GPU backend, or even more complex data-processing systems, such as TensorFlow.
In its current form, Atomese was primarily designed to allow the generalized manipulation of large networks of probabilistic data by means of rules and inferences and reasoning systems. It extends the idea of probabilistic logic networks to a generalized system for algorithmically manipulating and managing data. The current, actual design has been heavily influenced by practical experience with natural-language processing, question answering, inferencing and the specific needs of robot control.
The use of the AtomSpace, and the operation and utility of Atomese, remains a topic of ongoing research and design experimentation, as various AI and knowledge-processing subsystems are developed. These include machine learning, natural language processing, motion control and animation, deep-learning networks and vision processing, constraint solving and planning, pattern mining and data mining, question answering and common-sense systems, and emotional and behavioral psychological systems. Each of these impose sharply conflicting requirements on the AtomSpace architecture; the AtomSpace and "Atomese" is the current best-effort KR system for satisfying all these various needs in an integrated way. It is likely to change, as the various current short-comings, design flaws, performance and scalability issues are corrected.
Active researchers and theoreticians are invited to join! The current codebase is finally clean and well-organized enough that a large number of possibilities have opened up, offering many different and exciting directions to pursue. The system is clean and flexible, and ready to move up to the next level.
One of the primary conceptual distinctions in Atomese is between "Atoms" and "Values". The distinction is made for both usability and performance. Atoms are:
By contrast, Values, and valuations in general, are: * A way of holding on to rapidly-changing data, including streaming data. * Hold "truth values" and "probabilities", which change over time as new evidence is accumulated. * Provide a per-Atom key-value store (a mini noSQL database per-Atom). * Are not indexed, and are accessible only by direct reference. * Small, fast, fleeting (no indexes!)
Thus, for example, a piece of knowledge, or some proposition would be stored as an Atom. As new evidence accumulates, the truth value of the proposition is adjusted. Other fleeting changes, or general free-form annotations can be stored as Values. Essentially, the AtomSpace looks like a database-of-databases; each atom is a key-value database; the atoms are related to one-another as a graph. The graph is searchable, editable; it holds rules and relations and ontologies and axioms. Values are the data that stream and flow through this network, like water through pipes. Atoms define the pipes, the connectivity. Values flow and change. See the blog entry value flows as well as Atom and Value.
The primary documentation for the atomspace and Atomese is here:
The main project site is at https://opencog.org
Most users should almost surely focus their attention on one of the high-level systems built on top of the AtomSpace. The rest of this section is aimed at anyone who wants to work inside of the AtomSpace.
Most users/developers should think of the AtomSpace as being kind-of-like an operating system kernel, or the guts of a database: its complex, and you don't need to know how the innards work to use the system. These innards are best left to committed systems programmers and research scientists; there is no easy way for junior programmers to participate, at least, not without a lot of hard work and study. Its incredibly exciting, though, if you know what you're doing.
The AtomSpace is a relatively mature system, and thus fairly complex. Because other users depend on it, it is not very "hackable"; it needs to stay relatively stable. Despite this, it is simultaneously a research platform for discovering the proper way of adequately representing knowledge in a way that is useful for general intelligence. It turns out that knowledge representation is not easy. This project is a -good- excellent place to explore it, if you're interested in that sort of thing.
Experience in any of the following areas will make things easier for you; in fact, if you are good at any of these ... we want you. Bad.
Basically, Atomese is a mash-up of ideas taken from all of the above fields. It's kind-of trying to do and be all of these, all at once, and to find the right balance between all of them. Again: the goal is knowledge representation for general intelligence. Building something that the AGI developers can use.
We've gotten quite far; we've got a good, clean code-base, more-or-less, and we're ready to kick it to the next level. The above gives a hint of the directions that are now open and ready to be explored.
If you don't have at least some fair grounding in one of the above, you'll be lost, and find it hard to contribute. If you do know something about any of these topics, then please dive into the open bug list. Fixing bugs is the #1 best way of learning the internals of any system.
Looking ahead, some key major projects.
One of the major development goals for the 2019-2021 time frame is to gain experience with distributed data processing. Currently, the AtomSpace uses Postgres to provide distributed, scalable storage. We're also talking about porting to Apache Ignite, or possibly some other graph database, such as Redis, Riak or Grakn, all of which also support scalable, distributed storage.
However, despite the fact that Postgres is already distributed, and fairly scalable, none of the actual users of the AtomSpace use it in it's distributed mode. Exactly why this is the case remains unclear: is it the difficulty of managing a distributed Postgres database? (I guess you have to be a good DB Admin to know how to do this?) Is it the programming API offered by the AtomSpace? Maybe it's not yet urgent for them? Would rebasing on a non-SQL database (such as Ignite, Riak, Redis or Grakn) make this easier and simpler? This is quite unclear, and quite unknown at this stage.
If a port to one of the distributed graph databases is undertaken, there are several implementation issues that need to be cleared up. One is to eliminate many usages of SetLink ( Issues #1502 and #1507 ). Another is to change the AtomTable API to look like a bunch of MemberLink's. (Currently, the AtomTable conceptually looks and behaves like a large set, which makes scaling and distribution harder than it could be). How to transform the AtomTable into a bunch of MemberLinks without blowing up RAM usage or hurting performance is unclear.
The new Value system seems to provide a very nice way of working with fast-moving high-frequency data. It seems suitable for holding on to live-video feeds and audio streams and piping them through various data-processing configurations. It looks to be a decent API for declaring the structure and topology of neural nets (e.g. TensorFlow). However, it is more-or-less unused for these tasks. Apparently, there is still some missing infrastructure, as well as some important design decisions to be made. Developers have not begun to explore the depth and breadth of this subsystem, to exert pressure on it. Ratcheting up the tension by exploring new and better ways of using and working with Values will be an important goal for the 2020-2024 time-frame. See the value flows blog entry.
Many important types of real-world data, include parses of natural language and biochemical processes resemble the abstract mathematical concept of "sheaves", in the sense of sheaf theory. One reason that things like deep learning and neural nets work well is because some kinds of sheaves look like tensor algebras; thus one has things like Word2Vec and SkipGram models. One reason why neural nets still stumble on natural language processing is because natural language only kind-of-ish, partly looks like a tensor algebra. But natural language looks a whole lot more like a sheaf (because things like pre-group grammars and categorial grammars "naturally" look like sheaves.) Thus, it seems promising to take the theory and all the basic concepts of deep learning and neural nets, rip out the explicit tensor-algebra in those theories, and replace them by sheaves. A crude sketch is here.
Some primitive, basic infrastructure has been built. Huge remaining work items are using neural nets to perform the tensor-like factorization of sheaves, and to redesign the rule engine to use sheaf-type theorem proving techniques.
Current work is split between two locations: the "sheaf" subdirectory in this repo, and the generate repo.
The Atomspace runs on more-or-less any flavor of GNU/Linux. It does not run on any non-Linux operating systems (except maybe some of the BSD's). Sorry!
There are a small number of pre-requisites that must be installed before it can be built. Many users will find it easiest to use the install scripts provided in the ocpkg repo. Some users may find some success with one of the opencog Docker containers. Developers interested in working on the AtomSpace must be able to build it manually. If you can't do that, all hope is lost.
To build the OpenCog AtomSpace, the packages listed below are required. Essentially all Linux distributions will provide these packages.
apt-get install libboost-dev
apt-get install cmake3
sudo make installat the end.
apt-get install guile-2.2-dev
apt-get install cxxtest
The following packages are optional. If they are not installed, some optional parts of the AtomSpace will not be built. The
cmakecommand, during the build, will be more precise as to which parts will not be built.
apt-get install cython
apt-get install postgresql postgresql-client libpq-dev
Be sure to install the pre-requisites first! Perform the following steps at the shell prompt:
cd to project root dir mkdir build cd build cmake .. make -j4 sudo make install make -j4 testLibraries will be built into subdirectories within build, mirroring the structure of the source directory root.
To build and run the unit tests, from the
./builddirectory enter (after building opencog as above):
make -j4 testMost tests (just not the database tests) can be run in parallel:
make -j4 test ARGS=-j4The database tests will fail if run in parallel: they will step on one-another, since they all set and clear the same database tables.
Specific subsets of the unit tests can be run:
make test_atomese make test_atomspace make test_guile make test_join make test_matrix make test_persist_sql make test_python make test_query make test_sheaf
After building, you MUST install the atomspace.
sudo make install
Atomese -- that is -- all of the different Atom types, can be thought of as the primary API to the AtomSpace. Atoms can, of course, be created and manipulated with Atomese; but, in practice, programmers will work with either scheme (guile), python, C++ or haskell.
Python is more familiar than scheme to most programmers, and it offers another way of interfacing to the atomspace. Unfortunately, it is not as easy and simple to use as scheme; it also has various technical issues. Thus, it is significantly less-used than scheme in the OpenCog project. None-the-less, it remains vital for various applications. See the