Posted:October 14, 2020

CWPK #53: Intro to Other Tools

Shifting Our Focus to How to Use the Knowledge Graph

Today’s installment marks a kind of a turning point in our Cooking with Python and KBpedia series. Now that our procedures for building, extracting, and managing the knowledge graph itself are in place, we can shift gears to explore how we may use this knowledge artifact. In today’s installment, I introduce a number of category of tools for doing so, and point to their more formal treatment in ensuing installments.

Some of our tools treat the knowledge graph as an object unto itself, providing statistics, logging, and traversals. Some tools allow us to publish these interactive Notebooks or enable external access to the knowlege graph. These measures are largely independent of the specific content in KBpedia. Some tools aid visualizations, sometimes dynamic. Some of the tools enable us to do advanced analysis or machine learning. We can also do much with natural language processing and understanding. We can also find novel representations of our knowledge graph — as a graph, as documents, as relations, or as terms — that we can embed in our learners.

These are the topics of most of our remaining installations in this CWPK series. Consider this installment as an introduction, then, to the remainder of this series. I present these tool clusters in approximate order of treament.

Stats

There is a wealth of counts and distributions of various resources within the KBpedia knowledge graph (or any graph, for that matter). Some of these statistics are automatically provided when using something like the Protégé editor. We will accept these statistics as given and concentrate on other counts and statistics not provided by Protégé that we may calculate directly from the knowledge graph with Python. We address stats in the next CWPK installment.

Logging

Python comes with a very capable logging module that is more useful than print statements sprinkled throughout the code. We will explore this module in some depth, and point to other useful logging extensions in the next CWPK installment.

Charting

There are many wonderful charting packages available in Python, some of which also are designed to work nicely with interactive Notebook pages. We’ll survey these options and present some charting utilities as part of cowpoke in CWPK #55. We will use some of the stats calculated in the prior installment to provide the data for these charts.

Graphing and Graph Extraction

A knowledge graph, duh, has a graph or network structure. Many properties (edges) connect multiple nodes (classes or reference concepts in the case of KBpedia). It is difficult to visualize these structures in their entirety, and sometimes computationally intense to render them. It is also possible to extract out local portions of the graph, and presenting a simpler sub-graph representation.

Graph visualization and extract has many fewer options than charting, and ease-of-use and performance can also be challenges. We will inspect what is available with Python and make some visualization selections in CWPK #56.

Publishing Stuff

The progress of covering these topics means that sufficient criteria for eventually going live with the Cooking with Python and KBpedia series have been met and it is time to start the public release of the series. In the process, we will need to set up remote instances of cowpoke and KBpedia, establish and endpoint for querying it, and begin to publish our Notebook series. We want to publish those Notebooks such that they retain their interactivity.

Researching, deciding upon, and then implementing the choices for these tools occupies four installments from CWPK #57 to #60.

Natural Language Processing

In our Part VI that concludes the new coding and substantive portions of this series (CWPK #61 to CWPK #71) we have much occasion to explore some additional specialty topics in natural language processing and understanding. These examples will tie into some of the most important Python packages available in NLP.

Embedding Models

One major use of knowledge graqhs is providing the grist to various embedding models. Because of KBpedia’s mappings to full-length articles in Wikipedia, we also have the option of employing document or word-rich embedding models. We will tee up the embedding approach in CWPK #63 and then develop specific models in CWPK #64. We will stage and test a number of different embedding models, ranging from single term or concept ones to ones that leverage virtually all structural aspects of the knowledge graph.

Machine Learning

The biggest chunk of installments in the CWPK series involves machine learning in a variety of forms. These examples undertake some aggressive uses of the knowledge graph, and then concludes the series with summaries of operating procedures and steps. We first look at ‘standard’ machine learning, with an emphasis on selecting models, creating training sets, setting parameters, and tuning performance. We then devote four installments (CWPK #67 to CWPK #70) to so-called ‘deep learning’ models. We are then able to assemble up and compare results from all classifier learning activities in CWPK #71.

The concluding last installments are all narrative in nature and wrap-up the series and provide summaries of use steps and general guidance.

NOTE: This article is part of the Cooking with Python and KBpedia series. See the CWPK listing for other articles in the series. KBpedia has its own Web site. The cowpoke Python code listing covering the series is also available from GitHub.

NOTE: This CWPK installment is available both as an online interactive file

or as a direct download to use locally. Make sure and pick the correct installment number. For the online interactive option, pick the *.ipynb file. It may take a bit of time for the interactive option to load.

I am at best an amateur with Python. There are likely more efficient methods for coding these steps than what I provide. I encourage you to experiment — which is part of the fun of Python — and to notify me should you make improvements.

Posted:October 13, 2020

CWPK #52: Distributed Interactions via Web Widgets – II

A Design for a Working Ecosystem of Existing Applications

In our last installment of the Cooking with Python and KBpedia series, I discussed the rationale for using distributed Web widgets as one means to bring the use and management of a knowledge graph into closer alignment with existing content tasks. The idea is to embed small Web controls as either plug-ins, enhancements to existing application Web interfaces, or as simply accompanying Web pages. Under this design, ease-of-use and immediacy are paramount in order to facilitate the capture of new knowledge if and where it arises.

More complicated knowledge graph tasks like initial builds, bulk changes, logic testing and the like are therefore kept as separate tasks. For our immediate knowledge purposes, knowledge workers should be able to issue suggestions for new concepts or modifications or deletions when they are discovered. These suggested modifications may be made to a working version of the knowledge graph that operates in parallel to a public (or staff-wide) release version.

The basic concept behind this and the previous installment is that we would like to have the ability to use simple widgets — embedded on any Web page for the applications we use — as a means of capturing and communicating immediate new information of relevance to our knowledge graphs. In a distributed manner, while working with production versions, we may want to communicate these updates to an interim version in the queue for review and promotion via a governance pipeline that might lead to a new production version.

On some periodic basis, this modified working version could be inspected and tested by assigned managers with the responsibility to vet new, public versions. There is much governance and policy that may guide these workflows, which may also be captured by their own ontologies, that is a topic separate from the widget infrastructure to make this vision operational.

While there are widgets available for analysis and visualization purposes, which we will touch upon in later installments, the ones we will emphasize in today’s CWPK installment are focused on CRUD (create – read – update – delete) activities. CRUD is the means by which we manage our knowledge graphs in the immediate sense. We will be looking to develop these widgets in a manner directly useful to distributed applications.

Getting Started

Remember from our last installment that we are basing our approach to code examples on the Jupyter Notebook ipywidgets package. That package does not come pre-installed, so we need to install it in our system using conda:

  conda install -c conda-forge ipywidgets

Once done, we fire up our Notebook and begin with our standard start-up, only now including a new autoFill module, which I explain next. This module is an example of an ipywidgets extension. Place this new autoFill.py page as a new module page within your Spyder cowpoke project. Make sure and copy that file into the project before beginning the startup:

from cowpoke.__main__ import *
from cowpoke.config import *
from cowpoke.autoFill import *
from owlready2 import *

Some Basic Controls (Widgets)

As noted, our basic controls for this installment revolve around CRUD, though we will not address them in that order. But before we can start manipulating individual objects, we need ways to discover what is already in the KBpedia (or your own) knowledge graph. Search and auto-completion are two essential tools for this job, particularly given the fact that KBpedia has more than 58,000 concepts and 5,000 different properties.

Basic Search

Recall in CWPK #23 that we covered owlready2’s basic search function and parameters. You may use the interactive commands shown in that documentation to search by type, subclasses, IRI, etc.

Auto-complete Helper

We list the auto-completion helper next because it is leveraged by virtually every widget. Auto-completion is used in a text entry box where as characters are typed, a dropdown list provides matching items that satisfy the search string as entered so far. It is a useful tool to discover what exists in a system as well as to provide the exact spelling and capitalization, which may be necessary for the match. As suggested items appear in the dropdown, if the right one appears you click it to make your current selection.

The added utility we found for this is the ipywidget-autocomplete module, which we have added as our own module to cowpoke. The module provides the underlying ‘autofill’ code that we import in the actual routine that we use within the Notebook. Here is an example of that Notebook code, with some explanations that follow below:

import cowpoke.autoFill as af
from ipywidgets import *                                  # Note 1

def open(value):
    show.value=value.new

strlist = []                                              # Note 2
listing = list(kb.classes())                              # Note 3
for item in listing:                                      # Note 4
    item = str(item)
    item = item.replace('rc.', '')
    strlist.append(item)

autofill = af.autoFill(strlist,callback=open)

show = HTML("Begin typing substring characters to see 'auto-complete'!")

display(HBox([autofill,show]))

Besides the ‘autofill’ component, we also are importing some of the basic ipywidgets code (1). In this instance, we want to auto-complete on the 58 K concepts in KBpedia (3), which we have to convert from a listing of classes to a listing of strings (1) and (4). We can just as easily do an auto-complete on properties by changing one line (3):

  listing = list(kb.classes())

or, of course, other specifications may be entered for the boundaries of our auto-complete.

To convert the owlready2 listing of classes to strings occurs by looping over all items in the list, converting each item to a string, replacing the ‘rc.’ prefix, and then appending the new string item to a new strlist (4). The ‘callback’ option relates the characters typed into the text box with this string listing. This particular expression will find string matches at any position (not just the beginning) and is case insensitive.

When the item appears in the dropdown list that matches your interest, pick it. This value can then be retrieved with the following statement:

show.value

'Mammal'

Read Item

Let’s use a basic record format to show how individual property values may be obtained for a given reference concept (RC), as we just picked with the auto-complete helper. We could obviously expand this example to include any of the possible RC property values, but we will use the most prominent ones here:

r_val = show.value 
r_val = getattr(rc, r_val)                               # Note 1                                   
a_val = str(r_val.prefLabel)                             # Note 2
b_val = r_val.altLabel
b_items = ''
for index, item in enumerate(b_val):                     # Note 3 
    item = str(item)
    if index == [0]:        
        b_items = item
    else: 
        b_items = b_items + '||' + item
b_val = b_items
c_val = str(r_val.definition)
d_val = r_val.subclasses()
d_items = ''
for index, item in enumerate(d_val):                     # Note 3
    item = str(item)
    if index == [0]:        
        d_items = item
    else: 
        d_items = d_items + '||' + item
d_val = d_items
e_val = str(r_val.wikidata_q_id)
f_val = str(r_val.wikipedia_id)
g_val = str(r_val.schema_org_id)
                                                         # Note 4 
a = widgets.Text(style={'description_width': '100px'}, layout=Layout(width='760px'), 
                 description='Preferred Label:', value = a_val)
b = widgets.Text(style={'description_width': '100px'}, layout=Layout(width='760px'), 
                 description='AltLabel(s):', value = b_val)
c = widgets.Textarea(style={'description_width': '100px'}, layout=Layout(width='760px'), 
                 description='Definition:', value = c_val)
d = widgets.Textarea(style={'description_width': '100px'}, layout=Layout(width='760px'), 
                 description='subClasses:', value = d_val)
e = widgets.Text(style={'description_width': '100px'}, layout=Layout(width='760px'), 
                 description='Q ID:', value = e_val)
f = widgets.Text(style={'description_width': '100px'}, layout=Layout(width='760px'), 
                 description='Wikipedia ID:', value = f_val)
g = widgets.Text(style={'description_width': '100px'}, layout=Layout(width='760px'), 
                 description='schema.org ID:', value = g_val)

def f_out(a, b, c, d, e, f, g):
    print(''.format(a, b, c, d, e, f, g))

out = widgets.interactive_output(f_out, {'a': a, 'b': b, 'c': c, 'd': d, 'e': e, 'f': f, 'g': g})

widgets.HBox([widgets.VBox([a, b, c, d, e, f, g]), out]) # Note 5

We begin by grabbing the show.value value (1) that came from our picking an item from the auto-complete list. We then start retrieving individual attribute values (2) for that resource, some of which we need to iterate over (3) because they return multiple items in a list. For display purposes, we need to convert all retrieved property values to strings.

We can style our widgets (4). ‘Layout’ applies to the entire widget, and ‘style’ applies to selected elements. Other values are possible, depending on the widget, which we can inspect with this statement (vary by widget type):

text = Text(description='text box')
print(text.style.keys)

Then, after defining a simple call procedure, we invoke the control on the Notebook page (5).

We could do more to clean up interim output values (such as removing brackets and quotes as we have done elsewhere), and can get fancier with grid layouts and such. In these regards, the Notebook widgets tend to work like and have parameter settings similar to other HTML widgets, though the amount of examples and degree of control is not as extensive as other widget libraries.

Again, though, since so much of this in an actual deployment would need to be tailored for other Web frameworks and environments, we can simply state for now that quite a bit of control is available, depending on language, for bringing your knowledge graph information local to your current applications.

Modify (Update) Item

Though strings in Python are what is known as ‘immutable’ (unchanging), it is possible through the string.replace('old', 'new') option to update or modify strings. For entire strings, ‘old’ may be brought into a text box via a variable name as shown above, altered, and then captured as show.value on a click event. The basic format of this approach can be patterned as follows (changing properties as appropriate):

from ipywidgets import widgets                      # Run the code cell to start

old_text = widgets.Text(style={'description_width': '100px'}, layout=Layout(width='760px'), description = 'Value to modify:', value = 'This is the old input.')
new_text = widgets.Text(description = 'New value:', value = 'This is the NEW value.')

def bind_old_to_new(sender):
    old_text.value = new_text.value
    old_text.description = new_text.description
old_text.on_submit(bind_old_to_new)

old_text                                 # Click on the text box to invoke

It is better to make modifications to an existing property or an existing class using something like Protégé, which is able to better account for internal cross-references. But, changes to attribute values may be pursued through the above approach.

New class names or properties, or modifications to existing class names or properties, should never be allowed to production systems. These CRUD actions, like create, are best slipstreamed into a reserved cache or made against a review copy of the knowledge base that is segregated away from the production system. This separation allows review steps and the enforcement of governance standards before changes are made to an operating version of the knowledge graph.

Create Item

Newly created items include new concepts and their attributes or subsumption relations, which deserve scrutiny before committing to a production version. Other items that perhaps deserve a lower degree of governance before adding include additional altLabels or mapping properties.

In the case of new concepts, the form of addition (see also CWPK #39, Note #6) has a number of forms, but one has worked best for KBpedia.

On the other hand, the general form for adding a new attribute has the form:

  CurResource.append(new_item)

As with modifications, creating items should be limited to working, and not production copies.

Delete Item

The form to delete an item is very simple, and takes the form:

  destroy_entity(xxxName)

Clearly, this is a function that should not be made available to casual users, and needs to be subject to governance policies and review. Again, working copies are the best versions to be the target of any such actions, and the acceptance of deletions should be an active subject of review. Forms to enable this are simple to build.

Not Part of the Package

For the reasons of diversity of use and applications, none of these routines has been formally added to the cowpoke package. We see examples of how such can be implemented, and I have argued for the use of distributed Web widgets under governance and using working copies as the best means for utilizing these functions. But, given this diversity, it does not make sense to add widgets to the cowpoke package at this time.

That being said, we also hope some of this discussion and related code commentary have encouraged you to think of how to apply workflows and life-cycle management to your own knowledge graphs. Embedding widgets into existing distributed applications is one means to bring the benefits and updates of knowledge graphs into immediate relevance.

Additional Documentation

Here is additional documentation of working with the ipywidgest package:

Official documentation
- The interact
- A ipywidgets tutorial
- Another good tutorial
- Explanation of ipywidgets layouts.

In addition, there are other interactive uses of Notebooks:

The Voilà option for publishing interactive Jupyter Notebook pages
Creating interactive dashboards with Jupyter.

NOTE: This CWPK installment is available both as an online interactive file

Posted:October 12, 2020

CWPK #51: Distributed Interactions via Web Widgets – I

Bringing Knowledge Graphs into the Content Workflow

The rationale for starting this Cooking with Python and KBpedia series is to better match knowledge graph creation and use with practical requirements and workflows. The notion of ‘roundtripping‘ has one major use when bulk changes are needed, such as making modifications to the domain or scope of the knowledge graph. But roundtripping also supports the idea of a knowledge graph as a computable object, not simply an editable one.

Think for a moment about this: your organization has a vetted knowledge graph at the center of many analytic and knowledge management functions. How does one grow and extend such a resource, while also being able to make bulk changes or have it operate as a constantly available schema? On the one hand, such a vision requires a dynamic, always-on resource that is subject to constant querying and modification. On the other hand, this vision also means staff extending and maintaining the knowledge graph according to organizational standards and governance. The versions of the artifact may differ at various stages when these two hands work together. The capabilities needed to support a roundtripping capability are also some of the same ones needed to support these twin dynamic and standard roles. At some points we work directly with the artifact as a standalone thing, extending it and testing it for consistency and logic. At some points, the current production version of the knowledge graph is being actively exercised for knowledge management, data analysis, data integration, machine learning, or natural language processing tasks.

Now that we have put in place a framework for bulk changes and roundtripping, we next need to turn our attention to how we can access and use that knowledge artifact in a dynamic way. Such serious deployments of knowledge graphs imply many other things in version control, version deployments, and release governance. Those are topics outside of our series on the cowpoke Python package. But dynamic access and use of the knowledge graph is not outside of that scope.

How we may approach this issue in relation to workflows and HTML widgets that we may deploy directly to knowledge workers in a distributed manner is the topic of today’s CWPK installment. Our next installment with then showcase a few code examples of these widgets. Note we will also return to this topic in CWPK #58 onward when we also discuss how to publish results from our knowledge graphs to the Web.

Let’s First Consider Workflows

If your interests at this point are simply in learning a bit more about knowledge graphs (KGs), and perhaps how to design and build them, you may want to set today’s and tomorrow’s installments aside until another day. But, assuming that your interest in knowledge graphs is for eventual productive use within your organization, you will need to grapple at some point with how to integrate KGs into your staff’s current activities and how KGs may contribute to new capabilities.

There are clearly many ways to look at content workflows, but here is our longstanding view of knowledge graphs (ontologies) and content management for what might occur in a ‘typical’ larger organization:

Content Workflow with a Knowledge Graph

Figure 1: Content Workflow with a Knowledge Graph

Starting at the center, we can understand the Manage/Store bubble to represent what we have in owlready2 (or any similarly hosted knowledge graph). That central hub (which might be a different deployment version) can be the target for gathering activities, creating, tagging, analyzing, managing knowledge, publishing or planning. A separate Ontologies bubble may be the source for bulk updates or changes or for reviewing submissions prior to committing to the central, deployed hub. Older hubs or retired versions may be shifted to an archive status.

There could be different stages and names for these basic activities, and one might also use an ontology to actually govern and provide the ‘tags’ for tracking where content stands. (Including such things as sign-offs.) But, this circle, in general, represents a common set of uses to which any organization would likely aspire. These steps can be expanded or collapsed as local circumstances dictate.

What is important to note, however, is that most activities around the circumference of the workflow occur in context with other existing activities. For example, when reviewing Internet search results or creating documents, both tagging of new content or updating the existing knowledge base would be made easier by having small plug-ins or widgets for the existing applications. Machine learning and statistical tests, or natural language applications, may all surface possible inconsistencies in an existing knowledge graph, which is best captured immediately in conjunction with the tests.

What emerges when inspecting opportunity or choke points for using knowledge graphs is that in nearly all cases new knowledge discoveries occur in conjunction with existing work activities, and if not captured immediately and easily, are all too often not recorded. This inability to capture knowledge in real time undercuts the usefulness of knowledge management and causes existing knowledge to go stale.

For these reasons, it makes sense to assemble a suite of widgets that may conduct simple activities against the knowledge graph, and can be embedded in HTML pages for interacting with current applications. Of course, the nature of the widget and what it does is dependent on the specific current activity at-hand. But these simple activities all tend to be one of the CRUD activities (create – read – update – delete) and are quite patterned.

Python Web Frameworks and Related

The reasons for me to choose Python as the basis for this CWPK series was based on data science applications with a broad user base and an abundance of useful packages and libraries. The usefulness of Python to Web page or site development was not an initial consideration.

Our own experience with Web site development, which as been extensive over the years, began with PHP and eventually migrated into the more modern JavaScript approaches like Bootstrap, our current preferred basis. Sometimes we have created intimate relationships with CMS systems, most prominently Drupal, though we have moved away from such monolithic frameworks to the more fluid ones like Bootstrap.

However, I have been quite impressed working with Jupyter Notebook and have also been following certain Web framework Python packages. Prominent ones that have impressed me (though for very different reasons) include Flask, Tornado, Flexx, Pyramid, Bottle and CherryPy. Useful supporting packages and libraries include Requests and WSGI.

My first thought about this part of the CWPK series was to pick one of these frameworks, learn some rudimentary things with it sufficient to support our widget orientation, and then use it consistently for all HTML aspects. Were I to make that decision, Flask would be my choice based on what I have studied. But I am not now choosing to move in that direction, for a number of reasons.

First, it is difficult to demo Web interactions when it it unlikely that some in the audience may not be readily able to set up their own Web sites. Second, a distributed work environment relies on many existing applications. Even if all of these use HTML interfaces in some manner, it is likely that an effective deployment would still require perhaps multiple frameworks and integrations. Third, though I could set up a dedicated demo site, that would still require adopting a full Web framework with socket, authentication, and content negotiation support. In an ultimate deployment, that is a likely step that must be taken, but our job right now is not to prototype a production system. And, fourth, the easiest way to showcase dynamic demos is to piggyback on what we already have installed and are using: Jupyter Notebook.

Fortunately, there is a nice library of widgets useful in Jupyter Notebook, the ipywidget package, and additional third-party extensions. Widgets are objects that have familiar user interfaces that may be embedded in an HTML page. Example widgets include textboxes, radiobuttons, regular buttons, dropdown lists, tabular displays, graph plots or charts, slider controls, or other forms for interacting with a Web page and its data. Dashboards, for example, are often a built-up combination of multiple widgets. Widgets are common to all modern languages used on the Web, though how they are set up and controlled varies widely.

Because we could relatively demo these widget capabilities directly through Notebook without much further complexity, and have that work in other user settings, we decided to go with the ipywidget option. We will install and demo some Flask uses in about ten installments from now. But, for our current interests in tieing our knowledge graphs to existing work activities, we introduce code for some exemplar widgets in our next CWPK installment.

Additional Documentation

Here are some background materials on the Web frameworks in Python that may be of some use to you:

The Requests package, and many supporting documents and tutorials
Material on Flask, including mega tutorials and other supporting materials
General background material on Python Web frameworks.

NOTE: This CWPK installment is available both as an online interactive file

Posted:October 8, 2020

CWPK #50: Querying External Sources

The Nearly Infinite Usefulness of SPARQL

We are now two-thirds of the way through our CWPK series. One reason we have emphasized ‘roundtripping‘ in Cooking with Python and KBpedia is to accommodate the incorporation of information from external sources into KBpedia. From hierarchical relationships to annotations like definitions or labels, external sources can be essential. Of course, one can find flat files or spreadsheets or CSV files directly, but often times we need specific information that can only come from querying the external source directly. Two of the ones we heavily rely on in particular — Wikidata and DBpedia — provide this access through SPARQL queries. We first introduced SPARQL in CWPK #25.

External SPARQL queries are the basis of getting instance data, values for instance attributes, missing fields like altLabels and skos.definition, existing crosswalks or mappings, longer descriptions, subsumption relations, related links, and interesting joins and intersections across external knowledge base content. Often, one is able to specify the format (serialization) of the desired results.

The outputs from these external queries can be manipulated as strings, and then written to flat files useful for ingest into the various build routines. Of course, it is important that the format and CSV-nature of the results be maintained in a form that the build routines expect. One may alter the build formats or the extract formats, but to work they need to match on both ends.

So, what we provide in today’s installment are some guidelines and recipes for using SPARQL to obtain information you need and to write them to flat files. Because of their importance, we emphasize Wikidata and DBpedia (also a stand-in for Wikipedia) in our examples. Once populated, you may need to do some intermediate wrangling of these files to get them into shape for direct import. We covered that topic in brief in CWPK #36, but really do not address file wrangling further here. There are way too many varieties to cover the topic in a meaningful way, though we certainly have examples in today’s installment and across the entire CWPK series that should provide a useful foundation to your own efforts.

Choosing Access Method

There are not that many public SPARQL endpoints available, and some are not always up and available. But the endpoints that do exist, with their identification in the Query Sources section at the conclusion of today’s installment, are often comprehensive and with high value. The two we will be emphasizing today, Wikidata and DBpedia (and, by extension, the linked open data (LOD) cloud beyond that), are among the most valuable. (Of course, many endpoints, like ones specific to a particular organization, are private, and can be parts of valuable, distributed information ecosystems.) Another notable endpoint worthy of your attention is the LOD endpoint maintained by OpenLink Software.

It is possible to query many of these sources directly online with an HTML interface, often also providing a choice of the output format desired. In some of the examples below, I provide a Try it! link that takes you directly to the source site and uses their native SPARQL interface. (Also, inspect the URI links for these Try it! options, since it shows how SPARQL gets communicated over the Web.) You may often find this is the fastest and cleanest way to get useful results, and sometimes better formatted than what our home-brewed options below produce. Your mileage may vary. In any case, it is useful to learn how to conduct direct SPARQL capabilities from within cowpoke. For that reason, I emphasize our home-brewed examples below.

Setting Up This Installment

Like we have been emphasing of late, we begin today’s installment with our standard start-up instructions:

from cowpoke.__main__ import *
from cowpoke.config import *
from owlready2 import *

from SPARQLWrapper import SPARQLWrapper, JSON
from rdflib import Graph

#sparql = SPARQLWrapper('http://dbpedia.org/sparql')
sparql = SPARQLWrapper('https://query.wikidata.org/sparql')
graph = world.as_rdflib_graph()

Of course, we actually have a very capable query method to our own internal stores:

form_1 = list(graph.query_owlready("""
  PREFIX rc: <http://kbpedia.org/kko/rc/>
  PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
  SELECT DISTINCT ?x ?label
  WHERE
  {
    ?x rdfs:subClassOf rc:Eutheria.
    ?x skos:prefLabel  ?label. 
  }
"""))

print(form_1)

Wikidata Queries

For the following Wikidata queries, Run these assignments first:

from SPARQLWrapper import SPARQLWrapper, JSON
from rdflib import Graph

sparql = SPARQLWrapper('https://query.wikidata.org/sparql', agent='cowpoke 0.1 (github.com/Cognonto/cowpoke)')

We need to assign an ‘agent=’ because of limits Wikidata occasionally puts on queries. If you do many requests, you may want to consider adding your own agent defintion.

One of the techniques I use most heavily is the VALUES statement. This construct allows a listing of IDs to be passed to the query source. Depending on various endpoint limits, you may be able to list 1000 or more IDs in such a listing; experience with a given endpoint will dictate. If you use the VALUES construct, just make sure you are using the proper format and prefix (wd: in this instance for a Q item within Wikidata) in front of each value.

Parent Class from Q IDs

The first query is to obtain the parent class from submitted listing of Q items. You may also Try it! directly from Wikidata:

sparql.setQuery("""
PREFIX schema: <http://schema.org/>
SELECT ?item ?itemLabel ?wikilink ?itemDescription ?subClass ?subClassLabel WHERE {
  VALUES ?item { wd:Q25297630
  wd:Q537127
  wd:Q16831714
  wd:Q24398318
  wd:Q11755880
  wd:Q681337
}
 ?item wdt:P910 ?subClass.
 SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()

#for result in results["results"]["bindings"]:
#    print(result["item"]["value"])

print(results)

Notice that once we set our SPARQL endpoint and user agent, we are able to cut-and-paste different SPARQL queries between the opening and ending triple quotes (“””). The bracketing statements around that can be used repeatedly for different queries.

Go ahead and toggle between the print statements above to see how we can start varying outputs. Chances are you will need to do some string manipulation before your flat files are ready for ingest, but we can vary these specifications to get the initial output closer to our requirements.

subClass and Instance listings for Q ID

Try it! as well.

sparql.setQuery("""
SELECT ?subclass ?subclassLabel ?instance ?instanceLabel
WHERE
{
 ?subclass wdt:P279 wd:Q183366.
 ?instance wdt:P31 wd:Q183366.
 SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}
ORDER BY xsd:integer(SUBSTR(STR(?subclass),STRLEN("http://www.wikidata.org/entity/Q")+1))
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()

#for result in results["results"]["bindings"]:
#    print(result["item"]["value"])

print(results)

Useful Q Item Attributes

Try it!

sparql.setQuery("""
PREFIX schema: <http://schema.org/>

SELECT ?item ?itemLabel ?class ?classLabel ?description ?article ?itemAltLabel WHERE {
  VALUES ?item { wd:Q1 wd:Q2 wd:Q3 wd:Q4 wd:Q5 }
  ?item wdt:P31 ?class;
        wdt:P5008 ?project.
#  ?article rdfs:comment ?description.
  
   OPTIONAL {
    ?article schema:about ?item.
    ?article schema:isPartOf <https://en.wikipedia.org/>.
  }
  
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()

print(results)

Get English Wikipedia Article Names from Q ID

Try it!

sparql.setQuery("""
SELECT DISTINCT ?lang ?item ?name WHERE {
 VALUES ?item { wd:Q1
wd:Q2
wd:Q3
wd:Q4
wd:Q5 
}
 ?article schema:about ?item; schema:inLanguage ?lang; schema:name ?name .
 FILTER(?lang in ('en')) .
 FILTER (!CONTAINS(?name, ':')) .
}
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()

print(results)

Listing of Q IDs from Property

Try it!

sparql.setQuery("""
 SELECT
 ?item ?itemLabel
 ?value ?valueLabel
 # valueLabel is only useful for properties with item-datatype
 WHERE 
 {
 ?item wdt:P2167 ?value
 # change P2167 to desired property        
 SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
 }
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()

print(results)

subClass and Instance Listings for Q ID

Try it!

sparql.setQuery("""
SELECT ?subclass ?subclassLabel ?instance ?instanceLabel
WHERE
{
 ?subclass wdt:P279 wd:Q183366.
 ?instance wdt:P31 wd:Q183366.
 SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}
ORDER BY xsd:integer(SUBSTR(STR(?subclass),STRLEN("http://www.wikidata.org/entity/Q")+1))
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()

print(results)

Missing Q Data from Wikidata

Try it!

sparql.setQuery("""
PREFIX schema: <http://schema.org/>
PREFIX w: <https://en.wikipedia.org/wiki/>
SELECT ?wikipedia ?item WHERE {
VALUES ?wikipedia { w:Tom_Hanks }
?wikipedia schema:about ?item .
SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()

print(results)

Q ID from Wikipedia ID

Try it!

sparql.setQuery("""
PREFIX schema: <http://schema.org/>
PREFIX w: <https://en.wikipedia.org/wiki/>
SELECT ?wikipedia ?item WHERE {
VALUES ?wikipedia { w:Euthanasia
w:Commercial_art_gallery
}
 ?wikipedia schema:about ?item .
 SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()

print(results)

schema.org ← → Wikidata Mapping

Try it!

sparql.setQuery("""
SELECT ?wd ?wdLabel ?type ?uri ?prefix ?localName WHERE {
 {
   { ?wd wdt:P1628 ?uri . BIND("equivalent property" AS ?type) } UNION
   { ?wd wdt:P1709 ?uri . BIND("equivalent class" AS ?type) } UNION
   { ?wd wdt:P2888 ?uri . BIND("exact match" AS ?type) } UNION
   { ?wd wdt:P2235 ?uri . BIND("superproperty" AS ?type) } UNION
   { ?wd wdt:P2236 ?uri . BIND("subproperty" AS ?type) } 
 }
 BIND( REPLACE(STR(?uri),'[^#/]+$',) AS ?prefix)
 BIND( REPLACE(STR(?uri),'^.*[#/]',) AS ?localName)
 # filter by ontology (otherwise timeout expected)
 FILTER(?prefix = "http://schema.org/")
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en" }
} ORDER BY ?prefix ?localName
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()

print(results)

Main Topic of Q ID

Try it!

sparql.setQuery("""
PREFIX schema: <http://schema.org/>
 SELECT ?item ?itemLabel ?mainTopic ?mainTopicLabel WHERE {
   VALUES ?item { wd:Q13307732
 wd:Q8953981
 wd:Q1458376
 wd:Q8953071
 }
  ?mainTopic wdt:P910 ?item.
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
 }
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()

print(results)

DBpedia Queries

DBpedia is a bit more tricky to deal with.

Again, we set up our major call, to be followed by a series of SPARQL queries to DBpedia:

from SPARQLWrapper import SPARQLWrapper, RDFXML
from rdflib import Graph

sparql = SPARQLWrapper("http://dbpedia.org/sparql")

Languages in DBpedia with schema.org Language Code

In this query, we are looking for items that have been already mapped or characterized in a second ontology (schema.org).

Try it!

sparql.setQuery("""
    PREFIX dbo: <http://dbpedia.org/ontology/>
    PREFIX schema: <http://schema.org/>

    CONSTRUCT {
      ?lang a schema:Language ;
      schema:alternateName ?iso6391Code .
    }
    WHERE {
      ?lang a dbo:Language ;
      dbo:iso6391Code ?iso6391Code .
      FILTER (STRLEN(?iso6391Code)=2) # to filter out non-valid values
    }
""")

sparql.setReturnFormat(RDFXML)
results = sparql.query().convert()
print(results.serialize(format='xml'))

Missing Definitions

Try it!

sparql.setQuery("""
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX : <http://dbpedia.org/resource/>
SELECT ?item, ?description WHERE {
  VALUES ?item { :Child_prostitution
  :Ice_Hockey_World_Championships
  :Major_League_Soccer
  :Tamil_language
  :Acne }
  ?item rdfs:comment ?description .

  FILTER ( LANG(?description) = "en" ) 
} 
""")

sparql.setReturnFormat(RDFXML)
results = sparql.query().convert()
print(results.serialize(format='xml'))

Get URIs from Aliases

Try it!

sparql.setQuery("""
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX dbo: <http://dbpedia.org/ontology/>
SELECT ?x ?redirectsTo WHERE {
 VALUES ?wikipedia { "Abies"@en 
 "Abolitionists"@en}
 ?x rdfs:label ?wikipedia .
 ?x dbo:wikiPageRedirects ?redirectsTo
}
""")

sparql.setReturnFormat(XML)
results = sparql.query().convert()
print(results.serialize(format='xml'))

Of course, SPARQL is a language unto itself, and it takes time to become fluent. The examples above are closer to baby-talk than Shakespearean speech. Nonetheless, one begins to gain a feel for the power of the language.

As we move forward, we will try to leverage SPARQL as the query language to our knowledge graph, since it provides the most powerful and flexible language for doing so. There will obviously be times when direct Python calls are more direct and shorter to implement. But the most flexible filters and intersections will come from our use of SPARQL.

Query Resources

A partial, but useful, list of public SPARQL endpoints is provided by:

https://www.w3.org/wiki/SparqlEndpoints

An assessment of their current availability is provided by:

https://sparqles.ai.wu.ac.at/availability

Here are the top 100 named graphs available with their triple counts:

https://docs.google.com/spreadsheets/d/1XUtRtWwEXN805pnlrsAg9qW7gxqneeXqrsPOwhVusTw/edit#gid=0

Wikidata provides its own listing of 100 SPARQL endpoints:

https://www.wikidata.org/wiki/Wikidata:Lists/SPARQL_endpoints

There is an excellent (and growing) compilation of useful SPARQL queries to Wikidata available from:

https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service/queries/examples

Two smaller, but similarly useful resource for DBpedia queries, are available from:

The latter also provides some SPARQL construction tips.

Example OpenStreetMap SPARQL examples are available from:

https://wiki.openstreetmap.org/wiki/SPARQL_examples.

NOTE: This CWPK installment is available both as an online interactive file

Posted:October 7, 2020

CWPK #49: Mapping External Sources

Sometimes KR is Kind of Tricky

I think I pretty much lied to you about ‘roundtripping‘ and the importance of the last major section that we just completed in this Cooking with Python and KBpedia series. We had indeed accomplished the extraction-and-build cycle for classes, properties, and their annotations. These are the same roundtrip objectives we had been pursuing and doing with our Clojure procedures for years. But we were incomplete.

There is another purpose to KBpedia as a ‘scaffolding’ to external sources that is not captured in this initial ’roundtripping’ sense. If you download KBpedia, right now as version 2.50, and load it up into an editor like Protégé, you will see the KBpedia structure and its properties and annotations, but you will not see the links to the external resources that KBpedia integrates. These mappings (correspondences) are provided in separate mapping files, and have been since KBpedia was first released. This has struck me for some time as an unneeded indirection. I think the better approach is to embed these links directly into KBpedia (or whatever your master knowledge graph may be).

OK, so if we do that, does that not become part of the baseline that needs to be roundtripped? And, if to be used, what is the best practice for representing these external relationships?

Like much that has an open world approach, these kinds of questions and open-ended project requirements can stretch into the unachievable future. I don’t want to keep moving the goalposts. But I do think the role of top-level knowledge graphs is integration and coverage. The addition of key mappings to our basic expectation for our central knowledge graph seems appropriate and reasonable. Thus, for a ‘truer’ conceptualization of ’roundtripping’, I now include mappings with our basic remit.

This correction (which, of course, is an addition), and some other utilities and tools explorations, are the focus of this next major Part V in our CWPK series. Think of this next major part as wrapping our basic knowledge graph into the tools of the trade.

Representing External Resources

The standard way to handle links with an external source in most ontologies is to ‘import’ the external source. In OWL terms, this means to actually import an ontology and to incorporate the import’s full aspects. This is a bit of a more blunderbuss approach when using semantic technologies like OWL. At least in Python, as we have learned, we can actually limit our imports to specific methods or procedures.

With the standard OWL import command one brings the entire knowledge graph into the active space. Perhaps this is OK, though it does seem excessive if one only wants a small portion of the external resource, perhaps only for a specific class or predicate or three. But we have a more fundamental problem in that some of our external resources are not defined in a formal ontology subject to an import, but are just persistent URIs. Actually, with external knowledge bases like Wikipedia or Wikidata, this design is more common than one where a single ontology call can bring in all of the external structure. Moreover, even if the external source has a complete OWL-based specification, there is no guarantee that this external source embodies the same assertions and world view of our main knowledge resource, KBpedia.

If we are not bringing in a full ontology import — or may not even be able to do so because the external resources are only accessible via a URI scheme and not a logical ontology — then we will need to formulate a different linkage protocol. The one we have chosen to follow is: Give each external source its own mapping predicate, and then annotate any KBpedia resource with a mapping to show the link to the external source and its specific mapping predicate. This design decision means we can not apply a reasoner to the external sources, but that we may identify and collect them using SPARQL queries. This demarks an important realization point where we see that we can now supplement our information access and aggregation for our knowledge graph not only using logical inferencing and subsumption chains, but through directed queries that make all aspects of our information characterization, including annotations, available for selection and retrieval.

External Mapping Approach

OK, given this design, we proceed to code up our build (load) routines to bring the external mappings into KBpedia. We begin with our standard start-up instructions:

from cowpoke.__main__ import *
from cowpoke.config import *
from owlready2 import *

And then proceed to develop up our routine in the standard manner, including a header showing key configuration inputs. I explain some of the important implementation notes below the routine:

### KEY CONFIG SETTINGS (see build_deck in config.py) ###             
# 'kb_src'        : 'standard'                                        # Set in master_deck
# 'loop_list'     : mapping_dict.values(),                             
# 'base'          : 'C:/1-PythonProjects/kbpedia/v300/build_ins/mappings/',              
# 'ext'           : '.csv',                                         
# 'out_file'      : 'C:/1-PythonProjects/kbpedia/v300/targets/ontologies/kbpedia_reference_concepts.csv',


def mapping_builder(**build_deck):
    print('Beginning KBpedia mappings build . . .')
    loop_list = build_deck.get('loop_list')
    base = build_deck.get('base')
    ext = build_deck.get('ext')
    out_file = build_deck.get('out_file')
    for loopval in loop_list:                                               # Note 1
        print('   . . . processing', loopval)                             
        in_file = (base + loopval + ext)
        print(in_file)
        with open(in_file, 'r', encoding='utf8') as input:
            is_first_row = True
            reader = csv.DictReader(input, delimiter=',', fieldnames=['s', 'p', 'o'])
            for row in reader:                                              # Note 2
                if is_first_row:
                    is_first_row = False                
                    continue
                r_s = row['s']                                              # Note 3
                if 'kko/rc' in r_s:                                         # Note 4
                    r_s = r_s.replace('http://kbpedia.org/kko/rc/', '')
                    r_s = getattr(rc, r_s)
                else:
                    r_s = r_s.replace('http://kbpedia.org/ontologies/kko#', '')
                    r_s = getattr(kko, r_s)                                 # Note 5        
                r_p = row['p']                                              # Note 6
                r_o = row['o']
                if loopval == 'dbpedia':                                    # Note 7                    
                    kb_frag = 'dbpedia_id'
                    kb_prop = getattr(rc, kb_frag)
                    r_s.dbpedia_id.append(r_o)                              # Note 8
                elif loopval == 'dbpedia-ontology':
                    kb_frag = 'dbpedia_ontology_id'
                    kb_prop = getattr(rc, kb_frag)
                    r_s.dbpedia_ontology_id.append(r_o)
                elif loopval == 'geonames':
                    kb_frag = 'geo_names_id'
                    kb_prop = getattr(rc, kb_frag)
                    r_s.geo_names_id.append(r_o)
                elif loopval == 'schema.org':
                    kb_frag = 'schema_org_id'
                    kb_prop = getattr(rc, kb_frag)
                    r_s.schema_org_id.append(r_o)
                elif loopval == 'wikidata':
                    kb_frag = 'wikidata_q_id'
                    kb_prop = getattr(rc, kb_frag)
                    r_s.wikidata_q_id.append(r_o)
                elif loopval == 'wikipedia':
                    kb_frag = 'wikipedia_id'
                    kb_prop = getattr(rc, kb_frag)
                    r_s.wikipedia_id.append(r_o)
                elif loopval == 'wikipedia-categories':
                    kb_frag = 'wikipedia_category_id'
                    kb_prop = getattr(rc, kb_frag)
                    r_s.wikipedia_category_id.append(r_o)
                else:
                    print(loopval, 'is not on list.')
                kko.mappingAsObject[r_s, kb_prop, r_o] = [r_p]              # Note 9         
                print(r_s)
    print('External mapping uploads are complete.')

We set up our standard looping routine (1), progressing through a new ‘loop_list’ that is contained in a new dictionary (mapping_dict) in our config.py file. Like our other build methods, we use the csv module and progress through the input files row-by-row (2). We pick up our column values (3) and (6) for each row, and assign them to a local variable, corresponding to the s-p-o ‘triples‘ for each row. We need to account for the fact that our KBpedia resources are under either the ‘rc’ or ‘kko’ namespaces (4), and pare them down to their fragments (since the resources are stored on file using their complete IRIs). Because these are string values we are extracting from the input files, we need to look them up and convert them to their internal attribute form (5), which are KBpedia classes in these instances.

We have a long list of switch statements (7) that detect the input file type and then pick the proper mapping predicate (there is one for each external mapping source). This long list of else-if statements would seem to lend themselves to a helper function of some sort, but I decide just to repeat the code blocks because they are simple and straightforward. (Rather than a ‘dict’ format I want something more akin to a ‘record’ where a single key may point to many different values, but that is an expansion of the ‘dict’ approach that actually seems a little complicated in Python). All of material up to this point is really set-up and processing for the two key owlready2 algorithms that we need to use.

The first algorithm (8) we have seen before and associates the external mapped IRI to the current KBpedia class in the loop routine, and has the form of:

  subject.predicate.append(object)

The only tricky part here is making sure that namespace prefixes are used as indicated and that we have converted the external mapping predicate to its internal form (kb_prop). This code is now associating the external IRI to the subject KBpedia class. However, we are still lacking the nature of the mapping predicate.

For this purpose, we need to “annotate the annotation”, or basically reify the annotation with its specific mapping predicate. There is a format in owlready2 where we basically treat the entire tuple of the s-p-o object as the target for the added annotation, shown in the form as (9). The predicate for this purpose is the kko.mappingAsObject. The basic format is:

  kko.mappingAsObject[s, p, o] = 'specific mapping predicate'

The ‘specific mapping predicate’ is either owl:equivalentClass, rdfs:subClassOf, kko:superClassOf, kko:isAbout, or kko:isRelatedTo. The kko:mappingAsObject is named as it is because this specifically assigned mapping predicate applies to the external IRI when it appears in the object slot of the s-p-o triple. In other words, the triple so formed has the s-p-o construction of:

   Kbpedia class - 'specific mapping predicate' - external IRI

Just as it appears in our external mapping files. It is important to keep this ordering straight, since reversing the order of the subject and object causes the inverse properties to be required (rdfs:subClassOf is the inverse of kko:superClassOf, kko:isAbout is the inverse of kko:isRelatedTo).

So, with this code block now set, we can run our routine:

mapping_builder(**build_deck)

Once processed, here is the form the mapping takes within the Protégé editor:

Figure 1: Mapping Annotations

The first callout (1) indicates we successfully changed our hyphens to underscores, as discussed in the last major part. The second callout (2) shows how our mapping annotations now appear, with the annotation itself being annotated with the specific mapping predicate used.

Since we like how these mappings turned out, we decide to save the file:

kb.save('C:/1-PythonProjects/kbpedia/v300/targets/ontologies/kbpedia_reference_concepts_test.owl', format="rdfxml")

Since these mappings add considerably to the file size, I actually save both mapped and unmapped versions for later use, depending.

Completing the Roundtrip

Now that our builds contain external mappings, it is appropriate we also create extraction routines to get these assignments back out. First, we can list all of the KBpedia RCs that have a specific external mapping (rc.dbpedia_ontology_id in this case), which also returns a string value for the external IRI:

list(rc.dbpedia_ontology_id.get_relations())

[(rc.Person, 'http://dbpedia.org/ontology/Person'),
 (rc.SoccerPlayer, 'http://dbpedia.org/ontology/SoccerPlayer'),
 (rc.ConceptualWork, 'http://dbpedia.org/ontology/Work'),
 (rc.Bird, 'http://dbpedia.org/ontology/Bird'),
 (rc.TextualPCW, 'http://dbpedia.org/ontology/WrittenWork'),
 (rc.Scientist, 'http://dbpedia.org/ontology/Scientist'),
 (rc.BasketballPlayer, 'http://dbpedia.org/ontology/BasketballPlayer'),
 (rc.Artist, 'http://dbpedia.org/ontology/Artist'),
 (rc.GeopoliticalEntity,
  'http://dbpedia.org/ontology/GeopoliticalOrganisation'),
 (rc.GeopoliticalEntity, 'http://dbpedia.org/ontology/Region'),
 (rc.Lighthouse, 'http://dbpedia.org/ontology/Lighthouse'),
 (rc.FootballPlayer_American,
  'http://dbpedia.org/ontology/GridironFootballPlayer'),
 (rc.FootballPlayer_American,
  'http://dbpedia.org/ontology/AmericanFootballPlayer'),
 (rc.Organization, 'http://dbpedia.org/ontology/Organisation'),
 (rc.Organization, 'http://dbpedia.org/ontology/EmployersOrganisation'),
 (rc.Organization, 'http://dbpedia.org/ontology/OrganisationMember'),
 (rc.Athlete, 'http://dbpedia.org/ontology/Athlete'),
 (rc.Athlete, 'http://dbpedia.org/ontology/AthleticsPlayer'),
 (rc.Athlete, 'http://dbpedia.org/ontology/ArcherPlayer'),
 (rc.Athlete, 'http://dbpedia.org/ontology/Biathlete'),
 (rc.Athlete, 'http://dbpedia.org/ontology/BobsleighAthlete'),
 (rc.Athlete, 'http://dbpedia.org/ontology/Canoeist'),
 (rc.Athlete, 'http://dbpedia.org/ontology/Curler'),
 (rc.Athlete, 'http://dbpedia.org/ontology/DartsPlayer'),
 (rc.Athlete, 'http://dbpedia.org/ontology/GaelicGamesPlayer'),
 (rc.Athlete, 'http://dbpedia.org/ontology/HorseRider'),
 (rc.Athlete,
  'http://dbpedia.org/ontology/NationalCollegiateAthleticAssociationAthlete'),
 (rc.Athlete, 'http://dbpedia.org/ontology/NetballPlayer'),
 (rc.Athlete, 'http://dbpedia.org/ontology/Rower'),
 (rc.Athlete, 'http://dbpedia.org/ontology/Skater'),
 (rc.Athlete, 'http://dbpedia.org/ontology/Ski-jumper'),
 (rc.Athlete, 'http://dbpedia.org/ontology/SnookerChamp'),
 (rc.Athlete, 'http://dbpedia.org/ontology/SnookerPlayer'),
 (rc.Athlete, 'http://dbpedia.org/ontology/SpeedSkater'),
 (rc.Athlete, 'http://dbpedia.org/ontology/SportsTeamMember'),
 (rc.Athlete, 'http://dbpedia.org/ontology/TableTennisPlayer'),
 (rc.Athlete, 'http://dbpedia.org/ontology/TeamMember'),
 (rc.Athlete, 'http://dbpedia.org/ontology/WaterPoloPlayer'),
 (rc.Athlete, 'http://dbpedia.org/ontology/WinterSportPlayer'),
 (rc.Building, 'http://dbpedia.org/ontology/Building'),
 (rc.Philosopher, 'http://dbpedia.org/ontology/Philosopher'),
 (rc.MusicalComposition, 'http://dbpedia.org/ontology/MusicalWork'),
 (rc.Horse, 'http://dbpedia.org/ontology/Horse'),
 (rc.MartialArtist, 'http://dbpedia.org/ontology/MartialArtist'),
 (rc.Golfer, 'http://dbpedia.org/ontology/GolfPlayer'),
 (rc.Amphibian, 'http://dbpedia.org/ontology/Amphibian'),
 (rc.RailroadStation, 'http://dbpedia.org/ontology/RailwayStation'),
 (rc.RailroadStation, 'http://dbpedia.org/ontology/TramStation'),
 (rc.Anime, 'http://dbpedia.org/ontology/Anime'),
 (rc.Monument, 'http://dbpedia.org/ontology/Monument'),
 (rc.Arachnid, 'http://dbpedia.org/ontology/Arachnid'),
 (rc.Architect, 'http://dbpedia.org/ontology/Architect'),
 (rc.Legislator, 'http://dbpedia.org/ontology/Senator'),
 (rc.Article_PCW, 'http://dbpedia.org/ontology/Article'),
 (rc.Cave, 'http://dbpedia.org/ontology/Cave'),
 (rc.ChemicalSubstanceType, 'http://dbpedia.org/ontology/ChemicalCompound'),
 (rc.ChemicalSubstanceType, 'http://dbpedia.org/ontology/ChemicalSubstance'),
 (rc.TennisPlayer, 'http://dbpedia.org/ontology/TennisPlayer'),
 (rc.TennisTournament, 'http://dbpedia.org/ontology/TennisTournament'),
 (rc.AdministrativeDistrict,
  'http://dbpedia.org/ontology/GovernmentalAdministrativeRegion'),
 (rc.AdministrativeDistrict,
  'http://dbpedia.org/ontology/ClericalAdministrativeRegion'),
 (rc.AdministrativeDistrict, 'http://dbpedia.org/ontology/MicroRegion'),
 (rc.Food, 'http://dbpedia.org/ontology/Food'),
 (rc.BaseballPlayer, 'http://dbpedia.org/ontology/BaseballPlayer'),
 (rc.FootballPlayer_Australian,
  'http://dbpedia.org/ontology/AustralianRulesFootballPlayer'),
 (rc.Municipality, 'http://dbpedia.org/ontology/Municipality'),
 (rc.Municipality, 'http://dbpedia.org/ontology/FormerMunicipality'),
 (rc.ElectedOfficial, 'http://dbpedia.org/ontology/OfficeHolder'),
 (rc.BadmintonPlayer, 'http://dbpedia.org/ontology/BadmintonPlayer'),
 (rc.PopulatedPlace, 'http://dbpedia.org/ontology/PopulatedPlace'),
 (rc.RugbyPlayer, 'http://dbpedia.org/ontology/RugbyPlayer'),
 (rc.School_AcademicOrganization, 'http://dbpedia.org/ontology/School'),
 (rc.BodyOfWater, 'http://dbpedia.org/ontology/BodyOfWater'),
 (rc.Temple, 'http://dbpedia.org/ontology/Temple'),
 (rc.VolleyballPlayer, 'http://dbpedia.org/ontology/VolleyballPlayer'),
 (rc.VolleyballPlayer, 'http://dbpedia.org/ontology/BeachVolleyballPlayer'),
 (rc.Brewery, 'http://dbpedia.org/ontology/Brewery'),
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 (rc.Shrine_Religious, 'http://dbpedia.org/ontology/Shrine'),
 (rc.SkiArea, 'http://dbpedia.org/ontology/SkiArea'),
 (rc.SkiResort_Attraction, 'http://dbpedia.org/ontology/SkiResort'),
 (rc.SolarEclipse, 'http://dbpedia.org/ontology/SolarEclipse'),
 (rc.SpaceShuttle, 'http://dbpedia.org/ontology/SpaceShuttle'),
 (rc.SpaceStation, 'http://dbpedia.org/ontology/SpaceStation'),
 (rc.SubwayStation, 'http://dbpedia.org/ontology/MetroStation'),
 (rc.Synagogue, 'http://dbpedia.org/ontology/Synagogue'),
 (rc.TVCharacter, 'http://dbpedia.org/ontology/SoapCharacter'),
 (rc.TVSeason, 'http://dbpedia.org/ontology/TelevisionSeason'),
 (rc.TVShow_Single, 'http://dbpedia.org/ontology/TelevisionEpisode'),
 (rc.TeamHandballPlayer, 'http://dbpedia.org/ontology/HandballPlayer'),
 (rc.TelevisionDirector, 'http://dbpedia.org/ontology/TelevisionDirector'),
 (rc.TelevisionStar, 'http://dbpedia.org/ontology/TelevisionPersonality'),
 (rc.TheHumanLifeCycle, 'http://dbpedia.org/ontology/LifeCycleEvent'),
 (rc.TheatreDirector, 'http://dbpedia.org/ontology/TheatreDirector'),
 (rc.TimeInPosition_Served, 'http://dbpedia.org/ontology/TermOfOffice'),
 (rc.ToDoList, 'http://dbpedia.org/ontology/TrackList'),
 (rc.TransformerSubstation,
  'http://dbpedia.org/ontology/ElectricalSubstation'),
 (rc.Treadmill, 'http://dbpedia.org/ontology/Treadmill'),
 (rc.USRepresentative, 'http://dbpedia.org/ontology/Congressman'),
 (rc.VicePresidentOfOrganization, 'http://dbpedia.org/ontology/VicePresident'),
 (rc.ViticulturalDomain, 'http://dbpedia.org/ontology/WineRegion'),
 (rc.WaterPoweredMill, 'http://dbpedia.org/ontology/Watermill'),
 (rc.WaterTower, 'http://dbpedia.org/ontology/WaterTower'),
 (rc.WaterwayLock, 'http://dbpedia.org/ontology/Lock'),
 (rc.WindPoweredTurbine, 'http://dbpedia.org/ontology/WindMotor'),
 (rc.Winery, 'http://dbpedia.org/ontology/Winery'),
 (rc.WomensTennisAssociationTournament,
  'http://dbpedia.org/ontology/WomensTennisAssociationTournament'),
 (rc.WorkQuantity, 'http://dbpedia.org/ontology/UnitOfWork'),
 (rc.ZooOrganization, 'http://dbpedia.org/ontology/Zoo'),
 (kko.TopicsCategories, 'http://dbpedia.org/ontology/TopicalConcept'),
 (kko.Eukaryotes, 'http://dbpedia.org/ontology/Eukaryote'),
 (kko.Geopolitical, 'http://dbpedia.org/ontology/AdministrativeRegion')]

Then, we can decompose those results to construct up our format for annotating a s-p-o tuple to get the exact mapping predicate used:

list(kko.mappingAsObject[rc.Currency, rc.dbpedia_ontology_id, 'http://dbpedia.org/ontology/Currency'])

['owl:equivalentClass']

With some string replacements and shifting the orders, we then can see how we can get a reconstruction of the input rows we used to load the external mappings in the first place, with our example becoming:

  rc.Currency, owl:equivalentClass, http://dbpedia.org/ontology/Currency

(Using ‘rc.’ rather than base IRI in this example.) For the moment, we will defer writing the full routine, but will eventually add it to our extract.py module. We will also embed this code block into a loop that writes our individual mapping files to disk, thereby completing the external mapping portion of our roundtrip.

NOTE: This CWPK installment is available both as an online interactive file

Main Links

Search

Shifting Our Focus to How to Use the Knowledge Graph

Stats

Logging

Charting

Graphing and Graph Extraction

Publishing Stuff

Natural Language Processing

Embedding Models

Machine Learning

A Design for a Working Ecosystem of Existing Applications

Getting Started

Some Basic Controls (Widgets)

Basic Search

Auto-complete Helper

Read Item

Modify (Update) Item

Create Item

Delete Item

Not Part of the Package

Additional Documentation

Bringing Knowledge Graphs into the Content Workflow

Let’s First Consider Workflows

Python Web Frameworks and Related

Additional Documentation

The Nearly Infinite Usefulness of SPARQL

Choosing Access Method

Setting Up This Installment

Wikidata Queries

Parent Class from Q IDs

subClass and Instance listings for Q ID

Useful Q Item Attributes

Get English Wikipedia Article Names from Q ID

Listing of Q IDs from Property

subClass and Instance Listings for Q ID

Missing Q Data from Wikidata

Q ID from Wikipedia ID

schema.org ← → Wikidata Mapping

Main Topic of Q ID

DBpedia Queries

Languages in DBpedia with schema.org Language Code

Missing Definitions

Get URIs from Aliases

Query Resources

Sometimes KR is Kind of Tricky

Representing External Resources

External Mapping Approach

Completing the Roundtrip