I've been struggling to understand when these technologies are useful from a practical standpoint, and how they are different from each other. Could an expert check my understanding?
Graph databases: These are easier to understand and manage than a relational databases when relationships are complex, inherited, inferred with varying degrees of confidence, and likely to change. Some examples: a user doesn't know how much depth they'll need in a hierarchy; is inferring relationships from social media with varying degrees of confidence in ID resolution, topic resolution, and the strength of a relationship; or doesn't know what kinds of call center data they're going to want to store; all of these can be stored in relational databases, but they will need constant updates. They're also more performative for certain tasks.
Ontologies: These formal and standardized representations of knowledge are used to break down data silos. For instance, let's say a B2B sales company derives revenue from several different lines of business, which take one-time payments, subscriptions, sales of IP, and consulting services. The revenue data is stored in many different databases with lots of idiosyncrasies. An ontology allows the user to define a "customer payment" as anything that "creates or refunds revenue," so that subject matter experts can appropriately label the payments in their databases. Ontologies can be used with either graph databases or relational databases, but the emphasis on class inheritance makes them far easier to implement in a graph database, where the taxonomy of classes can be easily modeled.
Knowledge graph: A knowledge graph is a graph database where language (meaning, the entity and node taxonomies) are governed by an ontology. So in our B2B example, "customer payment" edges have one-time payments, subscription, etc subtypes, and connect "customer" classes to "line of business" classes.
Is that basically correct?
A graph database (GD) is a database that can store graph data, which primarily has three types of elements: nodes, edges, and properties. Two popular types of graph databases are (1) Resource Description Framework (RDF)-based graph databases eg. Blazegraph and (2) Label Propagation Graph (LPG)-based graph databases eg. Neo4j. RDF represents knowledge in the form of subject, verb, and object (S-V-O) triplet, such as John livesIn London, and as its nodes and edges cannot hold properties, additional nodes or literals needs to be added to represent properties. LPG represents knowledge in the form of edges, nodes, and attributes where nodes and edges can hold properties in the form of key:value. Eg., a node can have label Person, and Person can have properties name:Tom Hanks, born:1956.
An ontology is a description of the concepts and their relationships, using instances of concepts, attributes of instances (and classes), restrictions of classes, and rules (if-then statements). These rules describe the logical inferences that can be drawn from the assertions/axioms that comprise the overall theory that the ontology describes. Upper level ontology (eg. DOLCE) describes general concepts and relations, whereas domain ontology (eg. Gene Ontology) describes concepts and relations in a particular domain. A graph database may have an ontology in its schema level for logical consistency checking.
Generally, a knowledge graph (KG) is an organization of a knowledge base as a graph having nodes and links between the nodes. An example of an early KG is Wordnet which captures semantic relationships between words and their meanings. Later Google developed their Google Knowledge Graph (GKG) building on DBpedia and Freebase using RDFa, Microdata and JSON-LD content extracted from indexed web pages, and used schema.org vocabulary to organize the nodes. Google reported that it held around 70 billion facts in GKG.
Graph databases supports queries, but not logical inference which needs an ontology. If the connections within the data are of primary focus (eg. friends of a friend), retrieval more important than storage, and data model changes often, then graph database would be a good fit.
Ontology is used when we need to infer new knowledge from the given knowledge. For eg, if given (1) Socrates is Man, and (2) AllMen are Mortal, then the reasoner or inference engine in an ontology can infer a new knowledge (3) Socrates is Mortal. This is made possible by description logic axioms that the Web Ontology Language (OWL) uses to describe resources. The OWL is serialized using Resource Description Framework (RDF).
Ontology is also used when we need to check consistency in the data model. For eg, if an axiom says Human and Sponge are disjoint classes and we make John (a human) instance of both Human and Sponge classes then it will fail consistency test.
Taxonomy is the IS-A class hierarchy which forms the backbone of an ontology.
Knowledge Graphs are often associated with linked open data (LOD) projects built upon standard Web technologies such as HTTP, RDF, URIs, and SPARQL. KG may use ontologies for reasoning and graph databases to store the knowledge. Several large organizations have introduced their KGs.
I am new to ontology. After some study, I still do not know what is ontology advantage in application.
I already know ontology can provide more meaningful querying interface than database, and ontology can use reasoner to find hidden info to get better result.
But.
With building a bool table in database to represent new concept for each instance, or simple if-else rule engine. We can get same result as ontology with better performance.
So, what is the most important reason of using ontology in application exactly?
Please refer to Databases vs Ontologies by Ian Horrocks
In short:
Databases has closed world assumption, ontologies has open world
assumption
In databases each individual has a single unique name, but in ontologies individuals might have more than one name
You can infer implicit information from ontologies, in databases you can't.
The schema in an ontology is large and complex but databases have simple and smaller schema. In other words, The focus on formal semantics is much stronger in ontologies than in databases. Because the aim of ontologies is to represent meaning rather than data. Please refer to Ontologies and DB Schema: What's the Difference by Mike Uschold
it's my first time writing here but i'm really struck with a problem:
is it possible to use the Jena reasoner on a No-SQL database, like Neo4J, already filled with data?
I've a Neo4J's graph rappresenting a bunch of triples and I would like to use the Jena API and the Jena reasoner on them. I thought about using the SDB/TDB component of Jena but I don't get how to actually load the data into my model since the SDB component seems to work with just SQL databases and the go throught the whole TDB javadoc seems to be a bit too much.
Should I define some kind of configuration file for the TDB model too ?
Thanks very much for the help.
You should have a look at this link which describes the connection between neo4j and triplestores. Or possible connections at least.
The neo4j model is very different than the RDF model, which Jena uses. RDF is composed of triples, meaning subjects, predicates, and objects. Here is an example of a graph composed of triples. Note the use of URIs for identifying resources, and note that the nodes are typically atomic data values. They're a URI, a simple number, a string, and so on.
In Neo4j, nodes are "Property Containers". Meaning that they're not just URIs, but they're actually bundles of information. Relationships connect nodes. So RDF "predicates" are sort of like Neo4j relationships, but neo4j nodes are not like RDF resources and literals.
Your main task if you want to use reasoners over a neo4j database is going to be to suck data out of neo4j, and format it as a set of RDF triples. You can then put those RDF triples into a Jena Model. When you have that jena model in memory, you can use existing jena APIs to use reasoners with that model.
I am in the process of creating a neo4j implementation of the jena API. For this I am subclassing ObjectProperty, Individual and OntClass and implement queries to the neo4j endpoint.
The main problem is that for reasoning the whole database must be loaded in memory in order to use Jena's inmemory reasoning. My solution at the moment is to use a "reasoning"-server to process this and write new results to the main persistence layer. This, of course, is only suitable for long term recommendation systems and not for UI interactions.
Have a look here for the current state of the project:
https://github.com/uzuzjmd/Wissensmodellierung
Path:
competence-database\src\main\scala\uzuzjmd\competence\persistence\neo4j
Anyone interested to participate in this open source project feel free to contact me.
I'm a bit late to the party but you can use https://github.com/neo4j-labs/neosemantics to output the Neo4J data into triples and read that into a Jena Model
Is there a tool that can infer an ontology from information contained in both a database schema and the content in that schema? Let's say that there are tables in the database defining the following:
The types of entities that can exist
Instances of those entities linked to type
The types of relationships that can exist
Instances of those relationships linked to type and the entities concerned
I feel that looking at the schema alone is going to give a much more general ontology than I would like.
Reveltyix has a partnership with Global IDs, a data goveranance and MDM. One of GID's data profiling tool can export RDF to us to boot strap up a good portion of the ontology, even when that spans multiple databases. The Revelytix technologies then use the resultant ontology to federate data and manage distributed information sources, without having to move the date.
Good Luck
Greg
201-232-9195
What are the other types of database systems out there. I've recently came across couchDB that handles data in a non relational way. It got me thinking about what other models are other people is using.
So, I want to know what other types of data model is out there. (I'm not looking for any specifics, just want to look at how other people are handling data storage, my interest are purely academic)
The ones I already know are:
RDBMS (mysql,postgres etc..)
Document based approach (couchDB, lotus notes)
Key/value pair (BerkeleyDB)
db4o
Quote from the "about" page:
db4o is the open source object database that enables Java and .NET developers to store and retrieve any application object with only one line of code, eliminating the need to predefine or maintain a separate, rigid data model.
Older non-relational databases:
Network Database
Hierarchical Database
Both mostly went out of style when relational became feasible.
Column-oriented databases are also a bit of a different animal. Many of them do support standard relational database SQL though. These are generally used for data warehouse type applications.
Semantic Web is also a non-relational data storage paradigm. There are no relations, all metadata is stored in the same way as data, and every entity has potentially its own unique set of attributes. Open-source projects that implement RDF, a Semantic Web standard, include Jena and Sesame.
Isn't Amazon's SimpleDB non-relational?
db4o, as mentioned by Eric, is an Object-Oriented database management system (OODBMS).
There's object-based databases(Gemstore, for example). Google's Big-Table and Amason's Simple Storage I am not sure how you would categorize, but both are map-reduce based.
A non-relational document oriented database we have been looking at is Apache CouchDB.
Apache CouchDB is a distributed, fault-tolerant and schema-free document-oriented database accessible via a RESTful HTTP/JSON API. Among other features, it provides robust, incremental replication with bi-directional conflict detection and resolution, and is queryable and indexable using a table-oriented view engine with JavaScript acting as the default view definition language.
Our interest was in providing a distributed access user preferences store that would be immune to shape changes to which we could serialize preference objects from Java and access those just as easily with Javascript from a XULRunner based client application.
I'd like to detail more on Bill Karwin's answer about semantic web and triplestores, since it's what I am working on at the moment, and I have something to say on it.
The idea behind a triplestore is to store a graph-based database, whose datamodel roots in RDF. With RDF, you describe nodes and associations among nodes (in other words, edges). Data is organized in triples :
start node ----relation----> end node
(in RDF speech: subject --predicate--> object). With this very simple data model, any data network can be represented by adding more and more triples, provided you give a meaning to nodes and relations.
RDF is very general, and it's a graph-based data model well suited for search criteria looking for all triples with a particular combination of subject, predicate, or object, in any combination. Eventually, through a query language called SPARQL, you can also perform more complex queries, an operation that boils down to a graph isomorphism search onto the graph, both in terms of topology and in terms of node-edge meaning (we'll see this in a moment). SPARQL allows you only SELECT (and similar) queries. No DELETE, no INSERT, no UPDATE. The information you query (e.g. specific nodes you are interested in) are mapped into a table, which is what you get as a result of your query.
Now, topology in itself does not mean a lot. For this, a Schema language has been invented. Actually, more than one, and calling them schema languages is, in some cases, very limitative. The most famous and used today are RDF-Schema, OWL (Lite and Full), and they predate from the obsolete DAML+OIL. The point of these languages is, boiling down stuff, to give a meaning to nodes (by granting them a type, also described as a triple) and to relationships (edges). Also, you can define the "range" and "domain" of these relationships, or said differently what type is the start node and what type is the end node: you can say for example, that the property "numberOfWheels" can be applied only to connect a node of type Vehicle to a non-zero integer value.
ns:MyFiat --rdf:type--> ns:Vehicle
ns:MyFiat --ns:numberOfWheels-> 4
Now, you can use these ontologies in two directions: validation and inference. Validation is not that fancy today, but I've seen instances of use. Inference is what is cool today, because it allows reasoning. Inference basically takes a RDF graph containing a set of triples, takes an ontology, mixes them into a triplestore database which contains an "inference engine" and like magic the inference engine invents triples according to your ontological description. Example: suppose you just store this information in the database
ns:MyFiat --ns:numberOfWheels--> 4
and nothing else. No type is specified about this node, but the inference engine will add automatically a triple saying that
ns:MyFiat --rdf:type--> ns:Vehicle
because you said in your ontology that only objects of type Vehicle can be described by a property numberOfWheels.
Conversely, you can use the inference engine to validate your data against the ontology so to refuse not compliant data (sort of like XML-Schema for XML). In this case, you will need both triples to have your data successfully accepted by the triplestore.
Additional characteristics of triplestores are Formulas and Context-aware storage. Formulas are statements (as usual, triples subject predicate object) that describe something hypothetical. I never used Formulas, so I won't go into more details of something I don't know. Context awareness are basically subgraphs: the problem with storing triples is that you don't have anything to say where these triples come from. Suppose you have two dealers that describe the same price of a component. One says that the price is 5.99 and the other 4.99. If you just store both triples into a database, now you don't know anything about who stated each information. There are two ways to solve this problem.
One is reification. Reification means that you store additional triples to describe another triple. It's wasteful, and makes life hell because you have to reify every and each triple you store. The alternative is context-awareness. Having a context-aware storage It's like being able to box a bunch of triples into a container with a label on it (the context identifier). You now can use this identifier as subject for additional statements, hence describing a bunch of triples in a single action.
4. Navigational. Includes Tree/Hierarchy and Graph/Network.
File systems, the semantic web, XML, Object databases, CODASYL, and many others all fit into this category.
Those 4 are pretty much it.
There is also what is referred to as an "inverted index" or "inverted list" database. Software AG's Adabas product would be an example. As with hierachical, these databases continue to be used in large corporate or university environments because of legacy considerations or due to a performance advantage in certain situations (typically high-end transactional applications).
There are BASE systems (Basically Available, Soft State, Eventually consistent) and they work well with simple data models holding vast volumes of data. Google's BigTable, Dojo's Persevere, Amazon's Dynamo, Facebook's Cassandra are some examples.
See LINK
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