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W3C Recommendation: Time Ontology in OWL

October 26th, 2017, by Tim Finin, posted in KR, Ontologies, OWL, Semantic Web

W3C Recommendation: Time Ontology in OWL

The Spatial Data on the Web Working Group has published a W3C Recommendation of the Time Ontology in OWL specification. The ontology provides a vocabulary for expressing facts about  relations among instants and intervals, together with information about durations, and about temporal position including date-time information. Time positions and durations may be expressed using either the conventional Gregorian calendar and clock, or using another temporal reference system such as Unix-time, geologic time, or different calendars.

Agniva Banerjee on Managing Privacy Policies through Blockchain

October 16th, 2017, by Tim Finin, posted in Blockchain, cybersecurity, Policy, Privacy, Security, Semantic Web

Link before you Share: Managing Privacy Policies through Blockchain

Agniva Banerjee

11:00am Monday, 16 October 2017

An automated access-control and audit mechanism that enforces users’ data privacy policies when sharing their data across third parties, by utilizing privacy policy ontology instances with the properties of blockchain.

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.

PhD defense: Prajit Das, Context-dependent privacy and security management on mobile devices

August 17th, 2017, by Tim Finin, posted in Mobile Computing, OWL, Privacy, RDF, Semantic Web

Ph.D. Dissertation Defense

Context-dependent privacy and security management on mobile devices

Prajit Kumar Das

8:00-11:00am Tuesday, 22 August 2017, ITE325b, UMBC

There are ongoing security and privacy concerns regarding mobile platforms which 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.

Committee: Drs. Anupam Joshi (chair), Tim Finin (co-chair), Tim Oates, Nilanjan Banerjee, Arkady Zaslavsky, (CSIRO), Dipanjan Chakraborty (Shopperts)

PhD defense: Deep Representation of Lyrical Style and Semantics for Music Recommendation

July 16th, 2017, by Tim Finin, posted in Data Science, Machine Learning, NLP, Semantic Web

Dissertation Defense

Deep Representation of Lyrical Style and Semantics for Music Recommendation

Abhay L. Kashyap

11:00-1:00 Thursday, 20 July 2017, ITE 346

In the age of music streaming, the need for effective recommendations is important for music discovery and a personalized user experience. Collaborative filtering based recommenders suffer from popularity bias and cold-start which is commonly mitigated by content features. For music, research in content based methods have mainly been focused in the acoustic domain while lyrical content has received little attention. Lyrics contain information about a song’s topic and sentiment that cannot be easily extracted from the audio. This is especially important for lyrics-centric genres like Rap, which was the most streamed genre in 2016. The goal of this dissertation is to explore and evaluate different lyrical content features that could be useful for content, context and emotion based models for music recommendation systems.

With Rap as the primary use case, this dissertation focuses on featurizing two main aspects of lyrics; its artistic style of composition and its semantic content. For lyrical style, a suite of high level rhyme density features are extracted in addition to literary features like the use of figurative language, profanity and vocabulary strength. In contrast to these engineered features, Convolutional Neural Networks (CNN) are used to automatically learn rhyme patterns and other relevant features. For semantics, lyrics are represented using both traditional IR techniques and the more recent neural embedding methods.

These lyrical features are evaluated for artist identification and compared with artist and song similarity measures from a real-world collaborative filtering based recommendation system from Last.fm. It is shown that both rhyme and literary features serve as strong indicators to characterize artists with feature learning methods like CNNs achieving comparable results. For artist and song similarity, a strong relationship was observed between these features and the way users consume music while neural embedding methods significantly outperformed LSA. Finally, this work is accompanied by a web-application, Rapalytics.com, that is dedicated to visualizing all these lyrical features and has been featured on a number of media outlets, most notably, Vox, attn: and Metro.

Committee: Drs. Tim Finin (chair), Anupam Joshi, Tim Oates, Cynthia Matuszek and Pranam Kolari (Walmart Labs)

PhD Proposal: Analysis of Irregular Event Sequences using Deep Learning, Reinforcement Learning, and Visualization

July 12th, 2017, by Tim Finin, posted in AI, Machine Learning, Semantic Web

Analysis of Irregular Event Sequences using Deep Learning, Reinforcement Learning, and Visualization

Filip Dabek

11:00-1:00 Thursday 13 July 2017, ITE 346, UMBC

History is nothing but a catalogued series of events organized into data. Amazon, the largest online retailer in the world, processes over 2,000 orders per minute. Orders come from customers on a recurring basis through subscriptions or as one-off spontaneous purchases, resulting in each customer exhibiting their own behavioral pattern when it comes to the way in which they place orders throughout the year. For a company such as Amazon, that generates over $130 billion of revenue each year, understanding and uncovering the hidden patterns and trends within this data is paramount in improving the efficiency of their infrastructure ranging from the management of the inventory within their warehouses, distribution of their labor force, and preparation of their online systems for the load of users. With the ever increasingly availability of big data, problems such as these are no longer limited to large corporations but are experienced across a wide range of domains and faced by analysts and researchers each and every day.

While many event analysis and time series tools have been developed for the purpose of analyzing such datasets, most approaches tend to target clean and evenly spaced data. When faced with noisy or irregular data, it has been recommended to undergo a pre-processing step of converting and transforming the data into being regular. This transformation technique arguably interferes on a fundamental level as to how the data is represented, and may irrevocably bias the way in which results are obtained. Therefore, operating on raw data, in its noisy natural form, is necessary to ensure that the insights gathered through analysis are accurate and valid.

In this dissertation novel approaches are presented for analyzing irregular event sequences using a variety of techniques ranging from deep learning, reinforcement learning, and visualization. We show how common tasks in event analysis can be performed directly on an irregular event dataset without requiring a transformation that alters the natural representation of the process that the data was captured from. The three tasks that we showcase include: (i) summarization of large event datasets, (ii) modeling the processes that create events, and (iii) predicting future events that will occur.

Committee: Drs. Tim Oates (Chair), Jesus Caban, Penny Rheingans, Jian Chen, Tim Finin

 

Jennifer Sleeman dissertation defense: Dynamic Data Assimilation for Topic Modeling

June 27th, 2017, by Tim Finin, posted in Big data, Earth science, Machine Learning, NLP, Ontologies, Semantic Web

Ph.D. Dissertation Defense

Dynamic Data Assimilation for Topic Modeling

Jennifer Sleeman
9:00am Thursday, 29 June 2017, ITE 325b, UMBC

Understanding how a particular discipline such as climate science evolves over time has received renewed interest. By understanding this evolution, predicting the future direction of that discipline becomes more achievable. Dynamic Topic Modeling (DTM) has been applied to a number of disciplines to model topic evolution as a means to learn how a particular scientific discipline and its underlying concepts are changing. Understanding how a discipline evolves, and its internal and external influences, can be complicated by how the information retrieved over time is integrated. There are different techniques used to integrate sources of information, however, less research has been dedicated to understanding how to integrate these sources over time. The method of data assimilation is commonly used in a number of scientific disciplines to both understand and make predictions of various phenomena, using numerical models and assimilated observational data over time.

In this dissertation, I introduce a novel algorithm for scientific data assimilation, called Dynamic Data Assimilation for Topic Modeling (DDATM), which uses a new cross-domain divergence method (CDDM) and DTM. By using DDATM, observational data in the form of full-text research papers can be assimilated over time starting from an initial model. DDATM can be used as a way to integrate data from multiple sources and, due to its robustness, can exploit the assimilating observational information to better tolerate missing model information. When compared with a DTM model, the assimilated model is shown to have better performance using standard topic modeling measures, including perplexity and topic coherence. The DDATM method is suitable for prediction and results in higher likelihood for subsequent documents. DDATM is able to overcome missing information during the assimilation process when compared with a DTM model. CDDM generalizes as a method that can also bring together multiple disciplines into one cohesive model enabling the identification of related concepts and documents across disciplines and time periods. Finally, grounding the topic modeling process with an ontology improves the quality of the topics and enables a more granular understanding of concept relatedness and cross-domain influence.

The results of this dissertation are demonstrated and evaluated by applying DDATM to 30 years of reports from the Intergovernmental Panel on Climate Change (IPCC) along with more than 150,000 documents that they cite to show the evolution of the physical basis of climate change.

Committee Members: Drs. Tim Finin (co-advisor), Milton Halem (co-advisor), Anupam Joshi, Tim Oates, Cynthia Matuszek, Mark Cane, Rafael Alonso

UMBC Seeks Professor of the Practice to Head new Data Science Program

June 7th, 2017, by Tim Finin, posted in Data Science, Semantic Web, UMBC

The University of Maryland, Baltimore County is looking to hire a Professor of the Practice to head a new graduate program in Data Science. See the job announcement for more information and apply online at Interfolio.

In addition to developing and teaching graduate data science courses, the new faculty member will serve as the Graduate Program Director of UMBC’s program leading to a master’s degree in Data Science. This cross-disciplinary program is offered to professional students through a partnership between the College of Engineering and Information Technology; the College of Arts, Humanities and Social Sciences; the College of Natural and Mathematical Sciences; the Department of Computer Science and Electrical Engineering; and UMBC’s Division of Professional Studies.

Modeling and Extracting information about Cybersecurity Events from Text

May 15th, 2017, by Tim Finin, posted in cybersecurity, Machine Learning, NLP, OWL, Semantic Web

Ph.D. Dissertation Proposal

Modeling and Extracting information about Cybersecurity Events from Text

Taneeya Satyapanich

Tuesday, 16 May 2017, ITE 325, UMBC

People rely on the Internet to carry out much of the their daily activities such as banking, ordering food and socializing with their family and friends. The technology facilitates our lives, but also comes with many problems, including cybercrimes, stolen data and identity theft. With the large and increasing number of transaction done every day, the frequency of cybercrime events is also increasing. Since the number of security-related events is too high for manual review and monitoring, we need to train machines to be able to detect and gather data about potential cybersecurity threats. To support machines that can identify and understand threats, we need standard models to store the cybersecurity information and information extraction systems that can collect information to populate the models with data from text.

This dissertation will make two major contributions. The first is to extend our current cyber security ontologies with better models for relevant events, from atomic events like a login attempt, to an extended but related series of events that make up a campaign, to generalized events, such as an increase in denial-of-service attacks originating from a particular region of the world targeted at U.S. financial institutions. The second is the design and implementation of a event extraction system that can extract information about cybersecurity events from text and populated a knowledge graph using our cybersecurity event ontology. We will extend our previous work on event extraction that detected human activity events from news and discussion forums. A new set of features and learning algorithms will be introduced to improve the performance and adapt the system to cybersecurity domain. We believe that this dissertation will be useful for cybersecurity management in the future. It will quickly extract cybersecurity events from text and fill in the event ontology.

Committee: Drs. Tim Finin (chair), Anupam Joshi, Tim Oates and Karuna Joshi

new paper: Modeling the Evolution of Climate Change Assessment Research Using Dynamic Topic Models and Cross-Domain Divergence Maps

May 15th, 2017, by Tim Finin, posted in AI, Machine Learning, NLP, Paper, Semantic Web

Jennifer Sleeman, Milton Halem, Tim Finin, and Mark Cane, Modeling the Evolution of Climate Change Assessment Research Using Dynamic Topic Models and Cross-Domain Divergence Maps, AAAI Spring Symposium on AI for Social Good, AAAI Press, March, 2017.

Climate change is an important social issue and the subject of much research, both to understand the history of the Earth’s changing climate and to foresee what changes to expect in the future. Approximately every five years starting in 1990 the Intergovernmental Panel on Climate Change (IPCC) publishes a set of reports that cover the current state of climate change research, how this research will impact the world, risks, and approaches to mitigate the effects of climate change. Each report supports its findings with hundreds of thousands of citations to scientific journals and reviews by governmental policy makers. Analyzing trends in the cited documents over the past 30 years provides insights into both an evolving scientific field and the climate change phenomenon itself. Presented in this paper are results of dynamic topic modeling to model the evolution of these climate change reports and their supporting research citations over a 30 year time period. Using this technique shows how the research influences the assessment reports and how trends based on these influences can affect future assessment reports. This is done by calculating cross-domain divergences between the citation domain and the assessment report domain and by clustering documents between domains. This approach could be applied to other social problems with similar structure such as disaster recovery.

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