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2017 September

Archive for September, 2017

talk: Automated Knowledge Extraction from the Federal Acquisition Regulations System

September 23rd, 2017, by Tim Finin, posted in NLP, Semantic Web

In this week’s meeting, Srishty Saha, Michael Aebig and Jiayong Lin will talk about their work on extracting knowledge from the US FAR System.

Automated Knowledge Extraction from the Federal Acquisition Regulations System

Srishty Saha, Michael Aebig and Jiayong Lin

11am-12pm Monday, 25 September 2017, ITE346, UMBC

The Federal Acquisition Regulations System (FARS) within the Code of Federal Regulations (CFR) includes facts and rules for individuals and organizations seeking to do business with the US Federal government. Parsing and extracting knowledge from such lengthy regulation documents is currently done manually and is time and human intensive. Hence, developing a cognitive assistant for automated analysis of such legal documents has become a necessity. We are developing a semantically rich legal knowledge base representing legal entities and their relationships, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules.

2018 Ontology Summit: Ontologies in Context

September 12th, 2017, by Tim Finin, posted in events, KR, Ontologies, Semantic Web

2018 Ontology Summit: Ontologies in Context

The OntologySummit is an annual series of online and in-person events that involves the ontology community and communities related to each year’s topic. The topic chosen for the 2018 Ontology Summit will be Ontologies in Context, which the summit describes as follows.

“In general, a context is defined to be the circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed. Some examples of synonyms include circumstances, conditions, factors, state of affairs, situation, background, scene, setting, and frame of reference. There are many meanings of “context” in general, and also for ontologies in particular. The summit this year will survey these meanings and identify the research problems that must be solved so that contexts can succeed in achieving the full understanding and assessment of an ontology.”

Each year’s Summit comprises of a series of both online and face-to-face events that span about three months. These include a vigorous three-month online discourse on the theme, and online panel discussions, research activities which will culminate in a two-day face-to-face workshop and symposium.

Over the next two months, there will be a sequence of weekly online meetings to discuss, plan and develop the 2018 topic. The summit itself will start in January with weekly online sessions of invited speakers. Visit the the 2018 Ontology Summit site for more information and to see how you can participate in the planning sessions.

Dissertation: Context-Dependent Privacy and Security Management on Mobile Devices

September 10th, 2017, by Tim Finin, posted in cybersecurity, Machine Learning, Mobile Computing, Ontologies, Semantic Web

Context-Dependent Privacy and Security Management on Mobile Devices

Prajit Kumar Das, Context-Dependent Privacy and Security Management on Mobile Devices, Ph.D. Dissertation, University of Maryland, Baltimore County, September 2017.

There are ongoing security and privacy concerns regarding mobile platforms that are being used by a growing number of citizens. Security and privacy models typically used by mobile platforms use one-time permission acquisition mechanisms. However, modifying access rights after initial authorization in mobile systems is often too tedious and complicated for users. User studies show that a typical user does not understand permissions requested by applications or are too eager to use the applications to care to understand the permission implications. For example, the Brightest Flashlight application was reported to have logged precise locations and unique user identifiers, which have nothing to do with a flashlight application’s intended functionality, but more than 50 million users used a version of this application which would have forced them to allow this permission. Given the penetration of mobile devices into our lives, a fine-grained context-dependent security and privacy control approach needs to be created.

We have created Mithril as an end-to-end mobile access control framework that allows us to capture access control needs for specific users, by observing violations of known policies. The framework studies mobile application executables to better inform users of the risks associated with using certain applications. The policy capture process involves an iterative user feedback process that captures policy modifications required to mediate observed violations. Precision of policy is used to determine convergence of the policy capture process. Policy rules in the system are written using Semantic Web technologies and the Platys ontology to define a hierarchical notion of context. Policy rule antecedents are comprised of context elements derived using the Platys ontology employing a query engine, an inference mechanism and mobile sensors. We performed a user study that proves the feasibility of using our violation driven policy capture process to gather user-specific policy modifications.

We contribute to the static and dynamic study of mobile applications by defining “application behavior” as a possible way of understanding mobile applications and creating access control policies for them. Our user study also shows that unlike our behavior-based policy, a “deny by default” mechanism hampers usability of access control systems. We also show that inclusion of crowd-sourced policies leads to further reduction in user burden and need for engagement while capturing context-based access control policy. We enrich knowledge about mobile “application behavior” and expose this knowledge through the Mobipedia knowledge-base. We also extend context synthesis for semantic presence detection on mobile devices by combining Bluetooth, low energy beacons and Nearby Messaging services from Google.

New paper: Cognitive Assistance for Automating the Analysis of the Federal Acquisition Regulations System

September 5th, 2017, by Tim Finin, posted in KR, Machine Learning, NLP

Cognitive Assistance for Automating the Analysis of the Federal Acquisition Regulations System

Srishty Saha and Karuna Pande Joshi, Cognitive Assistance for Automating the Analysis of the Federal Acquisition Regulations System, AAAI Fall Symposium on Cognitive Assistance in Government and Public Sector Applications, AAAI Press, November 2017

Government regulations are critical to understanding how to do business with a government entity and receive other bene?ts. However, government regulations are also notoriously long and organized in ways that can be confusing for novice users. Developing cognitive assistance tools that remove some of the burden from human users is of potential bene?t to a variety of users. The volume of data found in United States federal government regulation suggests a multiple-step approach to process the data into machine readable text, create an automated legal knowledge base capturing various facts and rules, and eventually building a legal question and answer system to acquire understanding from various regulations and provisions. Our work discussed in this paper represents our initial efforts to build a framework for Federal Acquisition Regulations System (Title 48, Code of Federal Regulations) in order to create an efficient legal knowledge base representing relationships between various legal elements, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules.

New paper: Generating Digital Twin models using Knowledge Graphs for Industrial Production Lines

September 2nd, 2017, by Tim Finin, posted in KR

Generating Digital Twin models using Knowledge Graphs for Industrial Production Lines

Agniva Banerjee, Raka Dalal, Sudip Mittal and Karuna Pande Joshi, Generating Digital Twin models using Knowledge Graphs for Industrial Production Lines, Workshop on Industrial Knowledge Graphs, co-located with the 9th International ACM Web Science Conference, 2017.

Digital Twin models are computerized clones of physical assets that can be used for in-depth analysis. Industrial production lines tend to have multiple sensors to generate near real-time status information for production. Industrial Internet of Things datasets are difficult to analyze and infer valuable insights such as points of failure, estimated overhead. etc. In this paper we introduce a simple way of formalizing knowledge as digital twin models coming from sensors in industrial production lines. We present a way on to extract and infer knowledge from large scale production line data, and enhance manufacturing process management with reasoning capabilities, by introducing a semantic query mechanism. Our system primarily utilizes a graph-based query language equivalent to conjunctive queries and has been enriched with inference rules.

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