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SPARQL query server

To integrate ICONCLASS with services like the Termen­netwerk we would like to have a SPARQL query service.

Easy! You might say. Let's just dump all the terms to a file on disk, and then load them into a triplestore, and be done. We tried that. It has some issues. Namely fulltext searches, and secondly, the exploding size of the number of nodes when taking IC "keys" into account.

Firstly, you would like to do some fulltext searches over the data that includes more than just the literals in each triple. For a hierarchical system like ICONCLASS, when you index an item lower down in the tree, you would also like to include the texts and keywords for all "parents" in the tree, to give better recall. There are ways to integrate a search index with the most well known triplestores, but it is not logistically trivial, nor cheap. (if you use commercial triplestore providers)

A first version of the IC sparql service used the Blazegraph store. While blazingly fast, freely available, and widely used, it has very much become "abandoware" after it's authors were hired by AWS. I do not consider it wise to invest more time in a product with no future.

Then the issue of the "exploding numbers" 🤯 when using IC keys. This is explained here in more detail. It boils down to the fact that the core IC system has around 40K terms, but when using keys this count increases to more than 1.2 million. We can't just ignore this feature, it is integral to the system and has been used in databases around the world for more than 40 years to catalog their collections in detail. So we have to support it. And actually, it is very useful from an Art Historians perspective...😉

This repository contains a custom Python RDFlib based sparql query engine, that integrates searching using the most excellent SQLITE FTS5 (the same index that is used in the ICONCLASS web service).

This is a work in progress and not Done yet! 🍴

An endpoint is available at https://test.iconclass.org/sparql DISCLAIMER: it may go down, it may be unresponsive, there is no crack super devops team that has made it foolproof. (yet)

Some test can be done with YASGUI

But we are hard at work crossing the t's and dotting the i's, if you encounter bugs, please let me know or you can mail me on info@iconclass.org or file some issues in this repo.

Or ideally, contribute some fixes in a pull request... 🎯

Credit

Work on this service has been done with support from FIZ Karlsruhe Information Service Engineering and NFDI4Culture

Related Work

rdflib-endpoint ✨️ SPARQL endpoint built with RDFLib to serve RDF files, machine learning models, or any other logic implemented in Python

SPARQL endpoint for Translator services A SPARQL endpoint to serve NCATS Translator services as SPARQL custom functions. Built with rdflib-endpoint

Hydra library for Python The primary goal is to provide a lib for easily writing Hydra-enabled clients

Python Linked Data Fragment Server. Python Linked Data Fragment server using asyncio and Redis

ODTMP-TPF Triple pattern matching over non-RDF datasources with inference

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Desirable queries

PREFIX skos: http://www.w3.org/2004/02/skos/core#

CONSTRUCT { ?uri a skos:Concept ; skos:prefLabel ?prefLabel ; skos:broader ?broader_uri ; skos:narrower ?narrower_uri ; skos:related ?related_uri .

?broader_uri skos:prefLabel ?broader_prefLabel .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
?related_uri skos:prefLabel ?related_prefLabel .

} WHERE { ?uri a skos:Concept ; skos:prefLabel ?prefLabel .

?uri <http://iconclass.org/search> ?query .
OPTIONAL {
?uri skos:broader ?broader_uri .
?broader_uri skos:prefLabel ?broader_prefLabel .
}
OPTIONAL {
?uri skos:narrower ?narrower_uri .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
}
OPTIONAL {
?uri skos:related ?related_uri .
?narrower_uri skos:prefLabel ?related_prefLabel .
}

} LIMIT 1000

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SPARQL query server

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks"); } } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); } })(); (function(){ try { var __m = "github.com"; var __re = new RegExp('^' + "github\\.com" + '
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SPARQL query server

To integrate ICONCLASS with services like the Termen­netwerk we would like to have a SPARQL query service.

Easy! You might say. Let's just dump all the terms to a file on disk, and then load them into a triplestore, and be done. We tried that. It has some issues. Namely fulltext searches, and secondly, the exploding size of the number of nodes when taking IC "keys" into account.

Firstly, you would like to do some fulltext searches over the data that includes more than just the literals in each triple. For a hierarchical system like ICONCLASS, when you index an item lower down in the tree, you would also like to include the texts and keywords for all "parents" in the tree, to give better recall. There are ways to integrate a search index with the most well known triplestores, but it is not logistically trivial, nor cheap. (if you use commercial triplestore providers)

A first version of the IC sparql service used the Blazegraph store. While blazingly fast, freely available, and widely used, it has very much become "abandoware" after it's authors were hired by AWS. I do not consider it wise to invest more time in a product with no future.

Then the issue of the "exploding numbers" 🤯 when using IC keys. This is explained here in more detail. It boils down to the fact that the core IC system has around 40K terms, but when using keys this count increases to more than 1.2 million. We can't just ignore this feature, it is integral to the system and has been used in databases around the world for more than 40 years to catalog their collections in detail. So we have to support it. And actually, it is very useful from an Art Historians perspective...😉

This repository contains a custom Python RDFlib based sparql query engine, that integrates searching using the most excellent SQLITE FTS5 (the same index that is used in the ICONCLASS web service).

This is a work in progress and not Done yet! 🍴

An endpoint is available at https://test.iconclass.org/sparql DISCLAIMER: it may go down, it may be unresponsive, there is no crack super devops team that has made it foolproof. (yet)

Some test can be done with YASGUI

But we are hard at work crossing the t's and dotting the i's, if you encounter bugs, please let me know or you can mail me on info@iconclass.org or file some issues in this repo.

Or ideally, contribute some fixes in a pull request... 🎯

Credit

Work on this service has been done with support from FIZ Karlsruhe Information Service Engineering and NFDI4Culture

Related Work

rdflib-endpoint ✨️ SPARQL endpoint built with RDFLib to serve RDF files, machine learning models, or any other logic implemented in Python

SPARQL endpoint for Translator services A SPARQL endpoint to serve NCATS Translator services as SPARQL custom functions. Built with rdflib-endpoint

Hydra library for Python The primary goal is to provide a lib for easily writing Hydra-enabled clients

Python Linked Data Fragment Server. Python Linked Data Fragment server using asyncio and Redis

ODTMP-TPF Triple pattern matching over non-RDF datasources with inference

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Desirable queries

PREFIX skos: http://www.w3.org/2004/02/skos/core#

CONSTRUCT { ?uri a skos:Concept ; skos:prefLabel ?prefLabel ; skos:broader ?broader_uri ; skos:narrower ?narrower_uri ; skos:related ?related_uri .

?broader_uri skos:prefLabel ?broader_prefLabel .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
?related_uri skos:prefLabel ?related_prefLabel .

} WHERE { ?uri a skos:Concept ; skos:prefLabel ?prefLabel .

?uri <http://iconclass.org/search> ?query .
OPTIONAL {
?uri skos:broader ?broader_uri .
?broader_uri skos:prefLabel ?broader_prefLabel .
}
OPTIONAL {
?uri skos:narrower ?narrower_uri .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
}
OPTIONAL {
?uri skos:related ?related_uri .
?narrower_uri skos:prefLabel ?related_prefLabel .
}

} LIMIT 1000

About

SPARQL query server

Resources

Stars

3 stars

Watchers

2 watching

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Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

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SPARQL query server

To integrate ICONCLASS with services like the Termen­netwerk we would like to have a SPARQL query service.

Easy! You might say. Let's just dump all the terms to a file on disk, and then load them into a triplestore, and be done. We tried that. It has some issues. Namely fulltext searches, and secondly, the exploding size of the number of nodes when taking IC "keys" into account.

Firstly, you would like to do some fulltext searches over the data that includes more than just the literals in each triple. For a hierarchical system like ICONCLASS, when you index an item lower down in the tree, you would also like to include the texts and keywords for all "parents" in the tree, to give better recall. There are ways to integrate a search index with the most well known triplestores, but it is not logistically trivial, nor cheap. (if you use commercial triplestore providers)

A first version of the IC sparql service used the Blazegraph store. While blazingly fast, freely available, and widely used, it has very much become "abandoware" after it's authors were hired by AWS. I do not consider it wise to invest more time in a product with no future.

Then the issue of the "exploding numbers" 🤯 when using IC keys. This is explained here in more detail. It boils down to the fact that the core IC system has around 40K terms, but when using keys this count increases to more than 1.2 million. We can't just ignore this feature, it is integral to the system and has been used in databases around the world for more than 40 years to catalog their collections in detail. So we have to support it. And actually, it is very useful from an Art Historians perspective...😉

This repository contains a custom Python RDFlib based sparql query engine, that integrates searching using the most excellent SQLITE FTS5 (the same index that is used in the ICONCLASS web service).

This is a work in progress and not Done yet! 🍴

An endpoint is available at https://test.iconclass.org/sparql DISCLAIMER: it may go down, it may be unresponsive, there is no crack super devops team that has made it foolproof. (yet)

Some test can be done with YASGUI

But we are hard at work crossing the t's and dotting the i's, if you encounter bugs, please let me know or you can mail me on info@iconclass.org or file some issues in this repo.

Or ideally, contribute some fixes in a pull request... 🎯

Credit

Work on this service has been done with support from FIZ Karlsruhe Information Service Engineering and NFDI4Culture

Related Work

rdflib-endpoint ✨️ SPARQL endpoint built with RDFLib to serve RDF files, machine learning models, or any other logic implemented in Python

SPARQL endpoint for Translator services A SPARQL endpoint to serve NCATS Translator services as SPARQL custom functions. Built with rdflib-endpoint

Hydra library for Python The primary goal is to provide a lib for easily writing Hydra-enabled clients

Python Linked Data Fragment Server. Python Linked Data Fragment server using asyncio and Redis

ODTMP-TPF Triple pattern matching over non-RDF datasources with inference

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Desirable queries

PREFIX skos: http://www.w3.org/2004/02/skos/core#

CONSTRUCT { ?uri a skos:Concept ; skos:prefLabel ?prefLabel ; skos:broader ?broader_uri ; skos:narrower ?narrower_uri ; skos:related ?related_uri .

?broader_uri skos:prefLabel ?broader_prefLabel .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
?related_uri skos:prefLabel ?related_prefLabel .

} WHERE { ?uri a skos:Concept ; skos:prefLabel ?prefLabel .

?uri <http://iconclass.org/search> ?query .
OPTIONAL {
?uri skos:broader ?broader_uri .
?broader_uri skos:prefLabel ?broader_prefLabel .
}
OPTIONAL {
?uri skos:narrower ?narrower_uri .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
}
OPTIONAL {
?uri skos:related ?related_uri .
?narrower_uri skos:prefLabel ?related_prefLabel .
}

} LIMIT 1000

About

SPARQL query server

Resources

Stars

3 stars

Watchers

2 watching

Forks

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Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

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SPARQL query server

To integrate ICONCLASS with services like the Termen­netwerk we would like to have a SPARQL query service.

Easy! You might say. Let's just dump all the terms to a file on disk, and then load them into a triplestore, and be done. We tried that. It has some issues. Namely fulltext searches, and secondly, the exploding size of the number of nodes when taking IC "keys" into account.

Firstly, you would like to do some fulltext searches over the data that includes more than just the literals in each triple. For a hierarchical system like ICONCLASS, when you index an item lower down in the tree, you would also like to include the texts and keywords for all "parents" in the tree, to give better recall. There are ways to integrate a search index with the most well known triplestores, but it is not logistically trivial, nor cheap. (if you use commercial triplestore providers)

A first version of the IC sparql service used the Blazegraph store. While blazingly fast, freely available, and widely used, it has very much become "abandoware" after it's authors were hired by AWS. I do not consider it wise to invest more time in a product with no future.

Then the issue of the "exploding numbers" 🤯 when using IC keys. This is explained here in more detail. It boils down to the fact that the core IC system has around 40K terms, but when using keys this count increases to more than 1.2 million. We can't just ignore this feature, it is integral to the system and has been used in databases around the world for more than 40 years to catalog their collections in detail. So we have to support it. And actually, it is very useful from an Art Historians perspective...😉

This repository contains a custom Python RDFlib based sparql query engine, that integrates searching using the most excellent SQLITE FTS5 (the same index that is used in the ICONCLASS web service).

This is a work in progress and not Done yet! 🍴

An endpoint is available at https://test.iconclass.org/sparql DISCLAIMER: it may go down, it may be unresponsive, there is no crack super devops team that has made it foolproof. (yet)

Some test can be done with YASGUI

But we are hard at work crossing the t's and dotting the i's, if you encounter bugs, please let me know or you can mail me on info@iconclass.org or file some issues in this repo.

Or ideally, contribute some fixes in a pull request... 🎯

Credit

Work on this service has been done with support from FIZ Karlsruhe Information Service Engineering and NFDI4Culture

Related Work

rdflib-endpoint ✨️ SPARQL endpoint built with RDFLib to serve RDF files, machine learning models, or any other logic implemented in Python

SPARQL endpoint for Translator services A SPARQL endpoint to serve NCATS Translator services as SPARQL custom functions. Built with rdflib-endpoint

Hydra library for Python The primary goal is to provide a lib for easily writing Hydra-enabled clients

Python Linked Data Fragment Server. Python Linked Data Fragment server using asyncio and Redis

ODTMP-TPF Triple pattern matching over non-RDF datasources with inference

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Desirable queries

PREFIX skos: http://www.w3.org/2004/02/skos/core#

CONSTRUCT { ?uri a skos:Concept ; skos:prefLabel ?prefLabel ; skos:broader ?broader_uri ; skos:narrower ?narrower_uri ; skos:related ?related_uri .

?broader_uri skos:prefLabel ?broader_prefLabel .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
?related_uri skos:prefLabel ?related_prefLabel .

} WHERE { ?uri a skos:Concept ; skos:prefLabel ?prefLabel .

?uri <http://iconclass.org/search> ?query .
OPTIONAL {
?uri skos:broader ?broader_uri .
?broader_uri skos:prefLabel ?broader_prefLabel .
}
OPTIONAL {
?uri skos:narrower ?narrower_uri .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
}
OPTIONAL {
?uri skos:related ?related_uri .
?narrower_uri skos:prefLabel ?related_prefLabel .
}

} LIMIT 1000

About

SPARQL query server

Resources

Stars

3 stars

Watchers

2 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
Skip to content

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30 Commits

Folders and files

NameName
Last commit message
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SPARQL query server

To integrate ICONCLASS with services like the Termen­netwerk we would like to have a SPARQL query service.

Easy! You might say. Let's just dump all the terms to a file on disk, and then load them into a triplestore, and be done. We tried that. It has some issues. Namely fulltext searches, and secondly, the exploding size of the number of nodes when taking IC "keys" into account.

Firstly, you would like to do some fulltext searches over the data that includes more than just the literals in each triple. For a hierarchical system like ICONCLASS, when you index an item lower down in the tree, you would also like to include the texts and keywords for all "parents" in the tree, to give better recall. There are ways to integrate a search index with the most well known triplestores, but it is not logistically trivial, nor cheap. (if you use commercial triplestore providers)

A first version of the IC sparql service used the Blazegraph store. While blazingly fast, freely available, and widely used, it has very much become "abandoware" after it's authors were hired by AWS. I do not consider it wise to invest more time in a product with no future.

Then the issue of the "exploding numbers" 🤯 when using IC keys. This is explained here in more detail. It boils down to the fact that the core IC system has around 40K terms, but when using keys this count increases to more than 1.2 million. We can't just ignore this feature, it is integral to the system and has been used in databases around the world for more than 40 years to catalog their collections in detail. So we have to support it. And actually, it is very useful from an Art Historians perspective...😉

This repository contains a custom Python RDFlib based sparql query engine, that integrates searching using the most excellent SQLITE FTS5 (the same index that is used in the ICONCLASS web service).

This is a work in progress and not Done yet! 🍴

An endpoint is available at https://test.iconclass.org/sparql DISCLAIMER: it may go down, it may be unresponsive, there is no crack super devops team that has made it foolproof. (yet)

Some test can be done with YASGUI

But we are hard at work crossing the t's and dotting the i's, if you encounter bugs, please let me know or you can mail me on info@iconclass.org or file some issues in this repo.

Or ideally, contribute some fixes in a pull request... 🎯

Credit

Work on this service has been done with support from FIZ Karlsruhe Information Service Engineering and NFDI4Culture

Related Work

rdflib-endpoint ✨️ SPARQL endpoint built with RDFLib to serve RDF files, machine learning models, or any other logic implemented in Python

SPARQL endpoint for Translator services A SPARQL endpoint to serve NCATS Translator services as SPARQL custom functions. Built with rdflib-endpoint

Hydra library for Python The primary goal is to provide a lib for easily writing Hydra-enabled clients

Python Linked Data Fragment Server. Python Linked Data Fragment server using asyncio and Redis

ODTMP-TPF Triple pattern matching over non-RDF datasources with inference

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Desirable queries

PREFIX skos: http://www.w3.org/2004/02/skos/core#

CONSTRUCT { ?uri a skos:Concept ; skos:prefLabel ?prefLabel ; skos:broader ?broader_uri ; skos:narrower ?narrower_uri ; skos:related ?related_uri .

?broader_uri skos:prefLabel ?broader_prefLabel .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
?related_uri skos:prefLabel ?related_prefLabel .

} WHERE { ?uri a skos:Concept ; skos:prefLabel ?prefLabel .

?uri <http://iconclass.org/search> ?query .
OPTIONAL {
?uri skos:broader ?broader_uri .
?broader_uri skos:prefLabel ?broader_prefLabel .
}
OPTIONAL {
?uri skos:narrower ?narrower_uri .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
}
OPTIONAL {
?uri skos:related ?related_uri .
?narrower_uri skos:prefLabel ?related_prefLabel .
}

} LIMIT 1000

About

SPARQL query server

Resources

Stars

3 stars

Watchers

2 watching

Forks

Releases

Packages

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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SPARQL query server

To integrate ICONCLASS with services like the Termen­netwerk we would like to have a SPARQL query service.

Easy! You might say. Let's just dump all the terms to a file on disk, and then load them into a triplestore, and be done. We tried that. It has some issues. Namely fulltext searches, and secondly, the exploding size of the number of nodes when taking IC "keys" into account.

Firstly, you would like to do some fulltext searches over the data that includes more than just the literals in each triple. For a hierarchical system like ICONCLASS, when you index an item lower down in the tree, you would also like to include the texts and keywords for all "parents" in the tree, to give better recall. There are ways to integrate a search index with the most well known triplestores, but it is not logistically trivial, nor cheap. (if you use commercial triplestore providers)

A first version of the IC sparql service used the Blazegraph store. While blazingly fast, freely available, and widely used, it has very much become "abandoware" after it's authors were hired by AWS. I do not consider it wise to invest more time in a product with no future.

Then the issue of the "exploding numbers" 🤯 when using IC keys. This is explained here in more detail. It boils down to the fact that the core IC system has around 40K terms, but when using keys this count increases to more than 1.2 million. We can't just ignore this feature, it is integral to the system and has been used in databases around the world for more than 40 years to catalog their collections in detail. So we have to support it. And actually, it is very useful from an Art Historians perspective...😉

This repository contains a custom Python RDFlib based sparql query engine, that integrates searching using the most excellent SQLITE FTS5 (the same index that is used in the ICONCLASS web service).

This is a work in progress and not Done yet! 🍴

An endpoint is available at https://test.iconclass.org/sparql DISCLAIMER: it may go down, it may be unresponsive, there is no crack super devops team that has made it foolproof. (yet)

Some test can be done with YASGUI

But we are hard at work crossing the t's and dotting the i's, if you encounter bugs, please let me know or you can mail me on info@iconclass.org or file some issues in this repo.

Or ideally, contribute some fixes in a pull request... 🎯

Credit

Work on this service has been done with support from FIZ Karlsruhe Information Service Engineering and NFDI4Culture

Related Work

rdflib-endpoint ✨️ SPARQL endpoint built with RDFLib to serve RDF files, machine learning models, or any other logic implemented in Python

SPARQL endpoint for Translator services A SPARQL endpoint to serve NCATS Translator services as SPARQL custom functions. Built with rdflib-endpoint

Hydra library for Python The primary goal is to provide a lib for easily writing Hydra-enabled clients

Python Linked Data Fragment Server. Python Linked Data Fragment server using asyncio and Redis

ODTMP-TPF Triple pattern matching over non-RDF datasources with inference

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Desirable queries

PREFIX skos: http://www.w3.org/2004/02/skos/core#

CONSTRUCT { ?uri a skos:Concept ; skos:prefLabel ?prefLabel ; skos:broader ?broader_uri ; skos:narrower ?narrower_uri ; skos:related ?related_uri .

?broader_uri skos:prefLabel ?broader_prefLabel .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
?related_uri skos:prefLabel ?related_prefLabel .

} WHERE { ?uri a skos:Concept ; skos:prefLabel ?prefLabel .

?uri <http://iconclass.org/search> ?query .
OPTIONAL {
?uri skos:broader ?broader_uri .
?broader_uri skos:prefLabel ?broader_prefLabel .
}
OPTIONAL {
?uri skos:narrower ?narrower_uri .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
}
OPTIONAL {
?uri skos:related ?related_uri .
?narrower_uri skos:prefLabel ?related_prefLabel .
}

} LIMIT 1000

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SPARQL query server

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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SPARQL query server

To integrate ICONCLASS with services like the Termen­netwerk we would like to have a SPARQL query service.

Easy! You might say. Let's just dump all the terms to a file on disk, and then load them into a triplestore, and be done. We tried that. It has some issues. Namely fulltext searches, and secondly, the exploding size of the number of nodes when taking IC "keys" into account.

Firstly, you would like to do some fulltext searches over the data that includes more than just the literals in each triple. For a hierarchical system like ICONCLASS, when you index an item lower down in the tree, you would also like to include the texts and keywords for all "parents" in the tree, to give better recall. There are ways to integrate a search index with the most well known triplestores, but it is not logistically trivial, nor cheap. (if you use commercial triplestore providers)

A first version of the IC sparql service used the Blazegraph store. While blazingly fast, freely available, and widely used, it has very much become "abandoware" after it's authors were hired by AWS. I do not consider it wise to invest more time in a product with no future.

Then the issue of the "exploding numbers" 🤯 when using IC keys. This is explained here in more detail. It boils down to the fact that the core IC system has around 40K terms, but when using keys this count increases to more than 1.2 million. We can't just ignore this feature, it is integral to the system and has been used in databases around the world for more than 40 years to catalog their collections in detail. So we have to support it. And actually, it is very useful from an Art Historians perspective...😉

This repository contains a custom Python RDFlib based sparql query engine, that integrates searching using the most excellent SQLITE FTS5 (the same index that is used in the ICONCLASS web service).

This is a work in progress and not Done yet! 🍴

An endpoint is available at https://test.iconclass.org/sparql DISCLAIMER: it may go down, it may be unresponsive, there is no crack super devops team that has made it foolproof. (yet)

Some test can be done with YASGUI

But we are hard at work crossing the t's and dotting the i's, if you encounter bugs, please let me know or you can mail me on info@iconclass.org or file some issues in this repo.

Or ideally, contribute some fixes in a pull request... 🎯

Credit

Work on this service has been done with support from FIZ Karlsruhe Information Service Engineering and NFDI4Culture

Related Work

rdflib-endpoint ✨️ SPARQL endpoint built with RDFLib to serve RDF files, machine learning models, or any other logic implemented in Python

SPARQL endpoint for Translator services A SPARQL endpoint to serve NCATS Translator services as SPARQL custom functions. Built with rdflib-endpoint

Hydra library for Python The primary goal is to provide a lib for easily writing Hydra-enabled clients

Python Linked Data Fragment Server. Python Linked Data Fragment server using asyncio and Redis

ODTMP-TPF Triple pattern matching over non-RDF datasources with inference

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Desirable queries

PREFIX skos: http://www.w3.org/2004/02/skos/core#

CONSTRUCT { ?uri a skos:Concept ; skos:prefLabel ?prefLabel ; skos:broader ?broader_uri ; skos:narrower ?narrower_uri ; skos:related ?related_uri .

?broader_uri skos:prefLabel ?broader_prefLabel .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
?related_uri skos:prefLabel ?related_prefLabel .

} WHERE { ?uri a skos:Concept ; skos:prefLabel ?prefLabel .

?uri <http://iconclass.org/search> ?query .
OPTIONAL {
?uri skos:broader ?broader_uri .
?broader_uri skos:prefLabel ?broader_prefLabel .
}
OPTIONAL {
?uri skos:narrower ?narrower_uri .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
}
OPTIONAL {
?uri skos:related ?related_uri .
?narrower_uri skos:prefLabel ?related_prefLabel .
}

} LIMIT 1000

About

SPARQL query server

Resources

Stars

3 stars

Watchers

2 watching

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Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
Skip to content

Latest commit

History

30 Commits

Folders and files

NameName
Last commit message
Last commit date

Repository files navigation

SPARQL query server

To integrate ICONCLASS with services like the Termen­netwerk we would like to have a SPARQL query service.

Easy! You might say. Let's just dump all the terms to a file on disk, and then load them into a triplestore, and be done. We tried that. It has some issues. Namely fulltext searches, and secondly, the exploding size of the number of nodes when taking IC "keys" into account.

Firstly, you would like to do some fulltext searches over the data that includes more than just the literals in each triple. For a hierarchical system like ICONCLASS, when you index an item lower down in the tree, you would also like to include the texts and keywords for all "parents" in the tree, to give better recall. There are ways to integrate a search index with the most well known triplestores, but it is not logistically trivial, nor cheap. (if you use commercial triplestore providers)

A first version of the IC sparql service used the Blazegraph store. While blazingly fast, freely available, and widely used, it has very much become "abandoware" after it's authors were hired by AWS. I do not consider it wise to invest more time in a product with no future.

Then the issue of the "exploding numbers" 🤯 when using IC keys. This is explained here in more detail. It boils down to the fact that the core IC system has around 40K terms, but when using keys this count increases to more than 1.2 million. We can't just ignore this feature, it is integral to the system and has been used in databases around the world for more than 40 years to catalog their collections in detail. So we have to support it. And actually, it is very useful from an Art Historians perspective...😉

This repository contains a custom Python RDFlib based sparql query engine, that integrates searching using the most excellent SQLITE FTS5 (the same index that is used in the ICONCLASS web service).

This is a work in progress and not Done yet! 🍴

An endpoint is available at https://test.iconclass.org/sparql DISCLAIMER: it may go down, it may be unresponsive, there is no crack super devops team that has made it foolproof. (yet)

Some test can be done with YASGUI

But we are hard at work crossing the t's and dotting the i's, if you encounter bugs, please let me know or you can mail me on info@iconclass.org or file some issues in this repo.

Or ideally, contribute some fixes in a pull request... 🎯

Credit

Work on this service has been done with support from FIZ Karlsruhe Information Service Engineering and NFDI4Culture

Related Work

rdflib-endpoint ✨️ SPARQL endpoint built with RDFLib to serve RDF files, machine learning models, or any other logic implemented in Python

SPARQL endpoint for Translator services A SPARQL endpoint to serve NCATS Translator services as SPARQL custom functions. Built with rdflib-endpoint

Hydra library for Python The primary goal is to provide a lib for easily writing Hydra-enabled clients

Python Linked Data Fragment Server. Python Linked Data Fragment server using asyncio and Redis

ODTMP-TPF Triple pattern matching over non-RDF datasources with inference

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Desirable queries

PREFIX skos: http://www.w3.org/2004/02/skos/core#

CONSTRUCT { ?uri a skos:Concept ; skos:prefLabel ?prefLabel ; skos:broader ?broader_uri ; skos:narrower ?narrower_uri ; skos:related ?related_uri .

?broader_uri skos:prefLabel ?broader_prefLabel .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
?related_uri skos:prefLabel ?related_prefLabel .

} WHERE { ?uri a skos:Concept ; skos:prefLabel ?prefLabel .

?uri <http://iconclass.org/search> ?query .
OPTIONAL {
?uri skos:broader ?broader_uri .
?broader_uri skos:prefLabel ?broader_prefLabel .
}
OPTIONAL {
?uri skos:narrower ?narrower_uri .
?narrower_uri skos:prefLabel ?narrower_prefLabel .
}
OPTIONAL {
?uri skos:related ?related_uri .
?narrower_uri skos:prefLabel ?related_prefLabel .
}

} LIMIT 1000

About

SPARQL query server

Resources

Stars

3 stars

Watchers

2 watching

Forks

Releases

Packages

Contributors

Languages