@equinor/hylar
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A rule-based incremental reasoner for the Web.
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HyLAR-Reasoner
A rule-based incremental reasoner for the Web.
To cite HyLAR: HyLAR+: improving Hybrid Location-Agnostic Reasoning with Incremental Rule-based Update
Table of contents
- Description
- Use HyLAR locally
- Use HyLAR in a browser
- Use HyLAR as a server
- Supported Inferences
- Publications
Description
HyLAR is a Hybrid Location-Agnostic incremental Reasoner that uses known rdf-based librairies such as rdfstore.js, sparqljs and rdf-ext while providing an additional incremental reasoning engine. HyLAR can be either used locally as a npm module or globally as a server, and comes with a browserified version.
HyLAR relies on the rdfstore.js triplestore and therefore supports JSON-LD, N3 and Turtle serializations. SPARQL support is detailed here. The inferences initially supported by HyLAR are described at the bottom of this page. HyLAR supports custom business rules.
Use HyLAR locally
Installation
To use HyLAR locally, just launch
npm install --save hylar
Loading an ontology
Import HyLAR, then classify your ontology and query it using load()
,
which takes three parameters:
- rawOntology: A string, the raw ontology.
- mimeType: A string, either
text/turtle
,text/n3
orapplication/ld+json
. - keepOldValues: A boolean: true to keep old values while classfying, false to overwrite the KB. Default is false.
const Hylar = require('hylar');
const h = new Hylar();
// async function
h.load(rawOntology, mimeType, keepOldValues);
Querying an ontology
Once loaded, HyLAR is able to process SPARQL queries using query()
, with the following parameters:
- query: A string, the SPARQL query
let results = await h.query(query);
Create your own rules
HyLAR supports insertion of custom forward-chaining conjunctive rules in the form:
triple_head_1 ^ ... ^ triple_head_n -> triple_body_3
Where triple_head_x
and triple_body_x
are respectively "cause" triples (i.e. the input) and "consequence" triples (i.e. the inferred output) in the form:
(subject predicate object)
Each subject/predicate/object can be one of the following:
- A variable, e.g.
?x
- An URI, e.g.
http://www.w3.org/2000/01/rdf-schema#subClassOf
- A literal, e.g.
"0.5"
,"Hello world!"
A predicate can also be any of these comparison operators: <
, >
, =
, <=
, =>
.
Rule add example (first param: the 'raw' rule, second param: the rule name)
h.parseAndAddRule('(?p1 http://www.w3.org/2002/07/owl#inverseOf ?p2) ^ (?x ?p1 ?y) -> (?y ?p2 ?x)', 'inverse-1');
Rule removal example (first and only param: either the rule name or the raw rule)
h.removeRule('inverse-1');
// Outputs "[HyLAR] Removed rule (?p1 inverseOf ?p2) ^ (?x ?p1 ?y) -> (?y ?p2 ?x)" if succeeded.
Use HyLAR in a browser
Run npm run clientize
, which will generate the file hylar-client.js
.
Include this script in your page with this line:
<script src="path-to/hylar-client.js"></script>
As in the node module version, you can instantiate HyLAR with const h = new Hylar();
and call the same methods query()
, load()
and parseAndAddRule()
.
Use HyLAR as a server
Installation
npm install -g hylar
Run the server
Command hylar
with the following optional parameters
--port <port_number>
(port 3000 by default)--no-persist
deactivates database persistence (activated by default)--graph-directory </your/base/graph/directory/>
where local datasets are saved--entailment
eitherOWL2RL
(default) orRDFS
--reasoning-method
eitherincremental
(default) ortag-based
(provides reasoning proofs)
Hylar server API
/classify/{FILE_NAME}
(GET)
Loads, parses and classify the file {FILE_NAME}
from the ontology directory.
Note: You don't have to specify the ontology file's mimetype as it is detected automatically using its extension.
/classify/
(GET)
Allows classifying an ontology as a string, which requires its original serialization type.
Body parameters
filename
the absolute path of the ontology file to be processed.mimetype
the serialization of the ontology (mimetype, one of text/turtle, text/n3 or application/ld+json).
/query
(GET)
SPARQL queries your loaded ontology as does Hylar.query()
.
Body parameters
query
the SPARQL query string.
/rule
(PUT)
Puts an list of custom rules and adds it to the reasoner.
Body parameters
rules
the array of conjunctive rules.
Supported inferences
HyLAR supports a subset of OWL 2 RL and RDFS.
- RDFS
- Rules:
rdf1, rdfs2, rdfs3, rdfs4a, rdfs4b, rdfs5, dfs6, rdfs7, rdfs8, rdfs9, rdfs10, rdfs11, rdfs12, rdfs13
. - Supports all RDFS axiomatic triples, except axioms related to
rdf:Seq
andrdf:Bag
.
- Rules:
- OWL 2 RL
- Rules:
prp-dom, prp-rng, prp-fp, prp-ifp, prp-irp, prp-symp, prp-asyp, prp-trp, prp-spo1, prp-spo2, prp-eqp1, prp-eqp2, prp-pdw, prp-inv1, prp-inv2, prp-npa1, prp-npa2, cls-nothing2, cls-com, cls-svf1, cls-svf2, cls-avf, cls-hv1, cls-hv2, cls-maxc1, cls-maxc2, cls-maxqc1, cls-maxqc2, cls-maxqc3, cls-maxqc4, cax-sco, cax-eqc1, cax-eqc2, cax-dw, scm-cls, scm-sco, scm-eqc1, scm-eqc2, scm-op, scm-dp, scm-spo, scm-eqp1, scm-eqp2, scm-dom1, scm-dom2, scm-rng1, scm-rng2, scm-hv, scm-svf1, scm-svf2, scm-avf1, scm-avf2
- Axiomatic triples are not yet supported.
- Rules:
Publications
Location-agnostic mechanism
Terdjimi, M., Médini, L., & Mrissa, M. (2015, May). Hylar: Hybrid location-agnostic reasoning 📚 In ESWC Developers Workshop 2015 (p. 1).
Incremental reasoning on the Web with HyLAR
Terdjimi, M., Médini, L., & Mrissa, M. (2016, April). HyLAR+: improving hybrid location-agnostic reasoning with incremental rule-based update 📚 In Proceedings of the 25th International Conference Companion on World Wide Web (pp. 259-262). International World Wide Web Conferences Steering Committee.
Tag-based maintenance
Terdjimi, M., Médini, L., & Mrissa, M. (2018, April). Web Reasoning Using Fact Tagging 📚 In Companion of the The Web Conference 2018 on The Web Conference 2018 (pp. 1587-1594). International World Wide Web Conferences Steering Committee.
PR's & releases version
Consistent title names help maintainers organise their projects better 📚
Prefixes:
patch: patch,fixes,fix,chore,Bump
minor: feat
major: major
Example: chore: Update README.md
, | fix: Colors for head