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Title: Learning Numerical Attributes in Knowledge Bases

Paper content:
Knowledge bases (KB) are often represented as a collection of facts in the form (HEAD, PREDICATE, TAIL), where HEAD and TAIL are entities while PREDICATE is a binary relationship that links the two.
It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction.
However, though frequently ignored, these repositories also contain numerical facts.
Numerical facts link entities to numerical values via numerical predicates; e.g., (PARIS, LATITUDE, 48.8).
Likewise, numerical facts also suffer from the incompleteness problem.
To address this issue, we introduce the numerical attribute prediction problem.
This problem involves a new type of query where the relationship is a numerical predicate.
Consequently, and contrary to link prediction, the answer to this query is a numerical value.
We argue that the numerical values associated with entities explain, to some extent, the relational structure of the knowledge base.
Therefore, we leverage knowledge base embedding methods to learn representations that are useful predictors for the numerical attributes.
An extensive set of experiments on benchmark versions of FREEBASE and YAGO show that our approaches largely outperform sensible baselines.
We make the datasets available under a permissive BSD-3 license.
Knowledge Bases (KBs) are playing an increasingly important role in a number of AI applications.
KBs can be seen as a collection of facts or triples of the form (head, predicate, tail) , denoted as (h, p, t) , where head and tail correspond to entities and predicate corresponds to a relationship that holds between these two entities.
This structured information is easily accessible by AI systems to enhance their performance.
A variety of AI applications such as recommender systems, natural language chatbots or question answering models, have benefited from the rich structural information archived in these repositories.
This is because much of human knowledge can be expressed with one or more conjunctions of knowledge facts.However, KBs' capabilities are limited due to their incompleteness 1 .
Consequently there has been a flurry of research on knowledge base completion methods in recent years.
Relationship extraction BID27 (i.e., classification of semantic relationship mentions), knowledge graph matching BID32 BID12 (i.e., alignment and integration of entities and predicates across KBs), or search-based question-answering BID36 (i.e., queries issued to a web search engine) are a few different ways to address the incompleteness problem.
However, the literature on the so-called link prediction methods BID22 has received more attention in the last few years in comparison to the aforementioned approaches.
Contrary to other solutions, link prediction methods aim to find missing links between entities exclusively based on the existing information contained in the KB.
This is achieved by ranking entities that are answer candidates for the query.
The queries these methods typically address are of the form (USA, /location/contains, ?), or (Madrid, /location/capitalOf, ?), whereas the missing element -represented by a question mark-is an entity contained in the KB.Many link prediction methods only harness feature types learned from the rich relational information contained in the KB to infer new links, and only very recently Niepert, 2018, Pezeshkpour et al., 2018] numerical attributes have been integrated along with other feature types to improve link prediction performance.
Similarly, numerical information is also represented as facts such as (Berlin, /location/latitude, 52.31) or (Albert Einstein, /person/birth year, 1879).
However, as shown in BID5 ] the application of numerical attributes is limited because of the same incompleteness problem: Many entities are missing numerical attribute values they are expected to possess.
For example, entities that represent locations should have numerical information regarding latitude, longitude or area, among others; whereas for entities representing people, numerical predicates such as the birth year, weight or height would be more appropriate.
In this work we focus on the problem of completing queries where the relationship is a numerical predicate.
Consequently, the answer to this new type of query is a numerical value.
This is contrary to the link prediction problem, wherein the answer to a query is always an element of a closed vocabulary.
Examples of queries addressed in this paper are (Apple Inc., revenue, ?) or (California, average salary, ?).
While one can interpret link prediction as a classification/ranking problem, this is rather a regression problem.The main contributions of this paper are:• We introduce the problem of predicting the value of entities' numerical attributes in KBs.
For the sake of simplicity we term this as 'numerical attribute prediction problem'.
To our knowledge, this is the first time this problem is addressed in the literature.•
We create benchmark datasets for this problem. We
use well-known subsets of Freebase and Yago as the blueprints for creating these benchmarks. We
also create versions of these datasets for different percentages of sparsity by artificially removing facts that involve numerical predicates. All
these benchmark datasets will be made publicly available.• We
propose two meaningful baselines for this problem. These
baselines are inspired by previous work done in the node classification and the imputation literature.• We propose
supervised and semi-supervised approaches to this problem. The semisupervised
approaches significantly outperform the baselines in all datasets and conditions.The paper is organized as follows: We discuss the related work in Section 2. Afterwards we formalize
the problem of predicting numerical values for entities' numerical attributes in KBs in Section 3. We describe our approaches
to this problem, as well as the two baselines. Section 4 reports the experimental
setting followed by an extensive set of experiments on different datasets with different degrees of sparsity in Section 5. Finally, we summarize the conclusions
of our study in Section 6.
We introduce a novel problem, namely numerical attribute prediction in knowledge bases.
Contrary to link prediction, the answer to this new query type is a numerical value, and not an element from a (small) closed vocabulary.
Our premise to this problem is that the relational structure of a KB can be partially explained by the numerical attribute values associated with entities.
This allows for leveraging KB embedding methods to learn representations that are useful predictors of numerical attributes.
An extensive set of experiments validates our premise.
Furthermore, we also show that learning KB representations enriched with numerical attribute information are helpful for addressing this task.
Finally, we believe that this new problem introduced in the paper will spur interest and deeper investigation from the research community.5.
Note that it is non-negative and is row normalized.
6. For all practical purposes he is deemed a philosopher in FB15K-237.
7. Julius Caesar belongs to profession Politician in FB15K-237

assistant: text: Prediction of numerical attribute values associated with entities in knowledge bases.