@hdelva/termennetwerk_client
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3.2.2 • Public • Published

Termennetwerk Autocompletion Client

Imagine a jQuery-style autocompletion widget without hardcoded options, which can scale to millions of values. This project contains a proof of concept of such a client, and is structured as a toolbox to build your own clients.

Installation

npm i @hdelva/termennetwerk_client

Require

import AutoComplete from "@hdelva/termennetwerk_client";

Using one of the preconfigured clients

import * as AutoComplete from "@hdelva/termennetwerk_client";
import * as RdfString from "rdf-string";

// creates a client that traverses 4 datasets for the 10 best results
const client = new AutoComplete.StrictAutoComplete([
    "https://termen.opoi.org/nta",
    "https://termen.opoi.org/vtmk",
    "https://termen.opoi.org/cht",
    "https://termen.opoi.org/rkdartists"
], 10);

client.on("data", (quad, _meta) => {
    console.log(quad.subject.value);
})

client.on("reset", (_meta) => {
    console.clear();
})

client.on("end", (_meta) => {
    //
})

Build your own

import * as AutoComplete from "@hdelva/termennetwerk_client";

function relationSimilarity(expected, found) {
    return AutoComplete.tokenwiseCompare(
        AutoComplete.fuzzyIndexSimilarity,
        expected,
        found,
    );
}

function relationFilter(_, __, similarity) {
    return similarity > 0.9;
}

function resultSimilarity(expected, found) {
    return AutoComplete.tokenwiseCompare(
        AutoComplete.asymmetricDiceCoefficient,
        expected,
        found,
    );
}

function lengthResult(_, found) {
    return -1 * found.length;
}

const resultConfigurations = [
    new AutoComplete.SimilarityConfiguration(resultSimilarity),
    new AutoComplete.SimilarityConfiguration(lengthResult),
]

const relationConfigurations = [
    new AutoComplete.SimilarityConfiguration(relationSimilarity, relationFilter),
]

export default class FuzzyAutoComplete extends AutoComplete.ResultEmitter {
    constructor(sources, size) {
        super();

        const agents = [];
        for (const source of sources) {
            agents.push(new AutoComplete.QueryAgent(source, relationConfigurations));
        }

        const aggregator = new AutoComplete.QueryAggregator(agents);
        const store = new AutoComplete.ResultStore(aggregator);
        const filter = new AutoComplete.ResultUniqueFilter(store);

        const sorted = new AutoComplete.ResultRanking(
            size,
            filter,
            new AutoComplete.NFKD(),
            resultConfigurations
        );
        this.subEmitter = sorted;

        this.subEmitter.on("data", (data, meta) => this.emit("data", data, meta));
        this.subEmitter.on("end", (meta) => this.emit("end", meta));
        this.subEmitter.on("reset", (meta) => this.emit("reset", meta));
    }

    query(input) {
        this.subEmitter.query(input);
    }

    resolveSubject(uri) {
        return this.subEmitter.resolveSubject(uri);
    }
}

Structure

An autocompletion client is a combination of multiple components which all implement the same interface. Currently, these components are implemented:

  • QueryAgent: used to traverse a single data source, looking for the requested query string
  • QueryAggregator: merges the results from several other components
  • ResultRanking: creates a top-n view of all discovered results
  • ResultStore: maintains an in-memory RDF graph to provide additional context for the results
  • ResultUniqueFilter: filters out duplicate results

Each component exposes two methods:query and resolveSubject. The query method does not return anything, instead it starts a sequence of asynchronous calls that will emit data events each time a relevant Quad is found. Optionally, all known information about a certain subject can be requested (by the subject's URI), in which case the components should return all known quads related to this subject.

Two components can be configured with sorting functions: QueryAgent and ResultRanking. The former uses the functions to determine the node traversal order, the second one uses them to sort the results. Optionally, an additional filter function can be added to these sorting functions, which sets a minimum similarity score for a relation/result to be considered useful.

The following image illustrates how the components are used in the preconfigured client:

query svg

Not shown in the image above are the additional optional resolveSubject calls, which are passed through to, and handled by, the ResultStore.

Readme

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Install

npm i @hdelva/termennetwerk_client

Weekly Downloads

4

Version

3.2.2

License

MIT

Unpacked Size

153 kB

Total Files

85

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Collaborators

  • hdlva