Archive for the 'Semantic Web' Category
July 17th, 2014, by Tim Finin, posted in Ontologies, RDF, Semantic Web
Varish Mulwad, Tim Finin and Anupam Joshi, Interpreting Medical Tables as Linked Data to Generate Meta-Analysis Reports, 15th IEEE Int. Conf. on Information Reuse and Integration, Aug 2014.
Evidence-based medicine is the application of current medical evidence to patient care and typically uses quantitative data from research studies. It is increasingly driven by data on the efficacy of drug dosages and the correlations between various medical factors that are assembled and integrated through meta–analyses (i.e., systematic reviews) of data in tables from publications and clinical trial studies. We describe a important component of a system to automatically produce evidence reports that performs two key functions: (i) understanding the meaning of data in medical tables and (ii) identifying and retrieving relevant tables given a input query. We present modifications to our existing framework for inferring the semantics of tables and an ontology developed to model and represent medical tables in RDF. Representing medical tables as RDF makes it easier for the automatic extraction, integration and reuse of data from multiple studies, which is essential for generating meta–analyses reports. We show how relevant tables can be identified by querying over their RDF representations and describe two evaluation experiments: one on mapping medical tables to linked data and another on identifying tables relevant to a retrieval query.
July 15th, 2014, by Tim Finin, posted in Big data, KR, Ontologies, RDF, Semantic Web
In The trouble with DBpedia, Paul Houle talks about the problems he sees in DBpedia, Freebase and Wikidata and offers up :BaseKB as a better “generic database” that models concepts that are in people’s shared consciousness.
:BaseKB is a purified version of Freebase which is compatible with industry-standard RDF tools. By removing hundreds of millions of duplicate, invalid, or unnecessary facts, :BaseKB users speed up their development cycles dramatically when compared to the source Freebase dumps.
:BaseKB is available for commercial and academic use under a CC-BY license. Weekly versions (:BaseKB Now) can be downloaded from Amazon S3 on a “requester-paid basis”, estimated at $3.00US per download. There are also BaseKB Gold releases which are periodic :BaseKB Now snapshots. These can be downloaded free via Bittorrent or purchased as a Blu Ray disc.
It looks like it’s worth checking out!
June 15th, 2014, by Tim Finin, posted in alumni, Ebiquity, Privacy, Security, Semantic Web
Congratulations to ebiquity alumna Lalana Kagal (Ph.D. 2004) for being featured on MIT’s home page recently for recent work with Ph.D. student Oshani Seneviratne on enabling people to track how their private data is used online. You can read more about their work via this MIT news item and in their paper Enabling Privacy Through Transparency which will be presented next month in the 2014 IEEE Privacy Security and Trust conference.
March 27th, 2014, by Prajit Kumar Das, posted in Ebiquity, Google, Mobile Computing, Policy, Semantic Web, Social, Wearable Computing
If you are a Google Glass user, you might have been greeted with concerned looks or raised eyebrows at public places. There has been a lot of chatter in the “interweb” regarding the loss of privacy that results from people taking your pictures with Glass without notice. Google Glass has simplified photography but as what happens with revolutionary technology people are worried about the potential misuse.
FaceBlock helps to protect the privacy of people around you by allowing them to specify whether or not to be included in your pictures. This new application developed by the joint collaboration between researchers from the Ebiquity Research Group at University of Maryland, Baltimore County and Distributed Information Systems (DIS) at University of Zaragoza (Spain), selectively obscures the face of the people in pictures taken by Google Glass.
Comfort at the cost of Privacy?
As the saying goes, “The best camera is the one that’s with you”. Google Glass suits this description as it is always available and can take a picture with a simple voice command (“Okay Glass, take a picture”). This allows users to capture spontaneous life moments effortlessly. On the flip side, this raises significant privacy concerns as pictures can taken without one’s consent. If one does not use this device responsibly, one risks being labelled a “Glasshole”. Quite recently, a Google Glass user was assaulted by the patrons who objected against her wearing the device inside the bar. The list of establishments which has banned Google Glass within their premises is growing day by day. The dos and donts for Glass users released by Google is a good first step but it doesn’t solve the problem of privacy violation.
Privacy-Aware pictures to the rescue
FaceBlock takes regular pictures taken by your smartphone or Google Glass as input and converts it into privacy-aware pictures. This output is generated by using a combination of Face Detection and Face Recognition algorithms. By using FaceBlock, a user can take a picture of herself and specify her policy/rule regarding pictures taken by others (in this case ‘obscure my face in pictures from strangers’). The application would automatically generate a face identifier for this picture. The identifier is a mathematical representation of the image. To learn more about the working on FaceBlock, you should watch the following video.
Using Bluetooth, FaceBlock can automatically detect and share this policy with Glass users near by. After receiving this face identifier from a nearby user, the following post processing steps happen on Glass as shown in the images.
What promises does it hold?
FaceBlock is a proof of concept implementation of a system that can create privacy-aware pictures using smart devices. The pervasiveness of privacy-aware pictures could be a right step towards balancing privacy needs and comfort afforded by technology. Thus, we can get the best out of Wearable Technology without being oblivious about the privacy of those around you.
FaceBlock is part of the efforts of Ebiquity and SID in building systems for preserving user privacy on mobile devices. For more details, visit http://face-block.me
February 26th, 2014, by Tim Finin, posted in Semantic Web
The next Central MD Semantic Web Meetup will be held at 6:00pm on Thursday, February 27, 2014 at Inovex Information Systems (7240 Parkway Dr., Suite 140, Hanover MD). Michael Grove, the Chief Software Architect at Clark & Parsia, will talk on their Stardog triple store technology. The meetup is a good way to meet and network with others working on or with semantic technologies in Maryland.
Our speaker, Michael Grove, is the Chief Software Architect at Clark & Parsia, where he also serves as the lead developer of Stardog, the leader in RDF databases featuring fast query performance and unmatched OWL & SWRL support.
A graduate in Computer Science at the University of Maryland, College Park, Michael first got started with semantic technologies in 2002 as a research assistant under Dr. Jim Hendler at the University of Maryland with the MINDSWAP group. Before joining the team at Clark & Parsia, he worked at Fujitsu Research Labs as the lead developer for the Task Computing project, an effort bring the semantic web to pervasive computing environments.
Michael is also active in open source where he is a contributor to Pellet the leading OWL DL reasoner and maintains Empire, an implementation of JPA backed by semantic technologies. Additionally, he is contributor to the Sesame project and active on the Jena development list.”
February 8th, 2014, by Tim Finin, posted in Big data, High performance computing, Ontologies, Semantic Web
In the first Ebiquity meeting of the semester, Vlad Korolev will talk about his work on using RDF for to capture, represent and use provenance information for big data experiments.
PROB: A tool for Tracking Provenance and Reproducibility of Big Data Experiments
10-11:30am, ITE346, UMBC
Reproducibility of computations and data provenance are very important goals to achieve in order to improve the quality of one’s research. Unfortunately, despite some efforts made in the past, it is still very hard to reproduce computational experiments with high degree of certainty. The Big Data phenomenon in recent years makes this goal even harder to achieve. In this work, we propose a tool that aids researchers to improve reproducibility of their experiments through automated keeping of provenance records.
January 30th, 2014, by Tim Finin, posted in KR, Ontologies, Semantic Web
Today’s online meeting (Jan 30, 12:30-2:30 EST) in the 2014 Ontology Summit series is part of the Tools, Services, and Techniques track and features presentations by
- Dr. ChrisWelty (IBM Research) on “Inside the Mind of Watson – a Natural Language Question Answering Service Powered by the Web of Data and Ontologies”
- Prof. AlanRector (U. Manchester) on “Axioms & Templates: Distinctions and Transformations amongst Ontologies, Frames, & Information Models
- Professor TillMossakowski (U. Magdeburg) on “Challenges in Scaling Tools for Ontologies to the Semantic Web: Experiences with Hets and OntoHub”
Audio via phone (206-402-0100) or Skype. See the session page for details and access to slides.
January 23rd, 2014, by Tim Finin, posted in KR, Ontologies, Semantic Web
The first online session of the 2014 Ontology Summit on “Big Data and Semantic Web Meet Applied Ontology” takes place today (Thurday January 23) from 12:30pm to 2:30pm (EST, UTC-5) with topic Common Reusable Semantic Content — The Problems and Efforts to Address Them. The session will include four presentations:
followed by discussion.
Audio connection is via phone (206-402-0100, 141184#) or Skype with a shared screen and participant chatroom. See the session page for more details.
January 18th, 2014, by Tim Finin, posted in Big data, Datamining, Machine Learning, Semantic Web
A free PDF version of the new second edition of Mining of Massive Datasets by Anand Rajaraman, Jure Leskovec and Jeffey Ullman is available. New chapters on mining large graphs, dimensionality reduction, and machine learning have been added. Related material from Professor Leskovec’s recent Stanford course on Mining Massive Data Sets is also available.
January 14th, 2014, by Tim Finin, posted in Big data, KR, Ontologies, Semantic Web
The ninth Ontology Summit starts on Thursday, January 16 with the theme “Big Data and Semantic Web Meet Applied Ontology.” The event kicks off a three month series of weekly online meetings on Thursdays that feature presentations from expert panels and discussions with all of the participants. The series will culminate with a two day symposium on April 28-29 in Arlington VA. The sessions are free and open to all, including researchers, practitioners and students.
The first virtual meeting will be held 12:30-
2:00 2:30 (EST) on Thursday, January 16 and will introduce the nine different topical tracks in the series, their goals and organizers. Audio connection is via phone (206-402-0100, 141184#) or Skype with a shared screen and participant chatroom. See the session page for more details.
This year’s Ontology Summit is an opportunity for building bridges between the Semantic Web, Linked Data, Big Data, and Applied Ontology communities. On the one hand, the Semantic Web, Linked Data, and Big Data communities can bring a wide array of real problems (such as performance and scalability challenges and the variety problem in Big Data) and technologies (automated reasoning tools) that can make use of ontologies. On the other hand, the Applied Ontology community can bring a large body of common reusable content (ontologies) and ontological analysis techniques. Identifying and overcoming ontology engineering bottlenecks is critical for all communities.
The 2014 Ontology Summit is chaired by Michael Gruninger and Leo Obrst.
January 9th, 2014, by Tim Finin, posted in Ontologies, Semantic Web
Computer Science and Electrical Engineering
University of Maryland, Baltimore County
Ph.D. Dissertation Proposal
Functional Reference Ontology Development:
a Design Pattern Approach
1:00pm Friday, January 10, 2014, ITE325b, UMBC
The next generation of smart manufacturing systems will be developed by composing advanced manufacturing components and IT services introducing new technologies. These new technologies can lead to dramatic improvements in the ability to monitor, control, and optimize all aspects of manufacturing. The ability to compose advanced manufacturing components and IT services enhances agility, resiliency, and productivity of a manufacturing system. In order to make the composition possible, functional knowledge of manufacturing components and IT services should be captured and shared explicitly. Recent researches have shown that a semantically precise and rich reference functional ontology enables effective composition. However, since domains of factories and production networks are large, evolving, and heterogeneous, developing a reference functional ontology is a challenging task. Specifically, conceptual functionality modeling that characterizes various features of manufacturing components and IT services at different levels of abstraction is a difficult task. Even if the reference functional ontology is developed successfully, there will certainly be interoperability issues between the reference functional ontology and local proprietary information models. Firstly, the conceptual conflict issues may arise primarily from the fact that the reference functional ontology does not reflect actual users’ or providers’ conceptualizations. Secondly, structural conflict issues may arise from diverse modeling choices in local, proprietary information models.
The objective of our research is to assess utility of design patterns in addressing the issues in the reference functional ontology development, specifically OWL ontology design patterns (ODPs). To achieve the objective, we will assess inductive approaches to identifying the ODPs, and explore development of a methodology for resolving structural differences between the reference functional ontology and local proprietary information models. The key potential contributions of this work include 1) new method to identify information patterns of functionalities in manufacturing components and IT services, 2) new inductive ODP development process which starts with the pattern definition of the specific functionality concepts, with subsequent grouping of these patterns into more general patterns, and 3) ODP-based ontology transformation to resolve structural conflicts between the reference functional ontology and local proprietary information models.
Committee: Drs. Yun Peng (chair), Tim Finin, Yelena Yesha, Milton Halem, Nenad Ivezic (NIST) and Boonserm Kulvatunyou (NIST)
January 1st, 2014, by Tim Finin, posted in Google, Semantic Web
“The app uses browser’s geolocation feature to find user’s location and displays a map of interesting objects that can be found nearby (within 50 000 ft). It uses the Freebase Search API to find relevant objects. When user clicks on one of the markers, the app calls the Freebase Topic API to fetch more information about that object. Once the information is retrieved, it populates a purejs template to display a knowledge card for the user.”
This sort of application has been done many times before with RDF and the Google approach can be adapted to query an arbitrary RDF resource for custom knowledge bases.
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