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PhD defense: Semantic Resolution Framework for Integrating Manufacturing Service Capability Data

April 25th, 2015, by Tim Finin, posted in AI, Ontologies, OWL, Semantic Web

Ph.D. Dissertation Defense

A Semantic Resolution Framework for Integrating
Manufacturing Service Capability Data

Yan Kang

10:00am Monday 27 April 2015, ITE 217b

Building flexible manufacturing supply chains requires availability of interoperable and accurate manufacturing service capability (MSC) information of all supply chain participants. Today, MSC information, which is typically published either on the supplier’s web site or registered at an e-marketplace portal, has been shown to fall short of interoperability and accuracy requirements. The issue of interoperability can be addressed by annotating the MSC information using shared ontologies. However, this ontology-based approach faces three main challenges: (1) lack of an effective way to automatically extract a large volume of MSC instance data hidden in the web sites of manufacturers that need to be annotated; (2) difficulties in accurately identifying semantics of these extracted data and resolving semantic heterogeneities among individual sources of these data while integrating them under shared formal ontologies; (3) difficulties in the adoption of ontology-based approaches by the supply chain managers and users because of their unfamiliarity with the syntax and semantics of formal ontology languages such as the web ontology language (OWL).

The objective of our research is to address the main challenges of ontology-based approaches by developing an innovative approach that is able to extract MSC instances from a broad range of manufacturing web sites that may present MSC instances in various ways, accurately annotate MSC instances with formal defined semantics on a large scale, and integrate these annotated MSC instances into formal manufacturing domain ontologies to facilitate the formation of supply chains of manufacturers. To achieve this objective, we propose a semantic resolution framework (SRF) that consists of three main components: a MSC instance extractor, a MSC Instance annotator and a semantic resolution knowledge base. The instance extractor builds a local semantic model that we call instance description model (IDM) for each target manufacturer web site. The innovative aspect of the IDM is that it captures the intended structure of the target web site and associates each extracted MSC instance with a context that describes possible semantics of that instance. The instance annotator starts the semantic resolution by identifying the most appropriate class from a (or a set of) manufacturing domain ontology (or ontologies) (MDO) to annotate each instance based on the mappings established between the context of that instance and the vocabularies (i.e., classes and properties) defined in the MDO. The primary goal of the semantic resolution knowledge base (SR-KB) is to resolve semantic heterogeneity that may occur in the instance annotation process and thus improve the accuracy of the annotated MSC instances. The experimental results demonstrate that the instance extractor and the instance annotator can effectively discover and annotate MSC instances while the SR-KB is able to improve both precision and recall of annotated instances and reducing human involvement along with the evolution of the knowledge base.

Committee: Drs. Yun Peng (Chair), Tim Finin, Yaacov Yesha, Matthew Schmill and Boonserm Kulvatunyou

Access control for a triplestore linked data fragments interface

April 19th, 2015, by Tim Finin, posted in OWL, Privacy, RDF, Security, Semantic Web

In this week’s meeting (10-11am Tue, April 21), Ankur Padia will present work in progress on providing access control to an RDF triple store.

Triple store access control for a linked data fragments interface
Ankur Padia, UMBC

The maturation of Semantic Web standards and associated web-based data representations such as schema.org have made RDF a popular model for representing graph data and semi-structured knowledge. Triple stores are used to store and query an RDF dataset and often expose a SPARQL endpoint service on the Web for public access. Most existing SPARQL endpoints support very simple access control mechanisms if any at all, preventing their use for many applications where fine-grained privacy or data security is important. We describe new work on access control for a linked data fragments interface, i.e. one that accepts queries consisting one or more triple patterns and responds with all matching triples that the authenticated querier can access.

2015 Ontology Summit: Internet of Things: Toward Smart Networked Systems and Societies

January 14th, 2015, by Tim Finin, posted in Agents, AI, Big data, Ontologies, Semantic Web, Web

The Internet of Things (IoT) is the interconnection of uniquely identifiable embedded computing devices within the existing Internet infrastructure.

The theme of the 2015 Ontology Summit is Internet of Things: Toward Smart Networked Systems and Societies. The Ontology Summit is an annual series of events (first started by Ontolog and NIST in 2006) that involve the ontology community and communities related to each year’s theme.

The 2015 Summit will hold a virtual discourse over the next three months via mailing lists and online panel sessions augmented conference calls. The Summit will culminate in a two-day face-to-face workshop on 13-14 April 2015 in Arlington, VA. The Summit’s goal is to explore how ontologies can play a significant role in the realization of smart networked systems and societies in the Internet of Things.

The Summit’s initial launch session will take place from 12:30pm to 2:00pm EDT on Thursday, January 15th and will include overview presentations from each of the four technical tracks. See the 2015 Ontology Summit for more information, the schedule and details on how to participate in these free an open events.

PhD defense: Varish Mulwad — Inferring the Semantics of Tables

December 29th, 2014, by Tim Finin, posted in KR, Machine Learning, NLP, Ontologies, Semantic Web

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Dissertation Defense

TABEL — A Domain Independent and Extensible Framework
for Inferring the Semantics of Tables

Varish Vyankatesh Mulwad

8:00am Thursday, 8 January 2015, ITE325b

Tables are an integral part of documents, reports and Web pages in many scientific and technical domains, compactly encoding important information that can be difficult to express in text. Table-like structures outside documents, such as spreadsheets, CSV files, log files and databases, are widely used to represent and share information. However, tables remain beyond the scope of regular text processing systems which often treat them like free text.

This dissertation presents TABEL — a domain independent and extensible framework to infer the semantics of tables and represent them as RDF Linked Data. TABEL captures the intended meaning of a table by mapping header cells to classes, data cell values to existing entities and pair of columns to relations from an given ontology and knowledge base. The core of the framework consists of a module that represents a table as a graphical model to jointly infer the semantics of headers, data cells and relation between headers. We also introduce a novel Semantic Message Passing scheme, which incorporates semantics into message passing, to perform joint inference over the probabilistic graphical model. We also develop and explore a “human-in-the-loop” paradigm, presenting plausible models of user interaction with our framework and its impact on the quality of inferred semantics.

We present techniques that are both extensible and domain agnostic. Our framework supports the addition of preprocessing modules without affecting existing ones, making TABEL extensible. It also allows background knowledge bases to be adapted and changed based on the domains of the tables, thus making it domain independent. We demonstrate the extensibility and domain independence of our techniques by developing an application of TABEL in the healthcare domain. We develop a proof of concept for an application to generate meta-analysis reports automatically, which is built on top of the semantics inferred from tables found in medical literature.

A thorough evaluation with experiments over dataset of tables from the Web and medical research reports presents promising results.

Committee: Drs. Tim Finin (chair), Tim Oates, Anupam Joshi, Yun Peng, Indrajit Bhattacharya (IBM Research) and L. V. Subramaniam (IBM Research)

Semantics for Privacy and Shared Context

December 15th, 2014, by Tim Finin, posted in Mobile Computing, OWL, Policy, RDF, Semantic Web

Roberto Yus, Primal Pappachan, Prajit Das, Tim Finin, Anupam Joshi, and Eduardo Mena, Semantics for Privacy and Shared Context, Workshop on Society, Privacy and the Semantic Web-Policy and Technology, held at Int. Semantic Web Conf., Oct. 2014.

Capturing, maintaining, and using context information helps mobile applications provide better services and generates data useful in specifying information sharing policies. Obtaining the full benefit of context information requires a rich and expressive representation that is grounded in shared semantic models. We summarize some of our past work on representing and using context models and briefly describe Triveni, a system for cross-device context discovery and enrichment. Triveni represents context in RDF and OWL and reasons over context models to infer additional information and detect and resolve ambiguities and inconsistencies. A unique feature, its ability to create and manage “contextual groups” of users in an environment, enables their members to share context information using wireless ad-hoc networks. Thus, it enriches the information about a user’s context by creating mobile ad hoc knowledge networks.

Wikidata article in CACM

October 12th, 2014, by Tim Finin, posted in Semantic Web, Web, Wikipedia

Wikidata-logo-en

I just noticed that Denny Vrandecic and Markus Krötzsch have an article on Wikidata in the latest CACM. Good work! Even better, it’s available without subscription.

Wikidata: a free collaborative knowledgebase, Denny Vrandecic and Markus Krötzsch, Communications of the ACM, v57, n10 (2014), pp 78-85.

“This collaboratively edited knowledgebase provides a common source of data for Wikipedia, and everyone else.

Unnoticed by most of its readers, Wikipedia continues to undergo dramatic changes, as its sister project Wikidata introduces a new multilingual “Wikipedia for data” (http://www.wikidata.org) to manage the factual information of the popular online encyclopedia. With Wikipedia’s data becoming cleaned and integrated in a single location, opportunities arise for many new applications.”

Infoboxer: using statistical semantic knowledge to help create Wikipedia infoboxes

September 29th, 2014, by Tim Finin, posted in OWL, RDF, Semantic Web, Web, Wikipedia


In this week’s ebiquity meeting (10am Tue. Oct 1 in ITE346), Varish Mulwad will present Infoboxer, a prototype tool he developed with Roberto Yus that overcomes these challenges using statistical and semantic knowledge from linked data sources to ease the process of creating Wikipedia infoboxes.

Wikipedia infoboxes serve as input in the creation of knowledge bases
such as DBpedia, Yago, and Freebase. Current creation of Wikipedia
infoboxes is manual and based on templates that are created and
maintained collaboratively. However, these templates pose several
challenges:

  • Different communities use different infobox templates for the same category articles
  • Attribute names differ (e.g., date of birth vs. birthdate)
  • Templates are restricted to a single category, making it harder to find a template for an article that belongs to multiple categories (e.g., actor and politician)
  • Templates are free form in nature and no integrity check is performed on whether the value filled by the user is of appropriate type for the given attribute

Infoboxer creates dynamic and semantic templates by suggesting attributes common for similar articles and controlling the expected values semantically. We will give an overview of our approach and demonstrate how Infoboxer can be used to create infoboxes for new Wikipedia articles as well as update erroneous values in existing infoboxes. We will also discuss our proposed extensions to the project.

Visit http://ebiq.org/p/668 for more information about Infoboxer. A demo can be found here.

Rafiki: A Semantic and Collaborative Approach to Community Health-Care in Underserved Areas

September 19th, 2014, by Tim Finin, posted in Mobile Computing, OWL, RDF, Semantic Web, Wearable Computing

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Primal Pappachan, Roberto Yus, Anupam Joshi and Tim Finin, Rafiki: A Semantic and Collaborative Approach to Community Health-Care in Underserved Areas, 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, 22-15 October2014, Miami.

Community Health Workers (CHWs) act as liaisons between health-care providers and patients in underserved or un-served areas. However, the lack of information sharing and training support impedes the effectiveness of CHWs and their ability to correctly diagnose patients. In this paper, we propose and describe a system for mobile and wearable computing devices called Rafiki which assists CHWs in decision making and facilitates collaboration among them. Rafiki can infer possible diseases and treatments by representing the diseases, their symptoms, and patient context in OWL ontologies and by reasoning over this model. The use of semantic representation of data makes it easier to share knowledge related to disease, symptom, diagnosis guidelines, and patient demography, between various personnel involved in health-care (e.g., CHWs, patients, health-care providers). We describe the Rafiki system with the help of a motivating community health-care scenario and present an Android prototype for smart phones and Google Glass.

Taming Wild Big Data

September 17th, 2014, by Tim Finin, posted in Database, Datamining, Machine Learning, RDF, Semantic Web

Jennifer Sleeman and Tim Finin, Taming Wild Big Data, AAAI Fall Symposium on Natural Language Access to Big Data, Nov. 2014.

Wild Big Data is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing Wild Big Data that uses techniques and resources that are popular for processing natural language text. The approach is applicable to data that is presented as a graph of objects and relations between them and to tabular data that can be transformed into such a graph. We start by applying topic models to contextualize the data and then use the results to identify the potential types of the graph’s nodes by mapping them to known types found in large open ontologies such as Freebase, and DBpedia. The results allow us to assemble coarse clusters of objects that can then be used to interpret the link and perform entity disambiguation and record linking.

Preprint: Interpreting Medical Tables as Linked Data to Generate Meta-Analysis Reports

July 17th, 2014, by Tim Finin, posted in Ontologies, RDF, Semantic Web

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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.

:BaseKB offered as a better Freebase version

July 15th, 2014, by Tim Finin, posted in Big data, KR, Ontologies, RDF, Semantic Web

:BaseKB

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!

Ebiquity alumna Lalana Kagal featured for privacy work

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.

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