We study a novel task of numerical relation extraction with the goal of extracting relations where one of the arguments is a number or a quantity ( e.g., atomic_number(Aluminium, 13), inflation_rate(India, 10.9%)). This task presents peculiar challenges not found in standard IE, such as the difficulty of matching numbers in distant supervision and the importance of units. We design two extraction systems that require minimal human supervision per relation: (1) NumberRule, a rule based extractor, and (2) NumberTron, a probabilistic graphical model. We find that both systems dramatically outperform MultiR, a state-of-the-art non-numerical IE model, obtaining up to 25 points F-score improvement.

The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime. The pro-active caching of certain subqueries further increases efficiency. Our empirical evaluation shows that this approach can often match or even surpass the performance of state-of-the-art systems, especially under restricted resources.

In this demo we present a semantic framework for innovation and patent analytics powered by Mined Semantic Analysis (MSA). Our framework provides cognitive assistance to its users through a Web-based visual and interactive interface. First, we describe building a conceptual knowledge graph by mining user-generated encyclopedic textual corpus for semantic associations. Then, we demonstrate applying the acquired knowledge to support many cognition and knowledge based use cases for innovation analysis including technology exploration and landscaping, competitive analysis, literature and prior art search and others.

Privacy preserving in association mining is an important research topic in the database security field. This paper has proposed a blocking-based method to solve the association rule hiding problem for data sharing. It aims at reducing undesirable side effects and increasing desirable side effects, while ensuring to conceal all sensitive rules. The candidate transactions are selected for sanitization based on their relations with border rules. Comparative experiments on real datasets demonstrate that the proposed method can achieve its goals.

Aspect extraction is a key task of fine-grained opinion mining. Although it has been studied by many researchers, it remains to be highly challenging. This paper proposes a novel unsupervised approach to make a major improvement. The approach is based on the framework of lifelong learning and is implemented with two forms of recommendations that are based on semantic similarity and aspect associations respectively. Experimental results using eight review datasets show the effectiveness of the proposed approach.

When we go grocery shopping, we often have a standard list of things to buy. Each shopper has a distinctive list, depending on one's needs and preferences. A housewife might buy healthy ingredients for a family dinner, while a bachelor might buy beer and chips. Understanding these buying patterns can help to increase sales in several ways. While we may know that certain items are frequently bought together, the question is, how do we uncover these associations? Besides increasing sales profits, association rules can also be used in other fields.

SUTime is a library for recognizing and normalizing time expressions. That is, it will convert next wednesday at 3pm to something like 2016-02-17T15:00 (depending on the assumed current reference time). SUTime is available as part of the Stanford CoreNLP pipeline and can be used to annotate documents with temporal information. It is a deterministic rule-based system designed for extensibility. SUTime was developed using TokensRegex, a generic framework for definining patterns over text and mapping to semantic objects.

The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages and standards for rule technologies. RuleML is a leading conference to build bridges between academe and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rule engines/business rule management systems; semantic web rule languages and rule standards; rule-based event processing languages (EPLs) and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules. The 9th International Web Rule Symposium (RuleML 2015) was held in Berlin, Germany, August 2-5.

The dinner speech at the Fischerhuette was given by Jörg Siekmann (University of Saarbrücken). The poster session, consisting of 18 posters and demos, was jointly organized as a get-together with the Berlin Semantic Web Meetup. At the session, wine, beer, and finger food were provided in the greenhouses of the Computer Science Department at The Thirty-First AAAI Conference on Artificial Intelligence the Freie Universität Berlin. The organizers also used (AAAI-17) and the Twenty-Ninth Conference on Innovative this unique opportunity to hold a joint public Applications of Artificial Intelligence (IAAI-17), will be RuleML and RR business meeting as well as an invited held in New Orleans, Louisiana, USA, during the mid-January dinner with all chairs, and invited keynote speakers to mid-February timeframe. AAAI-17 August 1, a boat sightseeing tour from lake Wannsee will arrive in New Orleans just prior to Mardi Gras and festivities to the Reichstag on Sunday, August 2, the CADE exhibitions will already be underway.

The general public shows great potential for utilizing scientific research. For example, a singer discovered her ectopic pregnancy by looking up clinical case reports. However, an exorbitant paywall impedes the public’s access to scientific literature. Our case study on a social network demonstrates a growing need for non-open access publications, especially for biomedical literature. The challenge is that non-open access papers are protected by copyright licenses that bar free distribution. In this paper, we propose a technical framework that leverages the doctrine of "idea-expression dichotomy" to bring ideas across paywalls. Idea-expression dichotomy prevents copyright holders from monopolizing ideas, theories, facts, and concepts. Therefore facts may pass through paywalls unencumbered by copyright license restrictions. Existing fact extraction methods (such as information extraction) require either large training sets or domain knowledge, which is intractable for the diverse biomedical scope spanning from clinical findings to genomics. We therefore develop a rule-based system to represent and extract facts. Social networkers and academics validated the effectiveness of our approach. 7 out of 9 users rated the paper’s information from the facts to be above average (≥6/10). Only 7% of the extracted facts were rated misleading.