A rule-based incremental reasoner for the Web.
To cite HyLAR: HyLAR+: improving Hybrid Location-Agnostic Reasoning with Incremental Rule-based Update
- Description
- Use HyLAR locally
- Use HyLAR in a browser
- Use HyLAR as a server
- Supported Inferences
- Publications
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.
To use HyLAR locally, just launch
npm install --save hylar
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/n3orapplication/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);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);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.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().
npm install -g hylar
Command hylar with the following optional parameters
--port <port_number>(port 3000 by default)--no-persistdeactivates database persistence (activated by default)--graph-directory </your/base/graph/directory/>where local datasets are saved--entailmenteitherOWL2RL(default) orRDFS--reasoning-methodeitherincremental(default) ortag-based(provides reasoning proofs)
/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
filenamethe absolute path of the ontology file to be processed.mimetypethe 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
querythe SPARQL query string.
/rule(PUT)
Puts an list of custom rules and adds it to the reasoner.
Body parameters
rulesthe array of conjunctive rules.
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:Seqandrdf: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:
Terdjimi, M., Médini, L., & Mrissa, M. (2015, May). Hylar: Hybrid location-agnostic reasoning 📚 In ESWC Developers Workshop 2015 (p. 1).
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.
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.
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