Comparison, Selection and Merging of Techniques, Methods and Tools for Operational CLIR Systems – The Case of the Greek Language.

C.SKOURLAS1, T.ALEVIZOS2, P.BELSIS1, C.DOULKERIDIS3, A. MALATRAS4
1Department. of Informatics
Technological Educational Institute of Athens
2Department. of Industrial Informatics
Technological Educational Institute of Kavala
3Department of Informatics
Athens University of Economics and Business
4 Department of Electronic Engineering
University of Surrey, London
1,2,3GREECE, 4UNITED KINGDOM

Abstract: - In this work, a framework for the implementation of Cross Language Information Retrieval (CLIR) systems is proposed and discussed. Special emphasis is given to capture a holistic study of bilingual Information Retrieval (IR) systems. First, traditional Information Retrieval (IR) and Content-Based (Image) Retrieval (CBR) as essential parts of a Multi-modal Information Retrieval approach are examined. Information Filtering (IF) is also examined and Learning and Adaptation in IR and IF are used for adding innovative characteristics to the traditional IR systems. Then, emphasis is given to the particularities of the Greek – English text, queries and test collections. Finally, CLIR systems are related to Secure Enhanced Knowledge and Data Management systems and specific techniques and methods are selected and discussed for possible implementation.

Key-Words: - Cross Language Information Retrieval, Content Based Information Retrieval, Word Sense Disambiguation, Information Filtering, Personalization, Knowledge Management, Security.

1   Introduction
Knowledge Management (KM) systems emerged the last decades and eventually transformed to core business function for competitive organizations. Among else, the main components of a successfully deployed KM system consist of information retrieval, image retrieval and tacit knowledge utilization-oriented components. Another challenge is related with the possibility to support several different languages. Cross Language Information Retrieval (CLIR) is a whole branch of Information Retrieval (IR) devoted to overcome language boundaries. Experiments were first initiated in the early seventies. During the last fifteen years much effort was spent experimenting with different techniques, and a collective effort of IR researchers (TREC, CLEF, NTCIR) produced systems able to retrieve effectively (e.g. Sanderson [6], Berger [7]). CLIR can be defined as the process of searching for texts written in foreign languages based on native language queries e.g. typing a query in English to retrieve documents written in Greek. For such a process to succeed, we must conduct work in one of the following two directions:
•	Translation of the user query written in the source language (e.g. English)  
•	Translation of the documents written in the target language (e.g. Greek).
In this paper, we investigate the importance and the correlation between the various type components of a KM system, which supports the effective knowledge distribution and dissemination for cross-language data. Moreover, this paper puts the foundations for the expansion of KM capabilities so as to support effectively a multiple organization’s schema.
The outline of this paper is as follows: in Section 2, we describe and motivate the need for Information Retrieval and Content Based Image Retrieval as essential characteristics for a KM system, with a special focus to Cross Language Information Retrieval. In Section 3, the role of Information Extraction and Filtering is examined as an added value component to traditional IR. In Section 4, we integrate the aforementioned parts of Multi-modal Information Retrieval in the context of a distributed Knowledge Management system and we present briefly its design and architecture. Finally in Section 5, we conclude the paper and sketch future research activities.

2   Problem Formulation & Discussion
There are two main monolingual information retrieval paradigms (e.g. [7], [8]) which could form a basis for (be incorporated in) a CLIR system:
1)	Statistical approach, which emphasizes statistical correlation of words/counts in document collections. Statistical schemes such as probabilistic and vector space models for document representation and retrieval are usually used. The Smart system is an example of a text processing and retrieval system based on the vector (space) processing model. Latent Semantic Indexing (LSI) is an example of a statistical method to capture the term associations in documents.
2)	Semantic approach, which characterizes the documents and queries in order to "understand" and then to represent the underlying meaning. Such an approach is usually based on Natural Language Processing and the use of AI-techniques.
Apart from the basic research on information retrieval techniques, further challenges are related with two facts. When it comes to extracting hidden knowledge, it can be extracted from multi-faceted sources. For example, an ideal IR system would be able to support knowledge extraction from heterogeneous sources. Furthermore, the adaptation of a KM system so as to support cross-language retrieval leads to specific research challenges related with the peculiarities of each language. In the following, we focus on explaining the choices that guided our research on these two axes: the incorporation of image based retrieval capabilities and the support for multi-lingual retrieval starting from the Greek language as a second language that our system supports. 

2.1	Content-Based Image Retrieval
Content-Based Image Retrieval (CBIR) systems are systems containing an image collection. In these systems retrieval can be performed using features based on pixels which form the image’s content. Forsyth [5] argues that the evaluation of CBIR systems at the moment is useless because systems are too bad. Hence, there is an interest in combining both textual and visual features for effective image retrieval. In many collections images are accompanied by some kind of text, e.g. metadata or captions, semantically related to the image. Such captions could be consisting of distinct fields, which can be used to facilitate image retrieval. Hence, image retrieval can be based on low-level features derived from an image (e.g. color, shape, spatial layout and texture) or by natural language text/metadata associated with the image.  Retrieval from an image collection offers distinct characteristics from one in which the document to be retrieved is natural language text. Not only the way in which a query is formulated but also the relevance of the results is different in the two approaches.

2.2	Text Retrieval and Greek Language
The Greek language is characterized by its highly inflectional nature and by its rich morphology. Tambouratzis and Carayannis [4] suggest that «to encompass the language evolution throughout the ages (ancient Greek, formal Greek (Katharevousa), and casual Greek (Dimotiki, Monotoniko)) one would need to generate a large number of different morphological lexica, each one covering a specific Language Evolution Sample». They also claim that «for text retrieval and information extraction purposes» when working with texts in the Greek language, it is important to be able to use two or three different stemming operators in order to represent fully the inflectional evolution of the language through time. 
        Ralli [3] mentions that in the case of the Greek language the morphological phenomenon of derivation, together with inflection leads to the formation of different word forms from the same stem. Hence, a word may contain several valid stems of the Greek language! According to Ralli [10] Modern Greek is particularly rich in compound formations and they are usually defined as an association of two stems or of a stem and a word.
Ralli and Galiotou [11] discuss the computational processing of phenomena such as stress and syllabification which are indispensable for the analysis of Greek compound words. Their recognition process of such constructions is still at a prototype level, and is based on PC-KIMMO version2 software.  Certain limitations of the system with respect to the principles of the compound formation are shown.
Fragos et al. [12] applied two statistical methods for extracting collocations from text corpora written in Modern Greek: the mean and variance method and a method based on the X2 test. The two methods extracted interesting collocations. The above mentioned research work [11, 12] forms a basis for our prototype IR system.

3   Building a Bilingual IR System
Several experiments have been carried out in the last years investigating the use of various resources and techniques (e.g., thesauri, synonyms, word sense disambiguation, etc.) to refine and enhance queries. However, the conclusions of these experiments vary widely. 
In the previous section, we have already mentioned that there are two main monolingual information retrieval paradigms which could form a basis for a CLIR / bilingual IR system covering the needs of the Greek Language. The particularities of the Greek Language were also discussed. Therefore, a prerequisite for Natural Language Processing of the Greek Language is some morphological analysis, a rather difficult task to be implemented. Alternatively, free-text searching could cover some needs. The existing systems for translating between Greek and other Languages are not mature in the moment.
Hence, it looks reasonable to build up a "traditional" bilingual (Greek/Latin) IR/Text Retrieval system using:
•	A well established model e.g. the traditional vector space model
•	Free-text searching techniques
•	Classification (coding) scheme for the organization of the documents/information
•	Uncontrolled terms (list) which will be the main source of a Keywords list in the future. 

3.1	Information Filtering and Personalization
There is relatively little difference between IR and IF at an abstract level, since both are concerned with the task of getting information to people who need it. However there are certain aspects of the general problem that have been ignored by the IR literature but are especially relevant in filtering contexts. Learning and adaptation is an issue of significance in the filtering context that has not been emphasized as much in IR research .

3.1.1   Information Extraction and Filtering as a basis for a new Definition of the Problems 
Information Extraction & Filtering deals with the delivery of information that is relevant to the users. An information extraction and filtering system assists users by extracting and filtering the data stream and delivering the relevant information to the user. Information preferences vary greatly across users, therefore such systems must be highly personalized to serve the individual interests of the user. 
Information Filtering aims in capturing the semantic contents of the documents. A variety of approaches have been used to get at the content (e.g. keyword-based approach, use of LSI). Two main problems could be outlined in the following way:
•	The first problem is the relevance of a document to a query ("relevance ranking") or learning how to separate the wheat from the chaff.
•	The second problem is to synthesize/summarize, with respect to a topic, an "executive briefing" of a document.
N. Karanikolas, C. Skourlas [14] consider the problem of extracting relevant key-phrases from bilingual corpora (Greek English text collections) and using them for text classification. They describe an algorithm for the discovery of key-phrases and a framework of handling bilingual texts and documents. This framework combines the use of the traditional vector space model with a new similarity measure, which is based on the key-phrases. 

3.1.2	(Machine) Learning and Adaptation
Learning and Adaptation in IR and in IF mainly refers to the reformulation of a search query in response to feedback provided by the user for the results of previous versions of the query. IR has mostly dealt with the learning issue through relevance feedback. Research also has established relationships between Text retrieval, Machine Learning and Data Mining/Data Warehousing.
Filtering systems are more ambitious. They must be responsive not only to user (communities) with long- term interests but also to cover dynamic user interests or (even) interests not well defined. For example, SONIA (Service for Organizing Networked Information Autonomously) system [17] is implemented as part of the Stanford Digital Libraries test bed. It enables the creation of dynamic hierarchical document categorizations based on the full-text of articles using probability theory [16].
Berger [7] proposed a general probabilistic framework which naturally accounts for word-relatedness issues: One sequence of words may be closely related to another one even if the pair of sequences has few or no words in common. The fact that a document contains the word automobile, for example, suggests that it may be relevant to the queries "Where can I find information on motor vehicles?" and "Tell me about car transmissions", even though the word automobile itself appears nowhere in these queries. Berger used techniques from statistical machine learning such as the Expectation-Maximization (EM) algorithm.
Karanikolas and Skourlas [15] focus on the induction of naive rules for classifying text documents. They described an algorithm for the creation of key-phrases from a given set of documents and these key-phrases are organized and used as features for the automatic classification of new documents. An Authority list of key-phrases is specified by the algorithm containing key-phrases that are frequent within the documents of only one or few classes in the training set. Eventually, they extract naive rules that measure the similarity of new documents with elements in the existing classes.

4   CLIR and Enhanced Distributed Knowledge Management Systems
Knowledge Management (KM) systems usually follow the single organization’s KM paradigm and are organized on a centralized architecture basis, which utilizes knowledge within a single organizational domain. Lately, the distributed/peer-to-peer approach emerges and attempts to overcome the traditional paradigm, develop inter-organizational knowledge exchange infrastructures, and manage resources from cooperating autonomous domains. The limitations of centralized approaches are mainly organizational. Bonifacio suggests that this paradigm incorporates a mismatch between social and technological architectures [1], which limit knowledge acquisition and dissemination possibilities. Distributed architectures in KM pose a number of serious problems, concerning the efficient and secure management of jointly owned resources. 
Our research attempts to investigate and to improve the distributed intra-organizational KM framework horizontally by selecting, incorporating and combining Cross Language Information Retrieval (CLIR) based techniques. It also focuses on establishing and extending vertically (in depth) a secure inter-organizational distributed KM framework to facilitate the bilingual or multilingual needs of a CLIR system. 
Relative to the first research axis, therefore, we propose the establishment of Knowledge Retrieval related components supporting Cross-lingual sets of data. Up to this moment, we have investigated so far knowledge retrieval system components, mainly text containing databases, which support Greek and English texts. When large volumes of data need to be processed in small time intervals parallel processing has to be considered and performed. For disambiguation issues the use of the Wordnet thesaurus is examined as a linguistic knowledge tool.
Relative to the latter research axis, we attempt to investigate how to support a distributed Intra-Organizational KM framework. Among else, we consider as a basic requirement for our system how to handle and to be able to resolve heterogeneity related problems. Heterogeneity issues arise, due to the various data types (structural heterogeneity) or it can be semantically related (semantic heterogeneity). The use of ontologies can facilitate towards the resolution of semantic conflicts. We utilize the RDF [2] ontology language in order to enable semantically enabled queries. Another requirement in the proposed framework is related with transparency. The user should be able to pose queries and retrieve Information independently of the location of knowledge assets, and retrieval issues should be performed entirely by the system. Software agents can facilitate knowledge retrieval on this direction.
In our system we handle several KM related activities: 
•	Knowledge assets discovery, which is handled by security agents and is based on the use of an appropriate ontology,
•	Authorization process, which identifies according to the user that requests access to an asset if he has the appropriate level of classification
•	Negotiation, when it comes to inter-organizational knowledge transfer

4.1	System Architecture and Implementation
Our architecture is materializing a distributed organizational memory. An Organizational Memory (OM) comprises a variety of information sources where information elements of all kinds, structures, contents and media types are available. In addition, a distributed OM utilizes knowledge from interconnected domains, representing knowledge assets in a location independent form. Several instances of an organizational memory are established in different organizational domains and are stored on local nodes. We provide a brief description of its core capabilities, in order to emphasize to its extensions that provide flexible authorization among distributed knowledge nodes. 
The organizational memory module supports storage and retrieval for semi-structured documents with multilingual support [18]. Organizational experience is being codified by subject in semi-structured documents, which consist of raw text, brief abstract description and keywords in order to facilitate retrieval. The system also attempts to provide support for tacit knowledge exploitation through its capability to interconnect users with experts based on multimedia modules [18]. It also provides inter-organizational cooperation for geographically dispersed organizations or cooperating organizations through a Geographical Information System (GIS) interface that provides users with facilities to seek experts in different locations and establish direct contact with them. Lately an image repository has been integrated in order to provide support for heterogeneous types of files. The image repository is implemented in Oracle 9i database environment, while access to the database is provided through a Java based module which provides the possibility for storing and retrieving images based on the usage of metadata for each item inserted in the database. 

Fig.1: Distributed KM architecture overview –
 system components

This implementation scheme is replicated on different nodes and it is supposed that different domains would like to contribute their knowledge potential. Our aim is to ensure that only authorized personnel among the two domains will have access to the knowledge sources. This situation is typical in medical environments, where due to the mobility of patients, a lot of patient related data are distributed in interconnected hospitals, or in e-Government environments, where all cooperating agencies need to share access to each other’s data for a common purpose. Relative to these issues, in our research, we consider two main problems: 
•	first the problem of knowledge discovery upon different domains
•	how a user from one domain can be authorized to access resources from another domain and how this procedure can happen transparently and securely, which means by minimizing the effort on the user’s behalf and at the same time without exposing the knowledge assets to unauthorized disclosure or modification.
Relative to the first question, the role of ontology is crucial. Each domain maintains its own domain ontology. We also introduce a central ontology repository, accessible from all the domains for retrieving domain ontologies. Ontologies define the concepts fro each domain and their properties and enable semantically enabled description and querying over the knowledge assets. In order to enable transparent knowledge assets identification we utilize software agents that act on the user’s behalf and query the distributed domains. Ontologies play also a crucial role in facilitating communication between software agents as they enable standardization of terminology in agent communication messages. 
Relative to the second question we adopt a security policy based approach and we introduce a software agent that handles the necessary negotiations in order to authorize a user to gain access over a specific asset. The security policy defines the roles that deserve access to a specific asset, and the agent carries the user credentials which enable user identification, and accordingly by a policy interpretation the user is authorized or not to retrieve the remote asset. 

5 	Conclusions & Future Work
Lately the distributed Knowledge Management (KM) approach emerges and attempts to overcome the limitations of the traditional KM paradigm. Our research attempts to investigate and to improve the distributed intra-organizational KM framework horizontally by selecting, combining and incorporating Cross Language Information Retrieval (CLIR) based techniques. It also focuses on establishing and extending vertically (in depth) a secure inter-organizational distributed KM framework to facilitate the multilingual retrieval and support the needs of a CLIR system. 
Knowledge Management has almost become a necessity for the modern organization, in order to become competitive and to respond quickly to technological challenges and changes. Usually, different organizations need to collaborate on the grounds of a common target; still their knowledge potential cannot be utilized uniformly due to the fear for unauthorized disclosure of knowledge assets to the other partner. This situation leads to limited performance while the collaboration is active. We present a solution to the aforementioned problem, where all the necessary resources can be shared and with clearly efficient and robust from security perspective manner. Our architecture is resilient and scalable. It enables the joint collaboration of several organizations and demands little extra cost in order to administer the system. We have presented the architecture of our prototype, the functionality of which is continuously extending, covering so far support for different types of files, and enabling transparent to the user knowledge assets identification and access control enforcement. We are attempting to expand the policy - based framework, so as to include negotiation support for different policy models and policy languages.

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