In this paper we investigate situation calculus action theories extended with ontologies, expressed as description logics TBoxes that act as state constraints. We show that this combination, while natural and desirable, is particularly problematic: it leads to undecidability of the simplest form of reasoning, namely satisfiability, even for the simplest kinds of description logics and the simplest kind of situation calculus action theories.

Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. A promising approach is to embed KBs into latent spaces and make inferences by learning and operating on latent representations. Such embedding models, however, do not make use of any rules during inference and hence have limited accuracy. This paper proposes a novel approach which incorporates rules seamlessly into embedding models for KB completion. It formulates inference as an integer linear programming (ILP) problem, with the objective function generated from embedding models and the constraints translated from rules. Solving the ILP problem results in a number of facts which 1) are the most preferred by the embedding models, and 2) comply with all the rules. By incorporating rules, our approach can greatly reduce the solution space and significantly improve the inference accuracy of embedding models. We further provide a slacking technique to handle noise in KBs, by explicitly modeling the noise with slack variables. Experimental results on two publicly available data sets show that our approach significantly and consistently outperforms state-of-the-art embedding models in KB completion. Moreover, the slacking technique is effective in identifying erroneous facts and ambiguous entities, with a precision higher than 90%.

In natural language processing and information retrieval, the bag of words representation is used to implicitly represent the meaning of the text. Implicit semantics, however, are insufficient in supporting text or natural language based interfaces, which are adopted by an increasing number of applications. Indeed, in applications ranging from automatic ontology construction to question answering, explicit representation of semantics is starting to play a more prominent role. In this paper, we introduce the task of conceptual labeling (CL), which aims at generating a minimum set of conceptual labels that best summarize a bag of words. We draw the labels from a data driven semantic network that contains millions of highly connected concepts. The semantic network provides meaning to the concepts, and in turn, it provides meaning to the bag of words through the conceptual labels we generate. To achieve our goal, we use an information theoretic approach to trade-off the semantic coverage of a bag of words against the minimality of the output labels. Specifically, we use Minimum Description Length (MDL) as the criteria in selecting the best concepts. Our extensive experimental results demonstrate the effectiveness of our approach in representing the explicit semantics of a bag of words.

However, as is well known, the but-for test is not always sufficient to determine causality. Consider the following The original Halpern-Pearl definition of causality well-known example, taken from [Paul and Hall, 2013]: [Halpern and Pearl, 2001] was updated in the journal Suzy and Billy both pick up rocks and throw them version of the paper [Halpern and Pearl, 2005] at a bottle. Suzy's rock gets there first, shattering to deal with some problems pointed out by Hopkins the bottle. Since both throws are perfectly accurate, and Pearl [2003]. Here the definition is modified Billy's would have shattered the bottle had it not yet again, in a way that (a) leads to a simpler definition, been preempted by Suzy's throw.

Knowledge and Action Bases (KABs) have been put forward as a semantically rich representation of a domain, using a DL KB to account for its static aspects, and actions to evolve its extensional part over time, possibly introducing new objects. Recently, KABs have been extended to manage inconsistency, with ad-hoc verification techniques geared towards specific semantics. This work provides a twofold contribution along this line of research. On the one hand, we enrich KABs with a high-level, compact action language inspired by Golog, obtaining so called Golog-KABs (GKABs). On the other hand, we introduce a parametric execution semantics for GKABs, so as to elegantly accomodate a plethora of inconsistency-aware semantics based on the notion of repair. We then provide several reductions for the verification of sophisticated first-order temporal properties over inconsistency-aware GKABs, and show that it can be addressed using known techniques, developed for standard KABs.

Large formal mathematical knowledge bases encode considerable parts of advanced mathematics and exact science, allowing deep semantic computer assistance and verification of complicated theories down to the atomic logical rules. An essential part of automated reasoning over such large theories are methods learning selection of relevant knowledge from the thousands of proofs in the corpora. Such methods in turn rely on efficiently computable features characterizing the highly structured and inter-related mathematical statements.  In this work we (i) propose novel semantic features characterizing the statements in such large semantic knowledge bases, (ii) propose and carry out their efficient implementation using deductive-AI data-structures such as substitution trees and discrimination nets, and (iii) show that they significantly improve the strength of existing knowledge selection methods and automated reasoning methods over the large formal knowledge bases. In particular, on a standard large-theory benchmark we improve the average predicted rank of a mathematical statement needed for a proof by 22% in comparison with state of the art. This allows us to prove 8% more theorems in comparison with state of the art.

Taxonomies hierarchically organize concepts in a domain. Building and maintaining them by hand is a tedious and time-consuming task. This paper proposes a novel, unsupervised algorithm for automatically learning an IS-A taxonomy from scratch by analyzing a given text corpus. Our approach is designed to deal with infrequently occurring concepts, so it can effectively induce taxonomies even from small corpora. Algorithmically, the approach makes two important contributions. First, it performs inference based on clustering and the distributional semantics, which can capture links among concepts never mentioned together. Second, it uses a novel graph-based algorithm to detect and remove incorrect is-a relations from a taxonomy. An empirical evaluation on five corpora demonstrates the utility of our proposed approach.

An author’s profile on Google Scholar consists of indexed articles and associated data, such as the number of citations and the H-index. The author is allowed to merge articles, which may affect the H-index. We analyze the parameterized complexity of maximizing the H-index using article merges. Herein, to model realistic manipulation scenarios, we define a compatability graph whose edges correspond to plausible merges. Moreover, we consider multiple possible measures for computing the citation count of a merged article. For the measure used by Google Scholar, we give an algorithm that maximizes the H-index in linear time if the compatibility graph has constant-size connected components. In contrast, if we allow to merge arbitrary articles, then already increasing the H-index by one is NP-hard. Experiments on Google Scholar profiles of AI researchers show that the H-index can be manipulated substantially only by merging articles with highly dissimilar titles, which would be easy to discover.

Cross-lingual sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of data in a label-scarce target language by exploiting labeled data from a label-rich language. The fundamental challenge of cross-lingual learning stems from a lack of overlap between the feature spaces of the source language data and that of the target language data. To address this challenge, previous work in the literature mainly relies on the large amount of bilingual parallel corpora to bridge the language gap. In many real applications, however, it is often the case that we have some partial parallel data but it is an expensive and time-consuming job to acquire large amount of parallel data on different languages. In this paper, we propose a novel subspace learning framework by leveraging the partial parallel data for cross-lingual sentiment classification. The proposed approach is achieved by jointly learning the document-aligned review data and un-aligned data from the source language and the target language via a non-negative matrix factorization framework. We conduct a set of experiments with cross-lingual sentiment classification tasks on multilingual Amazon product reviews. Our experimental results demonstrate the efficacy of the proposed cross-lingual approach.

Real world temporal planning often involves dealing with uncertainty about the duration of actions. In this paper, we describe a sound-and-complete compilation technique for strong planning that reduces any planning instance with uncertainty in the duration of actions to a plain temporal planning problem without uncertainty. We evaluate our technique by comparing it with a recent technique for PDDL domains with temporal uncertainty. The experimental results demonstrate the practical applicability of our approach and show complementary behavior with respect to previous techniques. We also demonstrate the high expressiveness of the translation by applying it to a significant fragment of the ANML language.