Barbara Bazzanella

An entity name system (ENS) for the semantic web

In this paper, we argue that implementing the grand vision of the Semantic Web would greatly benefit from a service which can enable the reuse of globally unique URIs across semantic datasets produced in a fully decentralized and open environment. Such a service, which we call Entity Name System (ENS), stores pre-existing URIs and makes them available for reuse mainly - but not only - in Semantic Web contents and applications. The ENS will make the integration of semantic datasets much easier and faster, and will foster the development of a whole family of applications which will exploit the data level integration through global URIs for implementing smart semantic-based solutions.

Towards a general entity representation model

In this paper, we argue for an infrastructure responsible for assigning and managing unique identifiers for entities in the semantic web, and we propose a conceptual model for the storage and management of these entities.

Searching for individual entities: A query analysis

Searching for information about individual entities such as persons, locations, events, is an important activity in Internet search today, and is in its core a very semantic-oriented task. Several ways for accessing such information exist, but for locating entity-specific information, search engines are the most commonly used approach. In this context, keyword queries are the primary means of retrieving information about a specific entity. We believe that an important first step of performing such a task is to understand what type of entity the user is looking for. We call this process Entity Type Disambiguation. In this paper we present a Naive Bayesian Model for entity type disambiguation that explores our assumption that an entity type can be inferred from the attributes a user specifies in a search query. The model has been applied to queries provided by a large sample of participants in an experiment performing an entity search task. The beneficial impact of this approach for the development of new search systems is discussed.

Can Persistent Identifiers Be Cool

The fast growth of scientific and non-scientific digital data, as well as the proliferation of new types of digital content, has led – among many other things – to a lot of innovative work on the concept of the identifier. Digital identifiers have become the key to preserving and accessing content, just as physical identifier tags have been the key to accessing paper-based content and other physical entities for millennia. Two main schools of thought have emerged: on the one hand, librarians and public repositories have pushed the concept of the Persistent Identifier (PI) as a way to guarantee long term identification and (sometimes) access; on the other hand, the extraordinary success of the web has led several researchers and web experts to push the concept of the Cool URI as the universal mechanism for identifying and accessing digital content. Both views have their pros and cons, but so far (with only a few exceptions) the two visions have developed in parallel, sometimes with a subtle underlying hostility. In this paper, we present the evolution of the Entity Name System (ENS), an open service-based platform developed as part of the OKKAM EU co-funded project, which can reconcile these two approaches. The new system, called ENS2.0, is currently under development and will enable data creators and curators to combine the technical strengths and opportunities of the (Semantic) Web vision with the organizational, economical and social requirements legitimately raised by the PI community and stakeholders.

Entity Type Disambiguation in User Queries

Searching for information about individual entities such as persons, locations, events, is an important activity in Internet search today, and is in its core a very semantic-oriented task. Several ways for accessing such information exist, but for locating entity-specific information, search engines are the most commonly used approach. In this context, keyword queries are the primary means of retrieving information about a specific entity. We believe that an important first step of performing such a task is to understand what type of entity the user is looking for. We call this process Entity Type Disambiguation. In this paper, we present a Naive Bayesian Model for entity type disambiguation that explores our assumption that an entity type can be inferred from the attributes a user specifies in a search query. The model has been applied to queries provided by a large sample of participants in an experiment performing an entity search task. The beneficial impact of this approach for the development of new search systems is discussed.

An Identification Ontology for Entity Matching

In this context we present the Identification Ontology, as an application ontology for a knowledge-based solution to the entity matching problem in the context of the Semantic Web. The Identification Ontology has a threefold role: 1 represent a selection of attributes that are relevant for identification or entity matching of a set of entity types; 2 supporting the definition of a set of contextual ontological mappings to ease the problem of semantic heterogeneity affecting entity matching in the Semantic Web; and 3 represent meta-properties of the considered features to highlight their roles in the definition of a knowledge-based entity matching solution. The Identification Ontology taxonomy is defined refining and extending the Okkam Conceptual Model, as a top level ontology modeling the identity and reference domain. Furthermore, it defines also a set of top-level entity types and relative features relying on a methodology that combines results from cognitive studies and a survey of existing vocabularies available through Linked Open Vocabulary initiative. The Identification Ontology is currently used as part of the Okkam Entity Name System matching framework, which was successfully tested in entity matching experiments and used in large-scale industrial linkage tasks to enable data integration for applications dealing with tax assessment and credit risk analysis.

Interoperability Knowledge Base for Persistent Identifiers Interoperability Framework

In this paper, we propose a semantic web based solution to implement the Interoperability Framework (IF) for Persistent Identifiers (PI) developed within the context of APARSEN EU project. The IF provides a comprehensive, semantics-aware solution for interoperability of heterogeneous Persistent Identifier systems. Such a solution aims to provide added-value services built on an Interoperability Knowledge Base. The IF ontology refinement and the related prototype specifics have been designed adopting a bottom-up approach that starts from a) the analysis of metadata provided by Content Providers and b) the collection of functional and semantic requirements of Persistent Identifier Domains (PID to fostering ontology-based metadata translation among different bodies. Conclusions and intended future work close the paper.

An Interoperability Infrastructure for Digital Identifiers in e-Science

The rapid increase of scientific digital assets in the last years has made clear that digital identifiers are crucial for effectively publishing, accessing and managing digital information in e-science contexts. From persistent keys for access to digital objects in network environments, the concept of persistent identifiers has been more recently extended to identify also physical objects like people, institutions and any type of relevant entity in the e-Science domain, opening the way to the creation of an integrated information space where a network of resources can be resolved, linked, navigated and analyzed, as the Linked Open Data approach envisions for the Web. However, the creation and full exploitation of this valuable network of connections is currently hindered by the fragmentation and lack of coordination of the digital identifier ecosystem. The aim of this paper is to propose an open, distributed and scalable infrastructure for interoperating existing Persistent Identifiers and other digital identifier systems (like Cool URIs) in e-science, overcoming geographical, disciplinary and organizational boundaries. The Digital Identifier interoperability infrastructure is presented as a cross-cutting solution of core services enabling interoperability at three different levels: identifier, co-reference and semantic.

OntoCleaning the Okkam Conceptual Model

The way people define and use identifiers to refer to entities has been under philosophical investigation for a long time. This problem gained attention also in the context of the Semantic Web, where the Identity Crisis has threatened one of the cornerstones of the proposed vision: the unambiguous identification of Resources. In this paper we present the application of OntoClean as a formal methodology to formally validate the Okkam Conceptual Model that was proposed to address the problem of identity and reference. In performing such analysis, we unfold some of the philosophical problems affecting the representation of several types of real world entities, presenting a literature review and arguing in favor of some interpretation. This is done with the twofold purpose of contributing to the ongoing debate, and fostering the definition of a shared vision about the problem that will support further advancements in development of the Semantic Web vision. Furthermore, we argue in favor of the adoption of the Okkam IDs as globally persistent and rigid identifiers as a sound solution to the problem of the Identity and Reference in the context of the Semantic Web.

Entity re-use in a large news corpus

In Digital content,named entities such as organizations, persons, locations are mantioned implicity or explicity using structured annotations. In this paper we attempt to measure an aspect of entity popularity by taking into consideration the occurrences of named entities in 20 years of New York Times articles. This study shows that entity re-use is an interest factor to take into account in the development of entity-centric applications and services.