Posted:May 28, 2015

AI3 Pulse

I was pleasantly surprised to discover that a diversity of my writings has been chosen for the syllabus for a text analytics course by Dr. Alianna Maren in Northwestern University’s master program in predictive analytics. Dr. Maren has chosen to feature some of my writings in NLP statistics, ontologies, and the open world assumption.

Dr. Maren has stated her intention is to present text analytics from a “top-down ontological perspective.” The syllabus looks very interesting.

I appreciate the recognition and wish the students and Dr. Maren a great course!

Posted by AI3's author, Mike Bergman Posted on May 28, 2015 at 4:17 pm in Ontologies, Pulse | Comments (0)
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Posted:May 18, 2015

Accurate ResultsTrying to Cut Through the Terminology Confusion and Offer Simple Guidelines

Semantics is a funny thing. All professionals come to know that communication with their peers and outside audiences requires accuracy in how to express things. Yet, even with such attentiveness, communications sometimes go awry. It turns out that background, perspective and context can all act to switch circuits at the point of communication. Despite, and probably because of, our predilection as a species to classify and describe things, all from different viewpoints, we can often exhort in earnest a thought that is communicated to others as something different from what we intended. Alas!

This reality is why, I suspect, we have embraced as a species things like dictionaries, thesauri, encyclopedias, specifications, standards, sacred tracts, and such, in order to help codify what our expressions mean in a given context. So, yes, while sometimes there is sloppiness in language and elocution, many misunderstandings between parties are also a result of difference in context and perspective.

It is important when we process information in order to identify relations or extract entities, to type them or classify them, or to fill out their attributes, that we have measures to gauge how well our algorithms and tests work, all attentive to providing adequate context and perspective. These very same measures can also tell us whether our attempts to improve them are working or not. These measures, in turn, also are the keys for establishing effective gold standards and creating positive and negative training sets for machine learning. Still, despite their importance, these measures are not always easy to explain or understand. And, truth is, sometimes these measures may also be mis-explained or mis-calculated. Aiding the understanding of important measures in improving the precision, completeness, and accuracy of communications is my purpose in this article.

Some Basic Statistics as Typically Described

The most common scoring methods for gauging the “accuracy” of natural language communications involves statistical tests based on the nomenclature of negatives and positives, true or false. Sometimes it can be a bit confusing about how to interpret these terms, a confusion which can be made all the more difficult in what kind of statistical environment is at play. Let me try to first confuse, and then more simply explain these possible nuances.

Standard science is based on a branch of statistics known as statistical hypothesis testing. This is likely the statistics that you were taught in school. In hypothesis testing, we begin with a hypothesis about what might be going on with respect to a problem or issue, but for which we do not know the cause or truth. After reviewing some observations, we formulate a hypothesis that some factor A is affecting or influencing factor B. We then formulate a mirror-image null hypothesis that specifies that factor A does not affect factor B; this is what we will actually test. The null hypothesis is what we assume the world in our problem context looks like, absent our test. If the test of our formulated hypothesis does not affect that assumed distribution, then we reject our alternative (meaning our initial hypothesis fails, and we keep the null explanation).

We make assumptions from our sample about how the entire population is distributed, which enables us to choose a statistical model that captures the shape of assumed probable results for our measurement sample. These shapes or distributions may be normal (bell-shaped or Gaussian), binomial, power law, or many others. These assumptions about populations and distribution shapes then tell us what kind of statistical test(s) to perform. (Misunderstanding the true shape of the distribution of a population is one of the major sources of error in statistical analysis.) Different tests may also give us more or less statistical power to test the null hypothesis, which is that chance results will match the assumed distribution. Different tests may also give us more than one test statistic to measure variance from the null hypothesis.

We then apply our test and measure and collect our sample from the population, with random or other statistical sampling important so as not to skew results, and compare the distribution of these results to our assumed model and test statistic(s). The null hypothesis is confirmed or not by whether the shape of our sampled results matches the assumed distribution or not. The significance of the variance from the assumed shape, along with a confidence interval based on our sample size and the test at hand, provides the information necessary to either accept or reject the null hypothesis.

Rejection of the null hypothesis generally requires both significant difference from the expected shape in our sample and a high level of confidence. Absent those results, we likely need to accept the null hypothesis, thus rejecting the alternative hypothesis that some factor A is affecting or influencing factor B. Alternatively, with significant differences and a high level of confidence, we can reject the null hypothesis, thereby accepting the alternative hypothesis (our actual starting hypothesis, which prompted the null) that factor A is affecting or influencing factor B.

This is all well and good except for the fact that either the sampling method or our test may be in error. There are two types of errors that are possible: Type I errors, where a positive result corresponds to rejecting the null hypothesis; and Type II errors, where a negative result corresponds to not rejecting the null hypothesis.

We can combine all of these thoughts into what is the standard presentation for capturing these true and false, positive and negative, results [1]:

Null hypothesis (H0) is
Valid/True Invalid/False
Judgment of Null Hypothesis (H0) Reject False Positive
Type I error
True Positive
Correct inference
Fail to reject (accept) True negative
Correct inference
False negative
Type II error

Clear as mud, huh?

Let’s Apply Some Simplifications

Fortunately, there are a couple of ways to sharpen this standard story in the context of information retrieval (IR), natural language processing (NLP) and machine learning (ML) — the domains of direct interest to us at Structured Dynamics — to make understanding all of this much simpler. Statistical tests will always involve a trade off between the level of false positives (in which a non-match is declared to be a match) and the level of false negatives (in which an actual match is not detected) [1]. Let’s see if we can simplify our recognition and understanding of these conditions.

First, let’s start with a recent explanation from the KDNuggets Web site [2]:

“Imagine there are 100 positive cases among 10,000 cases. You want to predict which ones are positive, and you pick 200 to have a better chance of catching many of the 100 positive cases. You record the IDs of your predictions, and when you get the actual results you sum up how many times you were right or wrong. There are four ways of being right or wrong:

  1. TN / True Negative: case was negative and predicted negative
  2. TP / True Positive: case was positive and predicted positive
  3. FN / False Negative: case was positive but predicted negative
  4. FP / False Positive: case was negative but predicted positive.”

The use of ‘case’ and ‘predictions’ help, but are still a bit confusing. Let’s hear another explanation from Benjamin Roth from his recently completed thesis [3]:

“There are two error cases when extracting training data: false positive and false negative errors. A false positive match is produced if a sentence contains an entity pair for which a relation holds according to the knowledge base, but for which the sentence does not express the relation. The sentence is marked as a positive training example for the relation, however it does not contain a valid signal for it. False positives introduce errors in the training data from which the relational model is to be generalized. For most models false positive errors are the most critical error type, for qualitative and quantitative reasons, as will be explained in the following.

“A false negative error can occur if a sentence and argument pair is marked as a negative training example for a relation (the knowledge base does not contain the argument pair for that relation), but the sentence actually expresses the relation, and the knowledge base was incomplete. This type of error may negatively influence model learning by omitting potentially useful positive examples or by negatively weighting valid signals for a relation.”

In our context, we can see a couple of differences from traditional scientific hypothesis testing. First, the problems we are dealing with in IR, NLP and ML are all statistical classification problems, specifically in binary classification. For example, is a given text token an entity or not? What type amongst a discrete set is it? Does the token belong to a given classification or not? This makes it considerably easier to posit an alternative hypothesis and the shape of its distribution. What makes it binary is the decision as to whether a given result is correct or not. We now have a different set of distributions and tests from more common normal distributions.

Second, we can measure our correct ‘hits’ by applying our given tests to a “gold standard” of known results. This gold standard provides a representative sample of what our actual population looks like, one we have characterized in advance whether all results in the sample are true or not for the question at hand. Further, we can use this same gold standard over and over again to gauge improvements in our test procedures.

Combining these thoughts leads to a much simpler matrix, sometimes called a confusion matrix in this context, for laying out the true and false, positive and negative characterizations:

Correctness Test Assertion
Positive Negative
True TP
True Positive
TN
True Negative
False FP
False Positive
FN
False Negative

As we can see, ‘positive’ and ‘negative’ are simply the assertions (predictions) arising from our test algorithm of whether or not there is a match or a ‘hit’. ‘True’ and ‘false’ merely indicate whether these assertions proved to be correct or not as determined by gold standards or training sets. A false positive is a false alarm, a “crying wolf”; a false negative is a missed result. Thus, all true results are correct; all false are incorrect.

Key Information Retrieval Statistics

Armed with these four characterizations — true positive, false positive, true negative, false negative — we now have the ability to calculate some important statistical measures. Most of these IR measures also have exact analogs in standard statistics, which I also note.

The first metric captures the concept of coverage. In standard statistics, this measure is called sensitivity; in IR and NLP contexts it is called recall. Basically it measures the ‘hit’ rate for identifying true positives out of all potential positives, and is also called the true positive rate, or TPR:

\mathit{TPR} = \mathit{TP} / P = \mathit{TP} / (\mathit{TP}+\mathit{FN})

Expressed as a fraction of 1.00 or a percentage, a high recall value means the test has a high “yield” for identifying positive results.

Precision is the complementary measure to recall, in that it is a measure for how efficient whether positive identifications are true or not:

\text{precision}=\frac{\text{number of true positives}}{\text{number of true positives}+\text{false positives}}

Precision is something, then, of a “quality” measure, also expressed as a fraction of 1.00 or a percentage. It provides a positive predictive value, as defined as the proportion of the true positives against all the positive results (both true positives and false positives).

So, we can see that recall gives us a measure as to the breadth of the hits captured, while precision is a statement of whether our hits are correct or not. We also see, as in the Roth quote above, why false positives need to be a focus of attention in test development, because they directly lower precision and efficiency of the test.

This recognition that precision and recall are complementary and linked is reflected in one of the preferred overall measures of IR and NLP statistics, the F-score, which is the adjusted (beta) mean of precision and recall. The general formula for positive real β is:

F_\beta = (1 + \beta^2) \cdot \frac{\mathrm{precision} \cdot \mathrm{recall}}{(\beta^2 \cdot \mathrm{precision}) + \mathrm{recall}}.

which can be expressed in terms of TP, FN and FP as:

F_\beta = \frac {(1 + \beta^2) \cdot \mathrm{true\ positive} }{(1 + \beta^2) \cdot \mathrm{true\ positive} + \beta^2 \cdot \mathrm{false\ negative} + \mathrm{false\ positive}}\,

In many cases, the harmonic mean is used, which means a beta of 1, which is called the F1 statistic:

F_1 = 2 \cdot \frac{\mathrm{precision} \cdot \mathrm{recall}}{\mathrm{precision} + \mathrm{recall}}

But F1 displays a tension. Either precision or recall may be improved to achieve an improvement in F1, but with divergent benefits or effects. What is more highly valued? Yield? Quality? These choices dictate what kinds of tests and areas of improvement need to receive focus. As a result, the weight of beta can be adjusted to favor either precision or recall. Two other commonly used F measures are the F2 measure, which weights recall higher than precision, and the F0.5 measure, which puts more emphasis on precision than recall [4].

Another metric can factor into this equation, though accuracy is a less referenced measure in the IR and NLP realm. Accuracy is the statistical measure of how well a binary classification test correctly identifies or excludes a condition:

\text{accuracy}=\frac{\text{number of true positives}+\text{number of true negatives}}{\text{number of true positives}+\text{false positives} + \text{false negatives} + \text{true negatives}}

An accuracy of 100% means that the measured values are exactly the same as the given values.

All of the measures above simply require the measurement of false and true, positive and negative, as do a variety of predictive values and likelihood ratios. Relevance, prevalence and specificity are some of the other notable measures that depend solely on these metrics in combination with total population.

By bringing in some other rather simple metrics, it is also possible to expand beyond this statistical base to cover such measures as information entropy, statistical inference, pointwise mutual information, variation of information, uncertainty coefficients, information gain, AUCs and ROCs. But we’ll leave discussion of some of those options until another day.

Bringing It All Together

Courtesy of one of the major templates in Wikipedia in the statistics domain [5], for which I have taken liberties, expansions and deletions, we can envision the universe of statistical measures in IR and NLP, based solely on population and positives and negatives, true and false, as being:

Condition (as determined by “Gold standard“)
Total population Condition positive Condition negative Prevalence =


Σ Condition positive

Σ Total population
Test
Assertion
Test assertion
positive
TP
True positive
FP
False positive
(Type I error)
Positive predictive value
(PPV), Precision =
Σ True positive
Σ Test outcome positive
False discovery rate (FDR) =
Σ False positive
Σ Test outcome positive
Test assertion
negative
FN
False negative
(Type II error)
TN
True negative
False omission rate (FOR) =
Σ False negative
Σ Test outcome negative
Negative predictive value (NPV) =
Σ True negative
Σ Test outcome negative
Accuracy (ACC) =
Σ True positive + Σ True negative
Σ Total population
True positive rate (TPR), Sensitivity, Recall =
Σ True positive
Σ Condition positive
False positive rate (FPR),Fall-out =
Σ False positive
Σ Condition negative
Positive likelihood ratio (LR+) =
TPR
FPR
F-score (F1 case) =
2 x (Precision * Recall)
(Precision + Recall)
False negative rate (FNR) =
Σ False negative
Σ Condition positive
True negative rate (TNR), Specificity (SPC) =
Σ True negative
Σ Condition negative
Negative likelihood ratio (LR−) =
FNR
TNR

Please note that the order and location of TP, FP, FN and TN differs from my simple layout presented in the confusion matrix above. In the confusion matrix, we are gauging whether the assertion of the test is correct or not as established by the gold standard. In this current figure, we are instead using the positive or negative status of the gold standard as the organizing dimension. Use the shorthand identifiers of TP, etc., to make the cross reference between “correct” and “condition”.

Relationships to Gold Standards and Training Sets

These basic measures and understandings have two further important roles beyond informing how to improve the accuracy and peformance of IR and NLP algorithms and tests. The first is gold standards. The second is training sets.

Gold standards that themselves contain false positives and false negatives, by definition, immediately introduce errors. These errors make it difficult to test and refine existing IR and NLP algorithms, because the baseline is skewed. And, because gold standards also often inform training sets, errors there propagate into errors in machine learning. It is also important to include true negatives in a gold standard, in the likely ratio expected by the overall population, so that this complement of the accuracy measurement is not overlooked.

Once a gold standard is created, you then run your current test regime against it when you run your same tests againt unknowns. Preferably, of course, the gold standard only includes true positives and true negatives (that is, the gold standard is the basis for judging “correctness'; see confusion matrix above). In the case of running an entity recognizer, your results against the gold standard can take one of three forms: you either have open slots (no entity asserted); slots with correct entities; or slots with incorrect entities. Thus, here is how you would create the basis for your statistical scores:

  • TP = test identifies the same entity as in the gold standard
  • FP = test identifies a different entity than what is in the gold standard (including no entity)
  • TN = test identifies no entity; gold standard has no entity, and
  • FN = test identifies no entity, but gold standard has one.

As noted before, these measures are sufficient to calculate the precision, recall, F-score and accuracy statistics. Also note that the F v T and P v N correspond to the gold standard “correctness” and what is asserted by the test(s), per the confusion matrix.

We can apply this same mindset to the second additional, important role in creating and evaluating training sets. Both positive and negative training sets are recommended for machine learning. Negative training sets are often overlooked. Again, if the learning is not based on true positives and negatives, then significant error may be introduced into the learning.

Clean, vetted gold standards and training sets are thus a critical component to improving our knowledge bases going forward [6]. The very practice of creating gold standards and training sets needs to receive as much attention as algorithm development because, without it, we are optimizing algorithms to fuzzy objectives.

The virtuous circle that occurs between more accurate standards and training sets and improved IR and ML algorithms is a central argument for knowledge-based artificial intelligence (KBAI). Continuing to iterate better knowledge bases and validation datasets is a driving factor in improving both the yield and quality from our rapidly expanding knowledge bases.


[2] Tilmann Bruckhaus, 2015. “How Are Precision and Recall Calculated?” from the KDNuggets Web site, retrieved May 10, 2015.
[3] Benjamin Roth, 2014. “Effective Distant Supervision for End-To-End Knowledge Base Population Systems,” D Engineering Thesis, Saarland University; quote is on p 33.
[6] Some would also argue for adequate gold standards in the ontology realm. See Dellschaft, Klaas, and Steffen Staab. “On how to perform a gold standard based evaluation of ontology learning.” In The Semantic Web-ISWC 2006, pp. 228-241. Springer Berlin Heidelberg, 2006. For ontologies, they state it “. . . is apparent that there does not exist a canonical way of performing gold-standard based evaluations of ontology learning. Moreover, we argue in this paper that existing gold-standard based evaluations are faulty and that a well-founded evaluation model is largely missing.”
Posted:May 11, 2015

AI3 Pulse

Mavlyutov et al. have posted a pre-print [1] of their upcoming paper to be presented at ESWC at the end of the month covering the most efficient representation of URIs in information systems. All of us who do large-scale work with the semantic Web or linked data should be interested in these findings.

To my knowledge, the paper is the first one to explicitly evaluate common data structures for encoding, storing and retrieving URIs at scale. As the unique identifiers for resources, there may be millions to billions needing to be stored and retrieved from triple stores or other database backends.

The authors compared a dozen different methods for storing URIs according to the standard needs to index, insert and retrieve URIs, including encoding and decoding, at scale.  Memory and operation times were measured. The methods evaulated were specific RDF systems; various hash maps; various hash tables; binary search, B+, ART (adaptive radix), and lexicographic trees; and the HAT-trie.

Different operational needs may point to different methods. However, the authors conclude that “overall, the HAT-trie appears to be a good compromise taking into account all aspects, i.e., memory consumption, loading time, and look-ups. ART also appears as an appealing structure, since it maintains the data in sorted order, which enables additional operations like range scans and pre fix lookups, and since it still remains time and memory efficient.”

This paper should be a useful reference for any group that needs to manage URIs at scale.


[1] Mavlyutov, Ruslan, Marcin Wylot, and Philippe Cudre-Mauroux. “A Comparison of Data Structures to Manage URIs on the Web of Data.”, accepted paper at the 12th ESWC Conference (2015), May 31-June 4, 2015, Portoroz, Slovenia.

Posted by AI3's author, Mike Bergman Posted on May 11, 2015 at 1:45 pm in Pulse, Semantic Web | Comments (0)
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Posted:April 21, 2015

UMBEL - Upper Mapping and Binding Exchange Layer Refining a Generator, Testing the Coherence, and Computing a KB

The six months since the last major release of UMBEL (Upper Mapping and Binding Exchange Layer) have been spent in improving the coherence and broadening the usefulness for the ontology. Structured Dynamics is today releasing version 1.20 of the open source UMBEL.

UMBEL’s first purpose is to provide a general vocabulary of classes and predicates for describing domain ontologies, with the specific aim of promoting interoperability with external datasets and domains. UMBEL’s second purpose is to provide a coherent framework of reference subjects and topics  for grounding relevant Web-accessible content. UMBEL presently has about 35,000 of these reference concepts drawn from the Cyc knowledge base, split into ‘core’ and a series of optional modules, which are organized into 32 mostly disjoint SuperTypes.

The key advances in this new 1.20 version of UMBEL include refinements to the UMBEL generator, improved tests for satisfiabliity and coherence, and additional mappings and structure to aid UMBEL’s role as a computing overlay for existing knowledge bases, such as Wikipedia. Part of the latter advance is being aided by the new addition of an Attributes Ontology to UMBEL as described in the prior articles of An UMBEL Extension for Attributes and Conceptual and Practical Distinctions in the Attributes Ontology.

Summary of Changes

These are the principal changes between the last public release, version 1.10, and this version 1.20:

  • Expanded mappings to OpenCyc to better capture coverage of Wikipedia content; there are now 35,533 reference concepts (RCs) in UMBEL, 35,302 of which are mapped to OpenCyc (the unmapped RCs are mostly used for organizational purposes in the Attributes Ontology and OpenCyc mismatches with key external ontologies)
  • Created a new Attributes Ontology (AO), with the purpose of enabling property (attribute) mappings to UMBEL (see further the UMBEL Annex L discussion for more details on this version update)
  • Created a new Attributes module, with 1,002 RCs assigned
  • Created a new Entities SuperType, with 20,393 RCs designated. The Entities ST is by definition non-disjoint with UMBEL’s other SuperTypes
  • Created a new Entities module, with 9,317 RCs assigned; the remainder of the Entites RCs are in core
  • Expanded the direct UMBEL RC to Wikipedia page mappings, with 25,582 currently mapped, or nearly three-quarters (72%) of RCs now assigned
  • Created a new Annex Z to hold updated statistics about UMBEL
  • Deprecated the Workplaces SuperType, and merged with the Facilities ST
  • Deprecated the MarketIndustries SuperType, and merged with the Attributes ST
  • Reviewed and greatly improved ST assignments across the board; notably, the distinction between the Events and Activities SuperTypes was improved. See Annex Z for the updated ST assignment statistics
  • Greatly expanded and improved the UMBEL generator to handle satisfiability tests and modules creation
  • Expanded and updated the UMBEL.org Web site.

A Short Primer on UMBEL

The Web and enterprises in general are characterized by growing, diverse and distributed information sources and data. Some of this information resides in structured databases; some resides in schema, standards, metadata, specifications and semi-structured sources; and some resides in general text or media where the content meaning is buried in unstructured form. Given these huge amounts of information, how can one bring together what subsets are relevant? And, then for candidate material that does appear relevant, how can it be usefully combined or related given its diversity? In short, how does one go about actually combining diverse information to make it interoperable and coherent?

UMBEL was conceived to provide a reference grounding to achieve these very aims. UMBEL’s vocabulary is designed to recognize that different sources of information have different contexts and different structures, and meaningful connections between sources are not always exact. UMBEL’s 35,000 reference concepts — drawn from the logically consistent Cyc knowledge base backed by 1000 person-years of development and testing — provide a set of fixed references by which we can orient, map and navigate external content. These UMBEL reference concepts form a knowledge graph (you can see a big graph visualization of this structure) of subject nodes that may be related to external classes and individuals (instances and named entities). Via this coherent structure, we gain some important benefits:

  • Mapping to other ontologies — disparate and heterogeneous datasets and ontologies may be related to one another by mapping to the UMBEL structure
  • A scaffolding for domain ontologies — more specific domain ontologies can be made interoperable by using and tieing their more general concepts into the UMBEL structure
  • Inferencing — the UMBEL reference concept structure is designed for inferencing, which supports better semantic search and look-ups
  • Semantic tagging — UMBEL, and ontologies mapped to it, can be used as input bases to ontology-based information extraction (OBIE) for tagging text or documents; UMBEL’s “semsets” broaden these matches and can be used across languages
  • Linked data mining — via the reference ontology, direct and related concepts may be retrieved and mined and then related to one another
  • Creating computable knolwedge bases — with complete mappings to key portions of a knowledge base, say, for Wikipedia articles, it is possible to use the UMBEL graph structure to create a computable knowledge source, with follow-on benefits in artificial intelligence and KB testing and improvements, and
  • Categorizing instances and named entities — UMBEL can bring a consistent framework for typing entities and relating their descriptive attributes to one another.

UMBEL is being developed and refined via large-scale use cases. A number of improvements have been brought to the system to make it more testable, manageable, and flexible.

The first improvement was to introduce the so-called SuperTypes to UMBEL. All UMBEL reference concepts are assigned to one or more of 32 SuperTypes, organized into nine dimensions (details may be found here). The four SuperTypes of Attributes, Abstract-level, Entities and Topics/Categories are designed to be fully non-disjoint, and do not participate in any disjoint assertions. The remaining 28 SuperTypes are designed to be as disjoint as possible:

Natural World Natural Phenomena
Natural Substances
Earthscape
Extraterrestrial
Living Things Prokaryotes
Protists & Fungus
Plants
Animals
Diseases
Person Types
Human Activities Organizations
Finance & Economy
Society
Activities
Time-related Events
Time
Human Works Products
Food or Drink
Drugs
Human Places Facilities
Geopolitical
Information Chemistry (n.o.c)
Audio Info
Visual Info
Written Info
Structured Info
Notations & References
Numbers
Descriptive Attributes
Classificatory Abstract-level
Entities
Topics/Categories

To make UMBEL more tractable, we have also modularized it into ‘core’, ‘geo’, ‘entities’, and ‘attributes’ modules (the latter two modules being added in this new release). The modules can be swapped out with other external options or left out of analysis if not needed for a given domain interest. We also have formal mappings to other important external reference sets such as Wikipedia, OpenCyc, schema.org, the DBpedia ontology,GeoNames and PROTON. UMBEL’s GitHub site provides these mappings.

Beginning with version 1.10, we also added a new UMBEL generator written in Clojure that allows the entire system to be built and tested from a series of simple input files. We are now using this system aggressively to discover gaps and mis-assignments in the UMBEL structure, as well as to achieve balance in scope and coverage. The system ties into the OWL API for certain tests and capabilities (UMBEL is OWL 2-compliant).

Still a Work in Progress

Though UMBEL retains its same mission as when the system was first formulated eight years ago, we also see its role expanding. The two key areas of expansion are in UMBEL’s use to model and map instance data attributes and in acting as a computable overlay for Wikipedia (and other knowledge bases). These two areas of expansion are still a work in progress.

This UMBEL version 1.20 marks the first expression of the Attributes Ontology. While we have organized what already had existed in attribute concepts (that is, those concepts that capture the descriptive data related to how to characterize instance records), some gaps remain in both UMBEL and the source Cyc. Using the new ontology to map against the properties in the DBpedia and schema.org vocabularies is the next priority. These direct use cases are needed to ground the ontology in important, real-world information markup systems. We will also be looking at linking to an existing units and measurements ontology such as QUDT. There likely will need to be a series of releases over time to capture and test these uses.

The mapping to Wikipedia is now about 72% complete. While we are testing automated mapping mechanisms, because of its central role we also need to vet all UMBEL-Wikipedia mapping assignments. This effort is pointing out areas of UMBEL that are over-specified, under-specified, and sometimes duplicative. By placing UMBEL in an intermediate position between Cyc and Wikipedia we are finding differences and gaps on both ends, as well as gaps within UMBEL itself. Our goal is to get to a 100% coverage point with Wikipedia, and then to exercise the structure for machine learning and other tests against the KB. These efforts will enable us to enhance the semsets in UMBEL as well as to move toward multilingual versions. This effort, too, is still a work in progress.

Despite these desired enhancements, we are using all aspects of UMBEL and its mappings to both aid these expansions and to test the existing mappings and structure. These efforts are proving the virtuous circle of improvements that is at the heart of UMBEL’s purposes.

Where to Get UMBEL and Learn More

The UMBEL Web site provides various online tools and Web services for exploring and using UMBEL. The UMBEL GitHub site is where you can download the UMBEL Vocabulary or the UMBEL Reference Concept ontology, both under a Creative Commons Attribution 3.0 license. Other documents and backup are also available from that location.

Technical specifications for UMBEL and its various annexes are available from the UMBEL wiki site. You can also download a PDF version of the specifications from there. You are also welcomed to participate on the UMBEL mailing list or LinkedIn group.

Posted by AI3's author, Mike Bergman Posted on April 21, 2015 at 2:33 pm in UMBEL | Comments (0)
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Posted:March 31, 2015

UMBEL -(Upper Mapping and Binding Exchange Layer Part of a Vision for Information Interoperability on the Web

Wikipedia is arguably the most important information source yet invented for natural language processing (NLP) and artificial intelligence, in addition to its role as humanity’s largest encyclopedia. Wikipedia is the principal information source for such prominent services as IBM’s Watson [1], Freebase [2], the Google Knowledge Graph [3], Apple’s Siri [4], YAGO [5], and DBpedia [6], the core reference structure for linked open data [7]. Wikipedia information has assumed a prominent role in NLP applications in word sense disambiguation, named entity recognition, co-reference resolution, and multi-lingual alignments; in information retrieval in query expansion, multi-lingual retrieval, question answering, entity ranking, text categorization, and topic indexing; and in semantic applications in topic extraction, relation extraction, entity extraction, entity typing, semantic relatedness, and ontology building [8].

The massive size of Wikipedia — with more than 26 million articles across 250 different language versions [9,10] — makes it a rich resource for reference entities and concepts. Structural features of Wikipedia that help to inform the understanding of relationships and connections include articles (and their embedded entities and concepts), article abstracts, article titles, infoboxes, redirects, internal and external links, editing histories, categories (in part), discussion pages, disambiguation pages, images and associated metadata, templates, tables, and pages of lists, not to mention Wikipedia as a whole being used as a corpus or graph [11]. It is no wonder that Wikipedia is referenced in about 665,000 academic articles and books [12]. And all of this occurs in a phenomenon that is not yet 15 years old!

Wikipedia is unparalleled as a resource for mining these resources of structure, concepts and entities. But, and here is the challenge, Wikipedia is never itself used as a computable knowledge base. It is a resource for other knowledge systems, but not a coherent knowledge base unto itself. Wikipedia feeds other useful knowledge bases, but does not play those roles alone. Why this is and how it can be remedied is the subject of this article.

Three Basic Problems

Wikipedia has been cited for three weaknesses relevant to its role as a knowledge base. The first is that its coverage is imbalanced. Various studies have evaluated the scope of Wikipedia [13, 14, 15, among many] and have found areas of popular culture such as games, movies, music and actors to be over-represented, while areas of philosophy, technology, academics and history, to be under-represented. While still perhaps true in terms of absolute numbers of articles, the actual domain coverage has been improving in recent years.

The second Wikipedia problem is incompleteness. Wikipedia tends to be spotty in terms of providing complete and equal representation in populating certain categories (or classes) with articles (instances). It also tends to be incomplete in how well embedded or structured various articles may be. An example of the representation problem is in economy or commerce and the coverage of companies or products. The notability criterion [16] is a tricky one here; some companies or products with seemingly equivalent notability get listed, while others do not. Another example is the kingdom of life where some life forms are extremely well represented, while others are not. The incompleteness of structure relates to which articles or entire categories have infoboxes or ones that are well populated, as well as how category assignments are incomplete or inconsistent. The existence of “stub” articles is one evidence for such incompleteness. As Wikipedia has gotten more structured and complicated, the number of active editors has declined. The growing use of bots, however, is often compensating for this and in some cases bringing better consistency and equivalent treatment [17,18].

But the biggest problem of Wikipedia has been its category structure. Categories were not part of the original design, but were added to Wikipedia in 2004. Various reviewers have likened Wikipedia more to a thesaurus than a classification scheme [19], others that it is different than classical knowledge organization systems in that it has no specified root or hierarchy [20]. This improved a wee bit from 2006 to 2010, when the main Wikipedia topics were organized according to top-level and main topics [21]. Still, typical commentaries point to the fact that Wikipedia’s category structure is “noisy, ill-formed, and difficult to make sense of” [22]. Its crowdsourced nature has led to various direct and indirect cycles in portions of the category structure [23]. All of these problems lead to the inability to do traditional reasoning or inference over the Wikipedia category graph [24].

Besides these lacks of computability, the Wikipedia graph is bloated with “artificial” categories (see further below) that just add noise to trying to understand or navigate the Wikipedia category structure. In short, while Wikipedia is a goldmine of resources and partial structure, its organization is incoherent at a global level, and it is unable to support reasoning and other tasks that might be expected from a truly functional knowledge base.

The real shame — but also the real opportunity — is that this lack of coherency makes it more difficult to validate and improve the information already in Wikipedia. So, there are both external reasons of linkage and internal reasons of improved authority for which it is desirable to shape Wikipedia into a true knowledge base.

Efforts to Recast Wikipedia

These faults are not unrecognized and the prospect of better leverage from Wikipedia has stimulated many efforts. Gazing inward, it is not uncommon to find efforts that attempt to clean up the existing Wikipedia structure [25], or various attempts to use the content of Wikipedia article categories [26] to re-constitute new taxonomies [27] or concept networks [28]. Clean up appears essential, and is a relative constant in other attempts to recast Wikipedia [29].

The choice of Wikipedia’s founders to make its full content available electronically for free and without restriction was a masterstroke. This has stimulated many to grab the Wikipedia content and to recast it in other ways. One of the first, and most successful, was DBpedia, with an emphasis on making (much of) Wikipedia available in RDF and linked data. DBpedia emphasized the structured content of Wikipedia’s infoboxes and eventually derived a typology of entities and their properties expressed as the DBpedia ontology [30]. It is not hyperbole to state that DBpedia nucleated the entire linked data phenomenon [7].

The key insight of YAGO [5] was the recognition that the resource richness of Wikipedia lacked a unifying structure, with WordNet chosen as the replacement organizing framework. Also, by patterned analysis of Wikipedia’s article titles structure, YAGO was able to infer and select many attribute relationships between entities. This enabled YAGO to posit what, in essence, was a much-expanded category structure for Wikipedia expressed as predicates. Many other efforts have also chosen WordNet as their organizing framework for Wikipedia [31,32].

Freebase [2], itself another attempt to use crowdsourcing with explicit attention to structured data, struggled in its early years until it embraced and incorporated Wikipedia. That marked the take-off point for Freebase, which was later acquired by Google to form the backbone of its knowledge graph. Freebase is now being shut down with its assets being transferred to Wikidata.

Wikidata [33] is itself an interesting case of how the Wikipedia model is being expanded. Wikidata, a sister project to Wikipedia under the Wikimedia banner, takes as its starting point the structured data about entities evident in Wikipedia infoboxes. Rather than extracting and cleaning that entity information as DBpedia does, the role of Wikidata is to be the multilingual source for all entities feeding the Wikimedia network, including Wikipedia. The approach leads to more uniformity and consistency, and provides a central Wikimedia access point for structured data. However, somewhat akin to Wikipedia, Wikidata also has struggled to find an appropriate typology (or ontology) for its millions of entities [34].

Other approaches to the Wikipedia classification challenge have been to map — or “express” — Wikipedia articles in relation to established external vocabularies or structures, such as the Library of Congress Classification [35], Library of Congress Subject Headings [23, 36], Universal Decimal classification (UDC) [37], Cyc [38] or UMBEL [39], among others. The idea here is that accepted organizational schemes provide more coherence than the Wikipedia category structure, with sometimes additional benefits as well.

Though not complete topical recastings, certain aspects of Wikipedia have also proven their usefulness for general knowledge acquisition. Using article (concept or entity) content can inform topical tagging using explicit semantic analysis (ESA) [40], automatic topic identification [41], information extraction [42] or a myriad of others.

Making a Natural Wikipedia Category Scheme

Whether “cleaned” or recasted, taking the existing Wikipedia structure in its existing form is problematic. Though some category cleaning sometimes takes place with some of these uses of Wikipedia, that is not uniformly nor universally so. The cleaning that does take place is often limited to administrative categories (relating to internal Wikipedia conventions or management). However, other Wikipedia conventions (such as lists) and the proliferation of user-generated “artificial” categories actually represent the bulk of the total number of categories.

Charles S. Peirce was the first, by my reading, who looked at the question of “natural classes,” which are now sometimes contraposed against what are called “artificial classes” (we tend to use the term “compound” classes instead). A “natural class” is a set with members that share the same set of attributes, though with different values (such as differences in age or hair color for humans, for example). Some of those attributes are also more essential to define the “type” of that class (such as humans being warm-blooded with live births and hair and use of symbolic languages). Artificial classes tend to only add one or a few shared attributes, and do not reflect the essence of the type [43].

“Compound” (or artificial) categories (such as Films directed by Pedro Almodóvar or Ambassadors of the United States to Mexico) are not “natural” categories, and including them in a logical evaluation only acts to confuse attributes from classification. To be sure, such existing categories should be decomposed into their attribute and concept components, but should not be included in constructing a schema of the domain.

“Artificial” categories may be identified in the Wikipedia category structure by both syntactical and heuristic signals. One syntactical rule is to look for the head of a title; one heuristic signal is to select out any category with prepositions. Across all rules, “compound” categories actually account for most of what is removed in order to produce “cleaned” categories.

We can combine these thoughts to show what a “cleaned” version of the Wikipedia category structure might look like. The 12/15/10 column in the table below reflects the approach used for determining the candidates for SuperTypes in the UMBEL ontology, last analyzed in 2010 [44]. The second column is from a current effort mapping Wikipedia to Cyc [45]:

12/15/10 3/1/15
Total Categories 100% 100%
Administrative Categories 14% 15%
Orphaned Categories 10% 20%
Working Categories 76% 66%
“Artificial” Categories 44% 34%
Single Head 23%
33%
Plural Head 24%
“Clean” Categories 33% 46%

Two implications can be drawn from this table. First, without cleaning, there is considerable “noise” in the Wikipedia category structure, equivalent to about half to two-thirds of all categories. Without cleaning these categories, any analysis or classification that ensues is fighting unnecessary noise and has likely introduced substantial assignment errors. Second, approaches, assumptions and how filters get sequenced differ between “cleaning” attempts, which both makes comparability a challenge but also represents areas for discussion and testing to derive best practices. This lack of comparability due to differences in staging Wikipedia for analysis makes it difficult to draw comparisons between different studies and approaches. One study is not necessarily relatable to other studies.

Today, in chaotic and uncoordinated ways, we see Wikipedia feeding much analysis through partial aspects of its structure and supplying many reference concepts and entities. But each analysis is done for different purposes using different bases; they are thus incompatible. Coherency, usability and insight suffer. Any prior efforts to map to or use Wikipedia categories that do not remove these artificial categories only introduce noise and are therefore likely to be in substantial error.

Benefits of a Reference Knowledge Base

If we could overcome these shortcomings by taking the steps to make Wikipedia a true reference knowledge base, what might the benefits be? Or, said another way, why should we care?

One benefit is that reference structures of any kind provide a focus, by definition, of common or canonical referents. This commonality leads to better defined, better understood and more widely used referents. Common referents become a kind of common vocabulary for the space, upon which other vocabularies and datasets can reference. A common language, of sorts, can begin to emerge.

Reference structures also provide a grounding, a spoke-and-hub design [46], that leads to an efficient basis for external vocabularies and datasets to refer to one another. Of course, any direct mapping can provide a means to relate this information, but such pairwise mappings are not scalable nor efficient. In a spoke-and-hub design, the number of mappings required goes down significantly with the number of datasets or items requiring mapping. The spoke-and-hub design, for example, is at the heart of such disciplines as master data management.

Another benefit of common reference structures is that they provide a common target for the development of tools and best practices. These kinds of “network effects” lead to still further tooling and practices. Thus, while we see literally tens of thousands of academic papers and approaches leveraging Wikipedia in one way or another, we see little of a practice or a community that has been built around it as a knowledge base. It is as if we are still looking a bit at the shadow of Wikipedia and its possible role, a chimera for its potential as a true knowledge base.

But the ultimate benefit of Wikipedia as a reference knowledge base will reside in its computability. When we can reason over Wikipedia’s content, use it for testing and analyzing assertions or new facts, when its coherent organization can be applied to such tasks as informing how to map and interoperate data together or remaking whole legacy applications such as enterprise information integration or MDM, all of which in cross-lingual ways, we will finally see the realization of Wikipedia’s inherent potential. And, as these latent capabilities get exploited, we will see supporting knowledge sources such as Wikidata also get pulled into the ecosystem.

Seven Requirements for a Computable Knowledge Base

So, if we buy into the benefits of a computable Wikipedia — or any other useful knowledge source for that matter — what are the guideposts for doing so? How do we assess the gaps and then fill them?

The importance of working with a “clean” version of the Wikipedia structure is obvious, yet ultimately there are higher-order requirements for what it takes, in our view, to become a “true” reference knowledge base. By our definition, such KBs have these aspects:

  • Coherent — does it hold together conceptually, logically, does it make sense? Either internally via consistency tests and such, or externally via testing against known facts and knowledge, the structure of the knowledge base should be defensible and meet the “common sense” test
  • Comprehensive — does the knowledge base have the scope of domains to which it is likely to interact? For a Web reference, the KB need not be global, but be relevant to an important domain of discourse. The biomedical domain, and its constituent and biological sub-domains, is an example. Something like Wikipedia represents a more “global” domain, and is thus central to the idea
  • Referencable — is the knowledge source authoritative? does it use URIs for referencing its objects?
  • Open Standards — which also implies, does it meet open standards? Open standards, by virtue of their decision processes, represent well-reasoned bases. Open standards are also easier to interoperate with and have more tooling available
  • Computable — the combination of the above can lead to a KB structure that supports reasoning, inference, set selection, relations, attributes, datatypes, and filtering and retrieval. These aspects make the KB “computable” [47], the threshold qualifier for a “true” knowledge base
  • Testable — but now, once the KBs are computable, they are also testable. That means the entire KB structure may be tested, verified, validated, scored, and evaluated
  • Multi-lingual — if not already multi-lingual, does it have a structure (such as ID v label-based) that supports multiple languages? Is there attention paid to encoding and transfer standards so as to promote consumption and use of the KB data? Multi-linguality may sound like icing on the cake, but it represents the next phase of bringing structure to the question of how to better identify, discern, and disambiguate information.

Wikipedia, and other publicly available knowledge sources [48], already fulfill many of these requirements. With focused attention, any current reference source should be able to be lifted to meet these seven major requirements.

Outlines of a General Staging Pipeline

OK, then: what might such a KB processing (or “lifting”) approach look like?

Well, the first point is that it should be a pipeline. It is important to be able to swap in and out various options at multiple points from input to desired output. Then, because there are disparate sources and different formats to accommodate, it is also important to use canonical syntaxes and standards for expressing the products and specifications at the various steps along that pipeline.

The very notion of pipeline implies workflows, which are the actual drivers for how the pipeline should be designed. Key workflow steps include:

  • Clean the input sources
  • Express the sources in a canonical form [49]
  • Identify and extract concepts
  • Map the structure to KB concepts
  • Identify and extract entities
  • Identify and extract relations
  • Type the entities, concepts, and relations
  • Extract attributes and values for identified entities
  • Test these against the existing KB
  • Update reference structures, including placement of the new assertions, as appropriate
  • Characterize and log to files
  • Commit to the KB
  • Rinse, repeat.

Much information gets processed in these pipelines, and the underlying sources update frequently. Thus, the pipelines themselves need to be performant and based on solid code. Automation, within the demanding bounds of quality, is also an essential condition to be scalable. Improving on that is a process, not a state.

Time to Make Some Sausage

Most of these observations are really not new or innovative [39,50]. Possibly what is new is to articulate the situation for major reference sources on the Web, and to then analyze and propose how to process them in the service of information interoperability.

Because, you see, we’re still at the very, very earliest phases of how the Internet is changing the abilities to gather, understand, and represent the information in our world. We’re about ready to embark on the next stage in that journey.


[1] IBM Journal of Research and Development 56(3/4), Special Issue on “This is Watson”, 2012
[2] Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. “Freebase: a collaboratively created graph database for structuring human knowledge,” in Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pages 1247–1250. ACM, 2008.
[3] A. Singhal: Introducing the Knowledge Graph: Things, not Strings. Google Blog, May 16, 2012
[4] Gruber, T. “Siri: a virtual personal assistant.” In keynote presentation at Semantic Technologies conference (SemTech09), 2009.
[5] Suchanek, Fabian M., Gjergji Kasneci, and Gerhard Weikum. “Yago: a core of semantic knowledge.” In Proceedings of the 16th international conference on World Wide Web, pp. 697-706. ACM, 2007.
[6] Sören Auer, Chris Bizer, Jens Lehmann, Georgi Kobilarov, Richard Cyganiak and Zachary Ives, 2007. DBpedia: A nucleus for a web of open data, in Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea, volume 4825 of LNCS, pages 715728, November 2007. See http://iswc2007.semanticweb.org/papers/ISWC2007_IU_Auer.pdf.
[7] Heath, Tom, and Christian Bizer. “Linked data: Evolving the web into a global data space.” Synthesis lectures on the semantic web: theory and technology 1, no. 1 (2011): 1-136.
[8] Olena Medelyan, Catherine Legg, David Milne and Ian H. Witten, 2008. Mining Meaning from Wikipedia, Working Paper Series ISSN 1177-777X, Department of Computer Science, The University of Waikato (New Zealand), September 2008, 82 pp. See http://arxiv.org/ftp/arxiv/papers/0809/0809.4530.pdf.
[9] Mesgari, Mostafa, Chitu Okoli, Mohamad Mehdi, Finn Årup Nielsen, and Arto Lanamäki. “The sum of all human knowledge: A systematic review of scholarly research on the content of Wikipedia,” Journal of the Association for Information Science and Technology 66, no. 2 (2015): 219-245.
[10] However, only 1/10th of the different language Wikipedias have more than 100,000 articles; see https://en.wikipedia.org/wiki/Wikipedia:Multilingual_statistics.
[11] See the discussion of ‘structural sources’ in M.K. Bergman, 2011. “In Search of ‘Gold Standards’ for the Semantic Web,” in AI3:::Adaptive Information blog, February 28, 2011.
[12] This count is from Google Scholar absent references in citations and patents with the query, http://scholar.google.com/scholar?as_vis=1&q=wikipedia&hl=en&as_sdt=1,16. Also, see the SWEETpedia listing 250 articles relating to this topic on this AI3:::Adaptive Information blog; I ceased updating the list about five years ago because it was growing too large to manage.
[13] Halavais, Alexander, and Derek Lackaff. “An analysis of topical coverage of Wikipedia.” Journal of Computer Mediated Communication 13, no. 2 (2008): 429-440.
[14] Holloway, Todd, Miran Bozicevic, and Katy Börner. “Analyzing and visualizing the semantic coverage of Wikipedia and its authors.” Complexity 12, no. 3 (2007): 30-40.
[15] Samoilenko, Anna, and Taha Yasseri. “The distorted mirror of Wikipedia: a quantitative analysis of Wikipedia coverage of academics.” EPJ Data Science 3, no. 1 (2014): 1-11.
[17] Halfaker, Aaron, and John Riedl. “Bots and cyborgs: Wikipedia’s immune system.Computer 3 (2012): 79-82.
[20] Suchecki, Krzysztof, Alkim Almila Akdag Salah, Cheng Gao, and Andrea Scharnhorst. “Evolution of Wikipedia’s Category Structure.” Advances in Complex Systems 15, no. supp01 (2012).
[22] Kittur, A., Chi, E. H. and Suh, B., What’s in Wikipedia? Mapping Topics and Conflict Using Socially Annotated Category Structure, in Proceedings of the 27th Annual CHI Conference on Human Factors in Computing Systems (CHI’2009), New York, USA, 2009, pp. 1509–1512.
[23] Joorabchi, Arash, and Abdulhussain E. Mahdi. “Towards linking libraries and Wikipedia: automatic subject indexing of library records with Wikipedia concepts.” Journal of Information Science 40, no. 2 (2014): 211-221.
[24] Paulheim, Heiko, and Christian Bizer. “Type inference on noisy rdf data,” In The Semantic Web–ISWC 2013, pp. 510-525. Springer Berlin Heidelberg, 2013.
[25] Maciej Janik and Krys Kochut, 2007. Wikipedia in Action: Ontological Knowledge in Text Categorization, University of Georgia, Computer Science Department Technical Report no. UGA-CS-TR-07-001. See http://lsdis.cs.uga.edu/~mjanik/UGA-CS-TR-07-001.pdf. Also, see Mohamed Ali Hadj Taieb, Mohamed Ben Aouicha, Mohamed Tmar, and Abdelmajid Ben Hamadou. “Wikipedia Category Graph and New Intrinsic Information Content Metric for Word Semantic Relatedness Measuring.” Computing 10, no. 13 (2012): 35-37.
[26] Vivi Nastase and Michael Strube, 2008. Decoding Wikipedia Categories for Knowledge Acquisition, in Proceedings of the AAAI08 Conference, Chicago, US, , pp.1219-1224.
[27] Simone Paolo Ponzetto and Michael Strube, 2007a. Deriving a Large Scale Taxonomy from Wikipedia, in Association for the Advancement of Artificial Intelligence (AAAI2007).
[28] Andrew Gregorowicz and Mark A. Kramer, 2006. Mining a Large-Scale Term-Concept Network from Wikipedia, Mitre Technical Report, October 2006.
[29] Wu, Fei, and Daniel S. Weld. “Autonomously semantifying wikipedia.” In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pp. 41-50. ACM, 2007.
[30] Bizer, Christian, Jens Lehmann, Georgi Kobilarov, Sören Auer, Christian Becker, Richard Cyganiak, and Sebastian Hellmann. “DBpedia-A crystallization point for the Web of Data.” Web Semantics: science, services and agents on the world wide web 7, no. 3 (2009): 154-165.
[31] Marius Pasca, 2009. Outclassing Wikipedia in Open-Domain Information Extraction: Weakly-Supervised Acquisition of Attributes over Conceptual Hierarchies, in Proceedings of the 12th Conference of the European Chapter of the ACL, pages 639–647, Athens, Greece, 30 March – 3 April 2009. See http://www.aclweb.org/anthology/E/E09/E09-1073.pdf.
[32] Fei Wu and Daniel S. Weld, 2008. Automatically Refining the Wikipedia Infobox Ontology, presented at the 17th International World Wide Web Conference (WWW 2008)
[33] Vrandečić, Denny, and Markus Krötzsch. “Wikidata: a free collaborative knowledgebase.” Communications of the ACM 57, no. 10 (2014): 78-85.
[34] From scratch, in a bit over three years, Wikidata has grown to cover about 19 million entities according to February 2015 statistics. However, there has yet to emerge an overarching typology or ontology for these entities, with the typing system that does exist growing from the bottom up. For some background, see https://www.wikidata.org/wiki/Wikidata:Requests_for_comment/Migrating_away_from_GND_main_type
[35] There is an alternate entry point to Wikipedia provided by http://en.wikipedia.org/wiki/Library_of_Congress_Classification
[36] Kiyota, Yoji, Hiroshi Nakagawa, Satoshi Sakai, Tatsuya Mori, and Hidetaka Masuda. “Exploitation of the wikipedia category system for enhancing the value of LCSH.” In Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries, pp. 411-412. ACM, 2009.
[37] Salah, Almila Akdag, Cheng Gao, Krzysztof Suchecki, and Andrea Scharnhorst. “Need to categorize: A comparative look at the categories of universal decimal classification system and Wikipedia,” Leonardo 45, no. 1 (2012): 84-85.
[38] Pohl, Aleksander. “Classifying the Wikipedia articles into the OpenCyc taxonomy.” In Proceedings of the Web of Linked Entities Workshop in conjuction with the 11th International Semantic Web Conference, vol. 5, p. 16. 2012.
[39] Upper Mapping and Binding Exchange Layer (UMBEL) Specification, UMBEL.org, retrieved February 16, 2015.
[40] Evgeniy Gabrilovich and Shaul Markovitch. 2007. Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis, in Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, January 2007.
[41] Hassan, Mostafa. “Automatic Document Topic Identification Using Hierarchical Ontology Extracted from Human Background Knowledge.” PhD dissertation, University of Waterloo, 2013.
[42] Fei Wu, Raphael Hoffmann and Daniel S. Weld, 2008b. Information Extraction from Wikipedia: Moving Down the Long Tail, in Proceedings of the 14th ACM SigKDD International Conference on Knowledge Discovery and Data Mining (KDD-08), Las Vegas, NV, August 24-27, 2008, pp. 635-644. See http://www.cs.washington.edu/homes/wufei/papers/kdd08.pdf.
[43] Menno Hulswit, 1997. “Peirce’s teleological approach to natural classes,” in Transactions of the Charles S. Peirce Society (1997): 722-772. See http://repository.ubn.ru.nl/bitstream/handle/2066/29577/29577___.PDF?sequence=1
[45] Aleksander Smywinski-Pohl, Krzysztof Wróbel, Michael K. Bergman and Bartosz Ziółko, 2015. “cycloped.io: An Interoperable Framework for Web Knowledge Bases,” manuscript in preparation.
[46] The main advantage of a grounding reference is that it allows a spoke-and-hub design for data mapping, which is tremendously more efficient than pairwise mappings. In a spoke-and-hub design, where the reference ontology is the common node at the hub, only n – 1 routes are necessary to connect all sources, meaning that it scales linearly with the number of sources and attributes. Without a grounding reference, these same mapping capabilities would require \frac{n(n-1)}{2}routes in a pairwise (point-to-point) approach, that also scales poorly as a quadratic function. A system of ten datasets would require 9 composite mappings in the reference grounding case, but 45 in a pairwise approach. And, of course, datasets themselves contain tens to thousands of attributes, compounding the map scaling problem further.
[47] For example, WordNet is a coherent lexical ontology, but is not computable.
[48] See the knowledge bases section of M.K. Bergman, 2014. “Knowledge-based Artificial Intelligence,” in AI3:::Adaptive Information blog, November 14, 2014.
[49] Galárraga, Luis, Geremy Heitz, Kevin Murphy, and Fabian M. Suchanek. “Canonicalizing open knowledge bases.” In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1679-1688. ACM, 2014.
[50] See, for example, Suchanek, Fabian M., and Gerhard Weikum. “Knowledge Bases in the Age of Big Data Analytics.” Proceedings of the VLDB Endowment 7, no. 13 (2014): 1713-1714.