Blaz Fortuna

OntoGen: semi-automatic ontology editor

In this paper we present a semi-automatic ontology editor as implemented in a new version of OntoGen system. The system integrates machine learning and text mining algorithms into an efficient user interface lowering the entry barrier for users who are not professional ontology engineers. The main features of the systems include unsupervised and supervised methods for concept suggestion and concept naming, as well as ontology and concept visualization. The system was tested in extensive user trails and in several real-world scenarios with very positive results.

Event registry: learning about world events from news

Event Registry is a system that can analyze news articles and identify in them mentioned world events. The system is able to identify groups of articles that describe the same event. It can identify groups of articles in different languages that describe the same event and represent them as a single event. From articles in each event it can then extract events core information, such as event location, date, who is involved and what is it about. Extracted information is stored in a database. A user interface is available that allows users to search for events using extensive search options, to visualize and aggregate the search results, to inspect individual events and to identify related events.

News across languages - cross-lingual document similarity and event tracking

In todays world, we follow news which is distributed globally. Significant events are reported by different sources and in different languages. In this work, we address the problem of tracking of events in a large multilingual stream. Within a recently developed system Event Registry we examine two aspects of this problem: how to compare articles in different languages and how to link collections of articles in different languages which refer to the same event. Taking a multilingual stream and clusters of articles from each language, we compare different cross-lingual document similarity measures based on Wikipedia. This allows us to compute the similarity of any two articles regardless of language. Building on previous work, we show there are methods which scale well and can compute a meaningful similarity between articles from languages with little or no direct overlap in the training data. Using this capability, we then propose an approach to link clusters of articles across languages which represent the same event. We provide an extensive evaluation of the system as a whole, as well as an evaluation of the quality and robustness of the similarity measure and the linking algorithm.

Towards building a global oracle: a physical mashup using artificial intelligence technology

In this paper, we describe Videk - a physical mashup which uses artificial intelligence technology. We make an analogy between human senses and sensors; and between human brain and artificial intelligence technology respectively. This analogy leads to the concept of Global Oracle. We introduce a mashup system which automatically collects data from sensors. The data is processed and stored by SenseStream while the meta-data is fed into ResearchCyc. SenseStream indexes aggregates, performs clustering and learns rules which then it exports as RuleML. ResearchCyc performs logical inference on the meta-data and transliterates logical sentences. The GUI mashes up sensor data with SenseStream output, ResearchCyc output and other external data sources: GoogleMaps, Geonames, Wikipedia and Panoramio.

OntoGen extension for exploring image collections

OntoGen is a semi-automatic and data-driven ontology editor focusing on editing of topic ontologies. It utilizes text mining tools to make the ontology-related tasks simpler to the user. This focus on building ontologies from textual data is what we are trying to bridge. We have successfully extended OntoGen to work with image data and allow for ontology construction and editing on collections of labeled or unlabeled images. Browsing large heterogenous image collections efficiently is certainly a challenging task - and we feel that semiautomatic ontology construction, as described in this paper, makes this task easier.

News Across Languages - Cross-Lingual Document Similarity and Event Tracking (Extended Abstract).

In todays world, we follow news which is distributed globally. Significant events are reported by different sources and in different languages. In this work, we address the problem of tracking of events in a large multilingual stream. Within a recently developed system Event Registry we examine two aspects of this problem: how to compare articles in different languages and how to link collections of articles in different languages which refer to the same event. Taking a multilingual stream and clusters of articles from each language, we compare different cross-lingual document similarity measures based on Wikipedia. This allows us to compute the similarity of any two articles regardless of language. Building on previous work, we show there are methods which scale well and can compute a meaningful similarity between articles from languages with little or no direct overlap in the training data. Using this capability, we then propose an approach to link clusters of articles across languages which represent the same event. We provide an extensive evaluation of the system as a whole, as well as an evaluation of the quality and robustness of the similarity measure and the linking algorithm.

Interpreting News Recommendation Models

This paper presents an approach for recommending news articles on a large news portal. Focus is given to interpretability of the developed models, analysis of their performance, and deriving understanding of short and long-term user behavior on a news portal.

On the robustness of event detection evaluation: a case study

Research on evaluation of IR systems has led to the insight that a robust evaluation strategy requires tests on a large number of events/queries. However, especially for event detection, the number of manually labeled events may be limited. In this paper we investigate how to optimize the evaluation strategy in those cases to maximize robustness. We also introduce two new vector space models for event detection that aim to incorporate bursty information of terms and compare these with existing models. Experiments show that exploiting graded relevance levels reduces the impact of subjectivity and ambiguity of event detection evaluation. We also show that although user disagreement is significant, it has no real impact on result ranking.

User Study of Ontology Generation Tool

We present design and results of a user study undertaken in order to evaluate ontology generation process. We have applied our study to an example tool for semi-automatic ontology generation OntoGen that covers different stages of the underlying process. In-depth analysis of the user experience delivered valuable insights into the requirements for the further system development.