The UMLS Metathesaurus was assembled by integrating its ancestors. We introduced an inconsistency definition for some 150 source vocabularies; it contains more than Metathesaurus concepts based on their hierarchical relations 2 million concepts (i.e., clusters of synonymous terms coming and compute all such inconsistent concepts. After that we from multiple source vocabularies identified by a Concept manually review some of the inconsistent concepts to determine Unique Identifier). The UMLS Metathesaurus contains the ones that have erroneous synonymy relations such also more than 36 million relations between these concepts, as wrong synonymy.

In this abstract, we compare semantic search (in the RDF model) with keyword search (in the relational model), and illustrate how these two search paradigms are different. This comparison addresses the following questions: (1) What can semantic search achieve that keyword search can not (in terms of behavior)? (2) Why is it difficult to simulate semantic search, using keyword search on the relational data model? We use the term keyword search, when the search is performed on data stored in the relational data model, as in traditional relational databases, and an example of keyword search in databases is [Hri02]. We use the term semantic search, when the search is performed on data stored in the RDF data model. Note that when the data is modeled in RDF, it inherently contains explicit typed relations or semantics, and hence the use of the term “semantic search.” Let us begin with an example, to illustrate the differences between semantic search and keyword search.

As the semantic web grows in popularity and enters the mainstream of computer technology, RDF (Resource Description Framework) datasets are becoming larger and more complex. Advanced semantic web ontologies, especially in medicine and science, are developing. As more complex ontologies are developed, there is a growing need for efficient queries that handle inference. In areas such as research, it is vital to be able to perform queries that retrieve not just facts but also inferred knowledge and uncertain information. OWL (Web Ontology Language) defines rules that govern provable inference in semantic web datasets. In this paper, we detail a database schema using bit vectors that is designed specifically for RDF datasets. We introduce a framework for materializing and storing inferred triples. Our bit vector schema enables storage of inferred knowledge without a query performance penalty. Inference queries are simplified and performance is improved. Our evaluation results demonstrate that our inference solution is more scalable and efficient than the current state-of-the-art. There are also standards being developed for representing probabilistic reasoning within OWL ontologies. We specify a framework for materializing uncertain information and probabilities using these ontologies. We define a multiple vector schema for representing probabilities and classifying uncertain knowledge using thresholds. This solution increases the breadth of information that can be efficiently retrieved.

Answering queries posed over knowledge bases is a central problem in knowledge representation and database theory. In the database area, checking query containment is an important query optimization and schema integration technique. In knowledge representation it has been used for object classification, schema integration, service discovery, and more. In the presence of a knowledge base, the problem of query containment is strictly related to that of query answering; indeed, the two are reducible to each other; we focus on the latter, and our results immediately extend to the former.

Revising knowledge bases (KBs) in description logics (DLs) in a syntax-independent manner is an important, nontrivial problem for the ontology management and DL communities. Several attempts have been made to adapt classical model-based belief revision and update techniques to DLs, but they are restricted in several ways. In particular, they do not provide operators or algorithms for general DL KB revision. The key difficulty is that, unlike propositional logic, a DL KB may have infinitely many models with complex (and possibly infinite) structures, making it difficult to define and compute revisions in terms of models. In this paper, we study general KBs in a specific DL in the DL-Lite family. We introduce the concept of features for such KBs, develop an alternative semantic characterization of KBs using features (instead of models), define two specific revision operators for KBs, and present the first algorithm for computing best approximations for syntax-independent revisions of KBs.

We carry out a comparative study of the expressive power of different ontologies of matter in terms of the ease with which simple physical knowledge can be represented. In particular, we consider five ontologies of models of matter: particle models, fields, two ontologies for continuous material, and a hybrid model. We evaluate these in terms of how easily eleven benchmark physical laws and scenarios can be represented.

Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently also to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visualizing and browsing social content, and also to help them in organizing their own content. However, learning from social metadata presents several challenges: sparseness, ambiguity, noise, and inconsistency. We describe an approach to folksonomy learning based on relational clustering that addresses these challenges by exploiting structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr. We evaluate the learned folksonomy quantitatively by automatically comparing it to a reference taxonomy. Our empirical results suggest that the proposed framework, which addresses the challenges listed above, improves on existing folksonomy learning methods.

Structured and semi-structured data describing entities, taxonomies and ontologies appears in many domains. There is a huge interest in integrating structured information from multiple sources; however integrating structured data to infer complex common structures is a difficult task because the integration must aggregate similar structures while avoiding structural inconsistencies that may appear when the data is combined. In this work, we study the integration of structured social metadata: shallow personal hierarchies specified by many individual users on the Social Web, and focus on inferring a collection of integrated, consistent taxonomies. We frame this task as an optimization problem with structural constraints. We propose a new inference algorithm, which we refer to as Relational Affinity Propagation (RAP) that extends affinity propagation(Frey and Dueck, 2007) by introducing structural constraints. We validate the approach on a real-world social media dataset, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures compared to an approach using only the standard affinity propagation algorithm.

Present-day programs are brittle as computers are notoriously lacking in common sense. While significant progress has been made in building large common sense knowledge bases, they are intrinsically incomplete and inconsistent. This paper presents a novel approach to bridging the gaps between multiple knowledge bases, making it possible to answer queries based on knowledge collected from multiple sources without a common ontology. New assertions are found by computing graph similarity with principle component analysis to draw analogies across multiple knowledge bases. Experiments are designed to find new assertions for a Chinese commonsense knowledge base using the OMCS ConceptNet and similarly for WordNet. The assertions are voted by online users to verify that 75.77% / 77.59% for Chinese ConceptNet / WordNet respectively are good, despite the low overlap in coverage among the knowledge bases.

We have been exploring the use of Web-derived knowledge bases through the development of Wikitology — a hybrid knowledge base of structured and unstructured information extracted from Wikipedia augmented by RDF data from DBpedia and other Linked Open Data resources. In this paper, we describe approaches that aid in enriching Wikipedia and thus the resources that derive from Wikipedia such as the Wikitology knowledge base, DBpedia, Freebase and Powerset.