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<title>Distributional–relational database</title>

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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Distributional–relational database</span></span>

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<div id="mw-content-text" class="mw-body-content mw-content-ltr" lang="en" dir="ltr"><div class="mw-content-ltr mw-parser-output" lang="en" dir="ltr"><p>A <b>distributional–relational database</b>, or <b>word-vector database</b>, is a <a href="Database_Management_System" class="mw-redirect" title="Database Management System">database management system</a> (DBMS) that uses distributional <a href="Word_embedding" title="Word embedding">word-vector</a> representations to enrich the semantics of <a href="Data_model" title="Data model">structured data</a>.

</p><p>As distributional word-vectors can be built automatically from large-scale <a href="Text_corpus" title="Text corpus">corpora</a>,<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> this enrichment supports the construction of databases which can embed large-scale commonsense background knowledge into their operations. Distributional-Relational models can be applied to the construction of <a href="Schema-agnostic_databases" title="Schema-agnostic databases">schema-agnostic databases</a> (databases in which users can query the data without being aware of its <a href="Database_schema" title="Database schema">schema</a>), <a href="Semantic_search" title="Semantic search">semantic search</a>, schema-integration and <a href="Inductive_reasoning" title="Inductive reasoning">inductive</a> and <a href="Abductive_reasoning" title="Abductive reasoning">abductive reasoning</a> as well as different applications in which a semantically flexible knowledge representation model is needed. The main advantage of distributional–relational models over purely logical / <a href="Semantic_Web" title="Semantic Web">semantic web</a> models is the fact that the core semantic associations can be automatically captured from corpora, in contrast to the definition of manually curated <a href="Ontology" title="Ontology">ontologies</a> and rule knowledge bases.<sup id="cite_ref-2" class="reference"><a href="#cite_note-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup>

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<div class="mw-heading mw-heading2"><h2 id="Distributional–relational_models">Distributional–relational models</h2></div>

<p>Distributional–relational models were first formalized,<sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> as a mechanism to cope with the vocabulary/semantic gap between users and the schema behind the data. In this scenario, <a href="Distributional_semantics" title="Distributional semantics">distributional semantic</a> relatedness measures, combined with semantic pivoting <a href="Heuristic" title="Heuristic">heuristics</a> can support the approximation between user queries (expressed in their own vocabulary), and <a href="Data" title="Data">data</a> (expressed in the vocabulary of the designer).

</p><p>In this model, the database symbols (entities and relations) are embedded into a distributional <a href="Semantic_space" title="Semantic space">semantic space</a> and have a <a href="Geometry" title="Geometry">geometric</a> interpretation under a latent or explicit semantic space. The geometric aspect supports the semantic approximation between entities from different databases, or between a query term and a database entity. The distributional relational model then becomes a double layered model where the semantics of the structured data provides the fine-grained semantics intended by the <a href="Database_designer" class="mw-redirect" title="Database designer">database designer</a>, which is extended by the distributional semantic model which contains the semantic associations expressed at a broader use.

These models support the generalization from a closed communication scenario (in which database designers and users live in the same context, e.g. the same organization) to an open communication scenario (e.g. different organizations, the Web), creating an abstraction layer between users and the specific representation of the conceptual model.

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<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

<li id="cite_note-1"><span class="mw-cite-backlink"><b><a href="#cite_ref-1">^</a></b></span> <span class="reference-text">Harris, Z. (1954). "Distributional structure". Word. 10 (23): 146–162.</span>

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</style><cite id="CITEREFMétaisRocheTeisseire2014" class="citation book cs1">Métais, Elisabeth; Roche, Mathieu; Teisseire, Maguelonne (2014-06-16). <a rel="nofollow" class="external text" href="https://books.google.com/books?id=6kUqBAAAQBAJ&q=Distributional%E2%80%93relational+database&pg=PA31"><i>Natural Language Processing and Information Systems: 19th International Conference on Applications of Natural Language to Information Systems, NLDB 2014, Montpellier, France, June 18-20, 2014. Proceedings</i></a>. Springer. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-3-319-07983-7</bdi>.</cite></span>

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<li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text">Freitas, A. <a rel="nofollow" class="external text" href="http://andrefreitas.org/papers/thesis_schema_agnostic.pdf">“Schema-agnostic queries over large-schema databases: a distributional semantics approach”</a> PhD Thesis, 2015</span>

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<li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text">Freitas, A., Handschuh, S., Curry, E., Distributional-Relational Models: Scalable Semantics for Databases, AAAI Spring Symposium, Knowledge Representation & Reasoning Track, Stanford, 2014</span>

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